Results 171 to 180 of about 4,968 (249)

GraphRAG for engineering diagrams: ChatP&ID enables LLM interaction with P&IDs

open access: yesAIChE Journal, EarlyView.
Abstract Piping and Instrumentation Diagrams (P&IDs) are central to process engineering workflows, yet extracting information from them remains a tedious and time‐consuming task. This work introduces ChatP&ID, a framework enabling natural‐language interaction with smart P&IDs through Graph Retrieval‐Augmented Generation (GraphRAG), to our knowledge ...
Achmad Anggawirya Alimin   +1 more
wiley   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

open access: yesAdvanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective

open access: yesAdvanced Intelligent Discovery, EarlyView.
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

Patient‐Mounted Neuro Optical Coherence Tomography for Targeted Minimally Invasive Micro‐Resolution Volumetric Imaging in Brain In Vivo

open access: yesAdvanced Intelligent Systems, Volume 7, Issue 3, March 2025.
Herein, a patient‐mounted neuro optical coherence tomography system that integrates a 5 degrees‐of‐freedom skull‐mounted robot (Skullbot) with a 0.6 mm neuroendoscope for targeted, minimally invasive deep brain imaging, is developed. The system offers high‐resolution imaging with precise deployment, demonstrated through successful tumor imaging in a ...
Chao Xu   +7 more
wiley   +1 more source

Machine Learning‐Based Estimation of Experimental Artifacts and Image Quality in Fluorescence Microscopy

open access: yesAdvanced Intelligent Systems, Volume 7, Issue 3, March 2025.
The use of image quality metrics in combination with machine learning enables automatic image quality assessment for fluorescence microscopy images. The method can be integrated into the experimental pipeline for optical microscopy and utilized to classify artifacts in experimental images and to build quality rankings with a reference‐free approach ...
Elena Corbetta, Thomas Bocklitz
wiley   +1 more source

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