Results 221 to 230 of about 2,646,492 (277)

Holographic Mapping of Orbital Angular Momentum using a Terahertz Diffractive Optical Neural Network

Advanced Intelligent Discovery, EarlyView.
A compact six‐layer diffractive optical neural network enables direct recognition and spatial mapping of terahertz (THz) orbital angular momentum (OAM) beams. Fabricated by 3D printing, the system distinguishes nine OAM modes and their superpositions with high fidelity, good efficiency, and low crosstalk, offering a scalable solution for THz ...
Wei Jia, Miguel Gomez, Steve Blair, Berardi Sensale‐Rodriguez   +3 more
wiley   +1 more source

Clinician and simulated patient perspectives on ambient AI scribes in psychiatric consultations: a qualitative study. [PDF]

Front Psychiatry
Bokhari SA, Nawaz FA, Usman FM, Arshad Z, Sudhir M, Krage R, Javaid SF, Kashyap R.   +7 more
europepmc   +1 more source

Factorization Machine with Iterative Quantum Reverse Annealing: A Python Package for Batch Black‐Box Optimization With Reverse Quantum Annealing

Advanced Intelligent Discovery, EarlyView.
Factorization machine with iterative quantum reverse annealing (FMIRA) leverages quantum reverse annealing to perform batch black‐box optimization. Factorization machine with quantum annealing (FMQA) is a widely used python package for solving black‐box optimization problems using D‐Wave quantum annealers.
Andrejs Tučs, Ryo Tamura, Koji Tsuda
wiley   +1 more source

Shear and Compression Wrinkling Experimental Analysis with a Sandwich Beam Submitted to Three-Point Bending. [PDF]

Materials (Basel)
Gashe B, Alawode A, Rivallant S, Castanié B.   +3 more
europepmc   +1 more source

AI‐Guided Co‐Optimization of Advanced Field‐Effect Transistors: Bridging Material, Device, and Fabrication Design

Advanced Intelligent Discovery, EarlyView.
This article outlines how artificial intelligence could reshape the design of next‐generation transistors as traditional scaling reaches its limits. It discusses emerging roles of machine learning across materials selection, device modeling, and fabrication processes, and highlights hierarchical reinforcement learning as a promising framework for ...
Shoubhanik Nath, Shauryaraj K. Jilkar, Aditya Udgaonkar, Yu (Joe) Lu, Ali Mesbah   +4 more
wiley   +1 more source

Science-Based Risk Assessment for the Categorization of Visual Inspection Defects of Sterile Dosage Forms. [PDF]

Pharmaceutics
Mahler HC, Folzer E, Ananthavettivelu R, Koehler J, Ferrari M, Allmendinger A.   +5 more
europepmc   +1 more source

Deep Learning–Based Extraction of Promising Material Groups and Common Features from High‐Dimensional Data: A Case of Optical Spectra of Inorganic Crystals

Advanced Intelligent Discovery, EarlyView.
We report a novel interpretation method for deep learning models based on feature extraction and clustering. Applying this method to an atomistic line graph neural network (ALIGNN) model trained on optical absorption spectra of 2,681 inorganic compounds obtained from first‐principles calculations, we successfully identify key factors underlying ...
Akira Takahashi, Yu Kumagai, Arata Takamatsu, Fumiyasu Oba   +3 more
wiley   +1 more source

Data-driven simulations to assess the impact of study imperfections in time-to-event analyses. [PDF]

Am J Epidemiol
Abrahamowicz M, Beauchamp ME, Boulesteix AL, Morris TP, Sauerbrei W, Kaufman JS, Stratos Simulation Panel OBOT.   +6 more
europepmc   +1 more source

AI‐BioMech: Deep Learning Prediction of Mechanical Behavior in Aperiodic Biological Cellular Materials

Advanced Intelligent Discovery, EarlyView.
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, Marcelo A. Dias, Parvez Alam   +2 more
wiley   +1 more source

STEEL-3dPointClouds: dataset supporting quantification of residual life and reusability of steel beam-columns. [PDF]

Sci Data
Gu T, Bijelić N, Katircioglu I, Obozinski G, Lignos DG.   +4 more
europepmc   +1 more source