Results 161 to 170 of about 35,332 (261)

Creating a data warehouse to support monitoring of NSQHS blood management standard from EMR data. [PDF]

BMC Med Inform Decis Mak
Cheng-Zarate D, Burns J, Ngo C, Haryanto A, Duncan G, Taniar D, Wybrow M.   +6 more
europepmc   +1 more source

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

Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning

Advanced Intelligent Discovery, EarlyView.
This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley   +1 more source

Suicide Death Prediction Using the Maryland Suicide Data Warehouse: A Sensitivity Analysis. [PDF]

Arch Suicide Res
Kitchen C, Zirikly A, Belouali A, Kharrazi H, Nestadt P, Wilcox HC.   +5 more
europepmc   +1 more source

Composition‐Aware Cross‐Sectional Integration for Spatial Transcriptomics

Advanced Intelligent Discovery, EarlyView.
Multi‐section spatial transcriptomics demands coherent cell‐type deconvolution, domain detection, and batch correction, yet existing pipelines treat these tasks separately. FUSION unifies them within a composition‐aware latent framework, modeling reads as cell‐type–specific topics and clustering in embedding space.
Qishi Dong, Zijian Huang, Xuanwu Wang, Zhenghui Feng, Wei Liu, Bingding Huang   +5 more
wiley   +1 more source

Radiochemotherapy for salivary gland adenoid cystic carcinoma: survival assessment through a retrospective study exploiting real-world data extracted from data warehouse. [PDF]

ESMO Real World Data Digit Oncol
Cavalieri S, Lombardi Stocchetti B, Crippa N, Silvestri C, Villa C, Ghelardi F, Baili P, Bonfarnuzzo S, Cavallo I, Iacovelli NA, Franceschini M, Filippi AR, Orlandi E, Deganello A, Cristofaro V, Bergamini C, Alfieri S, Nuzzolese I, Colombo E, Ottini A, Calareso G, Quattrone P, Licitra L.   +22 more
europepmc   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced 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

Feasibility of automated surveillance of implantable devices in orthopaedics via clinical data warehouse: the Studio study. [PDF]

BMC Med Inform Decis Mak
Ansoborlo M, Salpétrier C, Nail LL, Herbet J, Cuggia M, Rosset P, Grammatico-Guillon L.   +6 more
europepmc   +1 more source

Why Physics Still Matters: Improving Machine Learning Prediction of Material Properties With Phonon‐Informed Datasets

Advanced Intelligent Discovery, EarlyView.
Phonons‐informed machine‐learning predictive models are propitious for reproducing thermal effects in computational materials science studies. Machine learning (ML) methods have become powerful tools for predicting material properties with near first‐principles accuracy and vastly reduced computational cost.
Pol Benítez, Cibrán López, Edgardo Saucedo, Teruyasu Mizoguchi, Claudio Cazorla   +4 more
wiley   +1 more source