Results 251 to 260 of about 553,685 (362)

The Impact of Artificial Intelligence on Energy Conservation and Emission Reduction: Evidence From China's Listed Firms

International Studies of Economics, EarlyView.
ABSTRACT Artificial intelligence (AI) plays an increasingly pivotal role in advancing sustainable economic development. While existing literature predominantly examines the environmental impact of AI technologies from national or sectoral perspectives, this study provides a micro‐level analysis of its effects on energy conservation and emission ...
Qiannan Zhu, Zhengyu Zhang
wiley   +1 more source

Achieving inclusive healthcare through integrating education and research with AI and personalized curricula. [PDF]

Commun Med (Lond)
Bahmani A, Cha K, Alavi A, Dixit A, Ross A, Park R, Goncalves F, Ma S, Saxman P, Nair R, Akhavan-Sarraf R, Zhou X, Wang M, Contrepois K, Li-Pook-Than J, Monte E, Rodriguez DJF, Lai J, Babu M, Tondar A, Schüssler-Fiorenza Rose SM, Akbari I, Zhang X, Yegnashankaran K, Yracheta J, Dale K, Miller AD, Edmiston S, McGhee EM, Nebeker C, Wu JC, Kundaje A, Snyder M.   +32 more
europepmc   +1 more source

Choosing a library for the Python programming language for visualizing the operation of parallel algorithms

Сергій Шаров, Yurii Sitsylitsyn, Oleksii Naumuk, Dmytro Lubko, Віра Колмакова   +4 more
openalex   +1 more source

Quantitative Confounder Analysis of Electrocardiogram Signals in Cardiac Magnetic Resonance at 1.5, 3 and 7 T—Assessing Standardized Electrode Positions and Sequence Types—Towards Quality Assurance

Journal of Magnetic Resonance Imaging, EarlyView.
ABSTRACT Background The electrocardiogram (ECG) used for gating in cardiac MRI may be compromised by multiple confounders inside the scanner bore. Purpose To quantify the influence of magnetic field strengths (1.5 T/3 T/7 T), standardized electrode positions, and imaging sequences on ECG signals used for gating. Study Type Prospective.
Richard Hickstein, Stephanie Wiesemann, Darian Viezzer, Denise Kleindienst, Teodora Chitiboi, Bogdan Andrei Gheorghita, Jens Wetzl, Thomas Hadler, Sebastian Dietrich, Sebastian Schmitter, Jeanette Schulz‐Menger   +10 more
wiley   +1 more source

Co-Learning: code learning for multi-agent reinforcement collaborative framework with conversational natural language interfaces. [PDF]

Front Artif Intell
Yu J, Wu Y, Zhan Y, Guo W, Xu Z, Lee R.
europepmc   +1 more source

Real‐Time Deep‐Learning Image Reconstruction and Instrument Tracking in MR‐Guided Biopsies

Journal of Magnetic Resonance Imaging, EarlyView.
ABSTRACT Background Transrectal in‐bore MR‐guided biopsy (MRGB) is accurate but time‐consuming, limiting clinical throughput. Faster imaging could improve workflow and enable real‐time instrument tracking. Existing acceleration methods often use simulated data and lack validation in clinical settings.
Constant R. Noordman, Lauren P. W. te Molder, Marnix C. Maas, Christiaan G. Overduin, Jurgen J. Fütterer, Henkjan J. Huisman   +5 more
wiley   +1 more source

Ten quick tips for developing a reproducible Shiny application. [PDF]

PLoS Comput Biol
Brun J, Janée G, Curty RG.
europepmc   +1 more source

COMPARISON BETWEEN TWO PROGRAMMING LANGUAGES R AND PYTHON

, 2020
openaire   +2 more sources

Enhancing medical imaging education: integrating computing technologies, digital image processing and artificial intelligence

Journal of Medical Radiation Sciences, Volume 72, Issue 1, Page 148-155, March 2025.
This paper has proposed a comprehensive curriculum framework that integrates computing technologies, digital image processing and artificial intelligence into medical imaging education, aiming to prepare future professionals to navigate and innovate within this dynamic landscape.
Sibusiso Mdletshe, Alan Wang
wiley   +1 more source

Defining a Domain-Specific Language for Behavior Verification of Cyber-Physical Applications. [PDF]

Sensors (Basel)
Panayiotou K, Tsardoulias E, Tsampouris T, Symeonidis AL.   +3 more
europepmc   +1 more source