Power consumption in irreversible QCA logic circuits is a vital and a major issue; however in the practical cases, this focus is mostly omitted.The complete power depletion dataset of different QCA multiplexers have been worked out in this paper. At -271.15 °C temperature, the depletion is evaluated under three separate tunneling energy levels. All the circuits are designed with QCADesigner, a broadly used simulation engine and QCAPro tool has been applied for estimating the power dissipation.
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http://dx.doi.org/10.1016/j.dib.2017.03.001 | DOI Listing |
Int J Cardiol
January 2025
Center for Imaging, EPFL, Lausanne, Switzerland.
Background: Quantitative coronary angiography (QCA) typically employs traditional edge detection algorithms that often require manual correction. This has important implications for the accuracy of downstream 3D coronary reconstructions and computed haemodynamic indices (e.g.
View Article and Find Full Text PDFHeliyon
May 2024
Faculty of Pharmaceutical Sciences, Government College University, Faisalabad-38000, Pakistan.
This study underscores the effectiveness of Qualitative Comparative Analysis (QCA) when compared to conventional regression analysis (CRA) in the investigation of complex human systems. Utilizing historical secondary cross-national data from Lipset & Man (1960) spanning 18 countries, where CRA may be impractical, the research emphasizes the superior performance of QCA, specifically utilizing both crisp set QCA and fuzzy set QCA. The dataset includes variables such as democracy survival and its precursors, such as gross national product per capita, urbanization, literacy rate, and industrial labor force.
View Article and Find Full Text PDFInt J Cardiol
June 2024
Division of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea. Electronic address:
Catheter Cardiovasc Interv
October 2023
Structural and Coronary Heart Disease Unit, Cardiovascular Center of the University of Lisbon, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal.
Background: Visual assessment of the percentage diameter stenosis (%DS ) of lesions is essential in coronary angiography (CAG) interpretation. We have previously developed an artificial intelligence (AI) model capable of accurate CAG segmentation. We aim to compare operators' %DS in angiography versus AI-segmented images.
View Article and Find Full Text PDFNPJ Digit Med
August 2023
Division of Cardiology, Department of Medicine, University of California, San Francisco, Cardiology, 505 Parnassus Avenue, San Francisco, CA, 94143, USA.
Coronary angiography is the primary procedure for diagnosis and management decisions in coronary artery disease (CAD), but ad-hoc visual assessment of angiograms has high variability. Here we report a fully automated approach to interpret angiographic coronary artery stenosis from standard coronary angiograms. Using 13,843 angiographic studies from 11,972 adult patients at University of California, San Francisco (UCSF), between April 1, 2008 and December 31, 2019, we train neural networks to accomplish four sequential necessary tasks for automatic coronary artery stenosis localization and estimation.
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