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Akademický článek

SBMLToolkit.jl: a Julia package for importing SBML into the SciML ecosystem

Autor: Lang Paul F., Jain Anand, Rackauckas Christopher

Publikováno v: Journal of Integrative Bioinformatics, Vol 21, Iss 1, Pp 524-31 (2024)

Julia is a general purpose programming language that was designed for simplifying and accelerating numerical analysis and computational science. In particular the Scientific Machine Learning (SciML) ecosystem of Julia packages includes frameworks for

Externí odkaz: https://doaj.org/article/6ae0cf3ace624c3f97687bbf28d30f80

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AOTF based spectro-polarimeter for observing Earth as an Exoplanet

Autor: Jaiswal, Bhavesh, Singh, Swapnil, Jain, Anand, Sankarasubramanian, K, Nandi, Anuj

Publikováno v: Journal of Astronomical Telescopes, Instruments, and Systems, Volume 8, id. 044007 (2022)

Earth is the only known habitable planet and it serves as a testbed to benchmark the observations of temperate and more Earth-like exoplanets. It is required to observe the disc-integrated signatures of Earth for a large range of phase angles, resemb

Externí odkaz: http://arxiv.org/abs/2302.10712

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Akademický článek

CRISPR-based electrochemical biosensors for animal health: Recent advances

Autor: Gattani, Anil, Mandal, Sanju, Agrawal, Aditya, Patel, Pragati, Jain, Anand Kumar, Singh, Purnima, Garg, Akshay, Mishra, Aditya

Publikováno v: In Progress in Biophysics and Molecular Biology November 2024 193:7-18

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Akademický článek

Targeting PKCα alleviates iron overload in diabetes and hemochromatosis through the inhibition of ferroportin

Autor: Banerjee, Somesh, Lu, Shaolei, Jain, Anand, Wang, Irene, Tao, Hui, Srinivasan, Shanthi, Nemeth, Elizabeta, He, Peijian

Publikováno v: In Blood 26 September 2024 144(13):1433-1444

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Symbolic-Numeric Integration of Univariate Expressions based on Sparse Regression

Autor: Iravanian, Shahriar, Martensen, Carl Julius, Cheli, Alessandro, Gowda, Shashi, Jain, Anand, Ma, Yingbo, Rackauckas, Chris

Most computer algebra systems (CAS) support symbolic integration as core functionality. The majority of the integration packages use a combination of heuristic algebraic and rule-based (integration table) methods. In this paper, we present a hybrid (

Externí odkaz: http://arxiv.org/abs/2201.12468

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Composing Modeling and Simulation with Machine Learning in Julia

Autor: Rackauckas, Chris, Anantharaman, Ranjan, Edelman, Alan, Gowda, Shashi, Gwozdz, Maja, Jain, Anand, Laughman, Chris, Ma, Yingbo, Martinuzzi, Francesco, Pal, Avik, Rajput, Utkarsh, Saba, Elliot, Shah, Viral B.

In this paper we introduce JuliaSim, a high-performance programming environment designed to blend traditional modeling and simulation with machine learning. JuliaSim can build accelerated surrogates from component-based models, such as those conformi

Externí odkaz: http://arxiv.org/abs/2105.05946

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