Výsledky vyhledávání - "Maiko Inagaki"

Akademický článek

Day-Ahead Scheduling of Wind-Hydro Balancing Group Operation to Maximize Expected Revenue Considering Wind Power Output Uncertainty

Autor: Yu Fujimoto, Maiko Inagaki, Akihisa Kaneko, Shinichiro Minotsu, Yasuhiro Hayashi

Publikováno v: IEEE Access, Vol 11, Pp 119200-119218 (2023)

To use energy generated in wind farms (WFs), which contain uncertainty in their output, as a primary power source in a power system, a sophisticated balancing operation scheme is required. This study focuses on a balancing group (BG) scheme combining

Externí odkaz: https://doaj.org/article/30a813050d424b6b9e1a825946f6c5d0

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Akademický článek

Machine learning-based prediction of relapse in rheumatoid arthritis patients using data on ultrasound examination and blood test

Autor: Hidemasa Matsuo, Mayumi Kamada, Akari Imamura, Madoka Shimizu, Maiko Inagaki, Yuko Tsuji, Motomu Hashimoto, Masao Tanaka, Hiromu Ito, Yasutomo Fujii

Publikováno v: Scientific Reports, Vol 12, Iss 1, Pp 1-8 (2022)

Abstract Recent effective therapies enable most rheumatoid arthritis (RA) patients to achieve remission; however, some patients experience relapse. We aimed to predict relapse in RA patients through machine learning (ML) using data on ultrasound (US)

Externí odkaz: https://doaj.org/article/724e0bed2d3742a8b737930d231ca3e2

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Akademický článek

TAK1 (MAP3K7) signaling regulates hematopoietic stem cells through TNF-dependent and -independent mechanisms.

Autor: Giichi Takaesu, Maiko Inagaki, Keiyo Takubo, Yuji Mishina, Paul R Hess, Gregg A Dean, Akihiko Yoshimura, Kunihiro Matsumoto, Toshio Suda, Jun Ninomiya-Tsuji

Publikováno v: PLoS ONE, Vol 7, Iss 11, p e51073 (2012)

A cytokine/stress signaling kinase Tak1 (Map3k7) deficiency is known to impair hematopoietic progenitor cells. However, the role of TAK1 signaling in the stem cell function of the hematopoietic system is not yet well defined. Here we characterized he

Externí odkaz: https://doaj.org/article/6b707aa788c14c03ae9c4216e9ae859f

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Machine learning-based prediction of relapse in rheumatoid arthritis patients using data on ultrasound examination and blood test

Autor: Hidemasa Matsuo, Mayumi Kamada, Akari Imamura, Madoka Shimizu, Maiko Inagaki, Yuko Tsuji, Motomu Hashimoto, Masao Tanaka, Hiromu Ito, Yasutomo Fujii

Publikováno v: Scientific reports. 12(1)

Recent effective therapies enable most rheumatoid arthritis (RA) patients to achieve remission; however, some patients experience relapse. We aimed to predict relapse in RA patients through machine learning (ML) using data on ultrasound (US) examinat

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::27025f88e609ffc088a81a7b64aab4d4
https://pubmed.ncbi.nlm.nih.gov/35508670

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