Showing 64,941 - 64,960 results of 123,171 for search '(( 10 ((ng decrease) OR (mean decrease)) ) OR ( 5 ((a decrease) OR (nn decrease)) ))', query time: 1.96s Refine Results
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  10. 64950

    Fractionated Radiation Exposure of Rat Spinal Cords Leads to Latent Neuro-Inflammation in Brain, Cognitive Deficits, and Alterations in Apurinic Endonuclease 1 - Fig 3 by M. A. Suresh Kumar (774864)

    Published 2015
    “…<p>(A): left image. Western blot of thoracic (T5–T7) spinal cord extracts from 300 MeV/n <sup>28</sup>Si exposed rats, harvested after 6 months post exposure measured for APE1 and cell markers (A2B5 and NG2). …”
  11. 64951

    Major hyperparameters of RF-SVR. by Jintao Li (448681)

    Published 2024
    “…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R<sup>2</sup> value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”
  12. 64952

    Pseudo code for coupling model execution process. by Jintao Li (448681)

    Published 2024
    “…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R<sup>2</sup> value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”
  13. 64953

    Major hyperparameters of RF-MLPR. by Jintao Li (448681)

    Published 2024
    “…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R<sup>2</sup> value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”
  14. 64954

    Results of RF algorithm screening factors. by Jintao Li (448681)

    Published 2024
    “…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R<sup>2</sup> value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”
  15. 64955

    Schematic diagram of the basic principles of SVR. by Jintao Li (448681)

    Published 2024
    “…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R<sup>2</sup> value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”
  16. 64956

    Data_Sheet_1_A Potent Histone Deacetylase Inhibitor MPT0E028 Mitigates Emphysema Severity via Components of the Hippo Signaling Pathway in an Emphysematous Mouse Model.docx by Lu-Yang Yeh (12559033)

    Published 2022
    “…</p>Materials and Methods<p>A mouse model of porcine pancreatic elastase (PPE)-induced emphysema was orally administered 0, 25, or 50 mg/kg body weight (BW) of the MPT0E028 five times/week for 3 weeks. …”
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    DataSheet_1_Variable humic product effects on maize structural biochemistry across annual weather patterns and soil types in two Iowa (U.S.A.) production fields.pdf by D. C. Olk (2048794)

    Published 2023
    “…Stover and root tissues tended toward greater phenol concentrations in a drier upland transect but greater carbohydrate concentrations in a wetter lowland transect. …”