Showing 2,201 - 2,220 results of 28,654 for search '(( 50 ((na decrease) OR (((mean decrease) OR (a decrease)))) ) OR ( 12 nm decrease ))', query time: 0.86s Refine Results
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    Effects of chitin synthesis inhibitor treatment on <i>Lepeophtheirus salmonis</i> (Copepoda, Caligidae) larvae by Hulda María Harðardóttir (7419071)

    Published 2019
    “…All four CSIs interfered with the nauplius II molt to copepodids in a dose-dependent manner. The EC<sub>50</sub> values were 93.2 nM for diflubenzuron, 1.2 nM for hexaflumuron, 22.4 nM for lufenuron, and 11.7 nM for teflubenzuron. …”
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    Mean mitochondrial DNA content in response to benzo<i>(a)</i>pyrene. by Nicky Pieters (409800)

    Published 2013
    “…<p>Mean mitochondrial DNA (mtDNA) content of human TK6 cells exposed to 0; 0.05; 0.5; 5; 50 and 500 µM benzo<i>(a)</i>pyrene (BAP). …”
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    Paeameter ranges and optimal values. by Zhen Zhao (159931)

    Published 2025
    “…Firstly, recursive feature elimination using cross validation (RFECV), maximum information coefficient (MIC), and mean decrease accuracy (MDA) methods were utilized to select population distribution feature factors. …”
  18. 2218

    Improved random forest algorithm. by Zhen Zhao (159931)

    Published 2025
    “…Firstly, recursive feature elimination using cross validation (RFECV), maximum information coefficient (MIC), and mean decrease accuracy (MDA) methods were utilized to select population distribution feature factors. …”
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    Datasets used in the study area. by Zhen Zhao (159931)

    Published 2025
    “…Firstly, recursive feature elimination using cross validation (RFECV), maximum information coefficient (MIC), and mean decrease accuracy (MDA) methods were utilized to select population distribution feature factors. …”
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    Evaluation of the improved random forest model. by Zhen Zhao (159931)

    Published 2025
    “…Firstly, recursive feature elimination using cross validation (RFECV), maximum information coefficient (MIC), and mean decrease accuracy (MDA) methods were utilized to select population distribution feature factors. …”