Showing 1,661 - 1,680 results of 18,297 for search 'significant ((((gap decrease) OR (((mean decrease) OR (nn decrease))))) OR (a decrease))', query time: 0.70s Refine Results
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    Statins resulted in smaller neuronal soma size. by Shuk C. Tsoi (21192243)

    Published 2025
    “…Within control birds, the BrdU + /Hu+ neuron population was significantly smaller in soma size than the BrdU-/Hu+ older, heterogeneous population (A). …”
  16. 1676

    Experimental Investigation of the Impact of Mixed Wettability on Pore-Scale Fluid Displacement: A Microfluidic Study by Abdullah AlOmier (20402765)

    Published 2024
    “…Despite its common occurrence, the impact of mixed wettability on immiscible fluid displacement at the pore scale remains poorly understood, creating a gap in effective modeling and prediction of fluid behavior in porous media. …”
  17. 1677

    Experimental Investigation of the Impact of Mixed Wettability on Pore-Scale Fluid Displacement: A Microfluidic Study by Abdullah AlOmier (20402765)

    Published 2024
    “…Despite its common occurrence, the impact of mixed wettability on immiscible fluid displacement at the pore scale remains poorly understood, creating a gap in effective modeling and prediction of fluid behavior in porous media. …”
  18. 1678

    The technical route of the study. by Hongliang Zou (20707270)

    Published 2025
    “…The highest root-mean-square error recorded was 2.72m in five sequences from a multi-modal multi-scene ground robot dataset, which was significantly lower than competing approaches. …”
  19. 1679

    Point cloud fusion instance effect. by Hongliang Zou (20707270)

    Published 2025
    “…The highest root-mean-square error recorded was 2.72m in five sequences from a multi-modal multi-scene ground robot dataset, which was significantly lower than competing approaches. …”
  20. 1680

    Experimental results in the SubT-MRS dataset. by Hongliang Zou (20707270)

    Published 2025
    “…The highest root-mean-square error recorded was 2.72m in five sequences from a multi-modal multi-scene ground robot dataset, which was significantly lower than competing approaches. …”