Showing 1 - 5 results of 5 for search '(( significant decrease decrease ) OR ( significant ((broader decrease) OR (linear increase)) ))~', query time: 0.43s Refine Results
  1. 1

    Volitional control frequency and intensity in VH (Kapsner-Smith et al., 2025) by Mara R. Kapsner-Smith (22139315)

    Published 2025
    “…Group differences were tested with general linear models.</p><p dir="ltr"><b>Results: </b>No significant differences were found between people with and without HVDs on any of the measures. …”
  2. 2

    Association between FF Proximity and BMI by sex. by Kimberly Yuin Y’ng Wong (22766265)

    Published 2025
    “…An exponential decrease in FF proximity was associated with 0.7 kg/m<sup>2</sup> (p < 0.001) increase in BMI among males and 0.4 kg/m<sup>2</sup> (p < 0.05) decrease among females. …”
  3. 3

    Population structure impacts the relationship between the amount of environmental DNA particles and organism abundance by Toshiaki S. Jo (15869182)

    Published 2024
    “…On the other hand, under the allometric eDNA production, the <i>R</i><sup><em>2</em></sup> values decreased for the populations with broader population size range and broader/heterogeneous mass distribution by using biomass, which was not statistically significant by using ASM.…”
  4. 4

    Table 1_Global, regional, and national esophageal cancer deaths and DALYs attributable to diet low in vegetables and fruits, 1990–2019: analysis for the global burden of disease st... by Bing Cui (179696)

    Published 2025
    “…From 1990 to 2019, while the absolute numbers of deaths and DALYs followed a complex trajectory of initial increase followed by decline, age-standardized rates consistently decreased, reflecting the positive impact of epidemiological improvements. …”
  5. 5

    Ambient Air Pollutant Dynamics (2010–2025) and the Exceptional Winter 2016–17 Pollution Episode: Implications for a Uranium/Arsenic Exposure Event by Thomas Clemens Carmine (19756929)

    Published 2025
    “…<br><br><i>1. </i><i>Linear Interpolation: </i>Missing values for all pollutants except PM₂.₅ (i.e., NO₂, SO₂, CO, PM₁₀, EC) were initially filled using standard linear interpolation (pandas.DataFrame.interpolate). …”