Showing 13,781 - 13,800 results of 41,669 for search '(( 50 ((we decrease) OR (((nn decrease) OR (a decrease)))) ) OR ( a point decrease ))', query time: 1.12s Refine Results
  1. 13781

    Patient characteristics. by Ruijie Wang (5989841)

    Published 2024
    “…On the XJTU-EC dataset, CM-UNet achieves an excellent segmentation performance, and ECRNet obtains an accuracy of 98.50%, a precision of 99.32% and a sensitivity of 97.67% on the test set, which outperforms other competitive classical models. …”
  2. 13782

    Comparison with baseline methods. by Ruijie Wang (5989841)

    Published 2024
    “…On the XJTU-EC dataset, CM-UNet achieves an excellent segmentation performance, and ECRNet obtains an accuracy of 98.50%, a precision of 99.32% and a sensitivity of 97.67% on the test set, which outperforms other competitive classical models. …”
  3. 13783

    The details of the channel attention module. by Ruijie Wang (5989841)

    Published 2024
    “…On the XJTU-EC dataset, CM-UNet achieves an excellent segmentation performance, and ECRNet obtains an accuracy of 98.50%, a precision of 99.32% and a sensitivity of 97.67% on the test set, which outperforms other competitive classical models. …”
  4. 13784

    Ablation experiments. by Ruijie Wang (5989841)

    Published 2024
    “…On the XJTU-EC dataset, CM-UNet achieves an excellent segmentation performance, and ECRNet obtains an accuracy of 98.50%, a precision of 99.32% and a sensitivity of 97.67% on the test set, which outperforms other competitive classical models. …”
  5. 13785

    Pathological diagnosis. by Ruijie Wang (5989841)

    Published 2024
    “…On the XJTU-EC dataset, CM-UNet achieves an excellent segmentation performance, and ECRNet obtains an accuracy of 98.50%, a precision of 99.32% and a sensitivity of 97.67% on the test set, which outperforms other competitive classical models. …”
  6. 13786

    The details of ECRNet. by Ruijie Wang (5989841)

    Published 2024
    “…On the XJTU-EC dataset, CM-UNet achieves an excellent segmentation performance, and ECRNet obtains an accuracy of 98.50%, a precision of 99.32% and a sensitivity of 97.67% on the test set, which outperforms other competitive classical models. …”
  7. 13787
  8. 13788

    Impact of Manganese Carbonate Precipitation on Uranium(VI) Fate in Conditions Relevant to Carbonate-Buffered Aquifers by Surya Sujathan (11355082)

    Published 2024
    “…Reactive modeling of Mn and DIC data exhibited a decrease in the surface area normalized rate constants of MnCO<sub>3(s)</sub> precipitation from 5.9 to 3.3 × 10<sup>–8</sup> mol m<sup>–2</sup> h<sup>–1</sup> with an increase in initial U(VI) from 0 to 500 μM. …”
  9. 13789

    Impact of Manganese Carbonate Precipitation on Uranium(VI) Fate in Conditions Relevant to Carbonate-Buffered Aquifers by Surya Sujathan (11355082)

    Published 2024
    “…Reactive modeling of Mn and DIC data exhibited a decrease in the surface area normalized rate constants of MnCO<sub>3(s)</sub> precipitation from 5.9 to 3.3 × 10<sup>–8</sup> mol m<sup>–2</sup> h<sup>–1</sup> with an increase in initial U(VI) from 0 to 500 μM. …”
  10. 13790
  11. 13791
  12. 13792

    Flow chart of the study participants. by Milton W. Musaba (8431944)

    Published 2025
    “…We enrolled 150 mother-infant dyads into a pre-post study. …”
  13. 13793

    Suggested modifications for the BabySaver. by Milton W. Musaba (8431944)

    Published 2025
    “…We enrolled 150 mother-infant dyads into a pre-post study. …”
  14. 13794

    Mitochondrial morphologic changes in <i>A</i>. <i>lugdunensis</i>. by Min Seung Kang (9388806)

    Published 2023
    “…<p>After treatment with 50 μg/mL (B) of <i>T</i>. <i>nucifera</i>, structural damage and decreased wrinkling was observed compared with that in the control group (A). …”
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  17. 13797
  18. 13798
  19. 13799
  20. 13800

    Dynamic distribution decomposition applied to Nanog-Neo cell line data taken from Zunder <i>et. al</i>. [28]. by Jake P. Taylor-King (8282190)

    Published 2020
    “…<p>(a.) Fitting error plot showing log<sub>10</sub> transformed percentage error plotted against time for different values of <i>α</i> (described in main text); (b.) log-log plot of mean fitting error plotted against −log<sub>10</sub>(<i>α</i>), mean error decreases as alpha decreases; (c.) complex plot of eigenvalues λ of Perron–Frobenius matrix <i>P</i> for different values of <i>α</i>; (d.) …”