Showing 18,581 - 18,600 results of 21,342 for search '(( significant ((we decrease) OR (a decrease)) ) OR ( significant decrease decrease ))', query time: 0.57s Refine Results
  1. 18581

    Model comparison experimental results. by Chunhua Yang (346871)

    Published 2025
    “…The experimental results demonstrate that when compared to YOLOv10, S-YOLOv10-ASI shows significant improvements across various metrics. Specifically, Bounding Box Regression Loss decreases by over 30% in the training set, while Classification Loss and Bounding Box Regression Loss drop by more than 60% in the validation set. …”
  2. 18582

    Slicing aided hyper inference algorithm. by Chunhua Yang (346871)

    Published 2025
    “…The experimental results demonstrate that when compared to YOLOv10, S-YOLOv10-ASI shows significant improvements across various metrics. Specifically, Bounding Box Regression Loss decreases by over 30% in the training set, while Classification Loss and Bounding Box Regression Loss drop by more than 60% in the validation set. …”
  3. 18583

    Data Sheet 3_Genome-wide identification of GDPD gene family in foxtail millet (Setaria italica L.) and functional characterization of SiGDPD14 under low phosphorus stress.docx by Chaomin Meng (21496829)

    Published 2025
    “…Under low phosphorus stress, the expression levels of SiGDPD3 and SiGDPD14 significantly increased, while SiGDPD1, SiGDPD5, SiGDPD6, and SiGDPD11 showed significant decreases.To identify the function of SiGDPD14, an over-expressed transgenic Arabidopsis was generated. …”
  4. 18584

    Microbiome-host genetic association. by Tamizhini Loganathan (18538349)

    Published 2025
    “…Core microbiome and correlation analysis at the phylum and genus levels identified significant microbiota. Specifically, the abundance of genera such as <i>Pseudomonas</i> and <i>Akkermansia</i> decreased, while <i>Ruminococcus</i> and <i>Allistipes</i> increased, as determined by statistical and machine learning approaches. …”
  5. 18585

    Summary description of the samples. by Tamizhini Loganathan (18538349)

    Published 2025
    “…Core microbiome and correlation analysis at the phylum and genus levels identified significant microbiota. Specifically, the abundance of genera such as <i>Pseudomonas</i> and <i>Akkermansia</i> decreased, while <i>Ruminococcus</i> and <i>Allistipes</i> increased, as determined by statistical and machine learning approaches. …”
  6. 18586

    Improved YOLOv10 network structure. by Chunhua Yang (346871)

    Published 2025
    “…The experimental results demonstrate that when compared to YOLOv10, S-YOLOv10-ASI shows significant improvements across various metrics. Specifically, Bounding Box Regression Loss decreases by over 30% in the training set, while Classification Loss and Bounding Box Regression Loss drop by more than 60% in the validation set. …”
  7. 18587

    Image 2_Genome-wide identification of GDPD gene family in foxtail millet (Setaria italica L.) and functional characterization of SiGDPD14 under low phosphorus stress.png by Chaomin Meng (21496829)

    Published 2025
    “…Under low phosphorus stress, the expression levels of SiGDPD3 and SiGDPD14 significantly increased, while SiGDPD1, SiGDPD5, SiGDPD6, and SiGDPD11 showed significant decreases.To identify the function of SiGDPD14, an over-expressed transgenic Arabidopsis was generated. …”
  8. 18588

    Image 3_The global burden of hypertension and its epidemiological impacts on adolescents and young adults: projections to 2050.jpeg by Chaofeng Niu (17746406)

    Published 2025
    “…</p>Results<p>From 1990 to 2021, the absolute numbers of hypertension-related deaths, Disability-Adjusted Life Years (DALYs), and Years Lived with Disability (YLDs) increased significantly globally. The age-standardized mortality rate and DALY rate decreased to some extent, while the YLDs rate increased slightly. …”
  9. 18589

    Loss function variation curve. by Chunhua Yang (346871)

    Published 2025
    “…The experimental results demonstrate that when compared to YOLOv10, S-YOLOv10-ASI shows significant improvements across various metrics. Specifically, Bounding Box Regression Loss decreases by over 30% in the training set, while Classification Loss and Bounding Box Regression Loss drop by more than 60% in the validation set. …”
  10. 18590

    Type level landscape index changes in 1990-2020. by Chao Ma (207385)

    Published 2025
    “…The habitat quality shows a spatial distribution pattern of “high in the surrounding areas and low in the central areas”, and autocorrelation analysis shows that county-level units have significant spatial agglomeration effects. …”
  11. 18591

    Location map of the study area. by Chao Ma (207385)

    Published 2025
    “…The habitat quality shows a spatial distribution pattern of “high in the surrounding areas and low in the central areas”, and autocorrelation analysis shows that county-level units have significant spatial agglomeration effects. …”
  12. 18592

    Image 2_Exploring the antibacterial and anti-biofilm properties of Diacerein against methicillin-resistant Staphylococcus aureus.tif by Yingying Sun (568696)

    Published 2025
    “…Background<p>Methicillin-resistant Staphylococcus aureus (MRSA) poses a significant clinical challenge due to its multidrug resistance. …”
  13. 18593

    Different model detection results comparison. by Chunhua Yang (346871)

    Published 2025
    “…The experimental results demonstrate that when compared to YOLOv10, S-YOLOv10-ASI shows significant improvements across various metrics. Specifically, Bounding Box Regression Loss decreases by over 30% in the training set, while Classification Loss and Bounding Box Regression Loss drop by more than 60% in the validation set. …”
  14. 18594

    Data source. by Chao Ma (207385)

    Published 2025
    “…The habitat quality shows a spatial distribution pattern of “high in the surrounding areas and low in the central areas”, and autocorrelation analysis shows that county-level units have significant spatial agglomeration effects. …”
  15. 18595

    Research Technology Flow Chart. by Chao Ma (207385)

    Published 2025
    “…The habitat quality shows a spatial distribution pattern of “high in the surrounding areas and low in the central areas”, and autocorrelation analysis shows that county-level units have significant spatial agglomeration effects. …”
  16. 18596

    Inner-IoU. by Chunhua Yang (346871)

    Published 2025
    “…The experimental results demonstrate that when compared to YOLOv10, S-YOLOv10-ASI shows significant improvements across various metrics. Specifically, Bounding Box Regression Loss decreases by over 30% in the training set, while Classification Loss and Bounding Box Regression Loss drop by more than 60% in the validation set. …”
  17. 18597

    Image 5_The global burden of hypertension and its epidemiological impacts on adolescents and young adults: projections to 2050.jpeg by Chaofeng Niu (17746406)

    Published 2025
    “…</p>Results<p>From 1990 to 2021, the absolute numbers of hypertension-related deaths, Disability-Adjusted Life Years (DALYs), and Years Lived with Disability (YLDs) increased significantly globally. The age-standardized mortality rate and DALY rate decreased to some extent, while the YLDs rate increased slightly. …”
  18. 18598

    Quantitative results on WEDU dataset. by Dunlu Lu (19964225)

    Published 2024
    “…This is particularly true in the agricultural field, where there is a multitude of crop types and a complex and variable environment. …”
  19. 18599

    Table 5_Fatty acid synthase inhibition improves hypertension-induced erectile dysfunction by suppressing oxidative stress and NLRP3 inflammasome-dependent pyroptosis through activa... by Jiaochen Luan (9704384)

    Published 2025
    “…Multi-omics analysis revealed significant enrichment in lipid metabolic pathways, with Fasn identified as a hub gene. …”
  20. 18600

    Counting results on DRPD dataset. by Dunlu Lu (19964225)

    Published 2024
    “…This is particularly true in the agricultural field, where there is a multitude of crop types and a complex and variable environment. …”