Showing 18,341 - 18,360 results of 18,390 for search '(( significant impact decrease ) OR ( significantly ((less decrease) OR (a decrease)) ))', query time: 0.52s Refine Results
  1. 18341

    Spato-temporal changes in land use types. 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. …”
  2. 18342

    Table of dataset division. 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. 18343

    Pattern indices of landscape levels. 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. …”
  4. 18344

    Table 1_MicroRNA-27b alleviates septic cardiomyopathy by targeting the Mff/MAVS axis.docx by Xincai Wang (7819064)

    Published 2025
    “…</p>Results<p>Bioinformatics analysis revealed significant downregulation of miR-27b in SCM cardiac tissues (log2FC=-3.9, P<0.001). …”
  5. 18345

    Striking image. 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. …”
  6. 18346

    Precision, recall, F1-Score 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. …”
  7. 18347

    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. …”
  8. 18348

    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. …”
  9. 18349

    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. …”
  10. 18350

    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. …”
  11. 18351

    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. …”
  12. 18352

    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. …”
  13. 18353

    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. …”
  14. 18354

    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. …”
  15. 18355

    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. …”
  16. 18356

    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. …”
  17. 18357

    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. …”
  18. 18358

    Presentation1_Pressure-tolerant survival mechanism of Schizophyllum commune 20R-7-F01 isolated from deep sediments 2 kilometers below the seafloor.pdf by Mengshi Zhao (20157978)

    Published 2024
    “…In this study, we investigated Schizophyllum commune 20R-7-F01 isolated from coal-bearing sediments at a depth of 2 km below the seafloor. By assessing the cell viability, biomass, and cell wall thickness changes of strain 20-7-1 under different HHP conditions, we observed that, compared to 0.1 MPa, strain 20-7-1 exhibited slower growth rates and decreased cell viability at 15 MPa and 35 MPa, yet demonstrated significant pressure tolerance. …”
  19. 18359

    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. …”
  20. 18360

    PbCBP-O is required for efficient midgut traversal. by Dominika Kwecka (22170493)

    Published 2025
    “…<p><b>(A)</b> PbΔCBP-O ookinetes show regular motility. …”