Showing 18,141 - 18,160 results of 18,177 for search 'significantly ((((teer decrease) OR (((we decrease) OR (a decrease))))) OR (greater decrease))', query time: 0.47s Refine Results
  1. 18141

    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. …”
  2. 18142

    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). …”
  3. 18143

    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. …”
  4. 18144

    Flow chart. by Aysenur Karakus (21192337)

    Published 2025
    “…Both TTH and migraine groups received PRT twice a week for six weeks,</p><p>Results</p><p>Within-group comparisons showed significant decreases in attack frequency, VAS, HIT-6, PCS, and WHODAS-II scores in both groups post-intervention (p<0.001). …”
  5. 18145

    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. …”
  6. 18146

    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. …”
  7. 18147

    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. …”
  8. 18148

    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. …”
  9. 18149

    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. …”
  10. 18150

    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. …”
  11. 18151

    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. …”
  12. 18152

    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. …”
  13. 18153

    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. …”
  14. 18154

    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. …”
  15. 18155

    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. …”
  16. 18156

    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. …”
  17. 18157

    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. …”
  18. 18158

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

    Published 2025
    “…<p><b>(A)</b> PbΔCBP-O ookinetes show regular motility. …”
  19. 18159

    Data Sheet 1_Characteristics of cardiopulmonary exercise capacity in adults with different degrees of obesity.pdf by Shukun Deng (20583833)

    Published 2025
    “…</p>Conclusion<p>With the aggravation of obesity, the maximum exercise ability of adults decreases, VO<sub>2peak</sub>/kg and VO<sub>2</sub>/HR<sub>max</sub>%Pred decreases, and the breathing reserve decreases.…”
  20. 18160

    Table 1_Hemerocallis citrina Baroni leaf total phenol alleviates depressive-like behaviors via modulating “microbiota-gut-brain” axis in chronic unpredictable mild stress -induced... by Yanping Wang (275956)

    Published 2025
    “…</p>Methods<p>In this study, the chronic unpredictable mild stress (CUMS) method was used to establish a rat model of depression to investigate the ameliorative effects of HLTP on depression.…”