Showing 18,421 - 18,440 results of 18,449 for search 'significantly ((((step decrease) OR (((we decrease) OR (a decrease))))) OR (observed decrease))', query time: 0.53s Refine Results
  1. 18421

    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. …”
  2. 18422

    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). …”
  3. 18423

    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. …”
  4. 18424

    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. …”
  5. 18425

    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. …”
  6. 18426

    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. …”
  7. 18427

    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. …”
  8. 18428

    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. …”
  9. 18429

    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. 18430

    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. 18431

    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. 18432

    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. …”
  13. 18433

    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. …”
  14. 18434

    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. …”
  15. 18435

    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. …”
  16. 18436

    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.…”
  17. 18437

    Lower serum LH level was related to poor embryo quality and adverse pregnancy outcomes in fixed GnRH antagonist protocol with estradiol pretreatment by Ying Chen (9697)

    Published 2024
    “…</p> <p>Lower serum LH represents as a potential indicator for embryo quality and reproductive outcomes in GnRH antagonist fixed protocol pretreated with estradiol. …”
  18. 18438

    Data Sheet 1_Synchronized nutrition fertilizer improves soil microbial diversity and sugarcane yield through agrochemical and enzymatic regulation.docx by Xiuxiu Qi (22354858)

    Published 2025
    “…In 2020, soil microbial diversity was enriched by SNFert through regulating microbial communities and functions. As a new kind of chemical fertilizer, SNF1 overcame the shortcomings of CF in reducing the soil microbial diversity, that was, there were no statistically differences in microbial alpha diversity indices between SNF1 and CK. …”
  19. 18439

    Table 1_Enrichment of short-chain fatty acid-producing bacteria by pH-responsive sodium alginate and chitosan-encapsulated quercetin.docx by Qianyu Bai (21992984)

    Published 2025
    “…Compared to the DSS group, the SA-Q-CS MPs treatment group showed significant improvements, with the Disease Activity Index (DAI) and histopathological scores reduced by more than 66.9%, pro-inflammatory factor levels decreased by 65%, antioxidant levels increased over sevenfold, and tight junction protein expression elevated by more than threefold. …”
  20. 18440

    Neurodevelopmental deposition of H2AK119ub within acetylated euchromatin. by Aditya Parmar (22331873)

    Published 2025
    “…<b>(E)</b> Volcano plot depicting H2AK119ub in the early and late cerebellum, as quantified from normalized CUT&RUN data, within H3K4me3 peak regions identified the early cerebellum. The significance threshold was a p-value <0.05, as computed using edgeR and limma (n = 4). …”