Showing 8,041 - 8,060 results of 18,229 for search 'significantly ((((((linear decrease) OR (teer decrease))) OR (a decrease))) OR (greater decrease))', query time: 0.58s Refine Results
  1. 8041

    Freezing of soil samples. by Haotian Guo (6620120)

    Published 2025
    “…The cooling rate significantly affected the frost heave ratio: under closed conditions, the ratio decreased, whereas under open water supply conditions, the vertical frost heave displacement increased with higher cooling rates.Moisture migration within the specimen was notably different under the two replenishment conditions. …”
  2. 8042

    Local schematic diagram of freezing device. by Haotian Guo (6620120)

    Published 2025
    “…The cooling rate significantly affected the frost heave ratio: under closed conditions, the ratio decreased, whereas under open water supply conditions, the vertical frost heave displacement increased with higher cooling rates.Moisture migration within the specimen was notably different under the two replenishment conditions. …”
  3. 8043

    Temperature curve of sample D1. by Haotian Guo (6620120)

    Published 2025
    “…The cooling rate significantly affected the frost heave ratio: under closed conditions, the ratio decreased, whereas under open water supply conditions, the vertical frost heave displacement increased with higher cooling rates.Moisture migration within the specimen was notably different under the two replenishment conditions. …”
  4. 8044

    The apparent cold structure of soil. by Haotian Guo (6620120)

    Published 2025
    “…The cooling rate significantly affected the frost heave ratio: under closed conditions, the ratio decreased, whereas under open water supply conditions, the vertical frost heave displacement increased with higher cooling rates.Moisture migration within the specimen was notably different under the two replenishment conditions. …”
  5. 8045

    Test Schedule. by Haotian Guo (6620120)

    Published 2025
    “…The cooling rate significantly affected the frost heave ratio: under closed conditions, the ratio decreased, whereas under open water supply conditions, the vertical frost heave displacement increased with higher cooling rates.Moisture migration within the specimen was notably different under the two replenishment conditions. …”
  6. 8046

    Temperature curve of sample T1. by Haotian Guo (6620120)

    Published 2025
    “…The cooling rate significantly affected the frost heave ratio: under closed conditions, the ratio decreased, whereas under open water supply conditions, the vertical frost heave displacement increased with higher cooling rates.Moisture migration within the specimen was notably different under the two replenishment conditions. …”
  7. 8047

    Table 1_Analysis of the microbiota of pregnant women in relation to weight gain during pregnancy – a pilot study.docx by Katarzyna Kosinska-Kaczynska (6794522)

    Published 2025
    “…While the difference was not statistically significant after correction for multiple testing, the Chao index showed a persistent trend toward reduced species richness in the study group. …”
  8. 8048

    Supplementary Material for: A case report of adult lead-poisoning followed by use of non-prescription Ayurvedic medication in Germany by figshare admin karger (2628495)

    Published 2025
    “…Abstract Introduction: This case report describes a 65-year-old female patient from Germany who developed clinically significant lead poisoning following the use of Ayurvedic herbal preparations provided by an unlicensed practitioner in a major German city. …”
  9. 8049

    Table_1_Higher oxidative balance score is associated with lower female infertility: a cross-sectional study.DOCX by Xiong Lei (3847897)

    Published 2024
    “…When OBS was used as a categorical variable, female infertility decreased by 60% in the highest OBS group compared with the lowest OBS group (OR, 0.40; 95% CI, 0.21 to 0.74). …”
  10. 8050

    PCA-CGAN model parameter settings. by Chao Tang (10925)

    Published 2025
    “…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”
  11. 8051

    MIT-BIH dataset proportion analysis chart. by Chao Tang (10925)

    Published 2025
    “…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”
  12. 8052

    Wavelet transform preprocessing results. by Chao Tang (10925)

    Published 2025
    “…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”
  13. 8053

    PCAECG_GAN. by Chao Tang (10925)

    Published 2025
    “…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”
  14. 8054

    MIT dataset expansion quantities and Proportions. by Chao Tang (10925)

    Published 2025
    “…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”
  15. 8055

    Experimental hardware and software environment. by Chao Tang (10925)

    Published 2025
    “…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”
  16. 8056

    PCA-CGAN K-fold experiment table. by Chao Tang (10925)

    Published 2025
    “…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”
  17. 8057

    Classification model parameter settings. by Chao Tang (10925)

    Published 2025
    “…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”
  18. 8058

    MIT-BIH expanded dataset proportion chart. by Chao Tang (10925)

    Published 2025
    “…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”
  19. 8059

    AUROC Graphs of RF Model and ResNet. by Chao Tang (10925)

    Published 2025
    “…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”
  20. 8060

    PCA-CGAN Model Workflow Diagram. by Chao Tang (10925)

    Published 2025
    “…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”