Showing 13,101 - 13,120 results of 18,442 for search 'significantly ((((teer decrease) OR (((a decrease) OR (mean decrease))))) OR (linear decrease))', query time: 0.78s Refine Results
  1. 13101

    Correlation coefficient matrix. by Zhe Hu (787283)

    Published 2025
    “…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”
  2. 13102

    Actual measurement of shape errors. by Zhe Hu (787283)

    Published 2025
    “…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”
  3. 13103

    RMSE versus learning rate. by Zhe Hu (787283)

    Published 2025
    “…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”
  4. 13104

    RMSE versus training parameters. by Zhe Hu (787283)

    Published 2025
    “…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”
  5. 13105

    Assembly process of machine recognition form. by Zhe Hu (787283)

    Published 2025
    “…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”
  6. 13106

    Process of steel truss incremental launching. by Zhe Hu (787283)

    Published 2025
    “…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”
  7. 13107

    CGAN and AutoML stacking device. by Zhe Hu (787283)

    Published 2025
    “…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”
  8. 13108

    Comprehensive prediction process of shape errors. by Zhe Hu (787283)

    Published 2025
    “…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”
  9. 13109

    Shape error manual calculation process. by Zhe Hu (787283)

    Published 2025
    “…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”
  10. 13110

    Data Sheet 1_Temperature influences mood: evidence from 11 years of Baidu index data in Chinese provincial capitals.csv by Mengjiao Yin (10995604)

    Published 2025
    “…Conversely, a 1°C increase in DTR led to decreases of 30.35%, 31.19%, and 15.41% in these indices (p < 0.05). …”
  11. 13111

    U-wave estimates versus R-matrix noise variance. by Zhe Hu (787283)

    Published 2025
    “…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”
  12. 13112

    Sliding window process. by Zhe Hu (787283)

    Published 2025
    “…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”
  13. 13113

    Original record form of error matrix. by Zhe Hu (787283)

    Published 2025
    “…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”
  14. 13114

    Form for machine recognition. by Zhe Hu (787283)

    Published 2025
    “…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”
  15. 13115

    RMSE versus architectural parameters. by Zhe Hu (787283)

    Published 2025
    “…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”
  16. 13116

    Kalman process. by Zhe Hu (787283)

    Published 2025
    “…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”
  17. 13117

    Attention mechanism. by Zhe Hu (787283)

    Published 2025
    “…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”
  18. 13118

    Shape error measurement results statistics. by Zhe Hu (787283)

    Published 2025
    “…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”
  19. 13119

    Path regulates glial proliferation via the mTOR-S6K pathway. by Qian Dong (414788)

    Published 2025
    “…Path downregulation and defects in BBB expansion consequently led to a decrease in brain AAs, such as Leu, which hinders brain tumor protein synthesis to slow down tumor growth. …”
  20. 13120

    Five years comparation of efficacy and safety after ICL-V4c implantation for high and super high myopia correction by Qi Wan (271320)

    Published 2024
    “…Compared to baseline, we observed a significant increase in IOP at the 1-week follow-up, which decreased significantly at the one-month visit. …”