Showing 1,381 - 1,400 results of 18,130 for search 'significantly ((less decrease) OR (((we decrease) OR (((nn decrease) OR (a decrease))))))', query time: 0.71s Refine Results
  1. 1381
  2. 1382

    Battery parameters. by Peijian Jin (22265437)

    Published 2025
    “…We observed a pattern in which the time intervals between the waveforms decreased rapidly at first and then stabilized. …”
  3. 1383

    Additional file 3 of Single-cell profiling reveals a reduced epithelial defense system, decreased immune responses and the immune regulatory roles of different fibroblast subpopula... by Lin Lin (46073)

    Published 2025
    “…Changes in the expression of TLRs, the chemokine system and metabolic activities in macrophages. (A) Violin plot showing the differences in the gene signature scores of chemokines and Toll-like receptors between CAG and control samples. …”
  4. 1384

    The aging parameters of each group of batteries. by Peijian Jin (22265437)

    Published 2025
    “…We observed a pattern in which the time intervals between the waveforms decreased rapidly at first and then stabilized. …”
  5. 1385

    Minimal data set. by Peijian Jin (22265437)

    Published 2025
    “…We observed a pattern in which the time intervals between the waveforms decreased rapidly at first and then stabilized. …”
  6. 1386

    Experimental lithium-ion batteries. by Peijian Jin (22265437)

    Published 2025
    “…We observed a pattern in which the time intervals between the waveforms decreased rapidly at first and then stabilized. …”
  7. 1387

    Schematic diagram of two time intervals. by Peijian Jin (22265437)

    Published 2025
    “…We observed a pattern in which the time intervals between the waveforms decreased rapidly at first and then stabilized. …”
  8. 1388
  9. 1389

    Major hyperparameters of RF-SVR. by Jintao Li (448681)

    Published 2024
    “…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R<sup>2</sup> value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”
  10. 1390

    Pseudo code for coupling model execution process. by Jintao Li (448681)

    Published 2024
    “…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R<sup>2</sup> value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”
  11. 1391

    Major hyperparameters of RF-MLPR. by Jintao Li (448681)

    Published 2024
    “…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R<sup>2</sup> value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”
  12. 1392

    Results of RF algorithm screening factors. by Jintao Li (448681)

    Published 2024
    “…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R<sup>2</sup> value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”
  13. 1393

    Schematic diagram of the basic principles of SVR. by Jintao Li (448681)

    Published 2024
    “…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R<sup>2</sup> value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”
  14. 1394
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  16. 1396
  17. 1397

    Table 1_Prognostic significance of fibrinogen levels in sepsis-associated acute kidney injury: unveiling a nonlinear relationship and clinical implications.docx by Manqin Chen (20129541)

    Published 2024
    “…Particularly, when their fibrinogen levels were less than 1.6 g/l, a concomitant decrease in 28-day mortality was observed as fibrinogen levels increased.…”
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