Showing 1,201 - 1,220 results of 2,529 for search 'significantly ((((((teer decrease) OR (greater decrease))) OR (nn decrease))) OR (linear decrease))', query time: 0.49s Refine Results
  1. 1201
  2. 1202
  3. 1203

    LSTM model. by Longfei Gao (698900)

    Published 2025
    “…According to the experimental results, when the grinding depth increases to 21 μm, the average training loss of the model further decreases to 0.03622, and the surface roughness Ra value significantly decreases to 0.1624 μm. …”
  4. 1204

    CNN model. by Longfei Gao (698900)

    Published 2025
    “…According to the experimental results, when the grinding depth increases to 21 μm, the average training loss of the model further decreases to 0.03622, and the surface roughness Ra value significantly decreases to 0.1624 μm. …”
  5. 1205

    Ceramic bearings. by Longfei Gao (698900)

    Published 2025
    “…According to the experimental results, when the grinding depth increases to 21 μm, the average training loss of the model further decreases to 0.03622, and the surface roughness Ra value significantly decreases to 0.1624 μm. …”
  6. 1206

    Geometric contact arc length model. by Longfei Gao (698900)

    Published 2025
    “…According to the experimental results, when the grinding depth increases to 21 μm, the average training loss of the model further decreases to 0.03622, and the surface roughness Ra value significantly decreases to 0.1624 μm. …”
  7. 1207

    Indentation fracture mechanics model. by Longfei Gao (698900)

    Published 2025
    “…According to the experimental results, when the grinding depth increases to 21 μm, the average training loss of the model further decreases to 0.03622, and the surface roughness Ra value significantly decreases to 0.1624 μm. …”
  8. 1208

    Grinding particle cutting machining model. by Longfei Gao (698900)

    Published 2025
    “…According to the experimental results, when the grinding depth increases to 21 μm, the average training loss of the model further decreases to 0.03622, and the surface roughness Ra value significantly decreases to 0.1624 μm. …”
  9. 1209

    Three stages of abrasive cutting process. by Longfei Gao (698900)

    Published 2025
    “…According to the experimental results, when the grinding depth increases to 21 μm, the average training loss of the model further decreases to 0.03622, and the surface roughness Ra value significantly decreases to 0.1624 μm. …”
  10. 1210

    CNN-LSTM action recognition process. by Longfei Gao (698900)

    Published 2025
    “…According to the experimental results, when the grinding depth increases to 21 μm, the average training loss of the model further decreases to 0.03622, and the surface roughness Ra value significantly decreases to 0.1624 μm. …”
  11. 1211

    Differences in magnitude and velocity of decay of the different compartments of the viral reservoir. by Maria C. Puertas (8801768)

    Published 2025
    “…<p>A. The overall decrease in each fraction of the viral reservoir during the first year after ART initiation is expressed as the ratio of week 48 to baseline values. …”
  12. 1212

    Simulation datasets. by Xiao Mo (2430355)

    Published 2025
    “…These results offer significant theoretical guidance for the design and improvement of needle-free injection.…”
  13. 1213

    Grid division diagram. by Ming Zhang (9736)

    Published 2025
    “…When the height is 2.5m and above, the windbreak efficiency is greater than 90%, and the windbreak effect is significantly improved. (2) The change of sand barrier height has a significant effect on the windbreak efficiency between the second and third sand barriers. (3) Among the three sand-blocking fences, when the height of the sand-blocking fence is 2.5m, the thickness of the sand is 50.51% and 58.33% higher than that of the 2m high sand-blocking fence, and the sand-blocking effect is the most significant. …”
  14. 1214

    Model calculation diagram. by Ming Zhang (9736)

    Published 2025
    “…When the height is 2.5m and above, the windbreak efficiency is greater than 90%, and the windbreak effect is significantly improved. (2) The change of sand barrier height has a significant effect on the windbreak efficiency between the second and third sand barriers. (3) Among the three sand-blocking fences, when the height of the sand-blocking fence is 2.5m, the thickness of the sand is 50.51% and 58.33% higher than that of the 2m high sand-blocking fence, and the sand-blocking effect is the most significant. …”
  15. 1215

    Grid independence verification. by Ming Zhang (9736)

    Published 2025
    “…When the height is 2.5m and above, the windbreak efficiency is greater than 90%, and the windbreak effect is significantly improved. (2) The change of sand barrier height has a significant effect on the windbreak efficiency between the second and third sand barriers. (3) Among the three sand-blocking fences, when the height of the sand-blocking fence is 2.5m, the thickness of the sand is 50.51% and 58.33% higher than that of the 2m high sand-blocking fence, and the sand-blocking effect is the most significant. …”
  16. 1216

    Antibodies used in this study. by Kevin J. Kokesh (19859571)

    Published 2024
    “…Protein kinase C (PKC) activity was greater in the CF cells compared to the non-CF cells, but the activity was significantly attenuated in both cell types after infection with <i>M</i>. …”
  17. 1217

    Time and flowrate used for proteomics. by Kevin J. Kokesh (19859571)

    Published 2024
    “…Protein kinase C (PKC) activity was greater in the CF cells compared to the non-CF cells, but the activity was significantly attenuated in both cell types after infection with <i>M</i>. …”
  18. 1218

    S1 Graphical abstract - by Kevin J. Kokesh (19859571)

    Published 2024
    “…Protein kinase C (PKC) activity was greater in the CF cells compared to the non-CF cells, but the activity was significantly attenuated in both cell types after infection with <i>M</i>. …”
  19. 1219
  20. 1220

    Mean parameter values for the selected crops. by Gourab Saha (8987405)

    Published 2025
    “…Furthermore, crop yield is predicted using Linear Regression and Random Forest, achieving accuracies of 93.49% and 95.87%, respectively, while using RMSE (Root Mean Squared Error) as the loss function. …”