Showing 10,501 - 10,520 results of 21,342 for search '(( significant ((level decrease) OR (mean decrease)) ) OR ( significant decrease decrease ))', query time: 0.32s Refine Results
  1. 10501

    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. …”
  2. 10502

    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. …”
  3. 10503

    Appendices. by Tingting Cui (564531)

    Published 2024
    “…The factors within the Park attributes (G1) and the park’s social media level (G4) showed a two-way interaction strength increase. (4)The coefficients of influence of impact factors on the space heterogeneity of vacation park vitality exhibit significant variation. …”
  4. 10504

    Temporal change in average visitor vitality. by Tingting Cui (564531)

    Published 2024
    “…The factors within the Park attributes (G1) and the park’s social media level (G4) showed a two-way interaction strength increase. (4)The coefficients of influence of impact factors on the space heterogeneity of vacation park vitality exhibit significant variation. …”
  5. 10505

    Data source. by Tingting Cui (564531)

    Published 2024
    “…The factors within the Park attributes (G1) and the park’s social media level (G4) showed a two-way interaction strength increase. (4)The coefficients of influence of impact factors on the space heterogeneity of vacation park vitality exhibit significant variation. …”
  6. 10506

    Baidu heat map average. by Tingting Cui (564531)

    Published 2024
    “…The factors within the Park attributes (G1) and the park’s social media level (G4) showed a two-way interaction strength increase. (4)The coefficients of influence of impact factors on the space heterogeneity of vacation park vitality exhibit significant variation. …”
  7. 10507

    Overview of the study area. by Tingting Cui (564531)

    Published 2024
    “…The factors within the Park attributes (G1) and the park’s social media level (G4) showed a two-way interaction strength increase. (4)The coefficients of influence of impact factors on the space heterogeneity of vacation park vitality exhibit significant variation. …”
  8. 10508

    Single factor detection results. by Tingting Cui (564531)

    Published 2024
    “…The factors within the Park attributes (G1) and the park’s social media level (G4) showed a two-way interaction strength increase. (4)The coefficients of influence of impact factors on the space heterogeneity of vacation park vitality exhibit significant variation. …”
  9. 10509

    Types of interaction between two factors. by Tingting Cui (564531)

    Published 2024
    “…The factors within the Park attributes (G1) and the park’s social media level (G4) showed a two-way interaction strength increase. (4)The coefficients of influence of impact factors on the space heterogeneity of vacation park vitality exhibit significant variation. …”
  10. 10510

    Research frameworks. by Tingting Cui (564531)

    Published 2024
    “…The factors within the Park attributes (G1) and the park’s social media level (G4) showed a two-way interaction strength increase. (4)The coefficients of influence of impact factors on the space heterogeneity of vacation park vitality exhibit significant variation. …”
  11. 10511

    Model index comparison. by Tingting Cui (564531)

    Published 2024
    “…The factors within the Park attributes (G1) and the park’s social media level (G4) showed a two-way interaction strength increase. (4)The coefficients of influence of impact factors on the space heterogeneity of vacation park vitality exhibit significant variation. …”
  12. 10512

    Bioassay and NMR-HSQC-Guided Isolation and Identification of Phthalide Dimers with Anti-Inflammatory Activity from the Rhizomes of Angelica sinensis by Hongyan Wen (9226895)

    Published 2025
    “…Among them, compound <b>9</b> showed a remarkable inhibitory activity with IC<sub>50</sub> values of 425 nM and could significantly decrease IL-1β and IL-6 transcription levels. …”
  13. 10513

    Bioassay and NMR-HSQC-Guided Isolation and Identification of Phthalide Dimers with Anti-Inflammatory Activity from the Rhizomes of Angelica sinensis by Hongyan Wen (9226895)

    Published 2025
    “…Among them, compound <b>9</b> showed a remarkable inhibitory activity with IC<sub>50</sub> values of 425 nM and could significantly decrease IL-1β and IL-6 transcription levels. …”
  14. 10514

    An illustration of how GBT works. by Hoang Thanh Nhon (22311837)

    Published 2025
    “…The results indicate that incorporating business efficiency scores significantly enhances the models’ accuracy. For example, the deep neural network model shows a decrease in RMSE from 0.926 to 0.375, MAE from 0.337 to 0.196, and MAPE from 134.63 to 114.71. …”
  15. 10515

    The average performance of the test set. by Hoang Thanh Nhon (22311837)

    Published 2025
    “…The results indicate that incorporating business efficiency scores significantly enhances the models’ accuracy. For example, the deep neural network model shows a decrease in RMSE from 0.926 to 0.375, MAE from 0.337 to 0.196, and MAPE from 134.63 to 114.71. …”
  16. 10516

    The variables used and equations. by Hoang Thanh Nhon (22311837)

    Published 2025
    “…The results indicate that incorporating business efficiency scores significantly enhances the models’ accuracy. For example, the deep neural network model shows a decrease in RMSE from 0.926 to 0.375, MAE from 0.337 to 0.196, and MAPE from 134.63 to 114.71. …”
  17. 10517

    An illustration of how RF works. by Hoang Thanh Nhon (22311837)

    Published 2025
    “…The results indicate that incorporating business efficiency scores significantly enhances the models’ accuracy. For example, the deep neural network model shows a decrease in RMSE from 0.926 to 0.375, MAE from 0.337 to 0.196, and MAPE from 134.63 to 114.71. …”
  18. 10518

    The proposed method work-flow. by Hoang Thanh Nhon (22311837)

    Published 2025
    “…The results indicate that incorporating business efficiency scores significantly enhances the models’ accuracy. For example, the deep neural network model shows a decrease in RMSE from 0.926 to 0.375, MAE from 0.337 to 0.196, and MAPE from 134.63 to 114.71. …”
  19. 10519

    The basic flowchart of analysis in this study. by Jing Wang (6206297)

    Published 2025
    “…Finally, we discovered that the levels of <i>PDK1</i> expression in AD patients were remarkably upregulated, while <i>FDX1</i> and <i>GLS</i> were significantly decreased using qPCR.…”
  20. 10520

    The enriched GO terms of the 19 DECAGs. by Jing Wang (6206297)

    Published 2025
    “…Finally, we discovered that the levels of <i>PDK1</i> expression in AD patients were remarkably upregulated, while <i>FDX1</i> and <i>GLS</i> were significantly decreased using qPCR.…”