Absolute and relative ischaemic heart disease DALYs rate attributable to 2^nd level risk factors in Chile. by Camilo Briones-Valdivieso (21567535)

Absolute and relative stroke DALYs rate attributable to 2^nd level risk factors in Chile. by Camilo Briones-Valdivieso (21567535)

Absolute and relative overall CVD DALYs rate attributable to 2^nd-level risk factors in Chile. by Camilo Briones-Valdivieso (21567535)

Absolute and relative atrial fibrillation and flutter DALYs rate attributable to 2^nd level risk factors in Chile. by Camilo Briones-Valdivieso (21567535)

Absolute and relative hypertensive heart disease DALYs rate attributable to 2^nd level risk factors in Chile. by Camilo Briones-Valdivieso (21567535)

Absolute and relative aortic aneurysm DALYs rate attributable to 2^nd level risk factors in Chile. by Camilo Briones-Valdivieso (21567535)

Absolute and relative overall CVD DALYs rate attributable to 1^st-level risk factors in Chile. by Camilo Briones-Valdivieso (21567535)

Effects of clonal integration and <i>Bacillus subtilis</i> on total mass (A, E, I), leaf biomass (B, F, J), stolon biomass (C, G, K), and root biomass (D, H, L) of the basal, apica... by Bing-Nan Zhao (21553401)

Absolute and relative lower extremity peripheral arterial disease DALYs rate attributable to 2^nd level risk factors in Chile. by Camilo Briones-Valdivieso (21567535)

Effects of clonal integration and <i>Bacillus subtilis</i> on total flavonoids (A, C, E) and chlorogenic acid (B, D, F) of the basal, apical portion, and whole fragment of <i>Glech... by Bing-Nan Zhao (21553401)

Effects of clonal integration and <i>Bacillus subtilis</i> on ramet number (A, E, I), total stolon length (B, F, J), specific stolon length (C, G, K), and root-shoot ratio (D, H, L... by Bing-Nan Zhao (21553401)

Trends of the temperature, precipitation, and TVDI in the Santun River Irrigation Area, Xinjiang, during 2005–2023. by Yuxin Wei (11510974)

IRE1 depletion increases chromosome instability. by Iratxe Zuazo-Gaztelu (21071939)

Structure diagram of ensemble model. by Hongqi Wang (2208238)

“…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …”

Fitting formula parameter table. by Hongqi Wang (2208238)

“…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …”

Test plan. by Hongqi Wang (2208238)

“…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …”

Fitting surface parameters. by Hongqi Wang (2208238)

“…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …”

Model generalisation validation error analysis. by Hongqi Wang (2208238)

“…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …”

Empirical model prediction error analysis. by Hongqi Wang (2208238)

“…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …”

Fitting curve parameters. by Hongqi Wang (2208238)

“…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …”