Protein-protein interaction modeling reveals a putative CXCL10 binding site on GP63 that closely approximates the GP63 active site and CXCL10 cleavage site. by Alejandro L. Antonia (7147739)

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. …”

Test instrument. 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 establishment process. 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 prediction error trend chart. 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. …”

Basic physical parameters of red clay. 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. …”

BP neural network structure diagram. 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. …”

Structure diagram of GBDT 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. …”

Model prediction error analysis index. 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 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. …”

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. …”

Synthesis, Structure, and Dynamic Behavior of Cyclopentadienyl-Lithium, -Sodium, and -Potassium Annelated with Bicyclo[2.2.2]octene Units:  A Systematic Study on Site Exchange of A... by Tohru Nishinaga (1630696)

“…Within this range, a tendency was observed for the Δ<i>G</i>^⧧ values to increase as the size of the metal decreased. Theoretical calculations (B3LYP/6-31G(d)) afforded considerably large values as the gas-phase dissociation energy for <b>1</b>−M (162.7 kcal mol^-1 for M = Li; 131.6 kcal mol^-1 for M = Na; 110.9 kcal mol^-1 for M = K) and for <b>2</b>−M (170.0 kcal mol^-1 for M = Li; 137.5 kcal mol^-1 for M = Na; 115.4 kcal mol^-1 for M = K). …”

Synthesis, Structure, and Dynamic Behavior of Cyclopentadienyl-Lithium, -Sodium, and -Potassium Annelated with Bicyclo[2.2.2]octene Units:  A Systematic Study on Site Exchange of A... by Tohru Nishinaga (1630696)

“…Within this range, a tendency was observed for the Δ<i>G</i>^⧧ values to increase as the size of the metal decreased. Theoretical calculations (B3LYP/6-31G(d)) afforded considerably large values as the gas-phase dissociation energy for <b>1</b>−M (162.7 kcal mol^-1 for M = Li; 131.6 kcal mol^-1 for M = Na; 110.9 kcal mol^-1 for M = K) and for <b>2</b>−M (170.0 kcal mol^-1 for M = Li; 137.5 kcal mol^-1 for M = Na; 115.4 kcal mol^-1 for M = K). …”

Synthesis, Structure, and Dynamic Behavior of Cyclopentadienyl-Lithium, -Sodium, and -Potassium Annelated with Bicyclo[2.2.2]octene Units:  A Systematic Study on Site Exchange of A... by Tohru Nishinaga (1630696)

“…Within this range, a tendency was observed for the Δ<i>G</i>^⧧ values to increase as the size of the metal decreased. Theoretical calculations (B3LYP/6-31G(d)) afforded considerably large values as the gas-phase dissociation energy for <b>1</b>−M (162.7 kcal mol^-1 for M = Li; 131.6 kcal mol^-1 for M = Na; 110.9 kcal mol^-1 for M = K) and for <b>2</b>−M (170.0 kcal mol^-1 for M = Li; 137.5 kcal mol^-1 for M = Na; 115.4 kcal mol^-1 for M = K). …”