Ball valve pipeline geometric modeling. by Weiqing Xu (1279959)

“…<div><p>Hydrogen is a clean energy source, and blending it with natural gas in existing pipeline networks is a key transition solution for transportation cost reduction. …”

Hydrogen volume fraction change in the pipeline. by Weiqing Xu (1279959)

“…<div><p>Hydrogen is a clean energy source, and blending it with natural gas in existing pipeline networks is a key transition solution for transportation cost reduction. …”

Mesh independence verification. by Weiqing Xu (1279959)

“…<div><p>Hydrogen is a clean energy source, and blending it with natural gas in existing pipeline networks is a key transition solution for transportation cost reduction. …”

Role of ALIX and TSG101 in the entry of PEDV or PEAV VLP into Vero cells. by Xiongnan Chen (13307178)

Chitosan-Based Thermal-Coagulation Hydrogel System Driven by Multiple Interactions: Oxidation-Induced Fast Gelation and Enhanced Performance by Yifan Liu (219681)

Valve parameters and simulation results. by Weiqing Xu (1279959)

“…<div><p>Hydrogen is a clean energy source, and blending it with natural gas in existing pipeline networks is a key transition solution for transportation cost reduction. …”

Undulation pipeline geometric modeling. by Weiqing Xu (1279959)

“…<div><p>Hydrogen is a clean energy source, and blending it with natural gas in existing pipeline networks is a key transition solution for transportation cost reduction. …”

Chitosan-Based Thermal-Coagulation Hydrogel System Driven by Multiple Interactions: Oxidation-Induced Fast Gelation and Enhanced Performance by Yifan Liu (219681)

Chitosan-Based Thermal-Coagulation Hydrogel System Driven by Multiple Interactions: Oxidation-Induced Fast Gelation and Enhanced Performance by Yifan Liu (219681)

Time pressure: a controlled experiment of test case development and requirements review by Mika Mäntylä (406301)

“…<div>REF: Mäntylä M. V., Petersen K., Lehtinen, T. O. A., Lassenius, C. …”

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. Additionally, a secondary validation using experimental data from other researchers confirms the model’s good agreement with previous results, demonstrating its robust generalization ability. …”

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. Additionally, a secondary validation using experimental data from other researchers confirms the model’s good agreement with previous results, demonstrating its robust generalization ability. …”

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. Additionally, a secondary validation using experimental data from other researchers confirms the model’s good agreement with previous results, demonstrating its robust generalization ability. …”

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. Additionally, a secondary validation using experimental data from other researchers confirms the model’s good agreement with previous results, demonstrating its robust generalization ability. …”

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. Additionally, a secondary validation using experimental data from other researchers confirms the model’s good agreement with previous results, demonstrating its robust generalization ability. …”

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. Additionally, a secondary validation using experimental data from other researchers confirms the model’s good agreement with previous results, demonstrating its robust generalization ability. …”

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. Additionally, a secondary validation using experimental data from other researchers confirms the model’s good agreement with previous results, demonstrating its robust generalization ability. …”

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. Additionally, a secondary validation using experimental data from other researchers confirms the model’s good agreement with previous results, demonstrating its robust generalization ability. …”

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. Additionally, a secondary validation using experimental data from other researchers confirms the model’s good agreement with previous results, demonstrating its robust generalization ability. …”

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. Additionally, a secondary validation using experimental data from other researchers confirms the model’s good agreement with previous results, demonstrating its robust generalization ability. …”