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

Conceptual diagram of survival curve and loss of life expectancy. by Hiroaki Saito (24514)

This figure is the PRISMA used in synthesis of the systematic review. by Aahan Arif (20189512)

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Characteristics of included studies, mental health outcomes recorded, and study tools used. by Aahan Arif (20189512)

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Relationship between <i>β</i>₁ and <i>β</i>₂ and compressive strength. by YongHe Liu (20477123)

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Porosity changes in concrete after F–T under three curing conditions. by YongHe Liu (20477123)

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The association of cybervictimization with mental health outcomes. by Aahan Arif (20189512)

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This file includes the data availability tables that are relevant to data curation in the systematic review. by Aahan Arif (20189512)

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Fitting equation of material parameters <i>β</i>₁ and <i>β</i>₂ and compressive strength in Pei model. by YongHe Liu (20477123)

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

(a, b). The statistical error analyses for the models using the same dataset. by Fahd Saeed Alakbari (10701871)

Coal seam combustion (a), (c) and infrared thermometry map (b), (d) in Fire Zone 1 and 3 (Republished from Desheng Xie under a CC BY license, with permission from Desheng Xie, orig... by Desheng Xie (21439869)

The restoration effect carried out in the early stage of (a) Fire Zone 1 and (b) Fire Zone 3 (Republished from Desheng Xie under a CC BY license, with permission from Desheng Xie,... by Desheng Xie (21439869)

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. …”