Percent change in yearly crude rates (between 2019 and 2021 and between 2019 and 2024) by gender and state. by Tony Wong (779773)

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

iBAT characterization of the <i>aP2-Drp1</i>^f/f neonatal mice. by Yuta Ibayashi (20404811)

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

Facilities offering modern family planning services by division in 2014 (A) and 2017 (B). by Rajon Banik (12066099)

Modern family planning service readiness score by division in 2014 (A) and 2017 (B). by Rajon Banik (12066099)

A Comparison of Pediatric Prehospital Opioid Encounters and Social Vulnerability by Stephen Sandelich (19991783)

“…The analysis demonstrated that as socioeconomic status (SES) improves, the likelihood of opioid-related activations increases significantly supported by a significant negative linear trend (Estimate = −0.2971, SE = 0.1172, z = −2.54, <i>p</i> = 0.0112. …”

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. …”

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. …”