Increase in physical activity (100 step, 500 steps or 1000 steps) after the challenge according to decrease in screen time < or ≥60 minutes. by Annaëlle Le Steunf (19838110)

RMSE versus learning rate. by Zhe Hu (787283)

“…However, prediction accuracy diminishes for longer time steps as the step length increases. Following model updates with measured data, the accumulated prediction error rapidly decreases. …”

RMSE versus training parameters. by Zhe Hu (787283)

“…However, prediction accuracy diminishes for longer time steps as the step length increases. Following model updates with measured data, the accumulated prediction error rapidly decreases. …”

RMSE versus architectural parameters. by Zhe Hu (787283)

“…However, prediction accuracy diminishes for longer time steps as the step length increases. Following model updates with measured data, the accumulated prediction error rapidly decreases. …”

NN analysis to assign the importance of variables. by Sulmaz Ghahramani (3387635)

The test steps. by Ruihong Wang (1932328)

The RMSE of point to point movement results. by John David Prieto Prada (11883577)

RMSE. by Pengfei Wu (3200082)

RMSE metrics for different models at varying input sizes. by Xiangyue Zhang (9251306)

RMSE after embedding the CP module into different models. by Xiangyue Zhang (9251306)

Oxidative Nickel-Catalyzed <i>ortho</i>-C–H Amination of (Iso)quinolines with Alicyclic Amines Directed by a Sacrificial <i>N</i>‑Oxide Group by Weiqi Zhu (17845973)

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RMSE results. by YueSheng Jiang (19267984)

Point range comparisons of RMSE. by Steven M. Pawley (18265087)

Creep failure mode and sketch map of step-step fractured rock. by Dengke Yang (842532)

Mean values and 95% confidence intervals for recovery time, Average NN Interval (AVNN); Standard Deviation of NN Intervals (SDNN), Low Frequency/ High Frequency Ratio (LF/HF); and... by Robert Trybulski (10998702)

Definitions of treatment step classification. by Rieko Kondo (10010647)

Performance through MAE, MSE and RMSE. by Ghulam Mustafa (458105)

“…Artificial Intelligence (AI) has increased the yield accuracy significantly. The existing Machine Learning (ML) methods are using statistical measures as regression, correlation and chi square test for predicting crop yield, all such model’s leads to low accuracy when the number of factors (variables) such as the weather and soil conditions, the wind, fertilizer quantity, and the seed quality and climate are increased. …”

Effect of step-by-step filling and compaction on the coarse aggregates. by Zhijun Liu (35566)

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Results of the step-by-step filling test on the coarse aggregates. by Zhijun Liu (35566)

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Gradation of the fine aggregates determined by step-by-step filling. by Zhijun Liu (35566)

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