Spatial and temporal variation of the mean abundance of coral recruits at the 10 stations located on the three major habitats, for the five categories (all taxa pooled, Acroporidae... by Radonirina Lebely Botosoamananto (13989736)

Sample means, SDs, and selected correlations. by Erik Lykke Mortensen (7239170)

Mean amplitude values during a complete 360° rotation for the external oblique muscle. by Christoph Anders (4068499)

Mean amplitude values during a complete 360° rotation for the internal oblique muscle. by Christoph Anders (4068499)

Mean amplitude values during a complete 360° rotation for the rectus abdominis muscle. by Christoph Anders (4068499)

Effects of warming and nitrogen deposition on plant species diversity. Different lowercase letters indicated that there were significant differences in different nitrogen depositio... by Xuemei Xiang (20756894)

Subjects:

Means with standard errors across time points for the outcome of mental health knowledge. by Ana Beato (20489933)

Model-averaged coefficients, significance, and relative importance (RI) of the predictor variables for the GLMMs to evaluate effects on: (A) overall pollinator abundance; (B) fruit... by Carmelo Gómez-Martínez (9384385)

ATGL-1 levels increase and lipid droplets decrease in response to <i>N. parisii</i> infection. by Hala Tamim El Jarkass (16625309)

Mean uncertainty of <i>boro</i> rice yields estimated by random forest model from 2002–2021. by Varun Tiwari (9258130)

Decreased expression of tight junction proteins and disruption of intestinal barrier integrity in DSS-induced colitis mouse model. by Mariam Wed Eladham (21439745)

Mean counts of fish caught per unit effort (± SE) per reef type. by Maryann S. Watson (16879881)

Generated spline library. by Zhe Hu (787283)

“…Following model updates with measured data, the accumulated prediction error rapidly decreases. The proposed prediction method for shape errors during pushing exhibits high accuracy and versatility in similar projects, significantly reducing time spent on manual error handling and minimizing computational inaccuracies.…”

Correlation coefficient matrix. by Zhe Hu (787283)

“…Following model updates with measured data, the accumulated prediction error rapidly decreases. The proposed prediction method for shape errors during pushing exhibits high accuracy and versatility in similar projects, significantly reducing time spent on manual error handling and minimizing computational inaccuracies.…”

RMSE versus learning rate. by Zhe Hu (787283)

“…Following model updates with measured data, the accumulated prediction error rapidly decreases. The proposed prediction method for shape errors during pushing exhibits high accuracy and versatility in similar projects, significantly reducing time spent on manual error handling and minimizing computational inaccuracies.…”

RMSE versus training parameters. by Zhe Hu (787283)

“…Following model updates with measured data, the accumulated prediction error rapidly decreases. The proposed prediction method for shape errors during pushing exhibits high accuracy and versatility in similar projects, significantly reducing time spent on manual error handling and minimizing computational inaccuracies.…”

Assembly process of machine recognition form. by Zhe Hu (787283)

“…Following model updates with measured data, the accumulated prediction error rapidly decreases. The proposed prediction method for shape errors during pushing exhibits high accuracy and versatility in similar projects, significantly reducing time spent on manual error handling and minimizing computational inaccuracies.…”

Process of steel truss incremental launching. by Zhe Hu (787283)

“…Following model updates with measured data, the accumulated prediction error rapidly decreases. The proposed prediction method for shape errors during pushing exhibits high accuracy and versatility in similar projects, significantly reducing time spent on manual error handling and minimizing computational inaccuracies.…”

CGAN and AutoML stacking device. by Zhe Hu (787283)

“…Following model updates with measured data, the accumulated prediction error rapidly decreases. The proposed prediction method for shape errors during pushing exhibits high accuracy and versatility in similar projects, significantly reducing time spent on manual error handling and minimizing computational inaccuracies.…”

U-wave estimates versus R-matrix noise variance. by Zhe Hu (787283)

“…Following model updates with measured data, the accumulated prediction error rapidly decreases. The proposed prediction method for shape errors during pushing exhibits high accuracy and versatility in similar projects, significantly reducing time spent on manual error handling and minimizing computational inaccuracies.…”