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.…”

Actual measurement of shape errors. 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.…”

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.…”

Assembly error angle of a single spline. 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.…”

Original record form of error 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.…”

Form for machine recognition. 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 architectural 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.…”

Attention mechanism. 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.…”

Shape error measurement results statistics. 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.…”

RipU^K60 impacts actin cytoskeleton organization at 48 hours post infiltration. by Rachel Hiles (20477573)

Spectral shift of fluorophore NHS-esters. by Hyunwoo Kim (690467)

Table_3_Widely targeted metabolomics and SPME-GC-MS analysis revealed the quality characteristics of non-volatile/volatile compounds in Zheng’an Bai tea.xls by Li Liu (75607)

“…In fresh leaves, the most significant differential metabolites (VIP > 1, p < 0.05) among different samples mainly include substances such as ethyl gallate, theaflavin, isovitexin and linalool, while the main differential metabolites of samples in the processing stage include alkaloids, polyphenols and flavonoids such as zarzissine, methyl L-Pyroglutamate, theaflavin 3,3’-digallate, euscaphic acid and ethyl gallate. …”

Image_4_Widely targeted metabolomics and SPME-GC-MS analysis revealed the quality characteristics of non-volatile/volatile compounds in Zheng’an Bai tea.tif by Li Liu (75607)

“…In fresh leaves, the most significant differential metabolites (VIP > 1, p < 0.05) among different samples mainly include substances such as ethyl gallate, theaflavin, isovitexin and linalool, while the main differential metabolites of samples in the processing stage include alkaloids, polyphenols and flavonoids such as zarzissine, methyl L-Pyroglutamate, theaflavin 3,3’-digallate, euscaphic acid and ethyl gallate. …”

Table_1_Widely targeted metabolomics and SPME-GC-MS analysis revealed the quality characteristics of non-volatile/volatile compounds in Zheng’an Bai tea.xls by Li Liu (75607)

“…In fresh leaves, the most significant differential metabolites (VIP > 1, p < 0.05) among different samples mainly include substances such as ethyl gallate, theaflavin, isovitexin and linalool, while the main differential metabolites of samples in the processing stage include alkaloids, polyphenols and flavonoids such as zarzissine, methyl L-Pyroglutamate, theaflavin 3,3’-digallate, euscaphic acid and ethyl gallate. …”

Image_2_Widely targeted metabolomics and SPME-GC-MS analysis revealed the quality characteristics of non-volatile/volatile compounds in Zheng’an Bai tea.tif by Li Liu (75607)

“…In fresh leaves, the most significant differential metabolites (VIP > 1, p < 0.05) among different samples mainly include substances such as ethyl gallate, theaflavin, isovitexin and linalool, while the main differential metabolites of samples in the processing stage include alkaloids, polyphenols and flavonoids such as zarzissine, methyl L-Pyroglutamate, theaflavin 3,3’-digallate, euscaphic acid and ethyl gallate. …”

Image_3_Widely targeted metabolomics and SPME-GC-MS analysis revealed the quality characteristics of non-volatile/volatile compounds in Zheng’an Bai tea.tif by Li Liu (75607)

“…In fresh leaves, the most significant differential metabolites (VIP > 1, p < 0.05) among different samples mainly include substances such as ethyl gallate, theaflavin, isovitexin and linalool, while the main differential metabolites of samples in the processing stage include alkaloids, polyphenols and flavonoids such as zarzissine, methyl L-Pyroglutamate, theaflavin 3,3’-digallate, euscaphic acid and ethyl gallate. …”