Probing Field Cancerization in the Gastrointestinal Tract Using a Hybrid Raman and Partial Wave Spectroscopy Microscope by Elena Kriukova (21524544)

“…Similarly, in the normal mucosa of Villin-Cre, Apc^fl/wt mice, the intensities of RS bands associated with amino acids increase significantly (<i>p</i> < 0.05) compared to controls, while the intensities of lipid-associated bands decrease significantly (<i>p</i> < 0.05). …”

Probing Field Cancerization in the Gastrointestinal Tract Using a Hybrid Raman and Partial Wave Spectroscopy Microscope by Elena Kriukova (21524544)

“…Similarly, in the normal mucosa of Villin-Cre, Apc^fl/wt mice, the intensities of RS bands associated with amino acids increase significantly (<i>p</i> < 0.05) compared to controls, while the intensities of lipid-associated bands decrease significantly (<i>p</i> < 0.05). …”

Probing Field Cancerization in the Gastrointestinal Tract Using a Hybrid Raman and Partial Wave Spectroscopy Microscope by Elena Kriukova (21524544)

“…Similarly, in the normal mucosa of Villin-Cre, Apc^fl/wt mice, the intensities of RS bands associated with amino acids increase significantly (<i>p</i> < 0.05) compared to controls, while the intensities of lipid-associated bands decrease significantly (<i>p</i> < 0.05). …”

Discriminatory learning preferentially enhances reward associated input. by Rahul Garg (3064578)

Development of binaural auditory brainstem physiology. by Kelsey L. Anbuhl (22190594)

Development of guinea pig monaural auditory brainstem responses (ABRs). by Kelsey L. Anbuhl (22190594)

Impact of mobility in a three-region system with heterogeneous epidemic conditions. by Mousumi Roy (8869412)

Navigation error analysis. by Haichao Li (225035)

“…Results show that SIDFM reduces navigation errors by 12.09% at low acceleration and 11.43% at high acceleration while also significantly decreasing positioning errors. These improvements enhance the stability, precision, and safety of AGVs in dynamic manufacturing environments. …”

Summary of related works. by Haichao Li (225035)

“…Results show that SIDFM reduces navigation errors by 12.09% at low acceleration and 11.43% at high acceleration while also significantly decreasing positioning errors. These improvements enhance the stability, precision, and safety of AGVs in dynamic manufacturing environments. …”

Research methodology flow diagram. by Haichao Li (225035)

“…Results show that SIDFM reduces navigation errors by 12.09% at low acceleration and 11.43% at high acceleration while also significantly decreasing positioning errors. These improvements enhance the stability, precision, and safety of AGVs in dynamic manufacturing environments. …”

Positioning error analysis. by Haichao Li (225035)

“…Results show that SIDFM reduces navigation errors by 12.09% at low acceleration and 11.43% at high acceleration while also significantly decreasing positioning errors. These improvements enhance the stability, precision, and safety of AGVs in dynamic manufacturing environments. …”

Error-Bar graph. by Haichao Li (225035)

“…Results show that SIDFM reduces navigation errors by 12.09% at low acceleration and 11.43% at high acceleration while also significantly decreasing positioning errors. These improvements enhance the stability, precision, and safety of AGVs in dynamic manufacturing environments. …”

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.…”

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.…”