Video 1_The clinical application value of mixed reality in robotic laparoscopic partial nephrectomy.mp4 by Xin Chang Zou (20129301)

Hypothesis 3: The mean difference between “Class 1” (a school attack happens within <i>k</i> kms. of a given school) and “Class 0” (no school attack happens within <i>k</i> kms. of... by Lirika Sola (21560710)

<i>Leptospira interrogans</i> from Okinawa induces epithelial barrier disruption with different kinetics. by Tetsuya Kakita (4922584)

Data_Sheet_1_Age-related changes in EEG signal using triple correlation values.docx by Yuri Watanabe (19735819)

“…<p>The alpha rhythm in human electroencephalography (EEG) is known to decrease in frequency with age. Previous study has shown that elderly individuals with dementia exhibit higher S values (spatial variability) and SD values (temporal variability) in the triple correlation of the occipital region (P3, P4, Oz) compared to healthy elderly individuals. …”

Individual numerical values for figures. by Ruiling Zhong (22339779)

Gas-Phase-like Inertial Rotation of a Platonic Solid in Reticular Amphidynamic Crystals of CUB-5: Molecular Dynamics and Experiment by Jing-Ran Shan (16641931)

Subjects:

Structure diagram of ensemble model. by Hongqi Wang (2208238)

“…Furthermore, we quantitatively analyze the specific influence of water content and other factors on the thermal conductivity of stabilized soil and construct a comprehensive prediction model encompassing BP neural network, gradient boosting decision tree, and linear regression models. …”

Fitting formula parameter table. by Hongqi Wang (2208238)

“…Furthermore, we quantitatively analyze the specific influence of water content and other factors on the thermal conductivity of stabilized soil and construct a comprehensive prediction model encompassing BP neural network, gradient boosting decision tree, and linear regression models. …”

Test plan. by Hongqi Wang (2208238)

“…Furthermore, we quantitatively analyze the specific influence of water content and other factors on the thermal conductivity of stabilized soil and construct a comprehensive prediction model encompassing BP neural network, gradient boosting decision tree, and linear regression models. …”

Fitting surface parameters. by Hongqi Wang (2208238)

“…Furthermore, we quantitatively analyze the specific influence of water content and other factors on the thermal conductivity of stabilized soil and construct a comprehensive prediction model encompassing BP neural network, gradient boosting decision tree, and linear regression models. …”

Model generalisation validation error analysis. by Hongqi Wang (2208238)

“…Furthermore, we quantitatively analyze the specific influence of water content and other factors on the thermal conductivity of stabilized soil and construct a comprehensive prediction model encompassing BP neural network, gradient boosting decision tree, and linear regression models. …”

Empirical model prediction error analysis. by Hongqi Wang (2208238)

“…Furthermore, we quantitatively analyze the specific influence of water content and other factors on the thermal conductivity of stabilized soil and construct a comprehensive prediction model encompassing BP neural network, gradient boosting decision tree, and linear regression models. …”

Fitting curve parameters. by Hongqi Wang (2208238)

“…Furthermore, we quantitatively analyze the specific influence of water content and other factors on the thermal conductivity of stabilized soil and construct a comprehensive prediction model encompassing BP neural network, gradient boosting decision tree, and linear regression models. …”

Test instrument. by Hongqi Wang (2208238)

“…Furthermore, we quantitatively analyze the specific influence of water content and other factors on the thermal conductivity of stabilized soil and construct a comprehensive prediction model encompassing BP neural network, gradient boosting decision tree, and linear regression models. …”

Empirical model establishment process. by Hongqi Wang (2208238)

“…Furthermore, we quantitatively analyze the specific influence of water content and other factors on the thermal conductivity of stabilized soil and construct a comprehensive prediction model encompassing BP neural network, gradient boosting decision tree, and linear regression models. …”

Model prediction error trend chart. by Hongqi Wang (2208238)

“…Furthermore, we quantitatively analyze the specific influence of water content and other factors on the thermal conductivity of stabilized soil and construct a comprehensive prediction model encompassing BP neural network, gradient boosting decision tree, and linear regression models. …”

Basic physical parameters of red clay. by Hongqi Wang (2208238)

“…Furthermore, we quantitatively analyze the specific influence of water content and other factors on the thermal conductivity of stabilized soil and construct a comprehensive prediction model encompassing BP neural network, gradient boosting decision tree, and linear regression models. …”

BP neural network structure diagram. by Hongqi Wang (2208238)

“…Furthermore, we quantitatively analyze the specific influence of water content and other factors on the thermal conductivity of stabilized soil and construct a comprehensive prediction model encompassing BP neural network, gradient boosting decision tree, and linear regression models. …”

Structure diagram of GBDT model. by Hongqi Wang (2208238)

“…Furthermore, we quantitatively analyze the specific influence of water content and other factors on the thermal conductivity of stabilized soil and construct a comprehensive prediction model encompassing BP neural network, gradient boosting decision tree, and linear regression models. …”

Model prediction error analysis index. by Hongqi Wang (2208238)

“…Furthermore, we quantitatively analyze the specific influence of water content and other factors on the thermal conductivity of stabilized soil and construct a comprehensive prediction model encompassing BP neural network, gradient boosting decision tree, and linear regression models. …”