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. …”

Fitting curve 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. …”

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. …”

Entangled Linear Polymer Solutions at High Shear: From Strain Softening to Hardening by Gengxin Liu (2050312)

“…The shear softening-to-hardening transition, which occurs as a function of the applied shear rate, happens at lower equivalent rate with decreasing temperature, violating the time–temperature superposition principle.…”

SMARD Performed on PacI-Linearized EBV Episomes Replicated in Raji Cells by Paolo Norio (3808)

“…<div><p>(A) Map of the PacI-linearized EBV genome with the positions of various genetic elements shown to scale. …”

Event-triggered fault detection for discrete-time linear multi-agent systems by Hajshirmohamadi, S.

“…The filter parameters and the event conditions are designed such that a mixed H?/H- performance index is guaranteed and it is shown that by using an event-triggered technique, the amount of data that is sent by each agent to its neighboring agents is dramatically decreased. …”
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Comparison of postoperative infection rate in linear mandibular fractures using antibiotic prophylaxis by Lucas Borin MOURA (6137987)

“…<p></p><p>Abstract Introduction Postoperative infections are a major complication in the treatment of mandibular fractures. …”

Investigation Into the Effects of Non-Linear Loading of Domestic Power Network on Home Appliances by Hossain, Ajad

“…This standard is of utmost importance on the present day situation due to increasing non-linear loading. Although the distortion limits are not applied to specific equipment, however, with a high penetration of non-linear loads, it is likely that some harmonic suppression may be necessary [8 − 12]. …”
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Threading Behavior and Dynamics of Ring-Linear Polymer Blends under Poiseuille Flow by Deyin Wang (6028850)

“…We investigate the ring-linear polymer blends under Poiseuille flow across a range of flow intensities. …”

Transcription of the Y-linked gigantic genes attenuates upon RNAi-mediated knockdown of <i>U2af38</i> and <i>SRPK</i>. by Jaclyn M. Fingerhut (6685103)

“…Linear best fit lines with negative slope indicate decreases in expression relative to the control for the RNAi conditions over the course of the gene span (5’ ➔ 3’).…”

Contribution of the through-mask leakages from the cavity and channel network distributed along the periphery of the mask; a–c and d–f are decreasing porosity <i>c</i><sub><i>k</i>... by Akshay Anand (118579)