Change in influenza vaccination uptake from May 2020 to October 2024 (weighted). by Hannah Melchinger (20234665)

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Major hyperparameters of RF-SVR. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Pseudo code for coupling model execution process. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Major hyperparameters of RF-MLPR. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Schematic diagram of the basic principles of SVR. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Example of sample data. by Xiying Wang (4859998)

“…The results reveal that as the number of nodes in the hidden layer increases, the model’s Mean Squared Error (MSE) and Relative Error (RE) show a decreasing trend, indicating an improvement in model prediction accuracy. …”

Structure of BPNN. by Xiying Wang (4859998)

“…The results reveal that as the number of nodes in the hidden layer increases, the model’s Mean Squared Error (MSE) and Relative Error (RE) show a decreasing trend, indicating an improvement in model prediction accuracy. …”

The workflow of EGA-BPNN. by Xiying Wang (4859998)

“…The results reveal that as the number of nodes in the hidden layer increases, the model’s Mean Squared Error (MSE) and Relative Error (RE) show a decreasing trend, indicating an improvement in model prediction accuracy. …”

S1 Data - by Xiying Wang (4859998)

“…The results reveal that as the number of nodes in the hidden layer increases, the model’s Mean Squared Error (MSE) and Relative Error (RE) show a decreasing trend, indicating an improvement in model prediction accuracy. …”

Algorithm flow of the GA-BPNN model. by Xiying Wang (4859998)

“…The results reveal that as the number of nodes in the hidden layer increases, the model’s Mean Squared Error (MSE) and Relative Error (RE) show a decreasing trend, indicating an improvement in model prediction accuracy. …”

Burden and trends of LC in the chinese annual cancer registry in 2004 and 2018. by Ze-Xing Zhang (18074512)

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EAPC in incidence and mortality of LC for age group. by Ze-Xing Zhang (18074512)

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Cases and deaths of LC from the Chinese Annual Cancer Registry, 2004–2018. by Ze-Xing Zhang (18074512)

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Changes in LC cases and deaths in male and female from 2004 to 2018, decomposed by three population-level determinants: population aging, population growth and epidemiological chan... by Ze-Xing Zhang (18074512)

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Projected ASIR and ASMR of LC in China over the next 15 years based on the Bayesian APC model. by Ze-Xing Zhang (18074512)

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The incidence and mortality data of LC by age group from the Chinese Annual Cancer Registry, 2004–2018. by Ze-Xing Zhang (18074512)

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Contribution of changes in population aging, population growth, and age-specific incidence rate to changes in incident cases of LC from 2004 to 2018 in China. by Ze-Xing Zhang (18074512)

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Description of Bivariable and multivariable binary logistic regression analysis showing an association between pathway to psychiatric care and associated factors among study partic... by Misrak Negash Shonor (22226735)

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Primers for qPCR. by Kaitao Zhao (3617825)

“…Results revealed the MRE11–RAD50–NBS1 (MRN) complex as a potential factor. Transiently or stably knockdown of MRE11, RAD50 or NBS1 in hepatocytes before HBV infection significantly decreased viral markers, including cccDNA, while reconstitution reversed the effect. …”

Antibodies used for western blotting. by Kaitao Zhao (3617825)

“…Results revealed the MRE11–RAD50–NBS1 (MRN) complex as a potential factor. Transiently or stably knockdown of MRE11, RAD50 or NBS1 in hepatocytes before HBV infection significantly decreased viral markers, including cccDNA, while reconstitution reversed the effect. …”