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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Population characteristics by quartiles of dietary niacin intake. by Yushan Shi (16440272)

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Multivariate logistic regression analysis of dietary niacin for risk of COPD. by Yushan Shi (16440272)

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The relationship between dietary niacin intake and COPD stratified using different characteristics. by Yushan Shi (16440272)

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Association between dietary niacin intake and COPD odds ratio. by Yushan Shi (16440272)

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Flow diagram of study participants. by Yushan Shi (16440272)

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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. …”

Results of RF algorithm screening factors. 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. …”

Minimal data set. by Pornpan Chalermkitpanit (22110121)

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Univariate and multivariate analysis of variables associated with good and poor responders to TFESI. by Pornpan Chalermkitpanit (22110121)

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The correlation between clinical presentations and baseline serum inflammatory cytokines before TFESI. by Pornpan Chalermkitpanit (22110121)

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The serum concentrations of cytokines between good responders and poor responders to TFESI. by Pornpan Chalermkitpanit (22110121)

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Demographic data and baseline characteristics between good and poor responders to TFESI. by Pornpan Chalermkitpanit (22110121)

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The correlation between the duration of pain symptoms and baseline serum inflammatory cytokine levels before TFESI. by Pornpan Chalermkitpanit (22110121)

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Supplementary results_detailed. by Pornpan Chalermkitpanit (22110121)

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Architecture of Multi-Layer U-Net Model. by Muhammad Zubair (728141)

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