The top four countries or territories with the highest ASRs of PUD in 2021. by Wende Hao (10315597)

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Joinpoint regression analysis of the sex-specific ASRs for PUD globally from 1990 to 2021. by Wende Hao (10315597)

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Predicted trends in Age-standardized rates of PUD for both sexes globally over the next 19 years (2022-2040). by Wende Hao (10315597)

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Age-specific numbers and rates of PUD globally in 2021 for both sexes. by Wende Hao (10315597)

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<i>SLC2A2</i> is associated with IGF1R pathways differentiation. by Yejin Kim (740789)

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Changes in <i>SLC2A2</i> gene expression in hepatocellular carcinoma (HCC) based on its stage and correlation with oncofetal and stemness genes. by Yejin Kim (740789)

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Gene expression dynamics during liver development. by Yejin Kim (740789)

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Protein levels of GLUT2 were analyzed by western blotting. by Yejin Kim (740789)

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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. …”

Key characteristics of non-healed DU wound records at adopted branches in 2022 and 2023. by Heba Tallah Mohammed (2822432)

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Overall characteristics of wound records at adopted branches in 2022 and 2023. by Heba Tallah Mohammed (2822432)

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Relative Index of Inequality (RII) and 95% Confidence Interval (CI) for access question between most a least deprived SIMD decile by A) Age and sex B) Number of Health Issues and c... by David Henderson (4612354)

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FDA-approved reference marker and capturing wound image. by Heba Tallah Mohammed (2822432)

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Longitudinal assessment of animal weights and tumor metrics. by Robert Botelho (20308297)

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Study timeline and methodological overview. by Robert Botelho (20308297)

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Mendelian randomization analyses for the relevance of anti-H. pylori IgG seropositivity with risk of CHD. by Jing Wang (6206297)

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