Numbers and age-standardized incidence, prevalence, deaths and DALYs rates of peptic ulcer disease in 2021. by Wende Hao (10315597)

Subjects:

Proportion of PUD DALYs and deaths attributable to smoking by gender, SDI quintile, and GBD region in 1990 and 2021. by Wende Hao (10315597)

Subjects:

S1 File - Global burden and risk factors of peptic ulcer disease between 1990 and 2021: An analysis from the global burden of disease study 2021 by Wende Hao (10315597)

Subjects:

Numbers and age-standardized incidence, prevalence, deaths and DALYs rates of peptic ulcer disease in 1990. by Wende Hao (10315597)

Subjects:

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

Subjects:

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

Subjects:

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

Subjects:

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

Subjects:

Frontier analysis based on SDI and age-standardized DALYs rate for thyroid cancer among adolescents and young adults. by Zijian Qiu (17560477)

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. …”

Comparison of left ventricular contractile function between male and female mutant mice 2 weeks after transverse aortic constriction (TAC). by Zhen Zhou (184390)

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

Subjects:

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

Subjects:

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)

Subjects:

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

Subjects:

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

Subjects: