Structure diagram of SPDConv. حسب Junyan Wang (4738518)

"…However, small object detection faces numerous challenges, such as significant difficulty, substantial interference from complex backgrounds, and inconsistent annotation quality. …"

Wise-IOU regression diagram. حسب Junyan Wang (4738518)

"…However, small object detection faces numerous challenges, such as significant difficulty, substantial interference from complex backgrounds, and inconsistent annotation quality. …"

Visualization of detection results. حسب Junyan Wang (4738518)

"…However, small object detection faces numerous challenges, such as significant difficulty, substantial interference from complex backgrounds, and inconsistent annotation quality. …"

Structure diagram of the SE attention mechanism. حسب Junyan Wang (4738518)

"…However, small object detection faces numerous challenges, such as significant difficulty, substantial interference from complex backgrounds, and inconsistent annotation quality. …"

Prevalence of low semen quality including 95% confidence interval (CI) for medication at the ATC 3 level. حسب P.G.J. ter Horst (21743830)

الموضوعات:

S1 Data - Medication, age and abstinence days associated with low semen quality: A cross-sectional study in more than 7000 men visiting the centre for reproductive medicine حسب P.G.J. ter Horst (21743830)

الموضوعات:

Univariable and multivariable analyses (age-adjusted), concerning a selection of medicaments p < 0.1 and n ≥ 10. حسب P.G.J. ter Horst (21743830)

الموضوعات:

Prevalence of low semen quality including 95% confidence interval (CI) for medication at the ATC 7 level. حسب P.G.J. ter Horst (21743830)

الموضوعات:

Univariable and multivariable analyses (age- and abstinence days-adjusted), concerning medicament(s) use and Low Semen Quality (LSQ). حسب P.G.J. ter Horst (21743830)

الموضوعات:

A. Proportion of men with Low Semen Quality (LSQ) including 95% confidence interval (CI) versus prevalence, overall and for men on medication(s) (M); B: Point estimates with 95% CI... حسب P.G.J. ter Horst (21743830)

الموضوعات:

Prevalence of low semen quality including 95% confidence interval (CI) for medication at the ATC 5 level. حسب P.G.J. ter Horst (21743830)

الموضوعات:

Univariable and multivariable analyses (age- and abstinence days-adjusted), concerning a selection of medicaments with n ≥ 10 medicament users and p < 0.1 by univariate analysis.... حسب P.G.J. ter Horst (21743830)

الموضوعات:

Top 10 significant functional annotations of up-regulated DEGs. حسب Meitner Cadena (22216261)

"…Functional annotations are ordered by decreasing significance, with color indicating significance according to the legend’s color scale, the ratio of genes on the horizontal axis, and DEG count represented by circle size.…"

Top 10 significant functional annotations of down-regulated DEGs. حسب Meitner Cadena (22216261)

"…Functional annotations are ordered by decreasing significance, with color indicating significance level based on the legend’s color scale, the ratio of genes on the horizontal axis, and DEG count represented by circle size.…"

The CDF of normal and tangential contact forces in numerical specimens after compression considering different particle morphologies: (a) normal contact force; (b) tangential conta... حسب Yongfeng Zhu (7361045)

Major hyperparameters of RF-SVR. حسب 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. حسب 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. حسب 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. حسب 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. حسب 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. …"