Col outcomes QR. by Felipe Agudelo-Hernández (20790521)

“…</p><p>Results</p><p>Statistically significant improvements were observed in human rights understanding, reduced stigmatizing attitudes toward mental health and decreased authoritarianism. …”

Pre-post comparison of study variables. by Felipe Agudelo-Hernández (20790521)

“…</p><p>Results</p><p>Statistically significant improvements were observed in human rights understanding, reduced stigmatizing attitudes toward mental health and decreased authoritarianism. …”

ELISA of the key proteins. by Du Leng (20421711)

“…KEGG pathway analysis showed a significant enrichment of DEPs in PI3K-Akt pathway and focal adhesion. …”

ONSD modifications induced by arterial paCO₂ changes. by Marco Carbonara (11483575)

Subjects:

Enrollment flow-chart. by Marco Carbonara (11483575)

Subjects:

Table 1 - by Marco Carbonara (11483575)

Subjects:

Middle cerebral artery mean flow velocity at three time points. by Marco Carbonara (11483575)

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Optic Nerve Sheath Diameter (ONSD) in mm, measured by ultrasound. by Marco Carbonara (11483575)

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ONSD at T14 and six months after ICU discharge. by Marco Carbonara (11483575)

Subjects:

Bland-Altman plot indicating the agreement between the optic nerve sheath diameter measured with CT scan and US. by Marco Carbonara (11483575)

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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. …”

Identification of differentially expressed proteins. by Du Leng (20421711)

Sectioning method. by Yihan Tu (22258445)

“…Additionally, welding sequences significantly affect residual stress magnitudes without altering their general distribution patterns. …”

Primer sequences used for RT-PCR. by Jingjing Chen (293564)

“…Notably, SIRT1 levels decrease with age in both mice and during cellular senescence, highlighting its significance in anti-aging processes. …”

Parametric studies. by Yihan Tu (22258445)

“…Additionally, welding sequences significantly affect residual stress magnitudes without altering their general distribution patterns. …”

FEM of HSS welded box-section. by Yihan Tu (22258445)

“…Additionally, welding sequences significantly affect residual stress magnitudes without altering their general distribution patterns. …”