Means scores of CAM and CAM-MYCS. by Senay Yildirim-Kahriman (22693464)

“…A moderate negative correlation was observed between CAM and CAM-MYCS scores (r = −0.511; p < 0.001), indicating that increased awareness of real causes is associated with decreased belief in myths. …”

Effect of ibuprofen (IBU) on cancer cell apoptosis, autophagy and migration ability. by Katarina Grossmannova (7543517)

Effects of alcohol on bone morphology and alcohol metabolism ability in female and male rats. by Ming Cheng (494553)

“…<p>(<b>A</b>) TRAP staining image of femur with green arrows indicate osteoclasts. …”

Effects of bortezomib/venetoclax on the growth suppression of sLCL 381 xenografts in SCID mice. by Kam Pui Tam (19746865)

Effect of preplant treatments on botrytis fruit rot (BFR) diseases during the 2022/23 and 2023/24 growing seasons. by Baker D. Aljawasim (22765921)

Effect of preplant treatments on the total soluble solids (TSS) during the 2022/23 and 2023/24 growing seasons. by Baker D. Aljawasim (22765921)

Effect of preplant treatments on the pH of the fruit juice during the 2022/23 and 2023/24 growing seasons. by Baker D. Aljawasim (22765921)

Effect of preplant treatments on the biomass of infected fruits by other diseases during the 2022/23 and 2023/24 growing seasons. by Baker D. Aljawasim (22765921)

Bortezomib/venetoclax suppressed G1-S transition in sLCLs expressing EBNA-3C, LMP-1 and bortezomib-induced Noxa. by Kam Pui Tam (19746865)

Area under the curve responses to breakfast, lunch and dinner during the baseline week and follow-up week. by Jennifer S. Barrett (20308363)

Effect of preplant treatments and post-plant treatments on fruit firmness. by Baker D. Aljawasim (22765921)

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. …”

TMS recapitulates epigenetic age predictions and tissue-dependent effects identified via other technologies. by Amy Longtin (21409957)

The quality of PPG signals measured at different sensor heights. by Peter H. Charlton (11389289)

Model parameter indicators. by Hongwei Bai (1447999)

“…The classification is performed using a global max-pooling layer followed by a Softmax layer. …”

Age prediction based on spatial patterns of nodal overlap. by Tianyuan Lei (19683861)