The six most abundant miRNAs within AdMSC-EVs. by Xiaoqin Li (1399105)

“…Colony formation assays showed that the colony-forming efficiency of LECs significantly increased in the presence of AdMSC-EVs. …”

Mixed embankment settlement monitoring results. by Yunke Liu (4839084)

“…The results show that the cement-phosphogypsum-red clay unconfined compressive strength decreases with the increase of the number of wet and dry cycles, with a larger decay in the first three times and leveling off thereafter. …”

Test road monitoring results. by Yunke Liu (4839084)

“…The results show that the cement-phosphogypsum-red clay unconfined compressive strength decreases with the increase of the number of wet and dry cycles, with a larger decay in the first three times and leveling off thereafter. …”

Schematic diagram of the wet/dry cycle process. by Yunke Liu (4839084)

“…The results show that the cement-phosphogypsum-red clay unconfined compressive strength decreases with the increase of the number of wet and dry cycles, with a larger decay in the first three times and leveling off thereafter. …”

Quantitative analysis table of mix composition. by Yunke Liu (4839084)

“…The results show that the cement-phosphogypsum-red clay unconfined compressive strength decreases with the increase of the number of wet and dry cycles, with a larger decay in the first three times and leveling off thereafter. …”

Basic physical indexes of red clay. by Yunke Liu (4839084)

“…The results show that the cement-phosphogypsum-red clay unconfined compressive strength decreases with the increase of the number of wet and dry cycles, with a larger decay in the first three times and leveling off thereafter. …”

Sample preparation process diagram. by Yunke Liu (4839084)

“…The results show that the cement-phosphogypsum-red clay unconfined compressive strength decreases with the increase of the number of wet and dry cycles, with a larger decay in the first three times and leveling off thereafter. …”

Layout plan of settlement monitoring points. by Yunke Liu (4839084)

“…The results show that the cement-phosphogypsum-red clay unconfined compressive strength decreases with the increase of the number of wet and dry cycles, with a larger decay in the first three times and leveling off thereafter. …”

SCA-2 curing agent basic parameters. by Yunke Liu (4839084)

“…The results show that the cement-phosphogypsum-red clay unconfined compressive strength decreases with the increase of the number of wet and dry cycles, with a larger decay in the first three times and leveling off thereafter. …”

Scatterplots of respiratory rate and age by sex. by Ina-Maria Rückert-Eheberg (2824901)

“…</p><p>Results</p><p>Respiratory rate decreased slightly from youngest to middle-aged women and men and increased in old age. …”

Flow chart of the study population. by Ina-Maria Rückert-Eheberg (2824901)

“…</p><p>Results</p><p>Respiratory rate decreased slightly from youngest to middle-aged women and men and increased in old age. …”

Neonatal deletion of Yap in Scx-lineage cells disrupts collagen production in the tendon during postnatal development. by Heather L. Dingwall (14573796)

Flow chart of the study design. by Ramita Gupta (21512558)

“…VO₂max increased by 4.4 ml/kg/min (95% CI: 2.9 to 6.0; p < 0.001, d = 1.31), and 10m sprint time decreased by 0.32 seconds (95% CI: -0.45 to -0.19; p < 0.001, d = 1.36) in forwards. …”

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. …”

HFD impairs autophagic activity in fly brains. by Tong Yue (6033305)

Probing the Histamine H₁ Receptor Binding Site to Explore Ligand Binding Kinetics by Sebastiaan Kuhne (1474948)

“…This study illustrates that for H₁R, there are several ways to increase RT but the different strategies differ significantly in SKR.…”