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. …”

Distance from the optimal direction for different levels of reward probabilities. by Jyotika Bahuguna (729510)

“…The distributions before plasticity are shown in blue. As the conflict increases, accuracy decreases and RTs show a lower average decrease after plasticity B) Cosine distances with respect to the RT (minimization), Accuracy (maximization) and Reward rate (maximization) vectors for the three reward probabilities. …”

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

The loss of bone in the femoral distal epiphysis is affected by housing type and weightlessness conditions in microgravity. by Rukmani Cahill (20939813)

“…(F) Conn.D is decreased in FL. Data shown are the mean ±  standard deviation with a scatter plot (ns: non-significant, * : p <  0.033, **: p <  0.002, ***: p <  0.0002). …”

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.…”

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.…”