Data Sheet 1_Beat-to-beat coronary wave intensity analysis: implications of backward waves originating from microvascular.docx by Xinzhou Xie (587158)

Supplementary file 1_Spatiotemporal monitoring in beidagang wetland using Landsat time-series images and Google Earth Engine during 2000–2022.docx by Xinyue Zhang (271120)

“…<p>Wetlands are composed of the interaction of water, soil and suitable vegetation, which has rich biological resources and strong ecological benefits. Due to increasing human disturbance and the effects of climate change, wetlands are being dramatically degraded and destroyed. …”

Data Sheet 1_A comparative analysis of nutritional content changes in six Chinese cuisines prepared using industrial versus traditional hand-cooked modes.docx by Xuan Wang (55634)

“…The fatty acid profiles were consistent with the fat content, and mineral content demonstrated a moderate increase under both cooking conditions. An inter-group t-test indicated no significant differences in nutrient content changes between the two cooking modes (p > 0.05), except for vitamin B6 retention, which was significantly lower in traditional hand-cooked modes compared to industrial modes (p < 0.05).…”

Additional file 7 of Regulatory changes in the fatty acid elongase eloF underlie the evolution of sex-specific pheromone profiles in Drosophila prolongata by Yige Luo (21224529)

Hourly loading variations. by Zuhair Alaas (20868907)

“…The simulation findings demonstrate the enhanced PO version’s efficacy, showing a significant decrease in losses of energy. With the Ajinde 62-node grid, the suggested PO version obtains a substantial 30.81% decrease in the total energy loss expenses in contrast to the initial scenario. …”

IEEE 69 node system. by Zuhair Alaas (20868907)

“…The simulation findings demonstrate the enhanced PO version’s efficacy, showing a significant decrease in losses of energy. With the Ajinde 62-node grid, the suggested PO version obtains a substantial 30.81% decrease in the total energy loss expenses in contrast to the initial scenario. …”

PV allowable capacity and voltage boundaries. by Zuhair Alaas (20868907)

“…The simulation findings demonstrate the enhanced PO version’s efficacy, showing a significant decrease in losses of energy. With the Ajinde 62-node grid, the suggested PO version obtains a substantial 30.81% decrease in the total energy loss expenses in contrast to the initial scenario. …”

Single-Line scheme of Ajinde 62-node grid. by Zuhair Alaas (20868907)

“…The simulation findings demonstrate the enhanced PO version’s efficacy, showing a significant decrease in losses of energy. With the Ajinde 62-node grid, the suggested PO version obtains a substantial 30.81% decrease in the total energy loss expenses in contrast to the initial scenario. …”

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. …”

LPGA-N₂ isotherms of coal samples. by Jin Wang (29560)

“…It shows that the role of peak cluster landform conditions on coal pore structure is significant, and the extent of the role decreases with the increase of vertical principal stresses. …”

Vertical geostress calculation data. by Jin Wang (29560)

“…It shows that the role of peak cluster landform conditions on coal pore structure is significant, and the extent of the role decreases with the increase of vertical principal stresses. …”

Critical pore size values of each coal sample. by Jin Wang (29560)

“…It shows that the role of peak cluster landform conditions on coal pore structure is significant, and the extent of the role decreases with the increase of vertical principal stresses. …”

Determination of critical aperture. by Jin Wang (29560)

“…It shows that the role of peak cluster landform conditions on coal pore structure is significant, and the extent of the role decreases with the increase of vertical principal stresses. …”

Sampling points and mountain elevation. by Jin Wang (29560)

“…It shows that the role of peak cluster landform conditions on coal pore structure is significant, and the extent of the role decreases with the increase of vertical principal stresses. …”

Pressurized mercury curve. by Jin Wang (29560)

“…It shows that the role of peak cluster landform conditions on coal pore structure is significant, and the extent of the role decreases with the increase of vertical principal stresses. …”

Pore size distribution of coal samples. by Jin Wang (29560)

“…It shows that the role of peak cluster landform conditions on coal pore structure is significant, and the extent of the role decreases with the increase of vertical principal stresses. …”