Patient selection flowchart. by Aihong Zheng (20571034)

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S2 Fig - by Aihong Zheng (20571034)

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(A-C) Force versus drill depth and torque versus drill depth. by Marsel Ganeyev (21453399)

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Increases in temperature for the two drill systems at different positions using different drilling procedures. The data in bold indicate the position where the highest mean tempera... by Marsel Ganeyev (21453399)

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Innovation and night light in large and small cities. by Saul Estrin (8629173)

Modeling results of 8-week-old female C57BL/6J mice. by Yan She (8639796)

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Sexual maturity status of female C57BL/6J Mice at different ages. by Yan She (8639796)

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Presents the bibliometric search results. by Yan She (8639796)

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Flow chart of data screening and analysis. by Yan She (8639796)

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S1 Data. Bibliometric analysis. S2 Data. Estrogen level analysis. S3 Data. MicroCT quantification. S4 Data. Markers of bone metabolism. by Yan She (8639796)

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Effects of ABZ on the viability of <i>M. corti.</i> by Inés Guarnaschelli (18102211)

“…Viability was not significantly affected for at least 3 days, and then decreased to 38% and 10% for ABZ 1 µM and 10 µM, respectively, by day 10. …”

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. …”

(A) Hierarchical clustering results of differential proteins in the Model.vs.WKY; (B) Differential protein enrichment bar chart in the Model.vs.WKY; (C) Significantly enriched path... by Xiao Zhang (152326)

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(a)(b)(c). Relationship between the OHAT-J score for “lips” or “saliva” or “dentures” and the BI at discharge. by Misaki Nakamura (756854)

Minimal data set. by Xiaoxia Liu (294088)

“…In contrast, with a negative Soret coefficient, the growth rate of the spherical crystals increases as the coefficient decreases, and the solute concentration near the interface decreases. …”

Demographic data of female participants (n = 30). by Vanessa Wellauer (18408477)

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