Illustrative images of the quantitative processing of polar maps from a myocardial perfusion study in one of the investigated patients. by Káryta Suely Macêdo Martins (22404155)

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Baseline and Post-Treatment Serum Cytokine Levels in Each Investigational Group. by Káryta Suely Macêdo Martins (22404155)

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Relative abundance of microbiota in colonic content at the genus level (n =  5). by Houkang Ding (20968091)

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Baseline and Post-Treatment Serum Levels of NT-proBNP in Each Investigational Group. by Káryta Suely Macêdo Martins (22404155)

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Variables obtained from the SF-36 quality of life questionnaire in each experimental group. by Káryta Suely Macêdo Martins (22404155)

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OPLS-DA score plot and volcano plot in the control and diarrhea groups. by Houkang Ding (20968091)

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Potential metabolites in the colonic tissue related with diarrhea induced by FSE. by Houkang Ding (20968091)

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Line graph showing the values of myocardial perfusion defects in the study groups. p = ANOVA Mixed Models. by Káryta Suely Macêdo Martins (22404155)

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Heatmap and pathway analysis of the potential metabolites in colon tissue related with diarrhea. by Houkang Ding (20968091)

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Data Sheet 1_Nicorandil reduces the antimicrobial effectiveness of polymyxin E against Klebsiella pneumoniae by decreasing reactive oxygen species accumulation.pdf by Rongqing Zhu (22261801)

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. …”

Table1_Organic fertilizer increases pumpkin production by improving soil fertility.docx by Fangfang Ren (1835083)

“…Compared to CK, the average soil bulk density was significantly decreased by 8.27–18.51% (P< 0.05); the soil organic carbon, available phosphorus, available potassium, and nitrate nitrogen under H treatment were increased by an average of 32.37%, 21.85%, 18.70%, and 36.97%, respectively. …”

Trend of total hospital attendance. by Yaw Nyarko Opoku-Boateng (20477302)

“…Month-on-month, antenatal and out-patient utilization decreased by 21,948.21 and 151,342.40, respectively. …”

Trend of outpatient (OPD) consultations. by Yaw Nyarko Opoku-Boateng (20477302)

“…Month-on-month, antenatal and out-patient utilization decreased by 21,948.21 and 151,342.40, respectively. …”

Variables used for the analysis. by Yaw Nyarko Opoku-Boateng (20477302)

“…Month-on-month, antenatal and out-patient utilization decreased by 21,948.21 and 151,342.40, respectively. …”

Trend of PNC attendance over the period. by Yaw Nyarko Opoku-Boateng (20477302)

“…Month-on-month, antenatal and out-patient utilization decreased by 21,948.21 and 151,342.40, respectively. …”