Results of multiple logistic regression analysis to identify potential factors associated with the successful reduction of HPC and F+ coliphage in the POU UVC LED effluent. Single... by Hania Zanib (21864477)

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Robustness test based on the matching approach. by Ximin Ma (19713237)

“…This study revealed that the use of sanitary toilets was significantly associated with decreased health poverty vulnerability in adults over 45 years of age. …”

Pre and post-treatment mean EPG values on days 0, 7, 14, 21, and 28. by Sohail Afzal (10850709)

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Fecal egg count reduction percentage (FECRP). by Sohail Afzal (10850709)

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Food intake in groups A, B and C. by Sohail Afzal (10850709)

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Regional Characteristics in Ultradeep MXene Slit Nanopores: Insights from Molecular Dynamics Simulation by Kaiqing Sun (18715965)

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Regional Characteristics in Ultradeep MXene Slit Nanopores: Insights from Molecular Dynamics Simulation by Kaiqing Sun (18715965)

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Regional Characteristics in Ultradeep MXene Slit Nanopores: Insights from Molecular Dynamics Simulation by Kaiqing Sun (18715965)

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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. …”

Busulfan treatment induces orchitis. by Lingjun Zhao (21763122)

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Effect of busulfan treatment on androgens and androgen receptors. by Lingjun Zhao (21763122)

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Effects of busulfan treatment (6 mg/kg) on the epididymis and sperm of mice over a three-week period. by Lingjun Zhao (21763122)

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Effect of busulfan treatment on vimentin, a supporting cytoskeletal protein. by Lingjun Zhao (21763122)

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Effects of busulfan treatment on CX43 and N-Cadherin. by Lingjun Zhao (21763122)

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Effects of busulfan treatment on the ultrastructure of the blood-testis barrier (BTB). by Lingjun Zhao (21763122)

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Effect of busulfan treatment on the tight junction protein, occludin. by Lingjun Zhao (21763122)

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