Interaction panel of Treat × Period effect (T × P) for eating time (min/8-h) and sorting index (%) on sieve 2.36 mm and bottom pan as influenced by feeding pasteurized waste milk (... حسب Shahryar Kargar (18880600)

Interaction panel of Treat × Period effect (T × P) for ether extract intake (g/d) as influenced by feeding pasteurized waste milk (WM) or milk replacer (MR) to newborn Holstein cal... حسب Shahryar Kargar (18880600)

Interaction panel of Treat × Period effect (T × P) for starter intake (g/d), body weight (kg), average daily gain (g/d), and feed efficiency (g/g) as influenced by feeding pasteuri... حسب Shahryar Kargar (18880600)

Interaction panel of Treat × Period effect (T × P) for body measurements as influenced by feeding pasteurized waste milk (WM) or milk replacer (MR) to newborn Holstein calves (n =... حسب Shahryar Kargar (18880600)

Major hyperparameters of RF-SVR. حسب 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. حسب 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. حسب 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. حسب 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. حسب 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. …"

Heart rate dynamics during an interview assessment.: The figure shows the raw heart rate (HR) interval (blue line), the cleaned HR interval after artifact removal (gray line), and... حسب David Vagni (8060432)

الموضوعات:

Work-related, and psychological characteristics of science communicators. حسب David Vagni (8060432)

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This is an additional table reporting supplementary data about external injury scores. حسب David Vagni (8060432)

الموضوعات:

Bidimensional High-Low Frequency (HF, LF) plane representation of the interaction among Phase and High-Low Clarity Groups_: In the Low Clarity (LC) group there was a larg... حسب David Vagni (8060432)

الموضوعات:

Descriptive Statistic and Principal Component Analysis of Interview Judgments. حسب David Vagni (8060432)

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Kubios output showing HR data segmented by interview phases. حسب David Vagni (8060432)

الموضوعات:

Experimental Setup and live ECG recording during an on-air primetime TV interview: On the left side, the science communicator was interviewed by Marco Dedola, director of the “Scie... حسب David Vagni (8060432)

الموضوعات:

Selected Heart Rate Variability (HRV) measures during each 5-min segment in the pre-interview, interview, and post-interview phases. حسب David Vagni (8060432)

الموضوعات:

This is an additional table reporting all data recorded our experiment. حسب David Vagni (8060432)

الموضوعات:

Bidimensional Low-High Frequency Normalized Difference and High-Low Frequency Power (LHFND, LHFP) plane representation of the interaction among Phase and High-Low Clarity Groups<su... حسب David Vagni (8060432)

الموضوعات:

Judgments loading on the two rotated Content and Performance components_. حسب David Vagni (8060432)

الموضوعات: