Sample of study stimuli by content condition. by Rhyan N. Vereen (10765678)

“…</p><p>Results</p><p>The CI campaign had significantly higher perceived cultural relevance (M = 4.61) than the general audience (M = 3.64) or control (M = 3.66; p’s<0.05) campaigns. …”

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. …”

Raw data_clean. by Carlos Sepúlveda (15272797)

“…Conversely, being overweight or obese is associated with lower CRF, which can lead to decreased daily energy expenditure and reduced physical activity. …”

Experimental design. by Carlos Sepúlveda (15272797)

“…Conversely, being overweight or obese is associated with lower CRF, which can lead to decreased daily energy expenditure and reduced physical activity. …”

Characterization of the participants. by Carlos Sepúlveda (15272797)

“…Conversely, being overweight or obese is associated with lower CRF, which can lead to decreased daily energy expenditure and reduced physical activity. …”

Spectral-domain optical coherence tomography (SD-OCT) images taken from a 72-year-old female patient with neovascular age-related macular degeneration (nAMD) who received ranibizum... by Zheyao Gu (20910794)

Experimental procedures. by Ellen Chan (22177558)

“…For ipsilateral erector spinae (ES) to rectus abdominis (RA) ratio, significant time effect (p = 0.022), between-group differences (p = 0.031), and real-time reduction during forward walking in left swing phase, and significant between-group differences (p = 0.024), time-and-group interaction effect (p = 0.009), and real-time increase during backward walking in right swing phase were noted. …”

Experimental procedures. by Ellen Chan (22177558)

“…For ipsilateral erector spinae (ES) to rectus abdominis (RA) ratio, significant time effect (p = 0.022), between-group differences (p = 0.031), and real-time reduction during forward walking in left swing phase, and significant between-group differences (p = 0.024), time-and-group interaction effect (p = 0.009), and real-time increase during backward walking in right swing phase were noted. …”

Supplementary file of datasets. by Ellen Chan (22177558)

“…For ipsilateral erector spinae (ES) to rectus abdominis (RA) ratio, significant time effect (p = 0.022), between-group differences (p = 0.031), and real-time reduction during forward walking in left swing phase, and significant between-group differences (p = 0.024), time-and-group interaction effect (p = 0.009), and real-time increase during backward walking in right swing phase were noted. …”

Cox risk curve. by Zhendong Sun (4723221)

“…Under different weather conditions, compared to sunny days, parking duration is longer during moderate rain, with the probability of vehicles departing decreased by 8.6%, whereas during heavy rain, parking duration is shorter, with the probability of vehicles departing increased by 3%. …”

Weather factor dummy variable. by Zhendong Sun (4723221)

“…Under different weather conditions, compared to sunny days, parking duration is longer during moderate rain, with the probability of vehicles departing decreased by 8.6%, whereas during heavy rain, parking duration is shorter, with the probability of vehicles departing increased by 3%. …”

Day factor dummy variable. by Zhendong Sun (4723221)

“…Under different weather conditions, compared to sunny days, parking duration is longer during moderate rain, with the probability of vehicles departing decreased by 8.6%, whereas during heavy rain, parking duration is shorter, with the probability of vehicles departing increased by 3%. …”

Desensitized original data. by Zhendong Sun (4723221)

“…Under different weather conditions, compared to sunny days, parking duration is longer during moderate rain, with the probability of vehicles departing decreased by 8.6%, whereas during heavy rain, parking duration is shorter, with the probability of vehicles departing increased by 3%. …”

Cox survival function curve. by Zhendong Sun (4723221)

“…Under different weather conditions, compared to sunny days, parking duration is longer during moderate rain, with the probability of vehicles departing decreased by 8.6%, whereas during heavy rain, parking duration is shorter, with the probability of vehicles departing increased by 3%. …”

Data sample example. by Zhendong Sun (4723221)

“…Under different weather conditions, compared to sunny days, parking duration is longer during moderate rain, with the probability of vehicles departing decreased by 8.6%, whereas during heavy rain, parking duration is shorter, with the probability of vehicles departing increased by 3%. …”

The PL estimation result. by Zhendong Sun (4723221)

“…Under different weather conditions, compared to sunny days, parking duration is longer during moderate rain, with the probability of vehicles departing decreased by 8.6%, whereas during heavy rain, parking duration is shorter, with the probability of vehicles departing increased by 3%. …”