Droplet boiling modes at different temperatures. by Lei Bai (631944)

“…With spray hole diameters ranging from 0.4 mm to 0.7 mm, the fractal dimensions of all droplet flames appear at around 2.6 seconds, but the values of <i>D</i>_<i>max</i> vary significantly. As the spray hole diameter (<i>S</i>) decreases, <i>D</i>_<i>max</i> approaches 2. …”

Risk Classification Diagram of Hot Surface. by Lei Bai (631944)

“…With spray hole diameters ranging from 0.4 mm to 0.7 mm, the fractal dimensions of all droplet flames appear at around 2.6 seconds, but the values of <i>D</i>_<i>max</i> vary significantly. As the spray hole diameter (<i>S</i>) decreases, <i>D</i>_<i>max</i> approaches 2. …”

Physical parameters of engine lubricating oil. by Lei Bai (631944)

“…With spray hole diameters ranging from 0.4 mm to 0.7 mm, the fractal dimensions of all droplet flames appear at around 2.6 seconds, but the values of <i>D</i>_<i>max</i> vary significantly. As the spray hole diameter (<i>S</i>) decreases, <i>D</i>_<i>max</i> approaches 2. …”

Variation of heat flow with wall temperature. by Lei Bai (631944)

“…With spray hole diameters ranging from 0.4 mm to 0.7 mm, the fractal dimensions of all droplet flames appear at around 2.6 seconds, but the values of <i>D</i>_<i>max</i> vary significantly. As the spray hole diameter (<i>S</i>) decreases, <i>D</i>_<i>max</i> approaches 2. …”

<i>RR</i> of the spatial effect and distribution of hot-cold spots. by Weiwei Meng (304234)

a Positive ion mode correlation heat map (A) Mild PTOA group VS Severe PTOA group (B) Mild PTOA group VS normal group (C) Severe PTOA group VS normal group. b. Negative ion mode co... by Peng-fei Han (694841)

KEGG enrichment pathway chart (bubble chart) (A) mild PTOA group VS severe PTOA group (B) mild PTOA group VS normal group (C) Severe PTOA group VS normal group. by Peng-fei Han (694841)

Differential abundance scores of differential metabolic pathways (classified according to Pathway_Hierarchy) (A) mild PTOA group VS severe PTOA group (B) mild PTOA group VS normal... by Peng-fei Han (694841)

Effects of message content condition on outcomes. 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. …”

Participant characteristics, N = 502. 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. …”

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. …”

<i>Aedes aegypti</i> database from SAGO traps. by Jesús A. Aguilar-Durán (9931967)

“…Nonetheless, further research is needed to verify and validate their effectiveness at larger operational scales.</p></div>…”