Pearson correlation of maternal variables and fetal heart rate. by Pedro Henrique Salles Brito (10441343)

Subjects:

Maternal cardiac output. by Pedro Henrique Salles Brito (10441343)

Subjects:

Maternal blood pH. by Pedro Henrique Salles Brito (10441343)

Subjects:

Maternal blood pO2. by Pedro Henrique Salles Brito (10441343)

Subjects:

Maternal central venous pressure. by Pedro Henrique Salles Brito (10441343)

Subjects:

Microhardness vs. depth diagram of sample No. 6 ( by Van-Thuc Nguyen (19469762)

“…The Travel speed factor appears to have the greatest effect, followed by the gas flow rate and current intensity. …”

Multi-organ differential gene expression changes statistically significant at hypertension onset. by Eden Hornung (20148295)

“…<i>Tgfb1</i> is significantly decreased in male SHR kidney compared to female at 16 weeks of age (p = 0.004). …”

Key safety measures including adverse events. by Anuja Dokras (8679261)

“…Waist circumference (mean change (MC) −2.23 cm; 95% CI [−3.98, −0.49]; <i>p</i> = 0.01), BMI (MC −0.49 kg/m²; 95% CI [−0.88, −0.10[; <i>p</i> = 0.01), and android fat mass measured by DXA (MC −167 g; 95% CI [−264, −71[; <i>p</i> < 0.001) decreased in the COCP group over the study period whilst there was no statistically significant changes in these parameters in the metformin only group when compared to baseline.. …”

Modified version of the Caregiving Stress Model for Post Intensive Care Syndrome – Family impairments. by Cristobal Padilla-Fortunatti (21376807)

Subjects:

SPIRIT schedule: Timeline of recruitment and assessment. <i>t</i>₀: Daily screening; <i>t</i>₁: Between the 3^rd and 10^th day of ICU admi... by Cristobal Padilla-Fortunatti (21376807)

Subjects:

Variables and generalized linear mixed models to explore PICS-F impairments. by Cristobal Padilla-Fortunatti (21376807)

Subjects:

Structure diagram of ensemble model. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Fitting formula parameter table. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Test plan. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Fitting surface parameters. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Model generalisation validation error analysis. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Empirical model prediction error analysis. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Fitting curve parameters. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Test instrument. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Empirical model establishment process. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”