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

الموضوعات:

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

الموضوعات:

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

الموضوعات:

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

الموضوعات:

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

الموضوعات:

Microhardness vs. depth diagram of sample No. 6 ( حسب 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. حسب 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. حسب 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. حسب Cristobal Padilla-Fortunatti (21376807)

الموضوعات:

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... حسب Cristobal Padilla-Fortunatti (21376807)

الموضوعات:

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

الموضوعات:

Structure diagram of ensemble model. حسب 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. حسب 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. حسب 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. حسب 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. حسب 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. حسب 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. حسب 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. حسب 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. حسب 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. …"