Model prediction error trend chart. 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. …”

Basic physical parameters of red clay. 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. …”

BP neural network structure diagram. 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. …”

Structure diagram of GBDT 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. …”

Model prediction error analysis index. 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 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. …”

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. …”

Association of covariates and COPD risk. by Yushan Shi (16440272)

“…Additionally, restricted cubic splines were utilized to assess linearity. Furthermore, we conducted stratified and interaction analyses to evaluate the stability of the relationship in diverse subgroups.…”

BMI groups by SES. by Krystal Hunter (6820052)

“…We also found that the relationship between BMI and PTB was not linear but curvilinear, bridging the gap in the conclusions of other studies. …”

BMISES_Data_Part2. by Krystal Hunter (6820052)

“…We also found that the relationship between BMI and PTB was not linear but curvilinear, bridging the gap in the conclusions of other studies. …”

Logistic regression for LSES population. by Krystal Hunter (6820052)

“…We also found that the relationship between BMI and PTB was not linear but curvilinear, bridging the gap in the conclusions of other studies. …”

Logistic regression for HSES population. by Krystal Hunter (6820052)

“…We also found that the relationship between BMI and PTB was not linear but curvilinear, bridging the gap in the conclusions of other studies. …”

Logistic regression for overall population. by Krystal Hunter (6820052)

“…We also found that the relationship between BMI and PTB was not linear but curvilinear, bridging the gap in the conclusions of other studies. …”

BMISES_Data_Part1. by Krystal Hunter (6820052)

“…We also found that the relationship between BMI and PTB was not linear but curvilinear, bridging the gap in the conclusions of other studies. …”

Baseline characteristics of HSES/LSES population. by Krystal Hunter (6820052)

“…We also found that the relationship between BMI and PTB was not linear but curvilinear, bridging the gap in the conclusions of other studies. …”

Baseline characteristics of overall population. by Krystal Hunter (6820052)

“…We also found that the relationship between BMI and PTB was not linear but curvilinear, bridging the gap in the conclusions of other studies. …”

Diagram of study population. by Krystal Hunter (6820052)

“…We also found that the relationship between BMI and PTB was not linear but curvilinear, bridging the gap in the conclusions of other studies. …”

Study-related adverse events. by Benjamin R. Lewis (22279166)

“…In a linear mixed model analysis (LMM), the MBSR + PAP arm evidenced a significantly larger decrease in QIDS-SR-16 score than the MBSR-only arm from baseline to 2-weeks post-intervention (between-groups effect = 4.6, 95% CI [1.51, 7.70]; <i>p</i> = 0.008). …”

Study flow chart. by Benjamin R. Lewis (22279166)

“…In a linear mixed model analysis (LMM), the MBSR + PAP arm evidenced a significantly larger decrease in QIDS-SR-16 score than the MBSR-only arm from baseline to 2-weeks post-intervention (between-groups effect = 4.6, 95% CI [1.51, 7.70]; <i>p</i> = 0.008). …”

Study CONSORT diagram. by Benjamin R. Lewis (22279166)

“…In a linear mixed model analysis (LMM), the MBSR + PAP arm evidenced a significantly larger decrease in QIDS-SR-16 score than the MBSR-only arm from baseline to 2-weeks post-intervention (between-groups effect = 4.6, 95% CI [1.51, 7.70]; <i>p</i> = 0.008). …”