Changes in alpha diversity in the weeks before anti-TNF-a treatment. by Katrine Carlsen (5477300)

BMI groups by SES. by Krystal Hunter (6820052)

“…Those who were LSES also had a curved relationship with PTB indicating that the as BMI increases, the odds of PTB decreases up until a BMI value, then the PTB rate increases. …”

BMISES_Data_Part2. by Krystal Hunter (6820052)

“…Those who were LSES also had a curved relationship with PTB indicating that the as BMI increases, the odds of PTB decreases up until a BMI value, then the PTB rate increases. …”

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

“…Those who were LSES also had a curved relationship with PTB indicating that the as BMI increases, the odds of PTB decreases up until a BMI value, then the PTB rate increases. …”

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

“…Those who were LSES also had a curved relationship with PTB indicating that the as BMI increases, the odds of PTB decreases up until a BMI value, then the PTB rate increases. …”

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

“…Those who were LSES also had a curved relationship with PTB indicating that the as BMI increases, the odds of PTB decreases up until a BMI value, then the PTB rate increases. …”

BMISES_Data_Part1. by Krystal Hunter (6820052)

“…Those who were LSES also had a curved relationship with PTB indicating that the as BMI increases, the odds of PTB decreases up until a BMI value, then the PTB rate increases. …”

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

“…Those who were LSES also had a curved relationship with PTB indicating that the as BMI increases, the odds of PTB decreases up until a BMI value, then the PTB rate increases. …”

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

“…Those who were LSES also had a curved relationship with PTB indicating that the as BMI increases, the odds of PTB decreases up until a BMI value, then the PTB rate increases. …”

Diagram of study population. by Krystal Hunter (6820052)

“…Those who were LSES also had a curved relationship with PTB indicating that the as BMI increases, the odds of PTB decreases up until a BMI value, then the PTB rate increases. …”

Data. by Aroon La-up (14095691)

“…Osteoporosis prevalence remained stable in both males and females. The Linear Mixed-Effects Model analysis revealed significant associations between BMD and several factors: increasing age, female sex, diabetes status and BMI. …”

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. …”