The linearly decreasing convergence factor a. by Junhao Wei (6816803)

The injectable contraceptives depot medroxyprogesterone acetate and norethisterone enanthate substantially and differentially decrease testosterone and sex hormone binding globulin... by Chanel Avenant (409756)

Univariate linear regression analysis of scales. by Xiangyu Wang (341093)

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. …”

BMI groups by SES. by Krystal Hunter (6820052)

“…This relationship was not found in higher economic status women. Our study had two significant findings. We first found an obesity paradox in PTB for those mothers who are LSES. …”

BMISES_Data_Part2. by Krystal Hunter (6820052)

“…This relationship was not found in higher economic status women. Our study had two significant findings. We first found an obesity paradox in PTB for those mothers who are LSES. …”

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

“…This relationship was not found in higher economic status women. Our study had two significant findings. We first found an obesity paradox in PTB for those mothers who are LSES. …”

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

“…This relationship was not found in higher economic status women. Our study had two significant findings. We first found an obesity paradox in PTB for those mothers who are LSES. …”

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

“…This relationship was not found in higher economic status women. Our study had two significant findings. We first found an obesity paradox in PTB for those mothers who are LSES. …”

BMISES_Data_Part1. by Krystal Hunter (6820052)

“…This relationship was not found in higher economic status women. Our study had two significant findings. We first found an obesity paradox in PTB for those mothers who are LSES. …”

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

“…This relationship was not found in higher economic status women. Our study had two significant findings. We first found an obesity paradox in PTB for those mothers who are LSES. …”

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

“…This relationship was not found in higher economic status women. Our study had two significant findings. We first found an obesity paradox in PTB for those mothers who are LSES. …”

Diagram of study population. by Krystal Hunter (6820052)

“…This relationship was not found in higher economic status women. Our study had two significant findings. We first found an obesity paradox in PTB for those mothers who are LSES. …”

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. …”