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)

Baseline patient characteristics. by Oscar F. C. van den Bosch (22184246)

“…While mean respiratory rate was not affected, midazolam resulted in a significant decrease in both VRR (ß = −0.071, 95% CI: −0.120 to −0.021) and VTV (ß = −0.117, 95% CI: −0.170 to −0.062). …”

Table 1_NSAID use may decrease serum Klotho levels.docx by Jingchao Yan (14079168)

“…Subgroup analyses did not reveal any statistically significant interactions.</p>Conclusion<p>Contrary to previous speculations, the use of NSAIDs is associated with a decrease in serum Klotho levels.…”

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

Contrasting Size Dependence of Photochemical Lifetimes of Polypropylene and Expanded Polystyrene Microplastics in Surface Waters by Ariana Patterson (22764051)

“…Sunlight-driven photochemistry can dissolve buoyant microplastics, producing dissolved organic carbon (DOC). We hypothesized that plastic dissolution would increase linearly with increasing surface area (SA)-to-volume (V) ratio as plastics decrease in size. …”

Cohort characteristics. by Fernanda Talarico (807333)

“…</p><p>Results</p><p>The analysis reveals a significant decrease in all health services utilization from 2016 to 2019, followed by an increase until 2022. …”

Threading Behavior and Dynamics of Ring-Linear Polymer Blends under Poiseuille Flow by Deyin Wang (6028850)

“…We investigate the ring-linear polymer blends under Poiseuille flow across a range of flow intensities. …”

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. …”

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. …”