Mean scores compared with paired-samples <i>t</i>-test (and related effect sizes) at pre- and post-test by gender (<i>n</i> = 900). by Maria Anna Donati (6735548)

Fitness, structure, and performance of best fit random forest regression models for the winter mean water temperatures and accumulated degree-days of studied stream sites. by Mussie T. Beyene (13123933)

Mean <i>d’</i> during the SSDT before (Pre) and after (Post) receiving the touch manipulation in the CT optimal and non-CT optimal group. by Sofia Sacchetti (9647845)

Effect of BXL-01-0029 on IFNγ+TNFα-induced class I and class II HLA in Hfsmc. by Luigi Di Luigi (478292)

“…<p>A, Immunocytochemistry revealed no constitutive expression of class I and class II HLA in control Hfsmc (left A); positive staining for both antigens was observed after 24–48 h incubation with IFNγ+TNFα (middle A); BXL-01-0029 significantly counteracted cytokine-induced class II HLA expression at both time points, with no effect onto class I HLA (right A). …”

Back transformed least squares means of gargle rates (± standard error bars) across sites and months. by Katherine E. (Gentry) Richardson (12035254)

Unadjusted mean monthly admission and mortality numbers during all lockdown levels and post-COVID period. by Kimesh Loganathan Naidoo (19697486)

Oral L-Arg and D-Arg increased arginase protein expression in the liver, but L-Arg decreased it in the upper small intestine (Sm. Intes.), whereas L-Arg decreased arginase activity... by Dain (Raina) Kim (16726470)

Comparison of ∑PCDD/F, PCDD/F TEQ, ∑PCB, PCB TEQ and ∑TEQ concentrations in eggs analyzed in this study and other relevant studies (pg/g ww except for ∑PCB ng/g ww, geometric mean... by Matti Viluksela (321291)

Bone histology in <i>Cyp27b1</i>^<i>+/+</i> or <i>Cyp27b1</i>^{<i>–/–</i>}mice administered ALF or ELD. by Yoshihisa Hirota (616015)

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. …”