The relationship between Δσ_n and τ₁, <i>JRC</i>. by Zhezhe Zhang (19704587)

“…Under the same <i>JRC</i>, σ_<i>i</i> increases with the increase of τ₁, and Δσ_n decreases with the increasing τ₁. Under the same <i>JRC</i> and σ_<i>i</i>, τ_<i>i</i> is significantly smaller under the UNLCSL path than the CNL path. …”

Relationship between τ_<i>i</i> and σ_<i>i</i>. by Zhezhe Zhang (19704587)

“…Under the same <i>JRC</i>, σ_<i>i</i> increases with the increase of τ₁, and Δσ_n decreases with the increasing τ₁. Under the same <i>JRC</i> and σ_<i>i</i>, τ_<i>i</i> is significantly smaller under the UNLCSL path than the CNL path. …”

Table_2_A novel anxiety-associated SNP identified in LYNX2 (LYPD1) is associated with decreased protein binding to nicotinic acetylcholine receptors.xlsx by Kristin R. Anderson (5478734)

Image_1_A novel anxiety-associated SNP identified in LYNX2 (LYPD1) is associated with decreased protein binding to nicotinic acetylcholine receptors.jpg by Kristin R. Anderson (5478734)

Table_1_A novel anxiety-associated SNP identified in LYNX2 (LYPD1) is associated with decreased protein binding to nicotinic acetylcholine receptors.pdf by Kristin R. Anderson (5478734)

Changes in circulatory variables during CPB. by Vincent Pey (21433304)

Prolonged starvation leads to a delay in cell cycle re-entry and decrease in H3K27ac in Vasa2+/Piwi1+ cells. by Eudald Pascual-Carreras (12115380)

“…During starvation (T_5ds, T_20ds), MFI levels of H3K27ac progressively decreased <b>(F)</b> while levels H3K27me3 did not change significantly <b>(G)</b>. …”

Evolution of arterial redox potential during CPB. by Vincent Pey (21433304)

Supplementary Material for: Significant Dry Weight Reduction After Transition from Peritoneal Dialysis to Hemodialysis by Lin Y.-T. (17065287)

“…After transitioning to HD, body weight decreased significantly, with a reduction of -2.8 kg at one month, -5.3 kg at three months, and -7.5 kg one year post-transition. …”

Cohort characteristics. by Vincent Pey (21433304)

“…</p><p>Results</p><p>Upon initiation of CPB we observed a significant decrease in arterial whole blood redox potential (101.90 mV + /- 11.52 vs. 41.80 mV + /- 10,26; p < 0.0001). …”

Analytical framework and statistical methods. by Na Chen (153323)

“…Our findings reveal significant variations in income insecurity and social protection responses across these groups. the pandemic had a significant impact on household incomes globally, with lower-middle-income countries experiencing the most significant income reductions. …”

Theoretical frameworks of social protection. by Na Chen (153323)

“…Our findings reveal significant variations in income insecurity and social protection responses across these groups. the pandemic had a significant impact on household incomes globally, with lower-middle-income countries experiencing the most significant income reductions. …”

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. …”