Prediction formulas for fitting different indicators. by Yi Wang (32470)

Subjects: “…diffusion coefficients increased…”

FTIR test results of asphalt. by Yi Wang (32470)

Subjects: “…diffusion coefficients increased…”

MSD simulation results in asphalt fractions. by Yi Wang (32470)

Subjects: “…diffusion coefficients increased…”

Simulation of asphalt density. by Yi Wang (32470)

Subjects: “…diffusion coefficients increased…”

Basic properties of asphalt at different aging times. by Yi Wang (32470)

Subjects: “…diffusion coefficients increased…”

Basic performance results of aged asphalt regenerated from WEO. by Yi Wang (32470)

Subjects: “…diffusion coefficients increased…”

Performance index of 70# MA. by Yi Wang (32470)

Subjects: “…diffusion coefficients increased…”

Original data. by Yi Wang (32470)

Subjects: “…diffusion coefficients increased…”

Microscopic morphology of different asphalts (200 × , 1000 × , and 1500×). by Yi Wang (32470)

Subjects: “…diffusion coefficients increased…”

Minimal data set. by Xiaoxia Liu (294088)

“…In contrast, with a negative Soret coefficient, the growth rate of the spherical crystals increases as the coefficient decreases, and the solute concentration near the interface decreases. …”

Baseline characteristics of participants. by Mei Zhou (269746)

“…</p><p>Results</p><p>After DRG implementation, the logarithmic mean of total hospitalization expenditures decreased significantly (3.914 ± 0.837 vs. 3.872 ± 1.004), while rates of unplanned readmissions, unplanned reoperations, postoperative complications, and patient complaints within 30 days increased significantly (3.784% vs 4.214%, 0.083% vs 0.166%, 0.207% vs 0.258%, 3.741% vs 5.133%). …”

The framework diagram of this study. by Mei Zhou (269746)

“…</p><p>Results</p><p>After DRG implementation, the logarithmic mean of total hospitalization expenditures decreased significantly (3.914 ± 0.837 vs. 3.872 ± 1.004), while rates of unplanned readmissions, unplanned reoperations, postoperative complications, and patient complaints within 30 days increased significantly (3.784% vs 4.214%, 0.083% vs 0.166%, 0.207% vs 0.258%, 3.741% vs 5.133%). …”

Receiver operating characteristic curve analyses for the prediction of hepatitis C. by Lu Han (151620)

Subjects:

Exploring the correlation between systemic inflammation biomarkers and HCV infection (logarithm base 2). by Lu Han (151620)

Subjects:

Threshold effect analysis of systemic inflammatory biomarkers on hepatitis C using two-piece wise linear regression. by Lu Han (151620)

Subjects:

Exclusion and enrollment summary from NHANES. by Lu Han (151620)

Subjects:

Demographic information and baseline population characteristics. by Lu Han (151620)

Subjects:

Stratified associations between systemic inflammation biomarkers and hepatitis C (OR 95%, <i>P</i> value, LOG base 2). by Lu Han (151620)

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General addictive models demonstrate the relationship between systemic inflammatory biomarkers and anti-HCV. by Lu Han (151620)

Subjects:

Comparison of systemic inflammation biomarkers in infected and uninfected populations. by Lu Han (151620)

Subjects: