Correlation between treatment-induced changes in lumbar motor control and N150 amplitude. by Soheila Qanbari (20455173)

Subjects:

Sensory-evoked potentials of N80 and N150. by Soheila Qanbari (20455173)

Subjects:

The mean and standard deviation of outcome measures. by Soheila Qanbari (20455173)

Subjects:

The demographical and clinical characteristics of subjects at baseline. by Soheila Qanbari (20455173)

Subjects:

Electrode montage and electric field distribution simulated with SimNIBS software. by Soheila Qanbari (20455173)

Subjects:

Stratification of cortisol and prolactin concentration by gravidity in peripheral blood and umbilical blood at delivery. by Francois Kiemde (5369657)

Subjects:

Cortisol and prolactin concentration in non-pregnant women. by Francois Kiemde (5369657)

Subjects:

S1 Data - by Francois Kiemde (5369657)

Subjects:

Interplay or correlation of cortisol on prolactin during pregnancy. by Francois Kiemde (5369657)

Subjects:

Dynamics of cortisol levels measured in pregnant women during pregnancy determined at different ANC visits. by Francois Kiemde (5369657)

Subjects:

Cortisol and prolactin concentration in peripheral blood and umbilical cord blood at delivery. by Francois Kiemde (5369657)

Subjects:

Malaria infection by gestation and gestational age. by Francois Kiemde (5369657)

Subjects:

Interplay or correlation of prolactin on cortisol during pregnancy. by Francois Kiemde (5369657)

Subjects:

Characteristic of study population. by Francois Kiemde (5369657)

Subjects:

Dynamic of prolactin in pregnant women during pregnancy. by Francois Kiemde (5369657)

Subjects:

The flexural lumber properties of Pinus patula Schiede ex Schltdl. & Cham. improve with decreasing initial tree spacing by Justin Erasmus (8702619)

“…</p><p dir="ltr">An 18- and a 19-year-old spacing experiment with four levels of initial tree spacing (1.83 m × 1.83 m, 2.35 m × 2.35 m, 3.02 m × 3.02 m and 4.98 m × 4.98 m) were sampled. Linear and non-linear mixed-effects models were developed to examine the effect of tree spacing on the quality of wood and lumber as defined by the modulus of elasticity, modulus of rupture and knot frequency of 208 boards and the ring-level microfibril angle and wood density of 86 radial strips.…”

Preference for the EIA – conjoint results. by Mehdi Mourali (10170245)

“…When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …”

Marginal means – Pooled across scenarios. by Mehdi Mourali (10170245)

“…When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …”

Sample attribute table. by Mehdi Mourali (10170245)

“…When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …”

Subgroup analysis – Political affiliation. by Mehdi Mourali (10170245)

“…When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …”