Significant clusters: Coronal PET images showing brain regions with significant (p < 0.001 and K > 50) metabolic inhibition (A-G) and activation (H-I) in rats following cocaine sel... by Christopher Rowan (21499924)

Cinacalcet administered early in the inactive phase markedly decrease parathyroid Ki-67 index. by Søren Egstrand (10906087)

“…All groups were compared by Kruskal Wallis test with <i>post hoc</i> test after Dunn with Bonferroni adjustment showing significant decreased Ki-67 labeling index of <i>Cina1</i> compared to <i>Cina2</i> (p = 0.006) and the untreated CKD groups (p = 0.0001 and p = 0.0002, respectively). …”

Microhardness of various samples. by Huashen Guan (20454677)

Subjects:

Shot peening process parameters. by Huashen Guan (20454677)

Subjects:

Schematic diagram of pin-disk wear. by Huashen Guan (20454677)

Subjects:

Parameters of the sliding wear test. by Huashen Guan (20454677)

Subjects:

Finite element model of shot peening. by Huashen Guan (20454677)

Subjects:

Wear morphology of the UT sample. by Huashen Guan (20454677)

Subjects:

Surface roughness of various samples. by Huashen Guan (20454677)

Subjects:

Material parameters of the sample and projectile [21]. by Huashen Guan (20454677)

Subjects:

RTA408 significantly reduces RGCs loss. by Hongmei Qian (538647)

Loss of RBM-26 causes mitochondria dysfunction in PLM. by Tamjid A. Chowdhury (14149846)

DALY and HLY in Portugal, 1990–2019. The first graph shows a decreasing trend of DALY and the second graph shows an increasing trend of HLY. by Aida Isabel Tavares (12080156)

Table 2 - by Rosanna Mary Rooney (17595801)

“…Despite a global decrease over the last 30 years, youth crime remains prevalent. …”

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. …”

Sample scenario description. 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. …”

AMCEs – 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. …”