Experimental design of this study. by Renya Kawakami (20469088)

“…However, only a few studies that investigated influences of male aging as well as male mating experience on their post-copulatory sexual selection. …”

All relevant data of this study. by Renya Kawakami (20469088)

“…However, only a few studies that investigated influences of male aging as well as male mating experience on their post-copulatory sexual selection. …”

Supplementary Material for: Off-label Use of Mycophenolate Mofetil in Immunoglobulin A Nephropathy: A Systematic Review and Meta-analysis by Feng L. (5056958)

“…Introduction: Mycophenolate mofetil (MMF) is widely used off-label in patients with immunoglobulin A nephropathy (IgAN), although the literature does not consistently agree on its efficacy and safety. …”

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. …”

Methodological flowchart. 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. …”

Preference for the EIA vs. ETA 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. …”

Baseline characteristics. by Seung Min Lee (1644409)

“…Such a change was relevant to the improvement in BCVA, but not metamorphopsia.…”

Data file used in this study. by Seung Min Lee (1644409)

“…Such a change was relevant to the improvement in BCVA, but not metamorphopsia.…”

Changes in foveal location after surgery. by Seung Min Lee (1644409)

“…Such a change was relevant to the improvement in BCVA, but not metamorphopsia.…”

Flowchart of Methodology. by Kamal Hossain (331680)

“…</p><p>Results</p><p>Dengue cases in Bangladesh exhibited a significant increase from 2019 to 2023 (excluding 2020 data), with a cumulative total of 513,344 reported cases. …”

A reas of actual and simulated LULC in 2023. by Firdissa Sadeta Tiye (21222102)

“…The analysis revealed significant LULC changes in BMNP, from 1993 to 2023, primarily due to anthropogenic activities, with further changes anticipated between 2023 and 2053.The results showed a notable increase in woodland and shrubs at the expense of grassland and Erica forest. …”

The spatial pattern of dengue including hotspot and coldspot in Bangladesh in 2019–2023. by Kamal Hossain (331680)

The spatial (districtwide) distribution of census population of Bangladesh in 2022. by Kamal Hossain (331680)

The spatial distribution of dengue incidence rate per million by month across districts of Bangladesh in 2023. by Kamal Hossain (331680)

The spatial distribution of dengue incidence rate per million by month across districts of Bangladesh in 2022. by Kamal Hossain (331680)