Servo pressure machine. by Hanqiu Wang (13204812)

“…The decrease reaches the maximum at a mortar strength of M40. …”

Fitting Stress-Strain Curves of M30 Specimens. by Hanqiu Wang (13204812)

“…The decrease reaches the maximum at a mortar strength of M40. …”

Preparation ratio of mortar/(kg·m^-3). by Hanqiu Wang (13204812)

“…The decrease reaches the maximum at a mortar strength of M40. …”

Relationships between peak stress. by Hanqiu Wang (13204812)

“…The decrease reaches the maximum at a mortar strength of M40. …”

Micro-parameters of mortar and sandstone. by Hanqiu Wang (13204812)

“…The decrease reaches the maximum at a mortar strength of M40. …”

Failure Modes of M30 Specimens. by Hanqiu Wang (13204812)

“…The decrease reaches the maximum at a mortar strength of M40. …”

Fitting Stress-Strain Curves of M40 Specimens. by Hanqiu Wang (13204812)

“…The decrease reaches the maximum at a mortar strength of M40. …”

Failure Modes of M40 Specimens. by Hanqiu Wang (13204812)

“…The decrease reaches the maximum at a mortar strength of M40. …”

Stress-strain curves of composite. by Hanqiu Wang (13204812)

“…The decrease reaches the maximum at a mortar strength of M40. …”

Contains all data. by Hanqiu Wang (13204812)

“…The decrease reaches the maximum at a mortar strength of M40. …”

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

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. …”

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. …”