Table 1_United States military working dogs from 2019 to 2021: analysis of causes of service discharge and decreased service life.docx by Dakota Discepolo (16804191)

“…ANOVA analysis comparing mean service life resulted in significant differences of mean overall service with main effects of breed (p = 0.0252), outcome (p = 0.0004), service discharge category (p < 0.0001), and subpopulation (p < 0.0001).…”

Patellar 3D-MRI before and after OWHTO in all cases. by Yusuke Fuchioka (22806178)

Survey sample distribution. by Yin Liu (50073)

“…The results show that: (1) Based on the counterfactual hypothesis, if the farmers who obtain microcredit for poverty-alleviated population are not loaned, their production and operating income will decrease by 3.31%, that is, microcredit has a significant income-increasing effect, and the income-increasing effect of obtaining microcredit for the poverty-alleviated population on the monitoring objects is greater than the households that have lifted out of poverty. (2) The mechanism of action shows that the microcredit policy for the poverty- alleviated population promotes income increase by promoting farmers to increase material capital investment and social capital investment. (3) The income-increasing effect of obtain microcredit for poverty-alleviated population on the low-income initial endowment farmers is greater than that on high-income initial endowment farmers, and farmers with low-land initial endowment have a higher income increase effect, that is, a ‘raising the low ‘ effect. …”

Variable definition and descriptive statistics. by Yin Liu (50073)

“…The results show that: (1) Based on the counterfactual hypothesis, if the farmers who obtain microcredit for poverty-alleviated population are not loaned, their production and operating income will decrease by 3.31%, that is, microcredit has a significant income-increasing effect, and the income-increasing effect of obtaining microcredit for the poverty-alleviated population on the monitoring objects is greater than the households that have lifted out of poverty. (2) The mechanism of action shows that the microcredit policy for the poverty- alleviated population promotes income increase by promoting farmers to increase material capital investment and social capital investment. (3) The income-increasing effect of obtain microcredit for poverty-alleviated population on the low-income initial endowment farmers is greater than that on high-income initial endowment farmers, and farmers with low-land initial endowment have a higher income increase effect, that is, a ‘raising the low ‘ effect. …”

Robustness test. by Yin Liu (50073)

“…The results show that: (1) Based on the counterfactual hypothesis, if the farmers who obtain microcredit for poverty-alleviated population are not loaned, their production and operating income will decrease by 3.31%, that is, microcredit has a significant income-increasing effect, and the income-increasing effect of obtaining microcredit for the poverty-alleviated population on the monitoring objects is greater than the households that have lifted out of poverty. (2) The mechanism of action shows that the microcredit policy for the poverty- alleviated population promotes income increase by promoting farmers to increase material capital investment and social capital investment. (3) The income-increasing effect of obtain microcredit for poverty-alleviated population on the low-income initial endowment farmers is greater than that on high-income initial endowment farmers, and farmers with low-land initial endowment have a higher income increase effect, that is, a ‘raising the low ‘ effect. …”

Key safety measures including adverse events. by Anuja Dokras (8679261)

“…Waist circumference (mean change (MC) −2.23 cm; 95% CI [−3.98, −0.49]; <i>p</i> = 0.01), BMI (MC −0.49 kg/m²; 95% CI [−0.88, −0.10[; <i>p</i> = 0.01), and android fat mass measured by DXA (MC −167 g; 95% CI [−264, −71[; <i>p</i> < 0.001) decreased in the COCP group over the study period whilst there was no statistically significant changes in these parameters in the metformin only group when compared to baseline.. …”

Coal mine fire area sampling data. (a) temperature, (b) humidity, (c) total nitrogen, (d) total phosphorus, and (e) available potassium (Republished from Desheng Xie under a CC BY... by Desheng Xie (21439869)

LEfSe Analysis. by Tejaswini Baral (14320736)

Trends of the temperature, precipitation, and TVDI in the Santun River Irrigation Area, Xinjiang, during 2005–2023. by Yuxin Wei (11510974)

Bluetooth beacons with colour coded lanyards. by J Mark Ansermino (13958512)

“…At both intervention sites, there was a significant reduction in antimicrobial utilization of 47% (Kenya) and 33% (Uganda) compared to baseline. …”

A Comparison of Pediatric Prehospital Opioid Encounters and Social Vulnerability by Stephen Sandelich (19991783)

“…The analysis demonstrated that as socioeconomic status (SES) improves, the likelihood of opioid-related activations increases significantly supported by a significant negative linear trend (Estimate = −0.2971, SE = 0.1172, z = −2.54, <i>p</i> = 0.0112. …”

(A) Mobility rate, (B) Explored areas, (5C) Immobility time, (D) Frozen events for hole board test. by Hammad Ismail (4227988)

IRE1 depletion increases chromosome instability. by Iratxe Zuazo-Gaztelu (21071939)

Antidepressant (N06A) prescribing changes from 2014 to 2022. by Dainty Ei (22265317)

Structure diagram of ensemble model. by Hongqi Wang (2208238)

“…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …”

Fitting formula parameter table. by Hongqi Wang (2208238)

“…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …”

Test plan. by Hongqi Wang (2208238)

“…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …”

Fitting surface parameters. by Hongqi Wang (2208238)

“…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …”

Model generalisation validation error analysis. by Hongqi Wang (2208238)

“…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …”

Empirical model prediction error analysis. by Hongqi Wang (2208238)

“…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …”