Association between FF Proximity and BMI by sex. by Kimberly Yuin Y’ng Wong (22766265)

“…The overall association between FF proximity and BMI was not significant but modified by sex (p-_{heterogeneity} <0.001). …”

A summary of calibration standards (n = 3). by Ewa Paszkowska (21246702)

“…<div><p>Purpose</p><p>Statins are the most commonly used drugs worldwide. Besides a significant decrease in cardiovascular diseases (CVDs) risk, the use of statins is also connected with a broad beneficial pleiotropic effect. …”

The observed MRM reactions. by Ewa Paszkowska (21246702)

“…<div><p>Purpose</p><p>Statins are the most commonly used drugs worldwide. Besides a significant decrease in cardiovascular diseases (CVDs) risk, the use of statins is also connected with a broad beneficial pleiotropic effect. …”

Intra- and inter-day precision and accuracy. by Ewa Paszkowska (21246702)

“…<div><p>Purpose</p><p>Statins are the most commonly used drugs worldwide. Besides a significant decrease in cardiovascular diseases (CVDs) risk, the use of statins is also connected with a broad beneficial pleiotropic effect. …”

Data. by Aroon La-up (14095691)

“…Osteoporosis prevalence remained stable in both males and females. The Linear Mixed-Effects Model analysis revealed significant associations between BMD and several factors: increasing age, female sex, diabetes status and BMI. …”

Scatter plots of the linear regression for the relationship between the duration of the vegetative phase and the average mass of single inflorescences. by Torsten Schober (20485754)

Means scores of CAM and CAM-MYCS. by Senay Yildirim-Kahriman (22693464)

The Date. by Chengyuan Hao (21615653)

“…Secondly, the daily minimum and maximum temperatures increased significantly, which were 0.395°C/10a and 0.200°C/10a respectively<b>—</b>less than the national mean. …”

Variation curve of the extreme temperature index. by Chengyuan Hao (21615653)

“…Secondly, the daily minimum and maximum temperatures increased significantly, which were 0.395°C/10a and 0.200°C/10a respectively<b>—</b>less than the national mean. …”

Univariate analyses. by Zachary E. M. Giovannini-Green (22008277)

“…Multivariate analysis showed the mean monthly ED visits increased significantly during the first year of COVID-19 than before the pandemic (Mean = 0.30 vs Mean = 0.21, p = 0.01). …”

Overview of individuals in the study. by Zachary E. M. Giovannini-Green (22008277)

“…Multivariate analysis showed the mean monthly ED visits increased significantly during the first year of COVID-19 than before the pandemic (Mean = 0.30 vs Mean = 0.21, p = 0.01). …”

Multivariate analyses. by Zachary E. M. Giovannini-Green (22008277)

“…Multivariate analysis showed the mean monthly ED visits increased significantly during the first year of COVID-19 than before the pandemic (Mean = 0.30 vs Mean = 0.21, p = 0.01). …”

COVID19 effect on essential services. by Admas Abera (11821659)

“…The difference in the trends of services before and during COVID-19 was compared using linear-by-linear tests and the difference of magnitude across the indicators was compared using Autoregressive Integrated Moving Average (ARIMA) interrupted time series analysis at a 5% significance level.…”

Mean muscle activation patterns across the gait cycle during walking with anteroposterior (AP) forces for <i>A)</i> able-bodied individuals and <i>B)</i> individuals after stroke d... by Sanne Ettema (18786312)

Mean muscle activation patterns across the gait cycle during walking with Body Weight Support (BWS) for <i>A)</i> able-bodied individuals and <i>B)</i> individuals after stroke dur... by Sanne Ettema (18786312)

Mean muscle activation patterns across the gait cycle during walking with mediolateral (ML) forces for <i>A)</i> able-bodied individuals and <i>B)</i> individuals after stroke duri... by Sanne Ettema (18786312)

Predictors used in the modelling analysis. The ‘Snail count’ model refers to the Generalised Linear Mixed Model (GLMM) used to model snail counts, ‘Prevalence’ refers to the diseas... by Roland Proud (11764892)

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

“…Furthermore, we quantitatively analyze the specific influence of water content and other factors on the thermal conductivity of stabilized soil and construct a comprehensive prediction model encompassing BP neural network, gradient boosting decision tree, and linear regression models. 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)

“…Furthermore, we quantitatively analyze the specific influence of water content and other factors on the thermal conductivity of stabilized soil and construct a comprehensive prediction model encompassing BP neural network, gradient boosting decision tree, and linear regression models. 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)

“…Furthermore, we quantitatively analyze the specific influence of water content and other factors on the thermal conductivity of stabilized soil and construct a comprehensive prediction model encompassing BP neural network, gradient boosting decision tree, and linear regression models. 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. …”