Reducing Dscam1 diversity decreases adult lifespan in a cluster-specific manner, also see S6 and S7 Figs. by Haiyang Dong (4783608)

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)

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. …”

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)

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. …”

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.…”

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. …”

Fitting surface parameters. 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. …”

Model generalisation validation error analysis. 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. …”

Empirical model prediction error analysis. 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 curve parameters. 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 instrument. 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. …”

Empirical model establishment process. 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. …”

Model prediction error trend chart. 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. …”