Evolution of mean values of posttraumatic stress symptomatology according to sociodemographic and occupational variables from baseline to 12-month. by Beatriz Arregui-Gallego (21553735)

nsP4-C483Y or Q192L mutations decrease sensitivity to 4′-FlU. by Peiqi Yin (6533747)

Boxplot mean cfPWV comparisons. by Johannes Nairz (18010746)

Estimated mean values for light interception. by Torsten Schober (20485754)

Fluctuation trend of the mean temperature index. by Chengyuan Hao (21615653)

“…The relative high temperature indices showed an increasing trend while the relevant low temperature indices tended to decrease. …”

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

“…The relative high temperature indices showed an increasing trend while the relevant low temperature indices tended to decrease. …”

Mann-Kendall test for the mean temperature index. by Chengyuan Hao (21615653)

“…The relative high temperature indices showed an increasing trend while the relevant low temperature indices tended to decrease. …”

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

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)

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)

The Date. by Chengyuan Hao (21615653)

“…The relative high temperature indices showed an increasing trend while the relevant low temperature indices tended to decrease. …”

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

“…The relative high temperature indices showed an increasing trend while the relevant low temperature indices tended to decrease. …”

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

“…FF spatial access was the sum of inverted distances to all FFR within an 8 km Euclidean distance and subsequently categorized into none, low, moderate, and high. Multilevel linear regression adjusted the associations of BMI for socio-demographics, district population density, and district median income. …”

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. …”