Comparison of ANN and XGBoost surrogate models trained on small numbers of building energy simulations.

Surrogate optimisation holds a big promise for building energy optimisation studies due to its goal to replace the use of lengthy building energy simulations within an optimisation step with expendable local surrogate models that can quickly predict simulation results. To be useful for such purpose, it should be possible to quickly train precise surrogate models from a small number of simulation results (10-100) obtained from appropriately sampled points in the desired part of the design space. Two sampling methods and two machine learning models are compared here.

Effect of epigallocatechin gallate on uncoupling protein 2 in acute liver injury.

Background: The aim of this study was to investigate the effect of epigallocatechin gallate (EGCG) on uncoupling protein 2 regulation in an acute liver injury-animal model.

Methods: Twenty seven male Wistar rats were divided into three groups: control group (n = 9), TAA group (n = 9): acute liver injury was induced by the intraperitoneal injection of thioacetamide (200 mg/kg) and EGCG/TAA (n = 9 rats): Epigallocatechin gallate was given two weeks prior to the induction of acute liver injury by thioacetamide. The levels of uncoupling protein 2, CRP, TNF-α and interleukins (IL) 6 and 18 were analyzed in the liver using PCR analysis.