Accurate retrieval of column-averaged dry-air mole fraction of methane (XCH4) in the atmosphere is important for greenhouse gas emission management.Traditional XCH4 retrieval methods are complex, while machine learning can be used to model nonlinear relationships by analyzing large datasets, providing an efficient alternative.This study proposes an
The aviation sector is facing a massive change in terms of replacing the currently used fossil jet fuels (Jet A, JP5, etc.) with non-fossil jet fuels from sustainable feedstocks.This involves Singlet several challenges and, among them, we have the fundamental issue of current jet engines being developed for the existing fossil jet fuels.To facilita
Predicting project cost overruns in the bidding stage has undergone significant changes with the application of state-of-the-art techniques.Both modeling techniques and domain knowledge should be integrated to enhance predictions of cost performance.This study developed an ensemble-learning classification model to predict the expected cost-overrun
Cardiovascular diseases (CVDs) continue to be the leading cause of mortality worldwide, necessitating the development of novel therapies.Despite therapeutic advancements, the underlying mechanisms remain elusive.Reactive oxygen species (ROS) show detrimental effects at high concentrations but act as essential signalling molecules at physiological l
Introduction The COVID-19 pandemic had devastating health and socioeconomic effects, partly due to policy decisions to mitigate them.Little evidence exists of approaches that guided decisions in settings with limited pre-pandemic modelling capacity.We thus sought to identify knowledge translation mechanisms, enabling factors and structures needed t