Abstract The study aims at forecasting the return volatility of cryptocurrencies using several machine learning algorithms, like neural network autoregressive (NNETAR), cubic smoothing spline (CSS), and group method data handling (GMDH-NN) algorithm. used in this is spanning from April 14, 2017, to October 30, 2020, covering 1296 observations. We predict four cryptocurrencies, namely Bitcoin, E...

With the increasing level of intelligence marine engines, there is an demand for online monitoring and NOx emissions have been great concern. In this paper, a simulation model developed based on virtual measurement technology, which can calculate predict current operating state parameters low-speed two-stroke diesel engines. First, calibrated 3D used to design experiments obtain experimental sa...

Abstract Developing fragility functions is the essential step in incorporating important uncertainties next-generation performance-based earthquake engineering (PBEE) methodology. The present paper aimed to involve record-to-record variability as well modelling uncertainty sources developing curves at collapse limit state. In this article, order reduce dispersion of uncertainties, Group Method ...