DEME Group, a leader in offshore energy and marine infrastructure, seeks to enhance the efficiency and accuracy of cable burial operations for offshore wind farms through the development of a user-friendly trencher mobility model. This research addresses the limitations of existing models and provides …

This paper investigates the application of neural basis expansion analysis with exogenous variables (NBEATSx) in the prediction of daily stock realized volatility for various time steps. It compares NBEATSx’s forecasting accuracy and robustness with several commonly used models, namely Long-Short Term …

Physical activity is crucial in human life, whether in everyday activities or elite sports. It is important to maintain or improve physical performance, which depends on various factors such as the amount of physical activity, the capability, and the capacity of the individual. In daily life, it is significant …

Background: Advanced statistical modeling techniques may help predict health outcomes. However, it is not the case that these modeling techniques always outperform traditional techniques such as regression techniques. In this study, external validation was carried out for five modeling strategies for …

The built environment requires energy-flexible buildings to reduce energy peak loads and to maximize the use of (decentralized) renewable energy sources. The challenge is to arrive at smart control strategies that respond to the increasing variations in both the energy demand as well as the variable …

This paper introduces and examines a novel realized volatility forecasting model that makes use of Long Short-Term Memory (LSTM) neural networks and the risk metric financial turbulence (FT). The proposed model is compared to five alternative models, of which two incorporate LSTM neural networks and …

The aim of this thesis is to assess the reliability and accuracy of Miedema's clay-cutting model by critically examining its underlying assumptions and investigating potential discrepancies. The research questions revolve around identifying the inconsistencies in nomenclature, evaluating the chosen strain …

The following paper refers to the final thesis for the curriculum at HZ University of Applied Science and was carried out at the request of the Italian firm Aig Associati and Partner. The structural team of the architecture and engineering firm requested a study on the most effective and least time-consuming …

Machine learning models have been an inevitable tool for analyzing medical images by radiologists. These models provide important information about the contents of these images using extracted radiomic features. However, the dimensionality of the feature space can cause reduction in the accuracy of prediction, …

Data-driven condition-based maintenance (CBM) and predictive maintenance (PdM) strategies have emerged over recent years and aim at minimizing the aviation maintenance costs and environmental impact by the diagnosis and prognosis of aircraft systems. As the use of data and relevant algorithms is essential …