Within the context of the Iliad project, the authors present technical challenges and the first results of having valid 3D scenes of (non-)existing offshore wind farms procedurally and automatically generated within either the Unreal or Unity game engine. The Iliad – Digital Twins of the Ocean project (EU Horizon 2020) aims to develop a ‘system of systems’ for creating cutting-edge digital twins of specific sea and ocean areas for diverse purposes related to their sustainable use and protection. One of the Iliad pilots addresses the topic of offshore floating wind farm construction or maintenance scenario testing and validation using the Unity 3D game engine. This work will speed up the development of these scenarios by procedurally and automatically creating the Unity 3D scene rather than manually (which is done at present). The main technical challenges concern the data-driven approach, in which a JSON configuration file drives the scene creation. The first results show a base wind farm running in Unreal 5.1. The final product will be able to handle environmental conditions, biological conditions, and specific human activities as input parameters.
In 2017 the municipality of Amsterdam launched a programme to combat a housingshortage and realise ambitious societal goals for 32 of its most deprived neighbourhoods. After decades of urban renewal projects, these areas still scored poorly on most socio-economic indicators. The programme aims to develop more affordable housing for low- and middleincome households, to revitalise the existing public spaces of these neighbourhoods and to improve the residents’ socio-economic position. In addition, the progressive municipal council installed in 2018 intends to democratise urban renewal processes with the aim of increasing community involvement.
Background & aims: Accurate diagnosis of sarcopenia requires evaluation of muscle quality, which refers to the amount of fat infiltration in muscle tissue. In this study, we aim to investigate whether we can independently predict mortality risk in transcatheter aortic valve implantation (TAVI) patients, using automatic deep learning algorithms to assess muscle quality on procedural computed tomography (CT) scans. Methods: This study included 1199 patients with severe aortic stenosis who underwent transcatheter aortic valve implantation (TAVI) between January 2010 and January 2020. A procedural CT scan was performed as part of the preprocedural-TAVI evaluation, and the scans were analyzed using deep-learning-based software to automatically determine skeletal muscle density (SMD) and intermuscular adipose tissue (IMAT). The association of SMD and IMAT with all-cause mortality was analyzed using a Cox regression model, adjusted for other known mortality predictors, including muscle mass. Results: The mean age of the participants was 80 ± 7 years, 53% were female. The median observation time was 1084 days, and the overall mortality rate was 39%. We found that the lowest tertile of muscle quality, as determined by SMD, was associated with an increased risk of mortality (HR 1.40 [95%CI: 1.15–1.70], p < 0.01). Similarly, low muscle quality as defined by high IMAT in the lowest tertile was also associated with increased mortality risk (HR 1.24 [95%CI: 1.01–1.52], p = 0.04). Conclusions: Our findings suggest that deep learning-assessed low muscle quality, as indicated by fat infiltration in muscle tissue, is a practical, useful and independent predictor of mortality after TAVI.
