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Space heating in buildings represents nearly half of the final heat demand in Europe. The potential to save emissions from existing fossil-based heating supply systems is substantial. The Netherlands announced in 2018 its decision to phase out natural gas by 2050 and to supply buildings from 2021 with sustainable heating. Models with a high level of spatial resolution can support the assessment of potential low-carbon heating systems at the local level. This study introduces the Vesta MAIS model, an open-source tool developed for local governments and urban planners in the Netherlands to support the development of municipal roadmaps. The method presented in this study can be applied for a neighbourhood or city and provides new insights for Dutch local authorities and researchers on the suitability and limitations of the Vesta MAIS model. Four scenarios, including individual and district heating technologies and building shell improvements, are compared up to 2030 from a techno-economic and environmental perspective. Our results demonstrate that district heating appears to be the most suitable strategy for the studied area, but with subtle underlying differences in the optimal levels of network temperature, heat density and building insulation. A further investigation of the most favourable combination of these parameters, the use of local data, and the inclusion of additional criteria, next to costs and CO2 emissions, is suggested to increase the practical use of model outcomes. This research serves as a showcase emphasizing the importance of a local analysis in the decision-making of potential heating strategies.

The decrease of the force of the magnetic field in altitudes above Low Earth Orbits (LEO) drives the need for miniaturized propulsion systems that can provide attitude control. In addition, these systems are already needed for orbit maintenance, phasing and lifetime extension of small satellite missions, and could also help with end-of-life decommissioning and debris mitigation. The I2T5 cold gas thruster, an iodine-based propulsion system, will be integrated on the Robusta-3A mission, developed by the CSUM, along with several educational and scientific payloads, related to meteorology and technology demonstration. The insights given on the development process of this mission are intended to provide enriching knowledge to the space community, mainly in three areas: small satellite missions design, when both scientific payloads and propulsion systems are used; off-axis primary propulsion systems, focusing on how to eventually overcome the problems related to attitude control and mission analysis of small satellite missions; the use of iodine as a propellant for scientific space missions, focusing on how to overcome the issues related to the use of this propellant, such as propellant bouncing, deposition on external surfaces and following corrosion risks.

First Online: 04 September 2017 Head motion is one of the most important nuisance variables in neuroimaging, particularly in studies of clinical or special populations, such as children. However, the possibility of estimating motion in structural MRI is limited to a few specialized sites using advanced MRI acquisition techniques. Here we propose a supervised learning method to retrospectively estimate motion from plain MRI. Using sparsely labeled training data, we trained a 3D convolutional neural network to assess if voxels are corrupted by motion or not. The output of the network is a motion probability map, which we integrate across a region of interest (ROI) to obtain a scalar motion score. Using cross-validation on a dataset of n=48 healthy children scanned at our center, and the cerebral cortex as ROI, we show that the proposed measure of motion explains away 37% of the variation in cortical thickness. We also show that the motion score is highly correlated with the results from human quality control of the scans. The proposed technique can not only be applied to current studies, but also opens up the possibility of reanalyzing large amounts of legacy datasets with motion into consideration: we applied the classifier trained on data from our center to the ABIDE dataset (autism), and managed to recover group differences that were confounded by motion. This study was supported by ERC Starting Grant 677697 (“BUNGEE-TOOLS”), UCL EPSRC CDT Award EP/L016478/1, and a GPU donated by Nvidia.