District heating (DH) systems can contribute substantially to the decarbonization of the heating sector by enabling the implementation of sustainable heating solutions such as ambient heat (AH) and waste heat (WH). However, these sources are subject to economic risks arising from uncertainties in future energy prices and availability. This paper presents a techno-economic analysis for a decarbonized DH network for a city in Poland, using a simulation model that evaluates levelized cost of heat (LCOH) across a wide range of heat supply portfolios including AH and WH sources. It considers uncertainties related to energy price and WH cessation scenarios as well as sensitivities related to WH prices, heat demand, and biomass availability. Results show that the DH system configuration plays a key role in determining the average cost, due to the relatively large share of CAPEX in the LCOH calculations, underscoring the importance of accurate investment cost assumptions. Further important factors are the development of the heat demand and the availability of biomass. The presence of low-cost industrial WH is beneficial. In general, there is a trade-off: minimizing average cost results in a higher economic risk.

Background: To develop a regression neural network for the reconstruction of lesion probability maps on Magnetic Resonance Fingerprinting using echo-planar imaging (MRF-EPI) in addition to T₁, T2∗, NAWM, and GM- probability maps. Methods: We performed MRF-EPI measurements in 42 patients with multiple sclerosis and 6 healthy volunteers along two sites. A U-net was trained to reconstruct the denoised and distortion corrected T₁ and T2∗ maps, and to additionally generate NAWM-, GM-, and WM lesion probability maps. Results: WM lesions were predicted with a dice coefficient of 0.61 ± 0.09 and a lesion detection rate of 0.85 ± 0.25 for a threshold of 33%. The network jointly enabled accurate T₁ and T2∗ times with relative deviations of 5.2% and 5.1% and average dice coefficients of 0.92 ± 0.04 and 0.91 ± 0.03 for NAWM and GM after binarizing with a threshold of 80%. Conclusion: DL is a promising tool for the prediction of lesion probability maps in a fraction of time. These might be of clinical interest for the WM lesion analysis in MS patients.

Purpose: To develop an accelerated postprocessing pipeline for reproducible and efficient assessment of white matter lesions using quantitative magnetic resonance fingerprinting (MRF) and deep learning. Methods: MRF using echo-planar imaging (EPI) scans with varying repetition and echo times were acquired for whole brain quantification of (Formula presented.) and (Formula presented.) in 50 subjects with multiple sclerosis (MS) and 10 healthy volunteers along 2 centers. MRF (Formula presented.) and (Formula presented.) parametric maps were distortion corrected and denoised. A CNN was trained to reconstruct the (Formula presented.) and (Formula presented.) parametric maps, and the WM and GM probability maps. Results: Deep learning-based postprocessing reduced reconstruction and image processing times from hours to a few seconds while maintaining high accuracy, reliability, and precision. Mean absolute error performed the best for (Formula presented.) (deviations 5.6%) and the logarithmic hyperbolic cosinus loss the best for (Formula presented.) (deviations 6.0%). Conclusions: MRF is a fast and robust tool for quantitative (Formula presented.) and (Formula presented.) mapping. Its long reconstruction and several postprocessing steps can be facilitated and accelerated using deep learning.

This chapter describes ICT solutions for planning, maintaining and assessing urban energy systems. There is no single urban energy system, but - like the city itself - a system of sub-systems with different scales, spatially ranging from buildings to blocks, districts and to the city, temporally ranging from real time data to hourly, daily, monthly and finally annual totals. ICT support must consider these different sub-systems which makes necessary dividing the chapter into different sections. The chapter starts with framework conditions and general requirements for ICT solutions, and continues discussing urban development simulating models. Then decision support tools are described for energy supply and demand as well as for energy efficiency improvement assessment. Later further instruments for Smart Grid-, district heating- and cooling-planning, as well as demand side management are addressed. In the final section tools are discussed for building automation systems as smallest physical entity within the urban energy system.