Equation of state for the two component Van der Waals gas with relativistic excluded volumes

A canonical partition function for the two-component excluded volume model is derived, leading to two di erent van der Waals approximations. The one is known as the Lorentz-Berthelot mixture and the other has been proposed recently. Both models are analysed in the canonical and grand canonical ensemble. In comparison with the one-component van der Waals excluded volume model the suppression of particle densities is reduced in these two-component formulations, but in two essentially di erent ways. Presently used multi-component models have no such reduction. They are shown to be not correct when used for components with di erent hard-core radii. For high temperatures the excluded volume interaction is refined by accounting for the Lorentz contraction of the spherical excluded volumes, which leads to a distinct enhancement of lighter particles. The resulting e ects on pion yield ratios are studied for AGS and SPS data

Automated Olfactory Bulb Segmentation on High Resolutional T2-Weighted MRI

The neuroimage analysis community has neglected the automated segmentation of the olfactory bulb (OB) despite its crucial role in olfactory function. The lack of an automatic processing method for the OB can be explained by its challenging properties. Nonetheless, recent advances in MRI acquisition techniques and resolution have allowed raters to generate more reliable manual annotations. Furthermore, the high accuracy of deep learning methods for solving semantic segmentation problems provides us with an option to reliably assess even small structures. In this work, we introduce a novel, fast, and fully automated deep learning pipeline to accurately segment OB tissue on sub-millimeter T2-weighted (T2w) whole-brain MR images. To this end, we designed a three-stage pipeline: (1) Localization of a region containing both OBs using FastSurferCNN, (2) Segmentation of OB tissue within the localized region through four independent AttFastSurferCNN - a novel deep learning architecture with a self-attention mechanism to improve modeling of contextual information, and (3) Ensemble of the predicted label maps. The OB pipeline exhibits high performance in terms of boundary delineation, OB localization, and volume estimation across a wide range of ages in 203 participants of the Rhineland Study. Moreover, it also generalizes to scans of an independent dataset never encountered during training, the Human Connectome Project (HCP), with different acquisition parameters and demographics, evaluated in 30 cases at the native 0.7mm HCP resolution, and the default 0.8mm pipeline resolution. We extensively validated our pipeline not only with respect to segmentation accuracy but also to known OB volume effects, where it can sensitively replicate age effects

Partisan and News Content on YouTube during 2019 Federal State Elections in Germany

YouTube has emerged as one of the most commonly used plat-forms for entertainment, information and political communi-cation, for media consumers as well as professional commu-nicators. News organizations and political parties alike haveadopted strategies for publishing video content–rangingfrom already broadcasted news programs to political speechesof local party members. In this ongoing study we interrogateYouTube ranking algorithm on the occasions of three fed-eral state elections in Germany. We retrieved ranked searchresults for every parties leading candidate in the elections,as well as the comments and replies which YouTube deter-mines as relevant for each video occurring in the rankings.Preliminary results show that content dealing with and con-tent authored by far-right parties is most widely consumedand interacted with. The ranking of the content is relativelystable over time but partially interrupted by short phasesthat jumble the previous order

Evaluating the value of concentrated solar power in electricity systems with fluctuating energy sources

The paper presents a method for evaluating the value of CSP in electricity systems in comparison to other technologies. The low parametrization effort of the model allows for conducting studies for different electricity systems and scenarios within a manageable time frame. CSP systems in possible German electricity systems 2050 can be used at its best, when the share of fluctuating renewables (FRES) is low. Under these conditions CSP is a cost-effective solution to meet CO2-reduction goals of 90 % in comparison to 1990. With FRES shares above 70 % the utilization of CSP systems would be too low to be competitive