TY - JOUR A1 - Rüdiger, Jens T1 - Enhancements of the numerical simulation algorithm for natural gas networks based on node potential analysis JF - IFAC-PapersOnLine N2 - A reliable energy supply for the economy of every country is a matter of national importance. Powerful simulation tools for natural gas networks are essential for operators of gas networks. In this paper, enhancement algorithms of previous developed node potential analysis algorithm are presented. These enhancement algorithms are used for a reasonable setting of initial values in the numerical gas net simulation algorithm. The setting of the initial values has a significant influence on the convergence behavior of the numerical simulation. The presented enhancement algorithms are explained and simulation results are evaluated. KW - energy supply KW - gas net KW - pipeline KW - natural gas KW - simulation KW - numerical algorithm Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-14129 SN - 2405-8963 VL - 53 IS - 2 SP - 13119 EP - 13124 ER - TY - JOUR A1 - Stumper, Nele A1 - Berger, Jörn A1 - Klotsche, Jens A1 - Gedat, Egbert A1 - Hoff, Paula A1 - Schmittat, Gabriela A1 - Burmester, Gerd-Rüdiger A1 - Krönke, Gerhard A1 - Backhaus, Marina A1 - Haugen, Ida Kristin A1 - Ohrndorf, Sarah T1 - To optimise the diagnostic process of rheumatic diseases affecting the hands using fluorescence optical imaging (FOI) JF - RMD Open N2 - Background Accurate and rapid diagnosis of rheumatic diseases is essential for further treatment decision. Different rheumatic diseases present characteristic patterns (image features) in fluorescence optical imaging (FOI). We developed an atlas of FOI image features and tested its ability to differentiate various rheumatic diseases. Methods FOI images from patients with rheumatoid arthritis (RA), psoriatic arthritis (PsA), connective tissue diseases (CTD) and osteoarthritis (OA) were analysed by two readers blinded for diagnosis and calibrated against each other, using the prima vista mode (PVM) and an automated 5-phase model. Twenty-six different reoccurring typical signal enhancement patterns (features) indicating inflamed joints, nail or skin were defined and all FOI images were scored accordingly. The feature frequency in each patient cohort and phase (PVM, 5-phase) was counted. Contingency tables were created with categorical variable counts and diagnosis using common formulae. Findings Four hundred thirty-eight patients with RA (n=117), PsA (n=110), CTD (n=121) and OA (n=90) were included. Once the data had been categorised, a two-step diagnostic pathway was developed: in the first step, OA was best distinguished from the other diseases with high specificity by five patterns (specificity >0.9, diagnostic OR between 2.34 and 8.24). In a second step, the remaining autoimmune diseases were differentiated from each other by a certain number of features (five for RA, 12 for PsA and four for CTD). Interpretation This was the first study to show that feature analysis in FOI helps to differentiate typical rheumatic diseases from each other, potentially simplifying and speeding up the diagnostic process. Therefore, FOI could be considered an additional component of a wider range of imaging techniques used in rheumatology. Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-20698 VL - 11 IS - 3 PB - EULAR ER -