Konsistenzbasierte Fehlerdiagnose nichtlinearer Systeme mittels Zustandsmengenbeobachtung

Diese Arbeit beschreibt ein Verfahren zur konsistenzbasierten Fehlerdiagnose zeitkontinuierlicher nichtlinearer Systeme mittels Zustandsmengenbeobachtung. Der Schwerpunkt liegt dabei auf der Durchführung der Zustandsmengenbeobachtung mittels Einschließungsverfahren für gewöhnliche Differenzialgleichungssysteme. Es werden zwei Konzepte zur Zustandsmengenbeobachtung vorgestellt und verglichen sowie anhand von Anwendungsbeispielen deren praktische Einsetzbarkeit verdeutlicht

The influence of El Niño–Southern Oscillation regimes on eastern African vegetation and its future implications under the RCP8.5 warming scenario

Abstract. The El Niño–Southern Oscillation (ENSO) is the main driver of the interannual variability in eastern African rainfall, with a significant impact on vegetation and agriculture and dire consequences for food and social security. In this study, we identify and quantify the ENSO contribution to the eastern African rainfall variability to forecast future eastern African vegetation response to rainfall variability related to a predicted intensified ENSO. To differentiate the vegetation variability due to ENSO, we removed the ENSO signal from the climate data using empirical orthogonal teleconnection (EOT) analysis. Then, we simulated the ecosystem carbon and water fluxes under the historical climate without components related to ENSO teleconnections. We found ENSO-driven patterns in vegetation response and confirmed that EOT analysis can successfully produce coupled tropical Pacific sea surface temperature–eastern African rainfall teleconnection from observed datasets. We further simulated eastern African vegetation response under future climate change as it is projected by climate models and under future climate change combined with a predicted increased ENSO intensity. Our EOT analysis highlights that climate simulations are still not good at capturing rainfall variability due to ENSO, and as we show here the future vegetation would be different from what is simulated under these climate model outputs lacking accurate ENSO contribution. We simulated considerable differences in eastern African vegetation growth under the influence of an intensified ENSO regime which will bring further environmental stress to a region with a reduced capacity to adapt effects of global climate change and food security

Identifying predictive features of autism spectrum disorders in a clinical sample of adolescents and adults using machine learning

Diagnosing autism spectrum disorders (ASD) is a complicated, time-consuming process which is particularly challenging in older individuals. One of the most widely used behavioral diagnostic tools is the Autism Diagnostic Observation Schedule (ADOS). Previous work using machine learning techniques suggested that ASD detection in children can be achieved with substantially fewer items than the original ADOS. Here, we expand on this work with a specific focus on adolescents and adults as assessed with the ADOS Module 4. We used a machine learning algorithm (support vector machine) to examine whether ASD detection can be improved by identifying a subset of behavioral features from the ADOS Module 4 in a routine clinical sample of N = 673 high-functioning adolescents and adults with ASD (n = 385) and individuals with suspected ASD but other best-estimate or no psychiatric diagnoses (n = 288). We identified reduced subsets of 5 behavioral features for the whole sample as well as age subgroups (adolescents vs. adults) that showed good specificity and sensitivity and reached performance close to that of the existing ADOS algorithm and the full ADOS, with no significant differences in overall performance. These results may help to improve the complicated diagnostic process of ASD by encouraging future efforts to develop novel diagnostic instruments for ASD detection based on the identified constructs as well as aiding clinicians in the difficult question of differential diagnosis

Continuous purification of cell culture-derived influenza A virus particles through pseudo- affinity membrane chromatography

Continuous manufacturing is a relevant trend in biopharmaceutical production to reduce the process footprint and to improve the process economy. Vaccines against world-spread diseases, such as influenza, should benefit in particular from such an approach, given the increasing demand for seasonal vaccines and the need for a fast response in case of a pandemic outbreak. Upstream processing of viral vaccines has seen important progress in continuous production of viral vaccines [1], which further supports the development of hybrid or fully continuous flow-schemes for downstream processing. Please click Additional Files below to see the full abstract

A fast radiotherapy paradigm for anal cancer with volumetric modulated arc therapy (VMAT)

Background/Purpose: Radiotherapy (RT) volumes for anal cancer are large and of moderate complexity when organs at risk ( OAR) such as testis, small bowel and bladder are at least partially to be shielded. Volumetric intensity modulated arc therapy (VMAT) might provide OAR-shielding comparable to step-and-shoot intensity modulated radiotherapy (IMRT) for this tumor entity with better treatment efficiency. Materials and methods: Based on treatment planning CTs of 8 patients, we compared dose distributions, comformality index (CI), homogeneity index ( HI), number of monitor units (MU) and treatment time (TTT) for plans generated for VMAT, 3D-CRT and step-and-shoot-IMRT (optimized based on Pencil Beam (PB) or Monte Carlo ( MC) dose calculation) for typical anal cancer planning target volumes (PTV) including inguinal lymph nodes as usually treated during the first phase (0-36 Gy) of a shrinking field regimen. Results: With values of 1.33 +/- 0.21/1.26 +/- 0.05/1.3 +/- 0.02 and 1.39 +/- 0.09, the CI's for IMRT (PB-Corvus/PB-Hyperion/MC-Hyperion) and VMAT are better than for 3D-CRT with 2.00 +/- 0.16. The HI's for the prescribed dose (HI36) for 3D-CRT were 1.06 +/- 0.01 and 1.11 +/- 0.02 for VMAT, respectively and 1.15 +/- 0.02/1.10 +/- 0.02/1.11 +/- 0.08 for IMRT (PB-Corvus/PB-Hyperion/MCHyperion). Mean TTT and MU's for 3D-CRT is 220s/225 +/- 11MU and for IMRT (PB-Corvus/PBHyperion/MC-Hyperion) is 575s/1260 +/- 172MU, 570s/477 +/- 84MU and 610s748 +/- 193MU while TTT and MU for two-arc-VMAT is 290s/268 +/- 19MU. Conclusion: VMAT provides treatment plans with high conformity and homogeneity equivalent to step-and-shoot-IMRT for this mono-concave treatment volume. Short treatment delivery time and low primary MU are the most important advantages

Visualizing Collocations in Religious Online Forums

We present results of a project examining the application of text visualization in the context of religious studies and sociology. Our goal is to analyze and compare the online communication of various religious directions. For this contribution we focus on the visualization of collocations for specific religious and spiritual key concepts. As a corpus, we acquired the content of the three religious subreddits /r/Islam, /r/Christianity and /r/Occult for a one-year time span. The overall corpus consists of 700,000 comments and around 50 million tokens. We explore and visualize collocations for the concepts “life”, “religion” and “love”. We discuss the results and to what extent we were able to gather new insights

Using Augmented Reality in Software Engineering Education? First insights to a comparative study of 2D and AR UML modeling

Although there has been much speculation about the potential of Augmented Reality (AR) in teaching for learning material, there is a significant lack of empirical proof about its effectiveness and implementation in higher education. We describe a software to integrate AR using the Microsoft Hololens into UML (Unified Modeling Language) teaching. Its user interface is laid out to overcome problems of existing software. We discuss the design of the tool and report a first evaluation study. The study is based upon effectiveness as a metric for students performance and components of motivation. The study was designed as control group experiment with two groups. The experimental group had to solve tasks with the help of the AR modeling tool and the control group used a classic PC software. We identified tendencies that participants of the experimental group showed more motivation than the control group. Both groups performed equally well