Publication Server of Aschaffenburg University
Not a member yet
1531 research outputs found
Sort by
Mentales Lagerfeuer
No full textEigenverantwortung und Kooperation sind in einer modernen Organisationskultur gleichermaßen gefragt. Das hier präsentierte «Peer-learning-Format» wirkt in diesem Sinne wie ein Lagerfeuergespräch: Kleine Gruppen treffen sich, tauschen sich vertieft zu Grundfragen des Zusammenlebens aus und gehen gewandelt daraus hervor. Eine Evaluation
der TH Aschaffenburg zeigt, dass mit diesem Format größere Teilnehmergruppen in die orientierte Entwicklung von Mindset und Kollaborationskultur eingebunden werden können. Dabei wird deutlich, dass diese «Learning-Circles» keine Selbstläufer sind, sondern eine Begleitarbeit im Sinne des Facilitation brauchen
Praxisschock im Studium - die Erstellung komplexer Angebote für Satellitenkomponenten
No full textDie Lehrveranstaltung „Projektstudie" im Rahmen des Studiengangs “Internationales Technisches Vertriebsmanagement“ verbindet Elemente der Ingenieurwissenschaften und der Betriebswirtschaftslehre mit einem realistischen Vertriebsprojekt, hier der Erstellung eines komplexen Angebotes für Satellitenkomponenten. Begleitend zu der Teamarbeit am praktischen Vertriebsprojekt werden Lehrsequenzen theoretischer Natur durchgeführt, die dem nachhaltigen Verständnis dienen und auf die Schwerpunktthemen vorbereiten. Die hohe Qualität der erstellten Angebotsunterlagen zeigt, dass die angestrebten Lernziele erreicht wurden. Der Praxisschock einer komplexen Angebotserstellung erfolgt für die Studierenden somit bereits im Studium unter Anleitung eines auf diesem Gebiet berufserfahrenen Professors und nicht erst während der Probezeit in einer ersten Vertriebsposition
Comparison of different AI systems for diagnosing sepsis, septic shock, and cardiogenic shock: a retrospective study
No full textSepsis, septic shock, and cardiogenic shock are life-threatening conditions associated with high mortality rates, but differentiating them is complex because they share certain symptoms. Using the Medical Information Mart for Intensive Care (MIMIC)-III database and artificial intelligence (AI), we aimed to increase diagnostic precision, focusing on Bayesian network classifiers (BNCs) and comparing them with other AI methods. Data from 5970 adults, including 950 patients with cardiogenic shock, 1946 patients with septic shock, and 3074 patients with sepsis, were extracted for this study. Of the original 51 variables included in the data records, 12 were selected for constructing the predictive model. The data were divided into training and validation sets at an 80:20 ratio, and the performance of the BNCs was evaluated and compared with that of other AI models, such as the one rule classifier
(OneR), classification and regression tree (CART), and an artificial neural network (ANN), in terms of accuracy, sensitivity, specificity, precision, and F1-score. The BNCs exhibited an accuracy of 87.6% to 91.5%. The CART model demonstrated a notable 91.6% accuracy when only three decision levels were used, whereas the intricate ANN model reached 90.5% accuracy. Both the BNCs and the CART model allowed clear interpretation of the predictions. BNCs have the potential to be valuable tools in diagnostic tasks, with an accuracy, sensitivity, and precision comparable, in some cases, to those of ANNs while demonstrating superior interpretability
Design Guide for Hybrid-Additive Manufacturing of Inconel 718 Combining PBF-LB/M and In Situ High-Speed Milling
No full textAs the correlation between design rules and process limitations is of the upmost importance for the full exploitation of any manufacturing technology, we report a design guide for hybrid-additive manufacturing of Inconel 718. Basic limitations need to be evaluated for this particular hybrid approach that combines laser powder bed fusion (PBF-LB/M) and in situ high-speed milling. Fundamental geometric limitations are examined with regard to the minimum feasible wall thickness, cylinders, overhanging structures, and chamfers. Furthermore, geometrical restrictions due to the integrated three-axis milling process with respect to inclinations, inner angles, notches, and boreholes are investigated. From these findings, we derive design guidelines for a reliable build process using this hybrid manufacturing. Additionally, a design guideline for the hybrid-additive manufacturing approach is presented, depicting a step-to-step guide for the adjustment of constructions. To demonstrate this, a powder nozzle for a direct energy deposition (DED-LB/M) process is redesigned following the previously defined guidelines. This redesign encompasses analysis of the existing component and identification of problematic areas such as flat angles, leading to a new construction that is suitable for a hybrid-additive manufacturing approach
Decoding Dystonia: unveiling neural patterns with interpretable EEG-Based Machine Learning
No full textDystonia has a multifaceted and complex pathogenesis. Current diagnostic proce-dures, which focus primarily on clinical signs, may lack accuracy due to the variable presentationsof different dystonia types. There is a need for objective, interpretable, and non-invasive diagnostictools. This study aims to develop an interpretable electroencephalography (EEG)-basedmachine learning (ML) and deep learning (DL) approach to distinguish between focal upper limbdystonia (ULD), cervical dystonia (CD), and healthy controls (HC). EEG data were recorded during resting-state, writing-from-memory, and finger-tapping tasks. The EEG signals were segmented into windows to generate connectivity matricesusing various pairwise correlation metrics. Machine learning models were trained to classify thegroups, with performance evaluated using accuracy and area under the curve (AUC) metrics. Our approach achieved accuracy and AUC scores close to 100%. Transfer entropyemerged as the most effective connectivity metric, revealing altered brain connections in dystonia.Complex network measures outperformed traditional EEG features, highlighting the relevance offunctional connectivity. Resting-state EEG showed the highest classification performance for ULD,suggesting strong diagnostic potential. Conclusions: This study provides the first machine learning-based comparison between differenttypes of dystonia, introduces novel cervical dystonia EEG data, and yields medically interpretableinsights into altered brain connectivity. The findings enhance our understanding of dystonia and support using EEG as alow-cost, interpretable tool for diagnosing and developing brain-machine interfaces
One decade of joint Bavarian-Czech projects on astronomical X-ray optics
No full textSince ten years, Aschaffenburg University of Applied Sciences and the Czech technical university in Prague are now cooperating. During the recent decade, eleven joint projects have been executed, mainly targeting the development of astronomical X-ray optics. The bilateral cooperation also included the exchange of PhD students and scientists, cost compensation for conference participations, and the sponsoring of scientific conferences (AXRO and IBWS). The scientific output up to date are more than twenty joint publications and many individual conference contributions of the project partners in addition. The Bavarian-Czech Academic Agency (BTHA) has funded all of these projects. It is the goal of the Bavarian-Czech Academic Agency to support the academic collaboration in research and education and to contribute to an increased cooperation of the two neighbored countries Bavaria and Czech Republic in general. We will give a review on one decade of our Bavarian-Czech cooperation and on the scientific results of our joint projects on astronomical X-ray optics
Evaluation of a Node-based Automatic Short Answer Tool “NodeGrade”
No full textNodeGrade tries to provide a suitable solution for the problem of time-intensive short answer grading. This research focuses simultaneously on performance, functionality and user experience, which is underlined by a triangulated approach. The evaluation results show comparable performance of NodeGrade on public datasets, even outperforming GPT-4 on the SemEval 2013 Task 7. Matching of NodeGrade’s output with multiple human expert raters reveals some weaknesses regarding cases at the lower and upper boundary. In terms of user experience, the interviewed and observed students recognized both positive facets, like better learning support and helpful feedback, and negative sides, including technical limitations and lack of transparency. Overall, NodeGrade promises high potential for further practical use and testing in the field of software engineering education and automatic short answer grading
Güterstandsschaukel als "Steuerfalle" - Vorteile nutzen und Fehler vermeiden
No full textAls Güterstandsschaukel wird die vorzeitige Beendigung des Güterstands der Zugewinngemeinschaft durch notarielle Beurkundung und die gleichzeitige Vereinbarung des Güterstands der Gütertrennung verstanden. In der gleichen notariellen Urkunde oder nach Verstreichen einer "Schamfrist" kann wieder der Güterstand der Zugewinngemeinschaft vereinbart und - bei Bedarf - wieder beendet werden - man "schaukelt" einmal weg vom Güterstand der Zugewinngemeinschaft und wieder zurück. Anhand von praxisrelevanten Fallgestaltungen will dieser Beitrag einen Überblick über die Vorteile und Risiken sowie die inhaltlichen Gestaltungsmöglichkeiten geben. Zudem wird der Frage nachgegangen, ob eine missglückte Güterstandsschaukel "reparabel" ist
