TY - CHAP A1 - Kramer, Pia A1 - Bragard, Michael A1 - Ritz, Thomas A1 - Ferfer, Ute A1 - Schiffers, Tim T1 - Visualizing, Enhancing and Predicting Students’ Success through ECTS Monitoring T2 - 2024 IEEE Global Engineering Education Conference (EDUCON) N2 - This paper serves as an introduction to the ECTS monitoring system and its potential applications in higher education. It also emphasizes the potential for ECTS monitoring to become a proactive system, supporting students by predicting academic success and identifying groups of potential dropouts for tailored support services. The use of the nearest neighbor analysis is suggested for improving data analysis and prediction accuracy. KW - Monitoring KW - Engineering education KW - Data visualization KW - Accuracy KW - Data analysis Y1 - 2024 U6 - https://doi.org/10.1109/EDUCON60312.2024.10578652 SN - 2165-9559 SN - 2165-9567 (eISSN) N1 - 2024 IEEE Global Engineering Education Conference (EDUCON), 08-11 May 2024, Kos Island, Greece PB - IEEE CY - New York, NY ER - TY - INPR A1 - Grieger, Niklas A1 - Mehrkanoon, Siamak A1 - Bialonski, Stephan T1 - Preprint: Data-efficient sleep staging with synthetic time series pretraining T2 - arXiv N2 - Analyzing electroencephalographic (EEG) time series can be challenging, especially with deep neural networks, due to the large variability among human subjects and often small datasets. To address these challenges, various strategies, such as self-supervised learning, have been suggested, but they typically rely on extensive empirical datasets. Inspired by recent advances in computer vision, we propose a pretraining task termed "frequency pretraining" to pretrain a neural network for sleep staging by predicting the frequency content of randomly generated synthetic time series. Our experiments demonstrate that our method surpasses fully supervised learning in scenarios with limited data and few subjects, and matches its performance in regimes with many subjects. Furthermore, our results underline the relevance of frequency information for sleep stage scoring, while also demonstrating that deep neural networks utilize information beyond frequencies to enhance sleep staging performance, which is consistent with previous research. We anticipate that our approach will be advantageous across a broad spectrum of applications where EEG data is limited or derived from a small number of subjects, including the domain of brain-computer interfaces. Y1 - 2024 ER - TY - BOOK A1 - Staat, Manfred A1 - Digel, Ilya A1 - Trzewik, Jürgen A1 - Sielemann, Stefanie A1 - Erni, Daniel A1 - Zylka, Waldemar T1 - Symposium Proceedings; 4th YRA MedTech Symposium 2024 : February 1 / 2024 / FH Aachen Y1 - 2024 SN - 978-3-940402-65-3 U6 - https://doi.org/10.17185/duepublico/81475 PB - Universität Duisburg-Essen CY - Duisburg ER - TY - CHAP A1 - Becker, Tim A1 - Bragard, Michael T1 - Low-Voltage DC Training Lab for Electric Drives - Optimizing the Balancing Act Between High Student Throughput and Individual Learning Speed T2 - 2024 IEEE Global Engineering Education Conference (EDUCON) N2 - After a brief introduction of conventional laboratory structures, this work focuses on an innovative and universal approach for a setup of a training laboratory for electric machines and drive systems. The novel approach employs a central 48 V DC bus, which forms the backbone of the structure. Several sets of DC machine, asynchronous machine and synchronous machine are connected to this bus. The advantages of the novel system structure are manifold, both from a didactic and a technical point of view: Student groups can work on their own performance level in a highly parallelized and at the same time individualized way. Additional training setups (similar or different) can easily be added. Only the total power dissipation has to be provided, i.e. the DC bus balances the power flow between the student groups. Comparative results of course evaluations of several cohorts of students are shown. KW - Synchronous machines KW - Power dissipation KW - Throughput KW - Low voltage KW - DC machines KW - Manifolds KW - Training Y1 - 2024 U6 - https://doi.org/10.1109/EDUCON60312.2024.10578902 SN - 2165-9559 SN - 2165-9567 (eISSN) N1 - 2024 IEEE Global Engineering Education Conference (EDUCON), 08-11 May 2024, Kos Island, Greece PB - IEEE CY - New York, NY ER - TY - RPRT A1 - Birmans, Katrin A1 - Schick, Elena A1 - Tambornino, Philipp A1 - Ullrich, Anna Valentine T1 - Ingenieurwissenschaften im Fokus: Zugänge zu einem effektiven Forschungsdatenmanagement an HAW N2 - Im Rahmen der Love Data Week vom 12. bis 16.02.2024 haben die BMBF-Projekte FDM2_TH_Koeln der TH Köln (FK 16FDFH105) und Persist@HAW der FH Aachen (FK 16FDFH129) am 15.02.2024 gemeinsam eine Online-Veranstaltung mit dem Titel „Ingenieurwissenschaften im Fokus: Zugänge zu einem effektiven Forschungsdatenmanagement an HAW“ angeboten. Diese richtete sich an Forschende aus den Ingenieurwissenschaften, die einen ersten Zugang zum Thema Forschungsdatenmanagement (FDM) suchen und erfahren möchten, welche speziellen Angebote für die Daten aus den Ingenieurwissenschaften existieren. In der Veranstaltung wurden wesentliche Aspekte des Forschungsdatenmanagements entlang des Datenlebenszyklus beleuchtet. Ziel war es, den Teilnehmenden praxisnahe Einblicke und Hilfestellungen zu einem effektiven Umgang mit Forschungsdaten an Hochschulen für Angewandte Wissenschaften (HAW) zu bieten. Durch Beispiele und konkrete Empfehlungen wurde das Thema zugänglich gemacht. Y1 - 2024 U6 - https://doi.org/10.5281/zenodo.12545429 N1 - Die Originalpräsentation ist über Conceptboard einsehbar: https://app.conceptboard.com/board/c651-e781-dr9h-zfyu-gkq9 ER - TY - RPRT A1 - Birmans, Katrin A1 - Tambornino, Philipp A1 - Ullrich, Anna Valentine T1 - 5 Gründe für Coscine - Handreichung für Forschende an HAW N2 - Welche Vorteile bietet die Forschungsdatenmanagement-Plattform Coscine für die Verwaltung von Daten in Forschungsprojekten? Hierzu gibt die Handreichung einen schnellen Überblick über den landesgeförderten Dienst Coscine für Forschende und FDM-Service-Personal an HAW in NRW (DH.NRW-Hochschulen). FDM-Service-Mitarbeitende können die Handreichung in ihrer Beratung zu Coscine einsetzen und mit der Eingabemaske in der Kopfzeile des Dokuments auf ihre Hochschule anpassen. Y1 - 2024 U6 - https://doi.org/10.5281/zenodo.12156734 N1 - Die Handreichung ist im Projekt Persist@HAW (FK: 16FDFH129), gefördert vom BMBF, entstanden. ER - TY - CHAP A1 - Rütters, René A1 - Bragard, Michael A1 - Dolls, Sarah T1 - The Inverted Rotary Pendulum: Facilitating Practical Teaching in Advanced Control Engineering T2 - 2024 IEEE Global Engineering Education Conference (EDUCON) N2 - This paper outlines a practical approach to teach control engineering principles, with an inverted rotary pendulum, serving as an illustrative example. It shows how the pendulum is embedded in an advanced course of control engineering. This approach is incorporated into a flipped-classroom concept, as well as classical teaching concepts, offering students practical experience in control engineering. In addition, the design of the pendulum is shown, using a Raspberry Pi as the target platform for Matlab Simulink. This pendulum can be used in the classroom to evaluate the controller design mentioned above. It is analysed if the use of the pendulum generates a deeper understanding of the learning contents. KW - Matlab KW - Engineering education KW - Online services KW - Software packages KW - Electronic learning KW - Control engineering Y1 - 2024 U6 - https://doi.org/10.1109/EDUCON60312.2024.10578937 SN - 2165-9559 SN - 2165-9567 (eISSN) N1 - 2024 IEEE Global Engineering Education Conference (EDUCON), 08-11 May 2024, Kos Island, Greece PB - IEEE CY - New York, NY ER - TY - JOUR A1 - Engelmann, Ulrich M. A1 - Simsek, Beril A1 - Shalaby, Ahmed A1 - Krause, Hans-Joachim T1 - Key contributors to signal generation in frequency mixing magnetic detection (FMMD): an in silico study JF - Sensors N2 - Frequency mixing magnetic detection (FMMD) is a sensitive and selective technique to detect magnetic nanoparticles (MNPs) serving as probes for binding biological targets. Its principle relies on the nonlinear magnetic relaxation dynamics of a particle ensemble interacting with a dual frequency external magnetic field. In order to increase its sensitivity, lower its limit of detection and overall improve its applicability in biosensing, matching combinations of external field parameters and internal particle properties are being sought to advance FMMD. In this study, we systematically probe the aforementioned interaction with coupled Néel–Brownian dynamic relaxation simulations to examine how key MNP properties as well as applied field parameters affect the frequency mixing signal generation. It is found that the core size of MNPs dominates their nonlinear magnetic response, with the strongest contributions from the largest particles. The drive field amplitude dominates the shape of the field-dependent response, whereas effective anisotropy and hydrodynamic size of the particles only weakly influence the signal generation in FMMD. For tailoring the MNP properties and parameters of the setup towards optimal FMMD signal generation, our findings suggest choosing large particles of core sizes dc > 25 nm nm with narrow size distributions (σ < 0.1) to minimize the required drive field amplitude. This allows potential improvements of FMMD as a stand-alone application, as well as advances in magnetic particle imaging, hyperthermia and magnetic immunoassays. KW - key performance indicators KW - magnetic biosensing KW - coupled Néel–Brownian relaxation dynamics KW - frequency mixing magnetic detection KW - magnetic relaxation KW - micromagnetic simulation KW - magnetic nanoparticles Y1 - 2024 U6 - https://doi.org/10.3390/s24061945 SN - 1424-8220 N1 - This article belongs to the Special Issue "Advances in Magnetic Sensors and Their Applications" VL - 24 IS - 6 PB - MDPI CY - Basel ER - TY - CHAP A1 - Fissabre, Anke ED - Naujokat, Anke ED - Hake, Verena ED - Schindler, Bruno ED - Schötten, Björn T1 - Eine moderne Sainte-Chapelle in Köln. Otto Bartnings Stahlkirche auf der Pressa 1928 T2 - Baugedanken: Einsichten, Ansichten, Aussichten für Jan Pieper zum 80. Geburtstag Y1 - 2024 SN - 978-3-943164-87-9 SP - 66 EP - 81 PB - Geymüller Verag für Architektur CY - Aachen ER - TY - JOUR A1 - Haeger, Gerrit A1 - Jolmes, Tristan A1 - Oyen, Sven A1 - Jaeger, Karl-Erich A1 - Bongaerts, Johannes A1 - Schörken, Ulrich A1 - Siegert, Petra T1 - Novel recombinant aminoacylase from Paraburkholderia monticola capable of N-acyl-amino acid synthesis JF - Applied Microbiology and Biotechnology N2 - N-Acyl-amino acids can act as mild biobased surfactants, which are used, e.g., in baby shampoos. However, their chemical synthesis needs acyl chlorides and does not meet sustainability criteria. Thus, the identification of biocatalysts to develop greener synthesis routes is desirable. We describe a novel aminoacylase from Paraburkholderia monticola DSM 100849 (PmAcy) which was identified, cloned, and evaluated for its N-acyl-amino acid synthesis potential. Soluble protein was obtained by expression in lactose autoinduction medium and co-expression of molecular chaperones GroEL/S. Strep-tag affinity purification enriched the enzyme 16-fold and yielded 15 mg pure enzyme from 100 mL of culture. Biochemical characterization revealed that PmAcy possesses beneficial traits for industrial application like high temperature and pH-stability. A heat activation of PmAcy was observed upon incubation at temperatures up to 80 °C. Hydrolytic activity of PmAcy was detected with several N-acyl-amino acids as substrates and exhibited the highest conversion rate of 773 U/mg with N-lauroyl-L-alanine at 75 °C. The enzyme preferred long-chain acyl-amino-acids and displayed hardly any activity with acetyl-amino acids. PmAcy was also capable of N-acyl-amino acid synthesis with good conversion rates. The best synthesis results were obtained with the cationic L-amino acids L-arginine and L-lysine as well as with L-leucine and L-phenylalanine. Exemplarily, L-phenylalanine was acylated with fatty acids of chain lengths from C8 to C18 with conversion rates of up to 75%. N-lauroyl-L-phenylalanine was purified by precipitation, and the structure of the reaction product was verified by LC–MS and NMR. KW - Chaperone KW - Biocatalysis KW - Aminoacylase KW - Acylation KW - Acyl-amino acids KW - Biosurfactants Y1 - 2024 U6 - https://doi.org/10.1007/s00253-023-12868-8 SN - 1432-0614 N1 - Corresponding author: Petra Siegert IS - 108 PB - Springer CY - Berlin ER -