TY - JOUR A1 - Monakhova, Yulia A1 - Diehl, Bernd W. K. T1 - A step towards optimization of the qNMR workflow: proficiency testing exercise at an GxP-accredited laboratory JF - Applied Magnetic Resonance N2 - Quantitative nuclear magnetic resonance (qNMR) is considered as a powerful tool for multicomponent mixture analysis as well as for the purity determination of single compounds. Special attention is currently paid to the training of operators and study directors involved in qNMR testing. To assure that only qualified personnel are used for sample preparation at our GxP-accredited laboratory, weighing test was proposed. Sixteen participants performed six-fold weighing of the binary mixture of dibutylated hydroxytoluene (BHT) and 1,2,4,5-tetrachloro-3-nitrobenzene (TCNB). To evaluate the quality of data analysis, all spectra were evaluated manually by a qNMR expert and using in-house developed automated routine. The results revealed that mean values are comparable and both evaluation approaches are free of systematic error. However, automated evaluation resulted in an approximately 20% increase in precision. The same findings were revealed for qNMR analysis of 32 compounds used in pharmaceutical industry. Weighing test by six-fold determination in binary mixtures and automated qNMR methodology can be recommended as efficient tools for evaluating staff proficiency. The automated qNMR method significantly increases throughput and precision of qNMR for routine measurements and extends application scope of qNMR. Y1 - 2021 U6 - https://doi.org/10.1007/s00723-021-01324-3 SN - 1613-7507 N1 - Corresponding author: Yulia Monakhova VL - 52 SP - 581 EP - 593 PB - Springer Nature CY - Wien ER - TY - JOUR A1 - Becht, Alexander A1 - Schollmayer, Curd A1 - Monakhova, Yulia A1 - Holzgrabe, Ulrike T1 - Tracing the origin of paracetamol tablets by near-infrared, mid-infrared, and nuclear magnetic resonance spectroscopy using principal component analysis and linear discriminant analysis JF - Analytical and Bioanalytical Chemistry N2 - Most drugs are no longer produced in their own countries by the pharmaceutical companies, but by contract manufacturers or at manufacturing sites in countries that can produce more cheaply. This not only makes it difficult to trace them back but also leaves room for criminal organizations to fake them unnoticed. For these reasons, it is becoming increasingly difficult to determine the exact origin of drugs. The goal of this work was to investigate how exactly this is possible by using different spectroscopic methods like nuclear magnetic resonance and near- and mid-infrared spectroscopy in combination with multivariate data analysis. As an example, 56 out of 64 different paracetamol preparations, collected from 19 countries around the world, were chosen to investigate whether it is possible to determine the pharmaceutical company, manufacturing site, or country of origin. By means of suitable pre-processing of the spectra and the different information contained in each method, principal component analysis was able to evaluate manufacturing relationships between individual companies and to differentiate between production sites or formulations. Linear discriminant analysis showed different results depending on the spectral method and purpose. For all spectroscopic methods, it was found that the classification of the preparations to their manufacturer achieves better results than the classification to their pharmaceutical company. The best results were obtained with nuclear magnetic resonance and near-infrared data, with 94.6%/99.6% and 98.7/100% of the spectra of the preparations correctly assigned to their pharmaceutical company or manufacturer. KW - IR KW - Manufacturer KW - Linear discriminant analysis KW - Principal component analysis Y1 - 2021 U6 - https://doi.org/10.1007/s00216-021-03249-z SN - 1618-2650 VL - 413 SP - 3107 EP - 3118 PB - Springer Nature ER - TY - CHAP A1 - Ferrein, Alexander A1 - Meeßen, Marcus A1 - Limpert, Nicolas A1 - Schiffer, Stefan ED - Lepuschitz, Wilfried T1 - Compiling ROS schooling curricula via contentual taxonomies T2 - Robotics in Education N2 - The Robot Operating System (ROS) is the current de-facto standard in robot middlewares. The steadily increasing size of the user base results in a greater demand for training as well. User groups range from students in academia to industry professionals with a broad spectrum of developers in between. To deliver high quality training and education to any of these audiences, educators need to tailor individual curricula for any such training. In this paper, we present an approach to ease compiling curricula for ROS trainings based on a taxonomy of the teaching contents. The instructor can select a set of dedicated learning units and the system will automatically compile the teaching material based on the dependencies of the units selected and a set of parameters for a particular training. We walk through an example training to illustrate our work. Y1 - 2021 SN - 978-3-030-67411-3 U6 - https://doi.org/10.1007/978-3-030-67411-3_5 N1 - RiE: International Conference on Robotics in Education (RiE); Advances in Intelligent Systems and Computing book series (AISC, volume 1316) SP - 49 EP - 60 PB - Springer CY - Cham ER - TY - JOUR A1 - Götten, Falk A1 - Havermann, Marc A1 - Braun, Carsten A1 - Marino, Matthew A1 - Bil, Cees T1 - Aerodynamic Investigations of UAV Sensor Turrets - A Combined Wind-tunnel and CFD Approach JF - SciTech 2021, AIAA SciTech Forum, online, WW, Jan 11-15, 2021 Y1 - 2021 U6 - https://doi.org/10.2514/6.2021-1535 SP - 1 EP - 12 PB - AIAA CY - Reston, Va. ER - TY - JOUR A1 - Hugenroth, Kristin A1 - Neidlin, Michael A1 - Engelmann, Ulrich M. A1 - Kaufmann, Tim A. S. A1 - Steinseifer, Ulrich A1 - Heilmann, Torsten T1 - Tipless Transseptal Cannula Concept Combines Improved Hemodynamic Properties and Risk‐Reduced Placement: an In Silico Proof‐of‐Concept JF - Artificial Organs Y1 - 2021 U6 - https://doi.org/10.1111/aor.13964 SN - 1525-1594 IS - Accepted Article PB - Wiley CY - Weinheim ER - TY - JOUR A1 - Jablonski, Melanie A1 - Poghossian, Arshak A1 - Severin, Robin A1 - Keusgen, Michael A1 - Wege, Christian A1 - Schöning, Michael Josef T1 - Capacitive Field-Effect Biosensor Studying Adsorption of Tobacco Mosaic Virus Particles JF - Micromachines N2 - Plant virus-like particles, and in particular, tobacco mosaic virus (TMV) particles, are increasingly being used in nano- and biotechnology as well as for biochemical sensing purposes as nanoscaffolds for the high-density immobilization of receptor molecules. The sensitive parameters of TMV-assisted biosensors depend, among others, on the density of adsorbed TMV particles on the sensor surface, which is affected by both the adsorption conditions and surface properties of the sensor. In this work, Ta₂O₅-gate field-effect capacitive sensors have been applied for the label-free electrical detection of TMV adsorption. The impact of the TMV concentration on both the sensor signal and the density of TMV particles adsorbed onto the Ta₂O₅-gate surface has been studied systematically by means of field-effect and scanning electron microscopy methods. In addition, the surface density of TMV particles loaded under different incubation times has been investigated. Finally, the field-effect sensor also demonstrates the label-free detection of penicillinase immobilization as model bioreceptor on TMV particles. KW - capacitive field-effect sensor KW - plant virus detection KW - tobacco mosaic virus (TMV) KW - TMV adsorption KW - Ta₂O₅ gate Y1 - 2021 U6 - https://doi.org/10.3390/mi12010057 VL - 12 IS - 1 PB - MDPI CY - Basel ER - TY - CHAP A1 - Funke, Harald A1 - Beckmann, Nils A1 - Keinz, Jan A1 - Horikawa, Atsushi T1 - 30 years of dry low NOx micromix combustor research for hydrogen-rich fuels: an overview of past and present activities T2 - Conference Proceedings Turbo Expo: Power for Land, Sea and Air, Volume 4B: Combustion, Fuels, and Emissions N2 - The paper presents an overview of the past and present of low-emission combustor research with hydrogen-rich fuels at Aachen University of Applied Sciences. In 1990, AcUAS started developing the Dry-Low-NOx Micromix combustion technology. Micromix reduces NOx emissions using jet-in-crossflow mixing of multiple miniaturized fuel jets and combustor air with an inherent safety against flashback. At first, pure hydrogen as fuel was investigated with lab-scale applications. Later, Micromix prototypes were developed for the use in an industrial gas turbine Honeywell/Garrett GTCP-36-300, proving low NOx characteristics during real gas turbine operation, accompanied by the successful definition of safety laws and control system modifications. Further, the Micromix was optimized for the use in annular and can combustors as well as for fuel-flexibility with hydrogen-methane-mixtures and hydrogen-rich syngas qualities by means of extensive experimental and numerical simulations. In 2020, the latest Micromix application will be demonstrated in a commercial 2 MW-class gas turbine can-combustor with full-scale engine operation. The paper discusses the advances in Micromix research over the last three decades. KW - Micromix KW - Hydrogen KW - Fuel-flexibility KW - NOx KW - Emissions Y1 - 2021 SN - 978-0-7918-8413-3 U6 - https://doi.org/10.1115/GT2020-16328 N1 - ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition September 21–25, 2020, Virtual, Online N1 - Paper No. GT2020-16328, V04BT04A069 PB - ASME CY - New York, NY ER - TY - CHAP A1 - Schulte, Maximilian A1 - Eggert, Mathias T1 - Predicting hourly bitcoin prices based on long short-term memory neural networks T2 - Proceedings of the International Conference on Wirtschaftsinformatik (WI) 2021 N2 - Bitcoin is a cryptocurrency and is considered a high-risk asset class whose price changes are difficult to predict. Current research focusses on daily price movements with a limited number of predictors. The paper at hand aims at identifying measurable indicators for Bitcoin price movements and the development of a suitable forecasting model for hourly changes. The paper provides three research contributions. First, a set of significant indicators for predicting the Bitcoin price is identified. Second, the results of a trained Long Short-term Memory (LSTM) neural network that predicts price changes on an hourly basis is presented and compared with other algorithms. Third, the results foster discussions of the applicability of neural nets for stock price predictions. In total, 47 input features for a period of over 10 months could be retrieved to train a neural net that predicts the Bitcoin price movements with an error rate of 3.52 %. Y1 - 2021 N1 - 16th International Conference on Wirtschaftsinformatik, March 2021, Essen, Germany ER - TY - JOUR A1 - Erpicum, Sebastien A1 - Crookston, Brian M. A1 - Bombardelli, Fabian A1 - Bung, Daniel Bernhard A1 - Felder, Stefan A1 - Mulligan, Sean A1 - Oertel, Mario A1 - Palermo, Michele T1 - Hydraulic structures engineering: An evolving science in a changing world JF - Wires Water Y1 - 2021 U6 - https://doi.org/10.1002/wat2.1505 SN - 2049-1948 VL - 8 IS - 2 PB - Wiley CY - Weinheim ER - TY - JOUR A1 - Givanoudi, Stella A1 - Cornelis, Peter A1 - Rasschaert, Geertrui A1 - Wackers, Gideon A1 - Iken, Heiko A1 - Rolka, David A1 - Yongabi, Derick A1 - Robbens, Johan A1 - Schöning, Michael Josef A1 - Heyndrickx, Marc A1 - Wagner, Patrick T1 - Selective Campylobacter detection and quantification in poultry: A sensor tool for detecting the cause of a common zoonosis at its source JF - Sensors and Actuators B: Chemical Y1 - 2021 U6 - https://doi.org/10.1016/j.snb.2021.129484 SN - 0925-4005 IS - In Press, Journal Pre-proof SP - Article 129484 PB - Elsevier CY - Amsterdam ER -