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IT-Sicherheit im Automobil
(2014)
A High-Throughput Functional Complementation Assay for Classification of BRCA1 Missense Variants
(2013)
Unsteady flow measurements in the wake behind a wind-tunnel car model by using high-speed planar PIV
(2015)
This study investigates unsteady characteristics of the wake behind a 28%-scale car model in a wind tunnel using highspeed planar particle image velocimetry (PIV). The car model is based on a hatchback passenger car that is known to have relatively high fluctuations in its aerodynamic loads. This study primarily focuses on the lateral motion of the flow on the horizontal plane to determine the effect of the flow motion on the straight-line stability and the initial steering response of the actual car on a track. This paper first compares the flow fields in the wake behind the above mentioned model obtained using conventional and high-speed planar PIV, with sampling frequencies of 8 Hz and 1 kHz, respectively. Large asymmetrically coherent flow structures, which fluctuate at frequencies below 2 Hz, are observed in the results of highspeed PIV measurements, whereas conventional PIV is unable to capture these features of the flow owing to aliasing. This flow pattern with a laterally swaying motion is represented by opposite signs of cross-correlation coefficients of streamwise velocity fluctuations for the two sides of the car model. Effects of two aerodynamic devices that are known to reduce the
fluctuation levels of the aerodynamic loads are then extensively investigated. The correlation analyses reveal that these devices indeed reduce the fluctuation levels of the flow and the correlation values around the rear combination-lamp, but it is found that the effects of these devices are different around the c-pillar.
Inkompressible Strömungen
(2015)
In the context of the increasing digitalization, the Internet of Things (IoT) is seen as a technological driver through which completely new business models can emerge in the interaction of different players. Identified key players include traditional industrial companies, municipalities and telecommunications companies. The latter, by providing connectivity, ensure that small devices with tiny batteries can be connected almost anywhere and directly to the Internet. There are already many IoT use cases on the market that provide simplification for end users, such as Philips Hue Tap. In addition to business models based on connectivity, there is great potential for information-driven business models that can support or enhance existing business models. One example is the IoT use case Park and Joy, which uses sensors to connect parking spaces and inform drivers about available parking spaces in real time. Information-driven business models can be based on data generated in IoT use cases. For example, a telecommunications company can add value by deriving more decision-relevant information – called insights – from data that is used to increase decision agility. In addition, insights can be monetized. The monetization of insights can only be sustainable, if careful attention is taken and frameworks are considered. In this chapter, the concept of information-driven business models is explained and illustrated with the concrete use case Park and Joy. In addition, the benefits, risks and framework conditions are discussed.
High aerodynamic efficiency requires propellers with high aspect ratios, while propeller sweep potentially reduces noise. Propeller sweep and high aspect ratios increase elasticity and coupling of structural mechanics and aerodynamics, affecting the propeller performance and noise. Therefore, this paper analyzes the influence of elasticity on forward-swept, backward-swept, and unswept propellers in hover conditions. A reduced-order blade element momentum approach is coupled with a one-dimensional Timoshenko beam theory and Farassat's formulation 1A. The results of the aeroelastic simulation are used as input for the aeroacoustic calculation. The analysis shows that elasticity influences noise radiation because thickness and loading noise respond differently to deformations. In the case of the backward-swept propeller, the location of the maximum sound pressure level shifts forward by 0.5 °, while in the case of the forward-swept propeller, it shifts backward by 0.5 °. Therefore, aeroacoustic optimization requires the consideration of propeller deformation.
Lifting propellers are of increasing interest for Advanced Air Mobility. All propellers and rotors are initially twisted beams, showing significant extension–twist coupling and centrifugal twisting. Torsional deformations severely impact aerodynamic performance. This paper presents a novel approach to assess different reasons for torsional deformations. A reduced-order model runs large parameter sweeps with algebraic formulations and numerical solution procedures. Generic beams represent three different propeller types for General Aviation, Commercial Aviation, and Advanced Air Mobility. Simulations include solid and hollow cross-sections made of aluminum, steel, and carbon fiber-reinforced polymer. The investigation shows that centrifugal twisting moments depend on both the elastic and initial twist. The determination of the centrifugal twisting moment solely based on the initial twist suffers from errors exceeding 5% in some cases. The nonlinear parts of the torsional rigidity do not significantly impact the overall torsional rigidity for the investigated propeller types. The extension–twist coupling related to the initial and elastic twist in combination with tension forces significantly impacts the net cross-sectional torsional loads. While the increase in torsional stiffness due to initial twist contributes to the overall stiffness for General and Commercial Aviation propellers, its contribution to the lift propeller’s stiffness is limited. The paper closes with the presentation of approximations for each effect identified as significant. Numerical evaluations are necessary to determine each effect for inhomogeneous cross-sections made of anisotropic material.
Operational Modal Analysis (OMA) is a promising candidate for flutter testing and Structural Health Monitoring (SHM) of aircraft wings that are passively excited by wind loads. However, no studies have been published where OMA is tested in transonic flows, which is the dominant condition for large civil aircraft and is characterized by complex and unique aerodynamic phenomena. We use data from the HIRENASD large-scale wind tunnel experiment to automatically extract modal parameters from an ambiently excited wing operated in the transonic regime using two OMA methods: Stochastic Subspace Identification (SSI) and Frequency Domain Decomposition (FDD). The system response is evaluated based on accelerometer measurements. The excitation is investigated from surface pressure measurements. The forcing function is shown to be non-white, non-stationary and contaminated by narrow-banded transonic disturbances. All these properties violate fundamental OMA assumptions about the forcing function. Despite this, all physical modes in the investigated frequency range were successfully identified, and in addition transonic pressure waves were identified as physical modes as well. The SSI method showed superior identification capabilities for the investigated case. The investigation shows that complex transonic flows can interfere with OMA. This can make existing approaches for modal tracking unsuitable for their application to aircraft wings operated in the transonic flight regime. Approaches to separate the true physical modes from the transonic disturbances are discussed.
With its need for high SNR and short acquisition times, Cardiac MRI (CMR) is an intriguing target application for ultrahigh field MRI. Due to the sheer size of the upper torso, however, the known RF issues of 7T MRI are also most prominent in CMR. Recent years brought substantial progress but the full potential of the ultrahigh field for CMR is yet to be exploited. Parallel transmission (pTx) is a promising approach in this context and several groups have already reported B1 shimming for 7T CMR. In such a static pTx application amplitudes and phases of all Tx channels are adjusted individually but otherwise imaging techniques established in current clinical practice 1.5 T and 3 T are applied. More advanced forms of pTx as spatially selective excitation (SSE) using Transmit SENSE promise additional benefits like faster imaging with reduced fields of view or improved SAR control. SSE requires the full dynamic capabilities of pTx, however, and for the majority of today's implemented pTx hardware the internal synchronization of the Tx array does not easily permit external triggering as needed for CMR. Here we report a software solution to this problem and demonstrate the feasibility of CINE CMR at 7 T using a Tx array.
Digital Shadows as the aggregation, linkage and abstraction of data relating to physical objects are a central vision for the future of production. However, the majority of current research takes a technocentric approach, in which the human actors in production play a minor role. Here, the authors present an alternative anthropocentric perspective that highlights the potential and main challenges of extending the concept of Digital Shadows to humans. Following future research methodology, three prospections that illustrate use cases for Human Digital Shadows across organizational and hierarchical levels are developed: human-robot collaboration for manual work, decision support and work organization, as well as human resource management. Potentials and challenges are identified using separate SWOT analyses for the three prospections and common themes are emphasized in a concluding discussion.
An interdisciplinary view on humane interfaces for digital shadows in the internet of production
(2022)
Digital shadows play a central role for the next generation industrial internet, also known as Internet of Production (IoP). However, prior research has not considered systematically how human actors interact with digital shadows, shaping their potential for success. To address this research gap, we assembled an interdisciplinary team of authors from diverse areas of human-centered research to propose and discuss design and research recommendations for the implementation of industrial user interfaces for digital shadows, as they are currently conceptualized for the IoP. Based on the four use cases of decision support systems, knowledge sharing in global production networks, human-robot collaboration, and monitoring employee workload, we derive recommendations for interface design and enhancing workers’ capabilities. This analysis is extended by introducing requirements from the higher-level perspectives of governance and organization.
The future of industrial manufacturing and production will increasingly manifest in the form of cyber-physical production systems. Here, Digital Shadows will act as mediators between the physical and digital world to model and operationalize the interactions and relationships between different entities in production systems. Until now, the associated concepts have been primarily pursued and implemented from a technocentric perspective, in which human actors play a subordinate role, if they are considered at all. This paper outlines an anthropocentric approach that explicitly considers the characteristics, behavior, and traits and states of human actors in socio-technical production systems. For this purpose, we discuss the potentials and the expected challenges and threats of creating and using Human Digital Shadows in production.
The constitutive androstane receptor (CAR) and the pregnane X receptor (PXR) are closely related nuclear receptors involved in drug metabolism and play important roles in the mechanism of phenobarbital (PB)-induced rodent nongenotoxic hepatocarcinogenesis. Here, we have used a humanized CAR/PXR mouse model to examine potential species differences in receptor-dependent mechanisms underlying liver tissue molecular responses to PB. Early and late transcriptomic responses to sustained PB exposure were investigated in liver tissue from double knock-out CAR and PXR (CARᴷᴼ-PXRᴷᴼ), double humanized CAR and PXR (CARʰ-PXRʰ), and wild-type C57BL/6 mice. Wild-type and CARʰ-PXRʰ mouse livers exhibited temporally and quantitatively similar transcriptional responses during 91 days of PB exposure including the sustained induction of the xenobiotic response gene Cyp2b10, the Wnt signaling inhibitor Wisp1, and noncoding RNA biomarkers from the Dlk1-Dio3 locus. Transient induction of DNA replication (Hells, Mcm6, and Esco2) and mitotic genes (Ccnb2, Cdc20, and Cdk1) and the proliferation-related nuclear antigen Mki67 were observed with peak expression occurring between 1 and 7 days PB exposure. All these transcriptional responses were absent in CARᴷᴼ-PXRᴷᴼ mouse livers and largely reversible in wild-type and CARʰ-PXRʰ mouse livers following 91 days of PB exposure and a subsequent 4-week recovery period. Furthermore, PB-mediated upregulation of the noncoding RNA Meg3, which has recently been associated with cellular pluripotency, exhibited a similar dose response and perivenous hepatocyte-specific localization in both wild-type and CARʰ-PXRʰ mice. Thus, mouse livers coexpressing human CAR and PXR support both the xenobiotic metabolizing and the proliferative transcriptional responses following exposure to PB.
Magnetic nanoparticles (MNPs) are used as therapeutic and diagnostic agents for local delivery of heat and image contrast enhancement in diseased tissue. Besides magnetization, the most important parameter that determines their performance for these applications is their magnetic relaxation, which can be affected when MNPs immobilize and agglomerate inside tissues. In this letter, we investigate different MNP agglomeration states for their magnetic relaxation properties under excitation in alternating fields and relate this to their heating efficiency and imaging properties. With focus on magnetic fluid hyperthermia, two different trends in MNP heating efficiency are measured: an increase by up to 23% for agglomerated MNP in suspension and a decrease by up to 28% for mixed states of agglomerated and immobilized MNP, which indicates that immobilization is the dominant effect. The same comparatively moderate effects are obtained for the signal amplitude in magnetic particle spectroscopy.
This work presents a methodology for automated
damage-sensitive feature extraction and anomaly
detection under multivariate operational variability
for in-flight assessment of wings. The
method uses a passive excitation approach, i. e.
without the need for artificial actuation. The
modal system properties (natural frequencies and
damping ratios) are used as damage-sensitive
features. Special emphasis is placed on the use
of Fiber Bragg Grating (FBG) sensing technology
and the consideration of Operational and
Environmental Variability (OEV). Measurements
from a wind tunnel investigation with a composite
cantilever equipped with FBG and piezoelectric
sensors are used to successfully detect an impact
damage. In addition, the feasibility of damage
localisation and severity estimation is evaluated
based on the coupling found between damageand
OEV-induced feature changes.
With the many achievements of Machine Learning in the past years, it is likely that the sub-area of Deep Learning will continue to deliver major technological breakthroughs [1]. In order to achieve best results, it is important to know the various different Deep Learning frameworks and their respective properties. This paper provides a comparative overview of some of the most popular frameworks. First, the comparison methods and criteria are introduced and described with a focus on computer vision applications: Features and Uses are examined by evaluating papers and articles, Adoption and Popularity is determined by analyzing a data science study. Then, the frameworks TensorFlow, Keras, PyTorch and Caffe are compared based on the previously described criteria to highlight properties and differences. Advantages and disadvantages are compared, enabling researchers and developers to choose a framework according to their specific needs.
Die Studie erörtert anhand eines Fallbeispiels aus der Mathematik für Ingenieur*innen, wie didaktische Gestaltungsprinzipien für Soziale Präsenz, Kollaboration und das Lösen von praxisnahen Problemen mit mathematischem Denken in einer Online-Umgebung aussehen können. Hierfür zieht der
Beitrag den forschungsmethodologischen Rahmen Design-Based Research (DBR) hinzu und berichtet über Zwischenergebnisse. DBR wird an dieser Stelle als eine systematische Herangehensweise an kurzfristige Lehrveränderungen und als Chance auf dem Weg zu einer neuen Hochschullehre nach der COVID-19-Pandemie dargestellt, die theoretische und empirische Erkenntnisse mit Praxisverknüpfung und -relevanz vereint.
The initial idea of Robotic Process Automation (RPA) is the automation of business processes through the presentation layer of existing application systems. For this simple emulation of user input and output by software robots, no changes of the systems and architecture is required. However, considering strategic aspects of aligning business and technology on an enterprise level as well as the growing capabilities of RPA driven by artificial intelligence, interrelations between RPA and Enterprise Architecture (EA) become visible and pose new questions. In this paper we discuss the relationship between RPA and EA in terms of perspectives and implications. As workin- progress we focus on identifying new questions and research opportunities related to RPA and EA.
The continuing growth of scientific publications raises the question how research processes can be digitalized and thus realized more productively. Especially in information technology fields, research practice is characterized by a rapidly growing volume of publications. For the search process various information systems exist. However, the analysis of the published content is still a highly manual task. Therefore, we propose a text analytics system that allows a fully digitalized analysis of literature sources. We have realized a prototype by using EBSCO Discovery Service in combination with IBM Watson Explorer and demonstrated the results in real-life research projects. Potential addressees are research institutions, consulting firms, and decision-makers in politics and business practice.
Im Rahmen der Digitalisierung ist die zunehmende Automatisierung von bisher manuellen Prozessschritten ein Aspekt, der massive Auswirkungen auf die zukünftige Arbeitswelt haben wird. In diesem Kontext werden an den Einsatz von Softwarerobotern zur Prozessautomatisierung hohe Erwartungen geknüpft. Bei den Implementierungsansätzen wird die Diskussion aktuell insbesondere durch Robotic Process Automation (RPA) und Chatbots geprägt. Beide Ansätze verfolgen das gemeinsame Ziel einer 1:1-Automatisierung von menschlichen Handlungen und dadurch ein direktes Ersetzen von Mitarbeitern durch Maschinen. Bei RPA werden Prozesse durch Softwareroboter erlernt und automatisiert ausgeführt. Dabei emulieren RPA-Roboter die Eingaben auf der bestehenden Präsentationsschicht, so dass keine Änderungen an vorhandenen Anwendungssystemen notwendig sind. Am Markt werden bereits unterschiedliche RPA-Lösungen als Softwareprodukte angeboten. Durch Chatbots werden Ein- und Ausgaben von Anwendungssystemen über natürliche Sprache realisiert. Dadurch ist die Automatisierung von unternehmensexterner Kommunikation (z. B. mit Kunden) aber auch von unternehmensinternen Assistenztätigkeiten möglich. Der Beitrag diskutiert die Auswirkungen von Softwarerobotern auf die Arbeitswelt anhand von Anwendungsbeispielen und erläutert die unternehmensindividuelle Entscheidung über den Einsatz von Softwarerobotern anhand von Effektivitäts- und Effizienzzielen.