RWTH Aachen University
  • Aachen, NRW, Germany
Recent publications
Objective Understanding the lateralization factors, including the anatomic and hemodynamic mechanisms, is essential for diagnosing cardio-embolic stroke. This study aims to investigate the elements, for the first time together, that could affect the laterality of stroke. Methods We performed a monocentric retrospective case-control study based on prospective registries of acute ischemic stroke patients in the comprehensive stroke center of the RWTH University hospital of Aachen for three years (June 2018–June 2021). We enrolled 222 patients with cardioembolic stroke (136 left stroke and 86 right stroke) admitted for first-ever acute ischemic stroke with unilateral large vessel occlusion of the anterior circulation. The peak systolic velocity (PSV) asymmetry of middle cerebral artery (MCA) was assessed by doppler as well as internal carotid artery (ICA) angle, aortic arch (AA) branching pattern and anatomy were assessed by CT-Angiography. Results We found that the increasing left ICA angle (p = 0.047), presence of bovine type AA anatomy (p = 0.041) as well as slow PSV of the right MCA with a value of >15% than left (p = 0.005) were the predictors for left stroke lateralization, while the latter was an independent predictor for the left stroke (OR=3.341 [1.415–7.887]). Inter-Rater Reliability ranged from moderate to perfect agreement. Conclusion The predictors for left stroke lateralization include the higher values of left ICA angle, presence of the bovine type AA and the slow right MCA PSV.
The transport sector faces two critical issues: a limited supply of fossil fuels and high greenhouse gas (GHG) emissions. Additionally, the demand for freight transport is steadily increasing, ultimately leading to higher GHG emissions. Since these emissions promote climate change, reducing the GHG emissions from the transport sector is necessary. Renewable drop-in fuels can play an essential role in this regard as those are CO2 neutral. Since these fuels come from so-called renewable sources, this represents a way to reduce carbon dioxide emissions from the current vehicle fleet to meet the EU’s Green Deal goals to become climate neutral by 2050. The drop-in fuels from renewable sources and later the purely renewable fuels serve as a bridging technology in this context. With this in mind, experiments were conducted with a Heavy-Duty Single Cylinder Engine (HD-SCE). The effects of four different renewable fuels or fuel blends – 93% RF/7% UCOME, 60% B0-Diesel/40% RF blend, 70% Diesel/30% Octanol blend and 100% Octanol – on engine performance and raw emissions were studied in comparison to fossil Diesel fuel. The investigations were conducted at three different load points — Rated Power (RP), best Brake Thermal Efficiency (BTE) and Cruise Point, covering all the relevant load points for HD engines. For all load points, the use of renewable fuels resulted in lower carbon dioxide (CO2), hydrocarbon (HC), carbon monoxide (CO) and FSN compared to fossil Diesel due to the fuel-borne oxygen and the lower C/H ratio of these alternative fuels. The blend of 60% B0-Diesel-40% RF shows the highest efficiency due to the paraffinic fuel structure, the fuel-borne oxygen, the higher calorific value, and the high cetane number. 100% Octanol resulted in a reduction in FSN by a factor of 3. All renewable fuels show a GHG emission reduction potential of around 2.5% to 5.5% in the Tank-to-Wheel (TtW) analysis.
Data lakes are becoming increasingly prevalent for big data management and data analytics. In contrast to traditional ‘schema-on-write’ approaches such as data warehouses, data lakes are repositories storing raw data in its original formats and providing a common access interface. Despite the strong interest raised from both academia and industry, there is a large body of ambiguity regarding the definition, functions and available technologies for data lakes. A complete, coherent picture of data lake challenges and solutions is still missing. This survey reviews the development, architectures, and systems of data lakes. We provide a comprehensive overview of research questions for designing and building data lakes. We classify the existing approaches and systems based on their provided functions for data lakes, which makes this survey a useful technical reference for designing, implementing and deploying data lakes. We hope that the thorough comparison of existing solutions and the discussion of open research challenges in this survey will motivate the future development of data lake research and practice.
This document provides a comparison of hairpin winding, pull-in winding and litz wire winding technology on the ac copper losses of a permanent magnet (PM) excited electrical machine. Current displacement effects under pure sinusoidal current and pulse width modulated (PWM) voltage supply are studied based on Finite Element Analysis (FEA). The resulting additional ac copper losses are analyzed for three winding technologies.
In this paper, we consider a unmanned aerial vehicle (UAV) aided integrated sensing and communications (ISAC) network with moving ground users in constant-velocity trajectory. A global optimal trajectory design scheme is proposed including a continuous analytic solution as well as optimization condition, which is theoretically different from numerical schemes obtaining a discrete piece-wise solution with approximate optimality. However, it is challenging to maximize the performance over entire infinite time slots in moving-user scenarios. By projecting the trajectory onto a user-relative coordinate frame, we reduce the performance to a location-determined function, which is in physical equivalence to an artificial potential field (APF). Accordingly, the optimization problem is reformulated to the shape determination problem of a density-varying catenary in the APF. By performing force analysis, we describe the topology of the catenary via a second-order differential equation determined by a boundary, i.e., any three combination of the location, orientation and turning curvature at arbitrary waypoints. Equivalently, the representation of the continuous solution is minimized in ultra-low-dimension parameter space and offers a flexible and lightning-speed design practice. Through complexity analysis, we find that the complexity of the proposed analytic scheme is significantly lower than the traditional discrete schemes. Further, we also prove that the global optimality, existence and uniqueness of the solution holds under a condition of a strong applicability to general sensing and communications (S $\&$ C) services. In simulation, the equivalence is confirmed and the results show global optimality, low-complexity and the high flexibility under avoidance, crossing and G-force limit.
Cities of all sizes around the world are facing increasing levels of congestion, which leads to increasing travel time and emissions, and ultimately affects the quality of life. Relevant research suggests that adaptive traffic light control systems can improve the traffic flow, but their impact on energy-efficiency of vehicle propulsion systems is not well understood. In this study, we use Proximal Policy Optimization, a Deep Reinforcement Learning algorithm, to develop an optimized adaptive traffic light control systems that controls three traffic lights simultaneously. For this purpose, we have created a microscopic traffic simulation of the city of Aachen, Germany, calibrated on the basis of traffic measurements, where the actual traffic light schedule, a green-wave, fixed-time control scheme, serves as a reference. The traffic simulation is coupled with detailed, physics-based powertrain models of both conventional and electric vehicles, which are validated against chassis dynamometer measurements. By analyzing the complex interactions between traffic light control, the resulting vehicle trajectories and the powertrain components, we show that Reinforcement Learning-based adaptive control can significantly improve the traffic flow, with a 41% increase in average velocity, without any drawbacks in CO2 emission (-1%). Furthermore, we find that maximizing traffic flow and minimizing CO2 emissions are not necessarily contradictory objectives, and identify an increased energy saving potential at low traffic densities. Thus, we prove that adaptive traffic light control can make traffic not only more time-efficient, but also more sustainable.
Neural Radiance Fields (NeRFs) have shown great potential for tasks like novel view synthesis of static 3D scenes. Since NeRFs are trained on a large number of input images, it is not trivial to change their content afterwards. Previous methods to modify NeRFs provide some control but they do not support direct shape deformation which is common for geometry representations like triangle meshes. In this paper, we present a NeRF geometry editing method that first extracts a triangle mesh representation of the geometry inside a NeRF. This mesh can be modified by any 3D modeling tool (we use ARAP mesh deformation). The mesh deformation is then extended into a volume deformation around the shape which establishes a mapping between ray queries to the deformed NeRF and the corresponding queries to the original NeRF. The basic shape editing mechanism is extended towards more powerful and more meaningful editing handles by generating box abstractions of the NeRF shapes which provide an intuitive interface to the user. By additionally assigning semantic labels, we can even identify and combine parts from different objects. We demonstrate the performance and quality of our method in a number of experiments on synthetic data as well as real captured scenes.
The pathologist Max Kuczynski (1890–1967) gained recognition for his bacteriological research but is also considered the founder of the so-called ethnopathology. As a “non-Aryan,” Kuczynski emigrated from Nazi Germany to Peru, where his elder son was later even to become president. However, the circumstances surrounding the end of Kuczynski’s career in Germany are hardly known. This article takes this research gap as an opportunity to reconstruct his life, the circumstances of his emigration, and his work in South America. Numerous archival documents serve as sources. In the mid-1920s, Kuczynski developed “ethnic pathology,” a new interdisciplinary approach that offered a counter-concept to the increasingly popular racial hygiene in Germany. But his career in Germany ended even before the Nazis came to power in 1933. He was dismissed from the Charité Pathological Institute in October 1932 at the instigation of its new director, Robert Rössle (1876–1956). Personal and financial reasons played a role, but Kuczynski’s rejection of racial hygiene may also have been a decisive factor: Rössle himself turned increasingly to questions of racial hygiene in the Third Reich and used the corpses of Nazi victims for his research. It can be shown that the circumstances of Kuczynski’s dismissal were already catalyzed by anti-Semitic and eugenic tendencies, which were to unleash themselves radically in Germany only a few months later – and even caught up with him in Peruvian exile.
Power conditioning systems are performing a key role in smart grid operation. The increase in renewable energy installation leads to an increase in the power electronic converters installation for distributed generation, rural electrification, and grid integration. As the numbers are increasing, the reliability of the grid is impacted mainly due to the power conditioning weak link. Thus, there is a need to improve the reliability and efficiency of overall power processing. One way to achieve this is to improve health by smart monitoring and make the controller fault-tolerant. For example, approaches that monitor the health of the devices along with fault-tolerant control architecture can be implemented in the controller to detect failures and to get timely alarm signals. For this, relevant data gathering and analysis are extremely critical. Machine learning (ML) and deep learning (DL) are approaches that analyze the data, learn from the data, and then apply it during the decision-making process. The Special Issue on Machine Learning Techniques in Power Electronics invites the articles related to data gathering/analysis and improvements of reliable operation of power conditioning and renewable energy resources with applications to the smart grid.
The German Federal Constitutional Court considers the civil rights of the younger generations to be violated by the fact that the climate protection provided for by law to date is not sufficient to protect them from excessive climate burdens in the future. To put it simply: If those living today do not reduce greenhouse gas emissions sufficiently, the young will have to do so even more and will then be overburdened. The German Federal Constitutional Court’s climate decision thus offers a good opportunity for more effective climate protection in Germany, which must, however, be reconciled with the more ambitious EU targets set by the EU Climate Act. Here, Germany has to make a weighty, as required solidary contribution according to its strong economic power. This is the real challenge, which the German Federal Constitutional Court unfortunately did not take into account. Climate protection is not only international, but also European, as Section 4 (1) Sentence 5 of the Climate Protection Act shows. The Russia-Ukraine war shows that climate protection is also about securing raw materials. This must therefore be ensured efficiently through international efforts. This is the only way to achieve the energy transition. Climate protection must also be conceived on a global scale. This means that all circumstances that are indispensable for climate protection must be considered globally. This also applies to the necessary raw materials. The supply of these raw materials must be secured across national borders.
The potential advantages of intelligent wireless communications with millimeter wave (mmWave) and massive multiple-input multiple-output (MIMO) are based on the availability of instantaneous channel state information (CSI) at the base station (BS). However, no existence of channel reciprocity leads to the difficult acquisition of accurate CSI at the BS in frequency division duplex (FDD) systems. Many researchers explored effective architectures based on deep learning (DL) to solve this problem and proved the success of DL-based solutions. However, existing schemes focused on the acquisition of complete CSI while ignoring the beamforming and precoding operations. In this paper, we propose an intelligent channel feedback architecture using eigenmatrix and eigenvector feedback neural network (EMEVNet). With the help of the attention mechanism, the proposed EMEVNet can be considered as a dual channel auto-encoder, which is able to jointly encode the eigenmatrix and eigenvector into codewords. Simulation results show great performance improvement and robustness with extremely low overhead of the proposed EMEVNet method compared with the traditional DL-based CSI feedback methods.
Aim Heart failure is an escalating burden on global health care systems. Modernizing heart failure care is inevitable, with eHealth products poised to play an important role. However, eHealth devices that can initiate and adjust heart failure medication are currently lacking. Consequently, this study aimed to develop an artificial intelligence-based decision engine to provide guideline-based recommendations for disease-modifying medication in heart failure patients. Methods and Results We developed the decision engine by converting the ESC heart failure guidelines into Business Process Model and Notation, a visual modeling language suitable for developing complex decision engines. A safety evaluation, based on clinical parameters, was conducted to ascertain the system’s applicability to specific cases. The decision engine renders specific decisions concerning disease- modifying therapy for heart failure patients. We defined 72 virtual heart failure patient scenarios, encompassing a broad spectrum of baseline characteristics and background medication. All recommendations offered by the engine were evaluated by an independent heart failure specialist. All but three recommendations (94%) were identical to the treatment decisions by the heart failure specialist and all (100%) were in line with the 2021 ESC heart failure guidelines. Conclusion The decision engine offers guideline-based recommendations for disease-modifying therapy, positioning it as a tool to enhance self-care among heart failure patients. To validate our results, the decision engine is being prospectively tested in real-world patients in a multicenter clinical trial (NCT04699253).
Hydrogen atom transfer (HAT) and photoredox dual catalysis provides a unique opportunity in organic synthesis, enabling the direct activation of C/Si/S–H bonds. However, the activation of O–H bonds of β,γ-unsaturated oximes poses a challenge due to their relatively high redox potential, which exceeds the oxidizing capacity of most currently developed photocatalysts. We here demonstrate that the combination of HAT and photoredox catalysis allows the activation of O–H bond of β,γ-unsaturated oximes. The strategy effectively addresses the oxime's high redox potential and offers a universal pathway for iminoxyl radical formation. Leveraging the versatility of this approach, a diverse array of valuable heterocycles have been synthesized with the use of different radical acceptors. Mechanistic studies confirm a HAT process for the O–H bond activation.
In this paper, we connect two research directions concerning numeral symbol systems and their epistemological significance. The first direction concerns the cognitive processes involved in acquiring and applying different numeral symbols, e.g. the Indo-Arabic or Roman numeral systems. The second direction is a semiotic one, with focus on Charles Peirce’s Philosophy of Notation. Peirce’s work on logical formalism is well known, but he also wrote extensively on numeral systems. Here we take Peirce’s considerations on central notions like iconicity and simplicity and examine their relevance for comparing different numeral symbol systems. We argue that simplicity and iconicity, for example, cannot be understood as single notions. Instead, they should be connected to different aims of numeral symbols that different systems fulfill to different degrees. Consequently, we focus on the kind of trade-offs that different symbol systems imply in acquiring and applying numeral symbol systems.
The coherent dynamics of a quantum mechanical two-level system passing through an anti-crossing of two energy levels can give rise to Landau-Zener-Stückelberg-Majorana (LZSM) interference. LZSM interference spectroscopy has proven to be a fruitful tool to investigate charge noise and charge decoherence in semiconductor quantum dots (QDs). Recently, bilayer graphene has developed as a promising platform to host highly tunable QDs potentially useful for hosting spin and valley qubits. So far, in this system no coherent oscillations have been observed and little is known about charge noise in this material. Here, we report coherent charge oscillations and T2*\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${T}_{2}^{*}$$\end{document} charge decoherence times in a bilayer graphene double QD. The charge decoherence times are measured independently using LZSM interference and photon assisted tunneling. Both techniques yield T2*\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${T}_{2}^{*}$$\end{document} average values in the range of 400–500 ps. The observation of charge coherence allows to study the origin and spectral distribution of charge noise in future experiments.
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Christina Regenbogen
  • Department of Psychiatry, Psychotherapy and Psychosomatics
Oliver Budde
  • Forschungsinstitut für Rationalisierung e. V.
Templergraben 55, D-52056, Aachen, NRW, Germany
Head of institution
Dr. rer. nat. Dr. h. c. mult., Universitätsprofessor Ulrich Rüdiger