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Lobster eye type X-ray telescope with chromium-iridium coated tri-layer mirrors
No full textFor space-born astronomical X-ray telescopes, iridium-based reflective layer systems are known as highly effective
mirrors coatings. During the recent years, Aschaffenburg University of Applied Sciences and the Czech Technical
University in Prague jointly developed stress compensated chromium-iridium coatings for this application. To overcome the disturbing reflectivity reduction of the iridium absorption edge around 2 keV photon energy, thin overcoat layers of chromium were applied in addition. Now a prototype of a wide-field, imaging X-ray telescope
of Lobster Eye type is assembled at the company RIGAKU. For this purpose a small series of 34 mirrors based on 100 x 50 mm semiconductor grade silicon substrates has been coated at Aschaffenburg University. The applied tri-layer system consists of a stack of 40 nm chromium, which act as adhesive layer and compensates layer stress, a 30 nm iridium thick reflective layer, and an additional overcoat layer of 6 nm chromium. This layer system
have been analysed by AFM and TEM images. The mirrors are assembled into an aluminium frame to build a 2D Lobster Eye type telescope. The designed focal length of this wide field X-ray telescope is two meter. To study the performance of the tri-layer coating system, a twin LE telescope with convenient gold coatings was manufactured also. Performance measurements of both telescopes and under same conditions are planned at the PANTER test facility at the Max-Planck Institute for Extraterrestrial Physics. First experimental results,
their comparison with theoretical simulations and the comparison between both models will be presented in this contribution
Testing of High-Power Traction Batteries
No full textElectric drives in road vehicles with a battery as energy storage are about to be introduced to the mass market. In order to guarantee the quality and operational safety of battery-powered vehicles, considerable efforts must be made by car manufacturers and their suppliers. Since the properties of the batteries significantly determine the performance of the vehicles, special test benches with precise measuring systems are required for the accurate determination of the battery properties. The safety devices of the traction batteries must also be tested on high-performance test benches. In this paper the necessary test benches and the corresponding measuring systems are presented. Estimations of the measurement uncertainty and possibilities to increase measurement accuracy are presented
Laser-Induced Period Surface Structures to Improve Solderability of Electrical Solder Pads
No full textA wavelet-based sparse row-action method for image reconstruction in magnetic particle imaging
No full textPurpose
Magnetic particle imaging (MPI) is a preclinical imaging technique capable of visualizing the spatio-temporal distribution of magnetic nanoparticles. The image reconstruction of this fast and dynamic process relies on efficiently solving an ill-posed inverse problem. Current approaches to reconstruct the tracer concentration from its measurements are either adapted to image characteristics of MPI but suffer from higher computational complexity and slower convergence or are fast but lack in the image quality of the reconstructed images.
Methods
In this work we propose a novel MPI reconstruction method to combine the advantages of both approaches into a single algorithm. The underlying sparsity prior is based on an undecimated wavelet transform and is integrated into a fast row-action framework to solve the corresponding MPI minimization problem.
Results
Its performance is numerically evaluated against a classical FISTA (Fast Iterative Shrinkage-Thresholding Algorithm) approach on simulated and real MPI data. The experimental results show that the proposed method increases image quality with significantly reduced computation times.
Conclusions
In comparison to state-of-the-art MPI reconstruction methods, our approach shows better reconstruction results and at the same time accelerates the convergence rate of the underlying row-action algorithm
Bridging organizational resilience and management control systems - a systematic review
No full textOrganizations have repeatedly faced challenges due to (natural) disasters such as pandemics, economic or financial crises, and unexpected events. One reason why some firms cope more efficiently with such unexpected events than others has long been the subject of research, might be found in their resilience design. However, there is still no consensus in the literature and there is no common understanding of the definitions, the conceptualizations of resilience at the organizational level, and the interaction with management control systems (MCS). This study bridges MCS and resilience literature and provides a broader understanding of the relationship between the organization and adversity. Due to its ability to successfully control an organization and provide an effective control environment, we use Simons’ levers of control framework (LOC) as framework for integrating organizational resilience into MCS.
We perform a systematic reviewing of analytical conceptualizations and definitions of management control systems (levers of control) and organizational resilience, supplemented by current empirical findings. Based on literature, we provide a framework which integrates organizational resilience into management control systems. Our findings show that the integration of resilience aspects into MCS enables firms to manage resilience at the organizational level
Pose Based Trajectory Forecast of Vulnerable Road Users Using Recurrent Neural Networks
No full textIn this work, we use Recurrent Neural Networks (RNNs) in form of Gated Recurrent Unit (GRU) networks to forecast trajectories of vulnerable road users (VRUs), such as pedestrians and cyclists, in road traffic utilizing the past trajectory and 3D poses as input. The 3D poses represent the postures and movements of limbs and torso and contain early indicators for the transition between motion types, e.g. wait, start, move, and stop. VRUs often only become visible from the perspective of an approaching vehicle shortly before dangerous situations occur. Therefore, a network architecture is required which is able to forecast trajectories after short time periods and is able to improve the forecasts in case of longer observations. This motivates us to use GRU networks, which are able to use time series of varying duration as inputs, and to investigate the effects of different observation periods on the forecasting results. Our approach is able to make reasonable forecasts even for short observation periods. The use of poses improves the forecasting accuracy, especially for short observation periods compared to a solely head trajectory based approach. Different motion types benefit to different extent from the use of poses and longer observation periods
Nanostructured iridium catalysts for effective hydrogen peroxide decomposition
No full textThe EU intends to restrict the use of critical fuels in space propulsion systems. These propellants like hydrazine are chemically aggressive, toxic and carcinogenic. Space propulsion of the future should be environmentally friendly, inexpensive, and easy to handle. Aschaffenburg University and DLR are currently developing environmentally friendly technologies for future satellite engines. Thereby rocket-grade hydrogen peroxide, which is foreseen as a substitute, is converted to uncritical water vapour and oxygen by an exothermic catalytic reaction on nanostructured iridium layers. The resulting hot gases provide the required amount of thrust. The iridium catalyst layers are coated onto ceramic pellets, using RF magnetron sputtering technology. The catalytic functionality was tested afterwards at the chemical laboratories of DLR. First experimental results show a significantly higher catalytic activity for nanostructured iridium surfaces than for smoother crystalline layers.Poste
COVID-19 and the kidney: A retrospective analysis of 37 critically ill patients using machine learning
No full textIntroduction
There is evidence that SARS-CoV2 has a particular affinity for kidney tissue and is often
associated with kidney failure.
Methods
We assessed whether proteinuria can be predictive of kidney failure, the development of
chronic kidney disease, and mortality in 37 critically ill COVID-19 patients. We used machine
learning (ML) methods as decision trees and cut-off points created by the OneR package to
add new aspects, even in smaller cohorts.
Results
Among a total of 37 patients, 24 suffered higher-grade renal failure, 20 of whom required kidney
replacement therapy. More than 40% of patients remained on hemodialysis after intensive
care unit discharge or died (27%). Due to frequent anuria proteinuria measured in two-thirds of
the patients, it was not predictive for the investigated endpoints; albuminuria was higher in
patients with AKI 3, but the difference was not significant. ML found cut-off points of >31.4 kg/
m2 for BMI and >69 years for age, constructed decision trees with great accuracy, and identified
highly predictive variables for outcome and remaining chronic kidney disease.
Conclusions
Different ML methods and their clinical application, especially decision trees, can provide
valuable support for clinical decisions. Presence of proteinuria was not predictive of CKD or
AKI and should be confirmed in a larger cohort