Investigation of the Capability of a Computational Fluid Dynamics Code for Low Reynolds Number Propeller

The amount of research and publication on low Reynolds number propellers has increased recently, especially because of the high number of UAVs produced during the past years. The use of CFD on propellers has been focused primarily on commercial propellers, propfans, and general aviation propellers. The aim of this work is to use a CFD code designed mainly for large scale (i.e. high Reynolds number) propellers to compute the performance characteristics of a low Reynolds number propeller and then compare those results with another software product that has been used more for low Reynolds number propellers

Intruder mobility in a vibrated granular packing

We study experimentally the dynamics of a dense intruder sinking under gravity inside a vibrated 2D granular packing. The surrounding flow patterns are characterized and the falling trajectories are interpreted in terms of an effectivive friction coefficient related to the intruder mean descent velocity (flow rules). At higher confining pressures i.e. close to jamming, a transition to intermittent dynamics is evidenced and displays anomalous "on-off" blockade statistics. A systematic analysis of the flow rules, obtained for different intruder sizes, either in the flowing regime or averaged over the flowing and blockade regimes, strongly suggest the existence of non-local properties for the vibrated packing rheology.

Acquiring reading comprehension skills in third grade students through the support of web based interactive technology

The purpose of the project is to develop an instructional tool, which will help third grade students in building their reading comprehension skills. The instructional tool will allow for more practice time to be worked into the day after a direct instruction lesson has been delivered. Students will be able to practice their reading skills in an interactive format rather than simple lecture or discussion

Induced QCD at Large N

We propose and study at large N a new lattice gauge model , in which the Yang-Mills interaction is induced by the heavy scalar field in adjoint representation. At any dimension of space and any $N$ the gauge fields can be integrated out yielding an effective field theory for the gauge invariant scalar field, corresponding to eigenvalues of the initial matrix field. This field develops the vacuum average, the fluctuations of which describe the elementary excitations of our gauge theory. At $N= \infty$ we find two phases of the model, with asymptotic freedom corresponding to the strong coupling phase (if there are no phase transitions at some critical $N$). We could not solve the model in this phase, but in the weak coupling phase we have derived exact nonlinear integral equations for the vacuum average and for the scalar excitation spectrum. Presumably the strong coupling equations can be derived by the same method.Comment: 20 page

Theorie und Numerik unidirektional verstärkter Faserverbundwerkstoffe: 3D Finite-Element-Untersuchungen der Faser-Matrix Mikroinstabilitäten

The topic of this work is a study of the microbuckling behavior of unidirectional fiber-reinforced composite materials. Some alternative finite element discretizations are compared in order to model the characteristic cell. Transversely isotropic material behavior is taken into account to model some anisotropic fibers. The free surface of the matrix has also influence on the fiber microbuckling. The results are compared qualitatively and quantitatively with analytical solutions and experiments

Phylogeography of E1b1b1b-M81 Haplogroup and Analysis of its Subclades in Morocco

In this work, we have analyzed a total of 295 unrelated Berber-speaking men from the northern, center and southern of Morocco, in order to characterize frequency of E1b1b1b-M81 haplogroup and to refine the phylogeny of its subclades: E1b1b1b1-M107, E1b1b1b2-M183 and E1b1b1b2a-M165. For this purpose, we have typed four biallelic polymorphisms: M81, M107, M183 and M165. As results, a large majority of the Berber-speaking male lineages belong to the Y chromosomal E1b1b1b-M81 haplogroup. The frequency ranged from 79.1 to 98.5% in all localities sampled. Then, the E1b1b1b2-M183 was the most dominant subclade in our samples, which ranged from 65.1% to 83.1%. In contrast, the E1b1b1b1-M107 and E1b1b1b2a-M165 subclades weren’t found in our samples. Our results suggest a predominance of E1b1b1b-M81 haplogroup among Moroccan Berber-speaking male with a decreasing gradient from south to north. Then, the most prevalent subclade in this haplogroup was E1b1b1b2-M183 in which difference between these three groups was statistically significant between central and southern groups

Angiotensin Converting Enzyme 2 (ACE2) and COVID-19: An overview of its structure, physiologic role and its involvement in SARS-COV2 infection and therapy

Coronavirus disease of 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is of a great global and national public health concern. Structural studies suggested that the SARS-CoV2 binds through its spike-protein to target cells by interacting with the angiotensin-converting enzyme 2 (ACE2) receptor which is widely expressed in the heart, kidneys, lungs, gut and testes cells. This article reviews the structural and physiologic roles of the human ACE2 and its correlation with the SARS-CoV2 infection and therapy. Evidence has been provided that the amino acids 318-510 of the viral spike protein represent the receptor-binding domain (RBD) which binds to ACE2, especially by means of the critical amino acids at positions 479 and 487, then allowing virus tropism and propagation. ACE2 play a crucial role in the down regulation of the renin-angiotensin-aldosterone system (RAAS). The RAAS ACE-Angiotensin II-AT1R regulatory axis promotes detrimental effects on the host, such as vasoconstriction, generation of reactive oxygen species, inflammation and matrix remodeling. However, the ACE2-Ang 1-7-MasR axis counterbalances the activation of the classical RAS system which inhibits cell growth, inflammation and fibrosis. The ACE2 has a protective effect against organ damage, lung injury and underlying chronic diseases such as hypertension, diabetes, and cardiovascular diseases wich are linked with poor prognosis of healing in patients with COVID-19. On account of the protective effects of ACE2, the design and development of drugs enhancing its activity may become one of the most promising strategies for the therapy of COVID-19 in the future

Fully Convolutional Networks for Semantic Segmentation from RGB-D images

In recent years new trends such as industry 4.0 boosted the research and development in the field of autonomous systems and robotics. Robots collaborate and even take over complete tasks of humans. But the high degree of automation requires high reliability even in complex and changing environments. Those challenging conditions make it hard to rely on static models of the real world. In addition to adaptable maps, mobile robots require a local and current understanding of the scene. The Bosch Start-Up Company is developing robots for intra-logistic systems, which could highly benefit from such a detailed scene understanding. The aim of this work is to research and develop such a system for warehouse environments. While the possible field of application is in general very broad, this work will focus on the detection and localization of warehouse specific objects such as palettes. In order to provide a meaningful perception of the surrounding a RGB-D camera is used. A pre-trained convolutional network extracts scene understanding in the form of pixelwise class labels. As this convolutional network is the core of the application, this work focuses on different network set-ups and learning strategies. One difficulty was the lack of annotated training data. Since the creation of densely labeled images is a very time consuming process it was important to elaborate on good alternatives. One interesting finding was that it’s possible to transfer learning to a high extent from similar models pre-trained on thousands of RGB-images. This is done by selective interventions on the net parameters. By ensuring a good initialization it’s possible to train towards a well performing model within few iterations. In this way it’s possible to train even branched nets at once. This can also be achieved by including certain normalization steps. Another important aspect was to find a suitable way to incorporate depth-information. How to fuse depth into the existing model? By providing the height over ground as an additional feature the segmentation accuracy was further improved while keeping the extra computational costs low. Finally the segmentation maps are refined by a conditional random field. The joint training of both parts results in accurate object segmentations comparable to recently published state-of-the-art models.Aktuelle Themen, wie zum Beispiel Industrie 4.0, haben Fortschritte im Bereich autonomer Systeme und Robotik vorangetrieben. Roboter kollaborieren mit Arbeitern oder übernehmen komplette Arbeitsschritte. Dieser hohe Automatisierungsgrad erfordert, dass solche Systeme, selbst in komplexen Situationen und Umgebungen, hochgradig zuverlässig und sicher arbeiten. Statische Modelle zur Abstrahierung der Umgebung sind unzureichend. Mobile Roboter benötigen neben dynamischen Lokalisierungskarten bestenfalls auch ein Verständnis der Umgebung. Die Bosch Start-Up GmbH entwickelt Roboter, welche zukünftig in Warenlagern eingesetzt werden sollen. Diese würden von einem solchen Verständnis profitieren. Das Ziel war es aktuelle Erkenntnisse aus der Forschung zur semantischen Segmentierung mithilfe von Deep Learning Techniken zu einer prototypischen Anwendung zu transferieren. Die entwickelte Anwendung im Allgemeinen zwar universell einsetzbar, der Fokus dieser Arbeit liegt jedoch auf der Segmentierung von Objekten aus einem typischen Warenlager (bspw. Paletten). Die Segmentierung basiert auf den Bildern einer RGB-D Kamera und ermöglicht gleichzeitig eine räumliche Lokalisierung von Objekten. Ein spezielles tiefes neuronales Netz (FCN) führt die komplette Segmentierung durch. Die Arbeit beschäftigt sich schwerpunktmäßig mit der Adaption und dem Training eines solches Netzes. Die Bereitstellung von annotatierten Daten ist äußerst aufwändig. Um die Zahl der nötigen Daten gering zu halten wurden geeignete Techniken eingesetzt. Dazu wurden Modellparameter frei zugänglicher Netze transferiert, um eine möglichst gute Initialisierung sicherzustellen. Außerdem wurden Normalisierungsschritte in die Netzarchitektur eingeführt, sodass auch verzweigte Strukturen in einem Trainingslauf trainiert werden können. Ein wichtiger Aspekt ist zudem die Einbeziehung von Tiefeninformation in den Segmentierungsprozess. Das finale Netz berücksichtigt neben RGB-Daten auch eine Höheninformation. Dadurch wurde die Segmentierungsqualität mit nur geringem zusätzlichen Rechenaufwand verbessert. Zudem wurde ein Conditional Random Field zur iterativen Verfeinerung der Segmentierung eingesetzt. Das gemeinsame Training beider Komponenten, FCN und CRF, hat dazu beigetragen, dass die Qualität der Ergebnisse sich im Bereich aktueller Forschungsarbeiten bewegen