Instability of a compressible circular free jet with consideration of the influence of the jet boundary layer thickness

The instability of a circular jet was investigated by means of the inviscid linearized stability theory. By variation of a jet parameter which takes the ratio of jet radius to boundary layer thickness into account, the influence of axisymmetry on the spatial growth rate and disturbance phase velocity is studied. The influence of Mach number and temperature ratio is discussed. A comparison with measurements shows that the instability of a turbulent jet boundary layer may also be explained by these results

Prediction of flyover jet noise spectra from static tests

A scaling law is derived for predicting the flyover noise spectra of a single-stream shock-free circular jet from static experiments. The theory is based on the Lighthill approach to jet noise. Density terms are retained to include the effects of jet heating. The influence of flight on the turbulent flow field is considered by an experimentally supported similarity assumption. The resulting scaling laws for the difference between one-third-octave spectra and the overall sound pressure level compare very well with flyover experiments with a jet engine and with wind tunnel experiments with a heated model jet

The effect of shared leadership on team performance in international undergraduate students

The current study examined how shared leadership influences a teams' performance among international undergraduate students. Shared leadership is a team property where two or more leaders are present in a team, sharing the lead and associated responsibilities. The study used a longitudinal design with five consecutive measurements during a six week period to inspect the temporal development and the various factors influencing shared leadership. The statistical analysis showed that shared leadership changed over time and both shared leadership and the personality trait conscientiousness predicted team performance. Correlation between motivation and team performance and an interaction effect between motivation and shared leadership could not be confirmed. Implications of the findings are discussed with reference to the current literature

The baroclinic secondary instability of the two-dimensional shear layer

The focus of this study is on the numerical investigation of two-dimensional, isovolume, high Reynolds and Froude numbers, variable-density mixing layers. Lagrangian simulations, of both the temporal and the spatial models, are performed. They reveal the breaking-up of the strained vorticity and density-gradient braids, connecting two neighboring primary structures. The secondary instability arises where the vorticity has been intensified by the baroclinic torque. A simplified model of the braid of the variable-density mixing layer, consisting of a strained vorticity and density-gradient filament, is analyzed. It is concluded that the physical mechanism responsible for the secondary instability is the forcing of the vorticity field by the baroclinic torque, itself sensitive to perturbations. This mechanism suggests a rapid route to turbulence for the variable-density mixing layer

A System-driven Automatic Ground Truth Generation Method for DL Inner-City Driving Corridor Detectors

Data-driven perception approaches are well-established in automated driving systems. In many fields even super-human performance is reached. Unlike prediction and planning approaches, mainly supervised learning algorithms are used for the perception domain. Therefore, a major remaining challenge is the efficient generation of ground truth data. As perception modules are positioned close to the sensor, they typically run on raw sensor data of high bandwidth. Due to that, the generation of ground truth labels typically causes a significant manual effort, which leads to high costs for the labelling itself and the necessary quality control. In this contribution, we propose an automatic labeling approach for semantic segmentation of the drivable ego corridor that reduces the manual effort by a factor of 150 and more. The proposed holistic approach could be used in an automated data loop, allowing a continuous improvement of the depending perception modules.Comment: 8 page

Neue Einsichten in die Toxizität und die Speziation von Mangan

ZusammenfassungMangan (Mn) ist seit mittlerweile 175 Jahren als neurotoxische Substanz bekannt. Daher ist es im Lauf des letzten Jahrhunderts intensiv erforscht worden. Von vorläufigen Beschreibungen ausschließlich über Symptomen bei Mn-exponierten bzw. überexponierten Arbeitern sind die Forschungsarbeiten zu detaillierteren Untersuchungen der toxischen Mechanismen von Mn fortgeschritten. Zur Aufklärung dieser neurotoxischen Mechanismen wurde eine Reihe von Studien durchgeführt, die z. T. in Übersichtsartikeln zusammengefasst wurden (z. B. Yokel RA. Neuromol Med 2009;11(4):297–310; Aschner M et al. Toxicology Appl Pharmacol 2007;221(2):131–47; Michalke B et al. J Environ Monit 2007;9(7):650). Seit unserem letzten Übersichtsartikel zur Mn-Speziation aus dem Jahr 2007 (Michalke B et al. J Environ Monit 2007;9(7):650) ist die Mn-Forschung beträchtlich vorangetrieben worden, und es sind mehrere neue Forschungsartikel erschienen. In den letzten Jahren fächerte sich jedoch die Erforschung der Mn-Toxizität in verschiedene Felder auf, wobei sehr detaillierte und komplexe Studiendesigns angewendet wurden. Insbesondere die Mechanismen der Mn-induzierten Nervenschädigung auf zellulärer und molekularer Ebene wurden genauer untersucht. Diskutiert wurden dabei Wechselwirkungen zwischen Neurotransmittern und Enzymen, Wirkmechanismen auf DNA-Ebene und auch die Einbeziehung genetischer Einflüsse. Ein wichtiges Thema war auch die Beschreibung spezieller Mn-Spezies, um so zu ermitteln, welches Molekül Mn an der Zellmembran transportiert und welches für die Schädigung des neuronalen Gewebes verantwortlich ist. Auch andere spezielle Schwerpunkte wie epidemiologische Studien wurden zunehmend wichtiger: Die betreffenden Arbeiten befassten sich mit Umwelteinflüssen von Mn insbesondere auf die Prävalenz der Parkinson-Krankheit sowie die Möglichkeit, Follow-up-Studien zur lebenslangen Exposition gegenüber Mn durchzuführen. Alle diese weit ausgreifenden Forschungsansätze können letztendlich dazu beitragen, mithilfe eines geeigneten Bio-Monitorings am Menschen in Zukunft das frühe Einsetzen von Manganismus zu verhindern oder zumindest rechtzeitig zu erkennen

Natural linewidth analysis of d-band photoemission from Ag(110)

We report a high-resolution angle-resolved study of photoemission linewidths observed for Ag(110). A careful data analysis yields k$-resolved upper limits for the inverse inelastic lifetimes of$d$-holes at the X-point of the bulk band structure. At the upper$d$-band edge the hole-lifetime is$\tau_h \geq 22 $fs, i.e. more than one order of magnitude larger than predicted for a free-electron gas. Following calculations for$d$-hole dynamics in Cu (I.\ Campillo et al., Phys. Rev. Lett., in press) we interpret the lifetime enhancement by a small scattering cross-section of $d$- and $sp$-states below the Fermi level. With increasing distance to $E_F$ the $d$-hole lifetimes get shorter because of the rapidly increasing density of d-states and contributions of intra-$d$-band scattering processes, but remain clearly above free-electron-model predictions.Comment: 14 pages, 7 figure

Explanation Retrieval in Semantic Networks : Understanding Spreading Activation based Recommendations

Spreading Activation is a well-known semantic search technique to determine the relevance of nodes in a semantic network. When used for decision support, meaningful explanations of semantic search results are crucial for the user’s acceptance and trust. Usually, explanations are generated based on the original network. Indeed, the data accumulated during the spreading activation process contains semantically extremely valuable information. Therefore, our approach exploits the so-called spread graph, a specific data structure that comprises the spreading progress data. In this paper, we present a three-step explanation retrieval method based on spread graphs. We show how to retrieve the most relevant parts of a network by minimization and extraction techniques and formulate meaningful explanations. The evaluation of the approach is then performed with a prototypical decision support system for automotive safety analyses