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Die Studiengangentwicklung ist ein komplexer Prozess, in dem strukturelle Vorgaben beachtet, viele unterschiedliche Akteure/-innen einbezogen und nicht zuletzt angemessene didaktische Lösungen zum Erreichen der angestrebten Lernergebnisse gefunden werden müssen. Der vorliegende Text nimmt besonders den letzten Punkt in den Blick: Er zeigt, wie Studiengangentwicklung zu einem Thema der (Hochschul-)Didaktik geworden ist und wie sich der didaktische Ansatz von struktur- und prozessorientierten Ansätzen unterscheidet, aber auch, wie er mit diesen zusammenhängt. An einem Beispiel aus dem Wirtschaftsingenieurwesen wird veranschaulicht, was didaktische Studiengangentwicklung in der Praxis ausmacht und wie eine konkrete Implementierung verlaufen kann. Auf dieser Grundlage wird abschließend ein erweitertes Modell der didaktischen Studiengangentwicklung vorgeschlagen.
Die bereits mit Koalitionsvertrag vom 16.12.2013 in Aussicht genommene Neuregulierung der Leiharbeit steht nunmehr kurz bevor. Nach diversen Korrekturen des ursprünglichen Referentenentwurfes des Bundesministeriums für Arbeit und Soziales liegt seit dem 20.7.2016 der endgültige Entwurf des Gesetzes zur Änderung des Arbeitnehmerüberlassungsgesetzes und anderer Gesetze vor (AÜG-E). Die Änderungen sollen zum 1.1.2017 in Kraft treten. Von größeren Änderungen des Gesetzesentwurfs wird allgemein nicht mehr ausgegangen. Für die betriebliche Praxis sollte dies Anlass sein, sich bereits jetzt mit den sich abzeichnenden wichtigsten Neuerungen vertraut zu machen und diese entsprechend umzusetzen, um nachteilige Konsequenzen zu vermeiden.
Wirksamkeit einer OT-Mitgliedschaft im Arbeitgeberverband – Anforderungen an die Verbandssatzung
(2016)
Vergabe öffentlicher Aufträge kann von der Zahlung eines Mindestlohns abhängig gemacht werden
(2016)
Manufacturing process simulation enables the evaluation and improvement of autoclave mold concepts early in the design phase. To achieve a high part quality at low cycle times, the thermal behavior of the autoclave mold can be investigated by means of simulations. Most challenging for such a simulation is the generation of necessary boundary conditions. Heat-up and temperature distribution in an autoclave mold are governed by flow phenomena, tooling material and shape, position within the autoclave, and the chosen autoclave cycle. This paper identifies and summarizes the most important factors influencing mold heat-up and how they can be introduced into a thermal simulation. Thermal measurements are used to quantify the impact of the various parameters. Finally, the gained knowledge is applied to develop a semi-empirical approach for boundary condition estimation that enables a simple and fast thermal simulation of the autoclave curing process with reasonably high accuracy for tooling optimization.
We present an effective and simple multiscale method for equilibrating Kremer Grest model polymer melts of varying stiffness. In our approach, we progressively equilibrate the melt structure above the tube scale, inside the tube and finally at the monomeric scale. We make use of models designed to be computationally effective at each scale. Density fluctuations in the melt structure above the tube scale are minimized through a Monte Carlo simulated annealing of a lattice polymer model. Subsequently the melt structure below the tube scale is equilibrated via the Rouse dynamics of a force-capped Kremer-Grest model that allows chains to partially interpenetrate. Finally the Kremer-Grest force field is introduced to freeze the topological state and enforce correct monomer packing. We generate 15 melts of 500 chains of 10.000 beads for varying chain stiffness as well as a number of melts with 1.000 chains of 15.000 monomers. To validate the equilibration process we study the time evolution of bulk, collective, and single-chain observables at the monomeric, mesoscopic, and macroscopic length scales. Extension of the present method to longer, branched, or polydisperse chains, and/or larger system sizes is straightforward.
Rubber materials filled with reinforcing fillers display nonlinear rheological behavior at small strain amplitudes below γ0 < 0.1. Nevertheless, rheological data are analyzed mostly in terms of linear parameters, such as shear moduli (G′, G″), which loose their physical meaning in the nonlinear regime. In this work styrene butadiene rubber filled with carbon black (CB) under large amplitude oscillatory shear (LAOS) is analyzed in terms of the nonlinear parameter I3/1. Three different CB grades are used and the filler load is varied between 0 and 70 phr. It is found that I3/1(φ) is most sensitive to changes of the total accessible filler surface area at low strain amplitudes (γ0 = 0.32). The addition of up to 70 phr CB leads to an increase of I3/1(φ) by a factor of more than ten. The influence of the measurement temperature on I3/1 is pronounced for CB levels above the percolation threshold.
Elastomers are exceptional materials owing to their ability to undergo large deformations before failure. However, due to their very low stiffness, they are not always suitable for industrial applications. Addition of filler particles provides reinforcing effects and thus enhances the material properties that render them more versatile for applications like tyres etc. However, deformation behavior of filled polymers is accompanied by several nonlinear effects like Mullins and Payne effect. To this day, the physical and chemical changes resulting in such nonlinear effect remain an active area of research. In this work, we develop a heterogeneous (or multiphase) constitutive model at the mesoscale explicitly considering filler particle aggregates, elastomeric matrix and their mechanical interaction through an approximate interface layer. The developed constitutive model is used to demonstrate cluster breakage, also, as one of the possible sources for Mullins effect observed in non-crystallizing filled elastomers.
The enormous diversity of seed traits is an intriguing feature and critical for the overwhelming success of higher plants. In particular, seed mass is generally regarded to be key for seedling development but is mostly approximated by using scanning methods delivering only two-dimensional data, often termed seed size. However, three-dimensional traits, such as the volume or mass of single seeds, are very rarely determined in routine measurements. Here, we introduce a device named phenoSeeder, which enables the handling and phenotyping of individual seeds of very different sizes. The system consists of a pick-and-place robot and a modular setup of sensors that can be versatilely extended. Basic biometric traits detected for individual seeds are two-dimensional data from projections, three-dimensional data from volumetric measures, and mass, from which seed density is also calculated. Each seed is tracked by an identifier and, after phenotyping, can be planted, sorted, or individually stored for further evaluation or processing (e.g. in routine seed-to-plant tracking pipelines). By investigating seeds of Arabidopsis (Arabidopsis thaliana), rapeseed (Brassica napus), and barley (Hordeum vulgare), we observed that, even for apparently round-shaped seeds of rapeseed, correlations between the projected area and the mass of seeds were much weaker than between volume and mass. This indicates that simple projections may not deliver good proxies for seed mass. Although throughput is limited, we expect that automated seed phenotyping on a single-seed basis can contribute valuable information for applications in a wide range of wild or crop species, including seed classification, seed sorting, and assessment of seed quality.