Internationale FuE-Standorte

In dieser Schwerpunktstudie wird die Internationalisierung von Forschung und Entwicklung (FuE) aus deutscher Perspektive untersucht. Aktuelle Trends wurden auf Basis der internationalen FuE-Statistik identifiziert. Darüber hinaus werden patentbasierte Indikatoren berechnet, die vor allen Dingen zu den Technologiefeldern der erfinderischen Aktivitäten deutscher Unternehmen im Ausland neue Erkenntnisse liefern. Eine mikro-ökonometrische Analyse gibt Aufschluss über die relevanten Standortfaktoren für ausländische FuE in Deutschland und der EU27. Zusätzlich wurden neue Befragungsdaten zu FuE deutscher Unternehmen an "neuen Standorten" in Ost- und Mitteleuropa analysiert. Abschließend wurden an Hand eines Datensatzes europäischer multinationaler Unternehmen sowie der Fallstudie eines deutschen Konzerns strategische Faktoren und Managementpraktiken identifiziert, welche den Wissenstransfer in multinationalen Unternehmen beeinflussen. Die Ergebnisse der Schwerpunktstudie bieten somit eine belastbare Basis, um wirtschaftspolitische Handlungsempfehlungen abzuleiten

Aharonov-Bohm interferometry with quantum dots: scattering approach versus tunneling picture

We address the question of how to model electron transport through closed Aharonov-Bohm interferometers which contain quantum dots. By explicitly studying interferometers with one and two quantum dots, we establish the connection between a tunneling-Hamiltonian formulation on the one hand and a scattering-matrix approach on the other hand. We prove that, under certain circumstances, both approaches are equivalent, i.e., both types of models can describe the same experimental setups. Furthermore, we analyze how the interplay of the Aharonov-Bohm phase and the orbital phase associated with the lengths of the interferometers' arms affect transport properties.Comment: 8 pages, 8 figures, published versio

An in-flight plasma diagnostic package for spacecraft with electric propulsion

The plasma diagnostics presented in this article target the plasma surrounding a spacecraft that is created by the electric thruster and its surface modifying effects. The diagnostic package includes a retarding potential analyzer, a plane Langmuir probe, and an erosion sensor. The paper describes the instrument as well as suitable test environments for mimicking the effects expected in space and shows test results. The system is to fly for the first time on the Heinrich Hertz satellite, which is scheduled to be launched in 2023. The spacecraft will be equipped with a pair of Highly Efficient Multistage Plasma Thrusters (HEMPT) and a pair of Hall thrusters for redundancy

Modification of the Electrochemical Surface Oxide Formation and the Hydrogen Oxidation Activity of Ruthenium by Strong Metal Support Interactions

A major hurdle for the wide spread commercialization of proton exchange membrane based fuel cells (PEMFCs) and water electrolyzers are the durability and high cost of noble metal catalysts. Here, alternative support materials might offer advantages, as they can alter the properties of a catalyst by means of a strong metal support interaction (SMSI) that has been shown to prevent platinum oxidation and suppress the oxygen reduction reaction on titanium oxide supported platinum nanoparticles deposited on a carbon support (Pt/TiOx/C). Herein, we report a novel Ru/TiOx/C catalyst that according to tomographic transmission electron microscopy analysis consists of partially encapsulated Ru particles in a Ru/TiOx-composite matrix supported on a carbon support. It is shown by cyclic voltammetry and X-ray photoelectron spectroscopy that ruthenium oxidation is mitigated by an SMSI between Ru and TiOx after reductive heat-treatment (Ru/TiOx/C400°C,H2). As a result, the catalyst is capable of oxidizing hydrogen up to the onset of oxygen evolution reaction, in stark contrast to a Ru/C reference catalyst. PEMFC-based hydrogen pump measurements confirmed the stabilization of the hydrogen oxidation reaction (HOR) activity on Ru/TiOx/C400°C,H2 and showed a ≈3-fold higher HOR activity compared to Ru/C, albeit roughly two orders of magnitude less active than Pt/C.DFG, 390776260, EXC 2089: e-conversionBMWi, 03ET6096A, Verbundprojekt: innoKA - Materialinnovationen für die PEM-Brennstoffzelle - Platinfreie Kathode, anodenseitige Materialstabilisierung durch neue Katalysatorkonzept; Teilvorhaben: Synthese neuer (Ko-)Katalysatoren für die Anode und deren Integration sowie die von platinfreien Kathodenkatalysatoren in MEAs

Mesoscopic Fano Effect in a Quantum Dot Embedded in an Aharonov-Bohm Ring

The Fano effect, which occurs through the quantum-mechanical cooperation between resonance and interference, can be observed in electron transport through a hybrid system of a quantum dot and an Aharonov-Bohm ring. While a clear correlation appears between the height of the Coulomb peak and the real asymmetric parameter $q$ for the corresponding Fano lineshape, we need to introduce a complex $q$ to describe the variation of the lineshape by the magnetic and electrostatic fields. The present analysis demonstrates that the Fano effect with complex asymmetric parameters provides a good probe to detect a quantum-mechanical phase of traversing electrons.Comment: REVTEX, 9 pages including 8 figure

The Lunar Lander Neutron and Dosimetry (LND) Experiment on Chang'E 4

Chang'E 4 is the first mission to the far side of the Moon and consists of a lander, a rover, and a relay spacecraft. Lander and rover were launched at 18:23 UTC on December 7, 2018 and landed in the von K\'arm\'an crater at 02:26 UTC on January 3, 2019. Here we describe the Lunar Lander Neutron \& Dosimetry experiment (LND) which is part of the Chang'E 4 Lander scientific payload. Its chief scientific goal is to obtain first active dosimetric measurements on the surface of the Moon. LND also provides observations of fast neutrons which are a result of the interaction of high-energy particle radiation with the lunar regolith and of their thermalized counterpart, thermal neutrons, which are a sensitive indicator of subsurface water content.Comment: 38 pages, submitted to Space Science Review

Modularization of biochemical networks based on classification of Petri net t-invariants

<p>Abstract</p> <p>Background</p> <p>Structural analysis of biochemical networks is a growing field in bioinformatics and systems biology. The availability of an increasing amount of biological data from molecular biological networks promises a deeper understanding but confronts researchers with the problem of combinatorial explosion. The amount of qualitative network data is growing much faster than the amount of quantitative data, such as enzyme kinetics. In many cases it is even impossible to measure quantitative data because of limitations of experimental methods, or for ethical reasons. Thus, a huge amount of qualitative data, such as interaction data, is available, but it was not sufficiently used for modeling purposes, until now. New approaches have been developed, but the complexity of data often limits the application of many of the methods. Biochemical Petri nets make it possible to explore static and dynamic qualitative system properties. One Petri net approach is model validation based on the computation of the system's invariant properties, focusing on t-invariants. T-invariants correspond to subnetworks, which describe the basic system behavior.</p> <p>With increasing system complexity, the basic behavior can only be expressed by a huge number of t-invariants. According to our validation criteria for biochemical Petri nets, the necessary verification of the biological meaning, by interpreting each subnetwork (t-invariant) manually, is not possible anymore. Thus, an automated, biologically meaningful classification would be helpful in analyzing t-invariants, and supporting the understanding of the basic behavior of the considered biological system.</p> <p>Methods</p> <p>Here, we introduce a new approach to automatically classify t-invariants to cope with network complexity. We apply clustering techniques such as UPGMA, Complete Linkage, Single Linkage, and Neighbor Joining in combination with different distance measures to get biologically meaningful clusters (t-clusters), which can be interpreted as modules. To find the optimal number of t-clusters to consider for interpretation, the cluster validity measure, Silhouette Width, is applied.</p> <p>Results</p> <p>We considered two different case studies as examples: a small signal transduction pathway (pheromone response pathway in <it>Saccharomyces cerevisiae</it>) and a medium-sized gene regulatory network (gene regulation of Duchenne muscular dystrophy). We automatically classified the t-invariants into functionally distinct t-clusters, which could be interpreted biologically as functional modules in the network. We found differences in the suitability of the various distance measures as well as the clustering methods. In terms of a biologically meaningful classification of t-invariants, the best results are obtained using the Tanimoto distance measure. Considering clustering methods, the obtained results suggest that UPGMA and Complete Linkage are suitable for clustering t-invariants with respect to the biological interpretability.</p> <p>Conclusion</p> <p>We propose a new approach for the biological classification of Petri net t-invariants based on cluster analysis. Due to the biologically meaningful data reduction and structuring of network processes, large sets of t-invariants can be evaluated, allowing for model validation of qualitative biochemical Petri nets. This approach can also be applied to elementary mode analysis.</p

Optical Dissection of Neural Circuits Responsible for Drosophila Larval Locomotion with Halorhodopsin

Halorhodopsin (NpHR), a light-driven microbial chloride pump, enables silencing of neuronal function with superb temporal and spatial resolution. Here, we generated a transgenic line of Drosophila that drives expression of NpHR under control of the Gal4/UAS system. Then, we used it to dissect the functional properties of neural circuits that regulate larval peristalsis, a continuous wave of muscular contraction from posterior to anterior segments. We first demonstrate the effectiveness of NpHR by showing that global and continuous NpHR-mediated optical inhibition of motor neurons or sensory feedback neurons induce the same behavioral responses in crawling larvae to those elicited when the function of these neurons are inhibited by Shibirets, namely complete paralyses or slowed locomotion, respectively. We then applied transient and/or focused light stimuli to inhibit the activity of motor neurons in a more temporally and spatially restricted manner and studied the effects of the optical inhibition on peristalsis. When a brief light stimulus (1–10 sec) was applied to a crawling larva, the wave of muscular contraction stopped transiently but resumed from the halted position when the light was turned off. Similarly, when a focused light stimulus was applied to inhibit motor neurons in one or a few segments which were about to be activated in a dissected larva undergoing fictive locomotion, the propagation of muscular constriction paused during the light stimulus but resumed from the halted position when the inhibition (>5 sec) was removed. These results suggest that (1) Firing of motor neurons at the forefront of the wave is required for the wave to proceed to more anterior segments, and (2) The information about the phase of the wave, namely which segment is active at a given time, can be memorized in the neural circuits for several seconds

Using graph theory to analyze biological networks

Understanding complex systems often requires a bottom-up analysis towards a systems biology approach. The need to investigate a system, not only as individual components but as a whole, emerges. This can be done by examining the elementary constituents individually and then how these are connected. The myriad components of a system and their interactions are best characterized as networks and they are mainly represented as graphs where thousands of nodes are connected with thousands of vertices. In this article we demonstrate approaches, models and methods from the graph theory universe and we discuss ways in which they can be used to reveal hidden properties and features of a network. This network profiling combined with knowledge extraction will help us to better understand the biological significance of the system