## Institut für Physik

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- CDA (1)
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In July 2004 the Cassini–Huygens mission reached the Saturnian system and started its orbital tour. A total of 75 orbits will be carried out during the primary mission until August 2008. In these four years Cassini crosses the ring plane 150 times and spends approx. 400 h within Titan's orbit. The Cosmic Dust Analyser (CDA) onboard Cassini characterises the dust environment with its extended E ring and embedded moons. Here, we focus on the CDA results of the first year and we present the Dust Analyser (DA) data within Titan's orbit. This paper does investigate High Rate Detector data and dust composition measurements. The authors focus on the analysis of impact rates, which were strongly variable primarily due to changes of the spacecraft pointing. An overview is given about the ring plane crossings and the DA counter measurements. The DA dust impact rates are compared with the DA boresight configuration around all ring plane crossings between June 2004 and July 2005. Dust impacts were registered at altitudes as high as 100 000 km above the ring plane at distances from Saturn between 4 and 10 Saturn radii. In those regions the dust density of particles bigger than 0.5 can reach values of 0.001m-3.

To develop and investigate detailed mathematical models of metabolic processes is one of the primary challenges in systems biology. However, despite considerable advance in the topological analysis of metabolic networks, kinetic modeling is still often severely hampered by inadequate knowledge of the enzyme-kinetic rate laws and their associated parameter values. Here we propose a method that aims to give a quantitative account of the dynamical capabilities of a metabolic system, without requiring any explicit information about the functional form of the rate equations. Our approach is based on constructing a local linear model at each point in parameter space, such that each element of the model is either directly experimentally accessible or amenable to a straightforward biochemical interpretation. This ensemble of local linear models, encompassing all possible explicit kinetic models, then allows for a statistical exploration of the comprehensive parameter space. The method is exemplified on two paradigmatic metabolic systems: the glycolytic pathway of yeast and a realistic-scale representation of the photosynthetic Calvin cycle.

This paper discusses translocation features of the 20S proteasome in order to explain typical proteasome length distributions. We assume that the protein transport depends significantly on the fragment length with some optimal length which is transported most efficiently. By means of a simple one-channel model, we show that this hypothesis can explain both the one- and the three-peak length distributions found in experiments. A possible mechanism of such translocation is provided by so-called fluctuation-driven transport.

It is shown that several polymers can form insoluble interfacial layers on a poly (ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) layer after annealing of the double-layer structure. The thickness of the interlayer is dependent on the characteristics of the underlying PEDOT.PSS and the molecular weight of the polymers. It is further shown that the electronic structures of the interlayer polymers have a significant effect on the properties of red-light-emitting polymer-based electrophosphorescent devices. Upon increasing the highest occupied molecular orbital and lowest unoccupied molecular orbital positions, a significant increase in current density and device efficiency is observed. This is attributed to efficient blocking of electrons in combination with direct injection of holes from the interlayer to the phosphorescent dye. Upon proper choice of the interlayer polymer, efficient red, polymer-based electrophosphorescent devices with a peak luminance efficiency of 5.5 cd A(-1) (external quantum efficiency = 6 %) and a maximum power-conversion efficiency of 5 Im W-1 can be realized.

Recent research using the complex network approach has revealed a rich and complicated network topology in the cortical connectivity of mammalian brains. It is of importance to understand the implications of such complex network structures in the functional organization of the brain activities. Here we study this problem from the viewpoint of dynamical complex networks. We investigate synchronization dynamics on the corticocortical network of the cat by modeling each node (cortical area) of the network with a sub-network of interacting excitable neurons. We find that the network displays clustered synchronization behavior, and the dynamical clusters coincide with the topological community structures observed in the anatomical network. Our results provide insights into the relationship between the global organization and the functional specialization of the brain cortex.

We investigate the bifurcation structures in a two-dimensional parameter space (PS) of a parametrically excited system with two degrees of freedom both analytically and numerically. By means of the Renyi entropy of second order K-2, which is estimated from recurrence plots, we uncover that regions of chaotic behavior are intermingled with many complex periodic windows, such as shrimp structures in the PS. A detailed numerical analysis shows that, the stable solutions lose stability either via period doubling, or via intermittency when the parameters leave these shrimps in different directions, indicating different bifurcation properties of the boundaries. The shrimps of different sizes offer promising ways to control the dynamics of such a complex system.

How do diverse dynamical patterns arise from the topology of complex networks? We study synchronization dynamics in the cortical brain network of the cat, which displays a hierarchically clustered organization, by modeling each node (cortical area) with a subnetwork of interacting excitable neurons. We find that in the biologically plausible regime the dynamics exhibits a hierarchical modular organization, in particular, revealing functional clusters coinciding with the anatomical communities at different scales. Our results provide insights into the relationship between network topology and functional organization of complex brain networks.