2‐(1‐Ethynylpyrene)‐Adenosine as a Folding Probe for RNA—Pyrene in or out

A series of short RNA duplexes containing one or two 1‐ethynylpyrene‐modified adenine bases was synthesised. The melting behaviour of these duplexes was examined by monitoring temperature‐dependent pyrene fluorescence. In the singly modified RNA duplexes, the bases flanking the ethynylpyrene‐rA were varied to examine the sequence specificity of the fluorescence change of pyrene upon RNA hybridisation. Because an increase in pyrene fluorescence upon melting of the duplex can be correlated with intercalation of pyrene, and a decrease is usually associated with the position of pyrene outside the strand, a relationship between the flanking bases and the tendency of the dye to intercalate has been established. It was found that pyrene intercalation is less likely to take place if the modified base is flanked only by A–U base pairs. Flanking G–C base pairs, even only in the 5′‐direction of the modified base, will favour intercalation. In addition, we examined a doubly modified compound that had a pyrene located on each strand. The spectra indicated that the two pyrenes were close enough for interaction. Upon melting of the strand, a fluorescence blue shift corresponding to the dissociation of the pyrene–pyrene complex could be observed in addition to the intensity effect already known from the singly modified compounds. Two melting curves based on the different properties of the fluorophore could be extracted, leading to different melting points corresponding to the global duplex melting and to the change of local pyrene environment, respectively.     

Blends of Two Perylene Derivatives: Mesogenic Properties and Application As Emitter Materials in OLEDs

Blends consisting of two components, namely a liquid crystalline perylene ester and a bilaterally extended perylene ester, were studied. The liquid crystalline properties of these blends were investigated in detail by means of polarized optical microscopy and differential scanning calorimetry. The resulting phase diagram was used to explore the potential of these blends as emitter layers in OLEDs, which were prepared via thermal evaporation in a vacuum and spin coating of solutions.     

Proton Conduction in Dense and Porous Nanocrystalline Ceria Thin Films

The electrical conductivity of ceria thin films (epitaxial as well as dense and porous nanocrystalline) is investigated in dry and wet atmosphere at temperatures below 500 °C. For the epitaxial and the fully dense nanocrystalline samples, no significant differences can be observed between dry and wet conditions. In marked contrast, the nanocrystalline porous films obtained via spin coating exhibit a considerable enhancement of the protonic conductivity below 300 °C in wet atmosphere. This outcome reveals that the residual open mesoporosity plays the key role for the enhancement of the proton transport at low temperatures and not the high density of grain boundaries. The quantitative analysis of the various pathways, along which the proton transport can take place, indicates that the observed proton conduction can arise not only from bulk water adsorbed in the open pores but also from the space charge zones on the water side of the water/oxide interface.     

Connecting Community-Grids by supporting job negotiation with coevolutionary Fuzzy-Systems

We utilize a competitive coevolutionary algorithm (CA) in order to optimize the parameter set of a Fuzzy-System for job negotiation between Community-Grids. In a Community-Grid, users are submitting jobs to their local High Performance Computing (HPC) sites over time. Now, we assume that Community-Grids are interconnected such that the exchange of jobs becomes possible: Each Community strives for minimizing the response time for their own members by trying to distribute workload to other communities in the Grid environment. For negotiation purpose, a Fuzzy-System is used to steer each site’s decisions whether to distribute or accept workload in a beneficial, yet egoistic direction. In such a system, it is essential that communities can only benefit if the workload is equitably (not necessarily equally) portioned among all participants. That is, if one community egoistically refuses to execute foreign jobs regularly, other HPC sites suffer from overloading. This, on the long run, deteriorates the opportunity to utilize them for job delegation. Thus, the egoistic community will degrade its own average performance. This scenario is particularly suited for the application of a competitive CA: the Fuzzy-Systems of the participating communities are modeled as species, which evolve in different populations while having to compete within the commonly shared ecosystem. Using real workload traces and Grid setups, we show that the opportunistic cooperation leads to significant improvements for both each community and the overall system.     

Counting decomposable multivariate polynomials

A polynomial f (multivariate over a field) is decomposable if f=g °h{f=g \circ h} with g univariate of degree at least 2. We determine the dimension (over an algebraically closed field) of the set of decomposables, and an approximation to their number over a finite field. The relative error in our approximations is exponentially decaying in the input size.     

Learning with few examples for binary and multiclass classification using regularization of randomized trees

The human visual system is often able to learn to recognize difficult object categories from only a single view, whereas automatic object recognition with few training examples is still a challenging task. This is mainly due to the human ability to transfer knowledge from related classes. Therefore, an extension to Randomized Decision Trees is introduced for learning with very few examples by exploiting interclass relationships. The approach consists of a maximum a posteriori estimation of classifier parameters using a prior distribution learned from similar object categories. Experiments on binary and multiclass classification tasks show significant performance gains     

The goal specificity effect on strategy use and instructional efficiency during computer-based scientific discovery learning

Using a computer-based scientific discovery learning environment on buoyancy in fluids we investigated the effects of goal specificity (nonspecific goals vs. specific goals) for two goal types (problem solving goals vs. learning goals) on strategy use and instructional efficiency. Our empirical findings close an important research gap, because in earlier studies the goal specificity effect either was restricted to one goal type or goal type was confounded with goal specificity. In addition, there is hardly a study with empirical evidence for the goal specificity effect on strategy use, which counts even more for a cognitive cost-benefit ratio as a dependent variable. Instead, in earlier studies the goal specificity effect has been attributed to differences in strategy use and cognitive cost-benefit ratio in a rather theoretical way. In the present study for strategy use an interaction was found between goal specificity and goal type, indicating that the goal specificity effect occurs only in case of problem solving goals, but not in case of learning goals. Compared to students provided with specific problem solving goals, students who worked on nonspecific problem solving goals, used a control of variables-strategy more frequently. Additionally, we found a main effect of goal specificity on instructional efficiency for both of the goal types, pointing at a more favorable relationship between performance gain and cognitive load caused by nonspecific goals.