Interfacial polymerization of pyrrole and in situ synthesis of polypyrrole/silver nanocomposites

We present a new synthetic approach leading to the formation of polypyrrole architectures in submicron level and to silver/polypyrrole nanocomposites via an interfacial polymerization in a water/chloroform interface. The oxidizing agent was either Ag(I) or Fe(III). In the first case, silver nanoparticles resulted. The mean diameter of the polypyrrole structures is in the range of 200-300 nm according to the addition or not of various surfactants. The progress of the reaction was studied by UV-visible spectroscopy, which also revealed the formation of a polaron band during the growth of the oligomers. The crystal structure of the polymers was examined by X ray diffractometry and all samples appeared to be amorphous, while the samples were further characterized by thermogravimetric analysis and FT-IR spectroscopy.     

Foam fractionation of spent sulphite liquor: Part I: Separation of surfactants

The surface activity of spent sulphite liquor (SSL) and the separation of surface active components by foam fractionation were studied. The maximum activity (lowest surface tension) of SSL was observed at a high pH. The surface tension was found to be 42 mN/m and 38 mN/m at acidic and basic pH values, respectively, and also a maximum foaminess (Σ) of 7 min was observed at pH 10. Foam fractionation reduced the surfactant concentration by 90% at an optimum pH of 10. The surfactants were concentrated in the collapsed foam as shown by the fact that the critical micelle concentrations of the unfractionated SSL and foam were 70% (v/v) and 10% (v/v), respectively. The removed surfactants were concentrated in 20% of the original liquid volume.     

Feature selection for k-means clustering stability: theoretical analysis and an algorithm

Stability of a learning algorithm with respect to small input perturbations is an important property, as it implies that the derived models are robust with respect to the presence of noisy features and/or data sample fluctuations. The qualitative nature of the stability property enhardens the development of practical, stability optimizing, data mining algorithms as several issues naturally arise, such as: how “much” stability is enough, or how can stability be effectively associated with intrinsic data properties. In the context of this work we take into account these issues and explore the effect of stability maximization in the continuous (PCA-based) k-means clustering problem. Our analysis is based on both mathematical optimization and statistical arguments that complement each other and allow for the solid interpretation of the algorithm’s stability properties. Interestingly, we derive that stability maximization naturally introduces a tradeoff between cluster separation and variance, leading to the selection of features that have a high cluster separation index that is not artificially inflated by the features variance. The proposed algorithmic setup is based on a Sparse PCA approach, that selects the features that maximize stability in a greedy fashion. In our study, we also analyze several properties of Sparse PCA relevant to stability that promote Sparse PCA as a viable feature selection mechanism for clustering. The practical relevance of the proposed method is demonstrated in the context of cancer research, where we consider the problem of detecting potential tumor biomarkers using microarray gene expression data. The application of our method to a leukemia dataset shows that the tradeoff between cluster separation and variance leads to the selection of features corresponding to important biomarker genes. Some of them have relative low variance and are not detected without the direct optimization of stability in Sparse PCA based k-means. Apart from the qualitative evaluation, we have also verified our approach as a feature selection method for $k$ -means clustering using four cancer research datasets. The quantitative empirical results illustrate the practical utility of our framework as a feature selection mechanism for clustering.     

Enabling user interface developers to experience accessibility limitations through visual,hearing, physical and cognitive impairment simulation

His paper presents a tool facilitating developers of user interfaces (UIs) to experience accessibility limitations that can be posed from various disabilities during the interaction of impaired users with their developments. In this respect, various aspects of visual, hearing, physical and cognitive impairments have been modelled through filters providing approximate, yet, realistic simulations over them. These filters have formed the basis for the developed tool, which can be used either on its own (as a standalone application), or be embedded in the NetBeans Integrated Development Environment. The tool, named DIAS, allows for impairment simulations to be performed over Java, mobile and web applications. Moreover, it integrates two of the most common assistive technologies (ATs), namely a screen reader and a magnifier. As a result, developers of UIs can not only experience how interaction would be affected from various impairments, but they can also understand how their developments would be perceived by impaired users through an AT. This work aims to provide an integrated, practical solution for impairment simulation, which could be easily adopted by developers, thus realistically increasing the possibilities for the future development of interactive applications that are more accessible to users with disabilities.     

Epsilon Flock: a model migration language

Model-driven engineering introduces additional challenges for controlling and managing software evolution. Today, tools exist for generating model editors and for managing models with transformation, validation, merging and weaving. There is limited support, however, for model migration—a development activity in which instance models are updated in response to metamodel evolution. In this paper, we propose conservative copy—a style of model transformation that we believe is well-suited to model migration—and Epsilon Flock—a compact model-to-model transformation language tailored for model migration. The proposed structures are evaluated by comparing the conciseness of model migration strategies written in different styles of transformation language, using several examples of evolution taken from UML and the graphical modelling framework.     

Speed Scaling for Maximum Lateness

We consider the power-aware problem of scheduling non-preemptively a set of jobs on a single speed-scalable processor so as to minimize the maximum lateness, under a given budget of energy. In the offline setting, our main contribution is a combinatorial polynomial time algorithm for the case in which the jobs have common release dates. In the presence of arbitrary release dates, we show that the problem becomes strongly \(\mathcal {N}\mathcal {P}\)-hard. Moreover, we show that there is no O(1)-competitive deterministic algorithm for the online setting in which the jobs arrive over time. Then, we turn our attention to an aggregated variant of the problem, where the objective is to find a schedule minimizing a linear combination of maximum lateness and energy. As we show, our results for the budget variant can be adapted to derive a similar polynomial time algorithm and an \(\mathcal {N}\mathcal {P}\)-hardness proof for the aggregated variant in the offline setting, with common and arbitrary release dates respectively. More interestingly, for the online case, we propose a 2-competitive algorithm.     

Efficient algorithms for counting parameterized list H-colorings

We study the fixed parameter tractability of the counting version of a parameterization of the restrictive list H-coloring problem. The parameterization is defined by fixing the number of preimages of a subset C of the vertices in H through a weight assignment K on C. We show the fixed parameter tractability of counting the number of list (H,C,K)-colorings, for the case in which (H,C,K) is simple. We introduce the concept of compactor and a new algorithmic technique, compactor enumeration, that allow us to design fixed parameter algorithms for parameterized counting problems.     

An interference-aware fair scheduling for multicast in wireless mesh networks

Multicast is a fundamental routing service in wireless mesh networks (WMNs) due to its many potential applications such as video conferencing, online games, and webcast. Recently, researchers proposed using link-quality-based routing metrics for finding high-throughput paths for multicast routing. However, the performance of such link-quality-based multicast routing is still limited by severe unfairness. Two major artifacts that exist in WMNs are fading which leads to low quality links, and interference which leads to unfair channel allocation in the 802.11 MAC protocol. These artifacts cause the multicast application to behave unfairly with respect to the performance achieved by the multicast receivers.