Study of Density Behavior During LHCD and IBW Heating in the HT-7 Superconducting Tokamak

A new method using special shape parameters has been introduced to study density behavior with synergy of lower hybrid current drive (LHCD) and ion Bernstein wave (IBW) heating in the HT-7 superconducting tokamak. During the synergy of the LHCD and IBW heating an improved confinement plasma was achieved, and the electron density was increased and peaked.     

Cost effective second generation AC-modules: Development and testing aspects

In the framework of the European research project PV2GO, a new AC-module inverter was developed, taking into account all relevant aspects from a European market's point of view (standards, market, application, and research and development goals). The project goal was to achieve the overall system costs of 3 Euro per Wp for a modular plug-and-play photovoltaic system. For the photovoltaic-module, a standard 130-Wp Eurosolare module was chosen. The research and development (R&D) goal was to develop an advanced DC-control system consisting of a state-of-the-art programmable digital device and an Application Specific Integrated Circuit (ASIC) for the AC-control of the inverter. According to the topology concept, thermal and magnetic designs were optimized with regard to production technology and packaging for large-scale production. The new AC-modules were tested in a number of field-test sites in various parts of Europe and their reliability was assessed through Highly Accelerated Stress Tests. Efficiency and power quality have been tested in the laboratory. Further in the PV2GO project an optimization study of the manufacturing process of the new generation of AC-modules for high volume output was done. Another task was the pre-certification procedure to assure compliance with the European guidelines and standards.     

Optical pattern recognition using two-channel joint transform correlation

A new joint transform correlation (JTC) technique, named two-channel JTC (TJTC), is proposed in this paper for optical pattern recognition applications. The TJTC technique independently evaluates the autocorrelation and crosscorrelation values of the reference and the target images and employs a modified decision algorithm. In addition, optical threshold operation and fringe-adjusted filter are incorporated in the proposed technique to enhance the correlation output and to improve the discrimination performance. The proposed technique shows better recognition performance compared to existing JTC techniques. Computer simulation are presented to investigate the salient features of the proposed TJTC technique with noise-free as well as noisy input scenes. The text was submitted by the authors in English.     

Cell signaling by the type IV pili of pathogenic Neisseria

Neisseria gonorrhoeae and Neisseria meningitidis are Gram-negative bacterial pathogens that infect human mucosal epithelia. Type IV pilus-mediated adherence of these bacteria is a crucial early event for establishment of infection. In this work, we show that the type IV pili transduce a signal into the eucaryotic host cell. Purified adherent pili, but not pili from a low binding mutant, trigger an increase in the cytosolic free calcium ([Ca2+]i) in target epithelial cells, a signal known to control many cellular responses. The [Ca2+]i increase was blocked by antibodies against CD46, a putative pilus receptor, suggesting a role for this protein in signal transduction. Pilus-mediated attachment was inhibited by depletion of host cell intracellular Ca2+ stores but not by removal of extracellular Ca2+. Further, kinase inhibition studies showed that pilus-mediated adherence is dependent on casein kinase II. In summary, these data reveal a novel function of the type IV pili, namely induction of signal transduction pathways in host cells.     

Variation resilient low-power memristor-based synchronous flip-flops: design and analysis

Microsystem Technologies - Flip-flops are the basic digital components for all types of complex digital electronics systems and sequential logic circuits. In this paper, new nonvolatile, low power,...     

Multiobjective optimization for classifier ensemble and feature selection: an application to named entity recognition

In this paper, the concept of finding an appropriate classifier ensemble for named entity recognition is posed as a multiobjective optimization (MOO) problem. Our underlying assumption is that instead of searching for the best-fitting feature set for a particular classifier, ensembling of several classifiers those are trained using different feature representations could be a more fruitful approach, but it is crucial to determine the appropriate subset of classifiers that are most suitable for the ensemble. We use three heterogenous classifiers namely maximum entropy, conditional random field, and support vector machine in order to build a number of models depending upon the various representations of the available features. The proposed MOO-based ensemble technique is evaluated for three resource-constrained languages, namely Bengali, Hindi, and Telugu. Evaluation results yield the recall, precision, and F-measure values of 92.21, 92.72, and 92.46%, respectively, for Bengali; 97.07, 89.63, and 93.20%, respectively, for Hindi; and 80.79, 93.18, and 86.54%, respectively, for Telugu. We also evaluate our proposed technique with the CoNLL-2003 shared task English data sets that yield the recall, precision, and F-measure values of 89.72, 89.84, and 89.78%, respectively. Experimental results show that the classifier ensemble identified by our proposed MOO-based approach outperforms all the individual classifiers, two different conventional baseline ensembles, and the classifier ensemble identified by a single objective?Cbased approach. In a part of the paper, we formulate the problem of feature selection in any classifier under the MOO framework and show that our proposed classifier ensemble attains superior performance to it.     

Correction to: Bismuth Yttrium Oxide (Bi3YO6), A New Electrode Material For Asymmetric Aqueous Supercapacitors

Journal of Inorganic and Organometallic Polymers and Materials - The original version of this article unfortunately contained mistakes. In line 9 of the abstract, 5% should read as 2%. The...     

Tensile behavior change depending on the varying tungsten content of W–Ni–Fe alloys

Tungsten heavy alloys (WHAs) are metal–metal composites consisting of nearly pure spherical tungsten particles embedded in a Ni–Fe–W or Ni–Co–W or Ni–Cu–W ductile matrix. In this dual phase alloy, there are several complicated relations between the ductile matrix and hard tungsten particles. The aim of this research was to examine the effect of varying tungsten content on the microstructure and mechanical properties of tungsten heavy alloys. The microstructural parameters (grain size, connectivity, contiguity and solid volume fraction) were measured and were found to have a significant effect on the mechanical properties of tungsten-based heavy alloys. The result shows that the binding strength between the W and the matrix phase has a major influence on the ductility of tungsten-based alloys. The larger this binding force is, the better the ductility is.     

A polar-based guided multi-objective evolutionary algorithm to search for optimal solutions interested by decision-makers in a logistics network design problem

In practical multi-objective optimization problems, respective decision-makers might be interested in some optimal solutions that have objective values closer to their specified values. Guided multi-objective evolutionary algorithms (guided MOEAs) have been significantly used to guide their evolutionary search direction toward these optimal solutions using by decision makers. However, most guided MOEAs need to be iteratively and interactively evaluated and then guided by decision-makers through re-formulating or re-weighting objectives, and it might negatively affect the algorithms performance. In this paper, a novel guided MOEA that uses a dynamic polar-based region around a particular point in objective space is proposed. Based on the region, new selection operations are designed such that the algorithm can guide the evolutionary search toward optimal solutions that are close to the particular point in objective space without the iterative and interactive efforts. The proposed guided MOEA is tested on the multi-criteria decision-making problem of flexible logistics network design with different desired points. Experimental results show that the proposed guided MOEA outperforms two most effective guided and non-guided MOEAs, R-NSGA-II and NSGA-II.