Utility Harmonic Impedance Measurement Based on Data Selection

Determination of the utility harmonic impedance based on measurements is a significant task for utility power-quality improvement and management. Compared to those well-established, accurate invasive methods, the noninvasive methods are more desirable since they work with natural variations of the loads connected to the point of common coupling (PCC), so that no intentional disturbance is needed. However, the accuracy of these methods has to be improved. In this context, this paper first points out that the critical problem of the noninvasive methods is how to select the measurements that can be used with confidence for utility harmonic impedance calculation. Then, this paper presents a new measurement technique which is based on the complex data-based least-square regression, combined with two techniques of data selection. Simulation and field test results show that the proposed noninvasive method is practical and robust so that it can be used with confidence to determine the utility harmonic impedances.

A complex networks approach for data clustering

This work proposes a method for data clustering based on complex networks theory. A data set is represented as a network by considering different metrics to establish the connection between each pair of objects. The clusters are obtained by taking into account five community detection algorithms. The network-based clustering approach is applied in two real-world databases and two sets of artificially generated data. The obtained results suggest that the exponential of the Minkowski distance is the most suitable metric to quantify the similarities between pairs of objects. In addition, the community identification method based on the greedy optimization provides the best cluster solution. We compare the network-based clustering approach with some traditional clustering algorithms and verify that it provides the lowest classification error rate. (C) 2012 Elsevier B.V. All rights reserved.

Development and characterization of light-emitting diodes (LEDs) based on ruthenium complex single layer for transparent displays

In this work, two ruthenium complexes, [Ru(bpy)(3)](PF6)(2) and [Ru(ph2phcn)(3)](PF6)(2) in poly(inethylinethacrylate) matrix were employed to build single-layer light-emitting electrochemical cells by spin coating on indium tin oxide substrate. In both cases the electroluminescence spectra exhibit a relatively broad band with maxima near to 625 rim and CIE (x, y) color coordinates of (0.64, 0.36), which are comparable with the photoluminescence data in the same medium. The best result was obtained with the [Ru(bpy)(3)](PF6)(2) device where the optical output power approaches 10 mu W at the band maximum with a wall-plug efficiency higher than 0.03%. The lowest driving voltage is about 4 V for an electrical current of 20 mA. (c) 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Admiralty Bay Benthos Diversity-A census of a complex polar ecosystem

A thorough census of Admiralty Bay benthic biodiversity was completed through the synthesis of data, acquired from more than 30 years of observations. Most of the available records arise from successive Polish and Brazilian Antarctic expeditions organized since 1977 and 1982, respectively, but also include new data from joint collecting efforts during the International Polar Year (2007-2009). Geological and hydrological characteristics of Admiralty Bay and a comprehensive species checklist with detailed data on the distribution and nature of the benthic communities are provided. Approximately 1300 species of benthic organisms (excluding bacteria, fungi and parasites) were recorded from the bay`s entire depth range (0-500 m). Generalized classifications and the descriptions of soft-bottom and hard-bottom invertebrate communities are presented. A time-series analysis showed seasonal and interannual changes in the shallow benthic communities, likely to be related to ice formation and ice melt within the bay. As one of the best studied regions in the maritime Antarctic Admiralty Bay represents a legacy site, where continued, systematically integrated data sampling can evaluate the effects of climate change on marine life. Both high species richness and high assemblage diversity of the Admiralty Bay shelf benthic community have been documented against the background of habitat heterogeneity. (C) 2010 Elsevier Ltd. All rights reserved.

A mixed model QTL analysis for sugarcane multiple-harvest-location trial data

Sugarcane-breeding programs take at least 12 years to develop new commercial cultivars. Molecular markers offer a possibility to study the genetic architecture of quantitative traits in sugarcane, and they may be used in marker-assisted selection to speed up artificial selection. Although the performance of sugarcane progenies in breeding programs are commonly evaluated across a range of locations and harvest years, many of the QTL detection methods ignore two- and three-way interactions between QTL, harvest, and location. In this work, a strategy for QTL detection in multi-harvest-location trial data, based on interval mapping and mixed models, is proposed and applied to map QTL effects on a segregating progeny from a biparental cross of pre-commercial Brazilian cultivars, evaluated at two locations and three consecutive harvest years for cane yield (tonnes per hectare), sugar yield (tonnes per hectare), fiber percent, and sucrose content. In the mixed model, we have included appropriate (co)variance structures for modeling heterogeneity and correlation of genetic effects and non-genetic residual effects. Forty-six QTLs were found: 13 QTLs for cane yield, 14 for sugar yield, 11 for fiber percent, and 8 for sucrose content. In addition, QTL by harvest, QTL by location, and QTL by harvest by location interaction effects were significant for all evaluated traits (30 QTLs showed some interaction, and 16 none). Our results contribute to a better understanding of the genetic architecture of complex traits related to biomass production and sucrose content in sugarcane.

Skeletal muscle major histocompatibility complex class I and II expression differences in adult and juvenile dermatomyositis

OBJECTIVE: To analyze major histocompatibility complex expression in the muscle fibers of juvenile and adult dermatomyositis. METHOD: In total, 28 untreated adult dermatomyositis patients, 28 juvenile dermatomyositis patients (Bohan and Peter's criteria) and a control group consisting of four dystrophic and five Pompe's disease patients were analyzed. Routine histological and immunohistochemical (major histocompatibility complex I and II, StreptoABComplex/HRP, Dakopatts) analyses were performed on serial frozen muscle sections. Inflammatory cells, fiber damage, perifascicular atrophy and increased connective tissue were analyzed relative to the expression of major histocompatibility complexes I and II, which were assessed as negatively or positively stained fibers in 10 fields (200X). RESULTS: The mean ages at disease onset were 42.0 +/- 15.9 and 7.3 +/- 3.4 years in adult and juvenile dermatomyositis, respectively, and the symptom durations before muscle biopsy were similar in both groups. No significant differences were observed regarding gender, ethnicity and frequency of organ involvement, except for higher creatine kinase and lactate dehydrogenase levels in adult dermatomyositis (p<0.050). Moreover, a significantly higher frequency of major histocompatibility complex I (96.4% vs. 50.0%, p<0.001) compared with major histocompatibility complex II expression (14.3% vs. 53.6%, p = 0.004) was observed in juvenile dermatomyositis. Fiber damage (p = 0.006) and increased connective tissue (p<0.001) were significantly higher in adult dermatomyositis compared with the presence of perifascicular atrophy (p<0.001). The results of the histochemical and histological data did not correlate with the demographic data or with the clinical and laboratory features. CONCLUSION: The overexpression of major histocompatibility complex I was an important finding for the diagnosis of both groups, particularly for juvenile dermatomyositis, whereas there was lower levels of expression of major histocompatibility complex II than major histocompatibility complex I. This finding was particularly apparent in juvenile dermatomyositis.

Tokamak TCABR: Acquisition system, data analysis, and remote participation using MDSplus

Each plasma physics laboratory has a proprietary scheme to control and data acquisition system. Usually, it is different from one laboratory to another. It means that each laboratory has its own way to control the experiment and retrieving data from the database. Fusion research relies to a great extent on international collaboration and this private system makes it difficult to follow the work remotely. The TCABR data analysis and acquisition system has been upgraded to support a joint research programme using remote participation technologies. The choice of MDSplus (Model Driven System plus) is proved by the fact that it is widely utilized, and the scientists from different institutions may use the same system in different experiments in different tokamaks without the need to know how each system treats its acquisition system and data analysis. Another important point is the fact that the MDSplus has a library system that allows communication between different types of language (JAVA, Fortran, C, C++, Python) and programs such as MATLAB, IDL, OCTAVE. In the case of tokamak TCABR interfaces (object of this paper) between the system already in use and MDSplus were developed, instead of using the MDSplus at all stages, from the control, and data acquisition to the data analysis. This was done in the way to preserve a complex system already in operation and otherwise it would take a long time to migrate. This implementation also allows add new components using the MDSplus fully at all stages. (c) 2012 Elsevier B.V. All rights reserved.

Word sense disambiguation via high order of learning in complex networks

Complex networks have been employed to model many real systems and as a modeling tool in a myriad of applications. In this paper, we use the framework of complex networks to the problem of supervised classification in the word disambiguation task, which consists in deriving a function from the supervised (or labeled) training data of ambiguous words. Traditional supervised data classification takes into account only topological or physical features of the input data. On the other hand, the human (animal) brain performs both low- and high-level orders of learning and it has facility to identify patterns according to the semantic meaning of the input data. In this paper, we apply a hybrid technique which encompasses both types of learning in the field of word sense disambiguation and show that the high-level order of learning can really improve the accuracy rate of the model. This evidence serves to demonstrate that the internal structures formed by the words do present patterns that, generally, cannot be correctly unveiled by only traditional techniques. Finally, we exhibit the behavior of the model for different weights of the low- and high-level classifiers by plotting decision boundaries. This study helps one to better understand the effectiveness of the model. Copyright (C) EPLA, 2012

Molecular support for the establishment of the new genus Laurenciella within the Laurencia complex (Ceramiales, Rhodophyta)

Currently, five genera are assigned to red seaweeds of the Laurencia complex worldwide: Chondrophycus, Laurencia s.s., Osmundea, Palisada and Yuzurua. The genera are segregated on the basis of morphological characters, especially the reproductive traits, and molecular sequences of the plastid-encoded gene rbcL. Four of the genera have been resolved as monophyletic, but not Laurencia s.s. In this study based on an rbcL gene phylogeny we show the presence of a sixth lineage within the Laurencia complex, viz., Laurencia marilzae plus two unidentified species of Laurencia from Brazil. The phylogenetic position of this group, combined with the high genetic divergence from Laurencia s.s. (8.2-11%), strongly support the establishment of a sixth genus for the complex, proposed here as Laurenciella gen. nov. This new taxon differs from Laurencia s.s. and from the other genera of the complex by molecular sequence data, but is indistinguishable from Laurencia s.s. by the usual morphological features.

Cryptic species in Iphisa elegans Gray, 1851 (Squamata: Gymnophthalmidae) revealed by hemipenial morphology and molecular data

Iphisa elegans Gray, 1851 is a ground-dwelling lizard widespread over Amazonia that displays a broadly conserved external morphology over its range. This wide geographical distribution and conservation of body form contrasts with the expected poor dispersal ability of the species, the tumultuous past of Amazonia, and the previously documented prevalence of cryptic species in widespread terrestrial organisms in this region. Here we investigate this homogeneity by examining hemipenial morphology and conducting phylogenetic analyses of mitochondrial (CYTB) and nuclear (C-MOS) DNA sequence data from 49 individuals sampled across Amazonia. We detected remarkable variation in hemipenial morphology within this species, with multiple cases of sympatric occurrence of distinct hemipenial morphotypes. Phylogenetic analyses revealed highly divergent lineages corroborating the patterns suggested by the hemipenial morphotypes, including co-occurrence of different lineages. The degrees of genetic and morphological distinctness, as well as instances of sympatry among mtDNA lineages/morphotypes without nuDNA allele sharing, suggest that I. elegans is a complex of cryptic species. An extensive and integrative taxonomic revision of the I. elegans complex throughout its wide geographical range is needed. (c) 2012 The Linnean Society of London, Zoological Journal of the Linnean Society, 2012, 166, 361376.

Magmatism and fenitization in the Cretaceous potassium-alkaline-carbonatitic complex of Ipanema So Paulo State, Brazil

The Ipanema alkaline-carbonatitic complex is part of the Meso-Cenozoic alkaline magmatism located within the southeastern part of the Brazilian Platform. Drill-core and field sampling have indicated the occurrence of glimmerites, with subordinate shonkinites (mela-syenites), clinopyroxene-bearing glimmerites, diorites and syenites. The glimmerites are cross-cut by lamprophyric dykes and calciocarbonatites. Fenitization has deeply affected the country rocks, originating dioritic and syenitic rocks. The Ipanema rocks show a distinct potassic affinity. The initial Sr-Nd- isotopic composition of the Ipanema rocks ((87)Sr/(86)Sr = 0.70661-0.70754 and (143)Nd/(144)Nd = 0.51169-0.51181) is similar to that of tholeiitic and potassium-rich-alkaline rocks of the Eastern Paraguay. Stable isotope data for the Ipanema calciocarbonatite suggest interaction with fluids at temperatures typical of hydrothermal stages, as hypothesized for other carbonatite complexes from southeastern Brazil. The chemical differences between the lamprophyre, glimmerites, carbonatites, apatitites and magnetitites, and the absence of marked REE enrichment in the evolved lithologies, all indicate that fractional crystallization and accumulus of liquidus phases in a magma reservoir, likely coupled with liquid immiscibility processes, may have played an important role in the genesis of the Ipanema rocks.

Automatic design of decision-tree induction algorithms tailored to flexible-receptor docking data

Background: This paper addresses the prediction of the free energy of binding of a drug candidate with enzyme InhA associated with Mycobacterium tuberculosis. This problem is found within rational drug design, where interactions between drug candidates and target proteins are verified through molecular docking simulations. In this application, it is important not only to correctly predict the free energy of binding, but also to provide a comprehensible model that could be validated by a domain specialist. Decision-tree induction algorithms have been successfully used in drug-design related applications, specially considering that decision trees are simple to understand, interpret, and validate. There are several decision-tree induction algorithms available for general-use, but each one has a bias that makes it more suitable for a particular data distribution. In this article, we propose and investigate the automatic design of decision-tree induction algorithms tailored to particular drug-enzyme binding data sets. We investigate the performance of our new method for evaluating binding conformations of different drug candidates to InhA, and we analyze our findings with respect to decision tree accuracy, comprehensibility, and biological relevance. Results: The empirical analysis indicates that our method is capable of automatically generating decision-tree induction algorithms that significantly outperform the traditional C4.5 algorithm with respect to both accuracy and comprehensibility. In addition, we provide the biological interpretation of the rules generated by our approach, reinforcing the importance of comprehensible predictive models in this particular bioinformatics application. Conclusions: We conclude that automatically designing a decision-tree algorithm tailored to molecular docking data is a promising alternative for the prediction of the free energy from the binding of a drug candidate with a flexible-receptor.

Divide to conquer: a complex pattern of biodiversity depicted by vertebrate components in the Brazilian Atlantic Forest

The identification of northern and southern components in different vertebrate species led researchers to accept a two-component hypothesis for the Brazilian Atlantic forest (BAF). Nevertheless, neither a formal proposal nor a meta-analysis to confirm this coincidence was ever made. Our main objective here was therefore to systematically test in how many vertebrate components the BAF could be divided by analysing existing empirical data. We used two approaches: (1) mapping and comparing the proposed areas of vertebrate endemism in the BAF and (2) analysing studies mentioning spatial subdivisions in distinct forest-dependent vertebrates within the biome, by the use of panbiogeography. The four large-scale endemism area components together with the six small-scale panbiogeographical ones allowed the definition of three BAF greater regions, subdivided into nine vertebrate components, latitudinally and longitudinally organized. Empirical time estimates of the diversification events within the BAF were also reviewed. Diversification of these vertebrates occurred not only in the Pleistocene but also throughout the Miocene. Our results confirm the BAF's complex history, both in space and time. We propose that future research should be small-scale and focused in the vertebrate components identified herein. Given the BAF's heterogeneity, studying via sections will be much more useful in identifying the BAF's historical biogeography. (c) 2012 The Linnean Society of London, Biological Journal of the Linnean Society, 2012, 107, 39-55.

Evaluation of physiologic complexity in time series using generalized sample entropy and surrogate data analysis

Complexity in time series is an intriguing feature of living dynamical systems, with potential use for identification of system state. Although various methods have been proposed for measuring physiologic complexity, uncorrelated time series are often assigned high values of complexity, errouneously classifying them as a complex physiological signals. Here, we propose and discuss a method for complex system analysis based on generalized statistical formalism and surrogate time series. Sample entropy (SampEn) was rewritten inspired in Tsallis generalized entropy, as function of q parameter (qSampEn). qSDiff curves were calculated, which consist of differences between original and surrogate series qSampEn. We evaluated qSDiff for 125 real heart rate variability (HRV) dynamics, divided into groups of 70 healthy, 44 congestive heart failure (CHF), and 11 atrial fibrillation (AF) subjects, and for simulated series of stochastic and chaotic process. The evaluations showed that, for nonperiodic signals, qSDiff curves have a maximum point (qSDiff(max)) for q not equal 1. Values of q where the maximum point occurs and where qSDiff is zero were also evaluated. Only qSDiff(max) values were capable of distinguish HRV groups (p-values 5.10 x 10(-3); 1.11 x 10(-7), and 5.50 x 10(-7) for healthy vs. CHF, healthy vs. AF, and CHF vs. AF, respectively), consistently with the concept of physiologic complexity, and suggests a potential use for chaotic system analysis. (C) 2012 American Institute of Physics. [http://dx.doi.org/10.1063/1.4758815]

Complex network classification using partially self-avoiding deterministic walks

Complex networks have attracted increasing interest from various fields of science. It has been demonstrated that each complex network model presents specific topological structures which characterize its connectivity and dynamics. Complex network classification relies on the use of representative measurements that describe topological structures. Although there are a large number of measurements, most of them are correlated. To overcome this limitation, this paper presents a new measurement for complex network classification based on partially self-avoiding walks. We validate the measurement on a data set composed by 40000 complex networks of four well-known models. Our results indicate that the proposed measurement improves correct classification of networks compared to the traditional ones. (C) 2012 American Institute of Physics. [http://dx.doi.org/10.1063/1.4737515]