Dielectric properties and physical features of phosphate glasses containing iron oxide

Microwave techniques were applied to the study of dielectric properties of phosphate glasses on the basis of contributions from permanent and induced dipolar polarization of local structural units interacting with the electrical component of the electromagnetic radiation. The dielectric constant of the selected glass system (100-x)(50P(2)O(5)center dot 25Li(2)O center dot 25Na(2)O)center dot xFe(2)O(3), where 0 <= x <= 21 is in mol%, was measured using a microwave setup assembled to measure the phase shift of the standing wave pattern produced by the insertion of the sample. It is shown that the Fe2+ ions contribute effectively to the dielectric constant, as expected from the interactions of the dipoles of the local charge compensation pairs with the microwave radiation. However, there is the possibility of occurrence of some ions Fe3+, in general, at low iron content, which reinforces the glass structure and, therefore, decreases the dielectric constant. There is a gradual conversion from Fe3+ to Fe2+ as the iron ions increases. This is possibly the reason of the anomaly in the dielectric constant values observed in the results. These assumptions can be checked by results of electronic paramagnetic resonance (EPR) and optical absorption (OA). The dielectric constant of the glasses studied in this work was found to increase with the temperature in the range of 25-330 degrees C. (C) 2007 Elsevier B.V. All rights reserved.

Climatological features of cutoff low systems in the Southern Hemisphere

Cutoff lows (COLs) pressure systems climatology for the Southern Hemisphere (SH), between 10 degrees S and 50 degrees S, using the National Center for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) and the ERA-40 European Centre for Medium Range Weather Forecast (ECMWF) reanalyses are analyzed for the period 1979-1999. COLs were identified at three pressure levels (200, 300, and 500 hPa) using an objective method that considers the main physical characteristics of the conceptual model of COLs. Independently of the pressure level analyzed, the climatology from the ERA-40 reanalysis has more COLs systems than the NCEP-NCAR. However, both reanalyses present a large frequency of COLs at 300 hPa, followed by 500 and 200 hPa. The seasonality of COLs differs at each pressure level, but it is similar between the reanalyses. COLs are more frequent during summer, autumn, and winter at 200, 300, and 500 hPa, respectively. At these levels, they tend to occur around the continents, preferentially from southeastern Australia to New Zealand, the south of South America, and the south of Africa. To study the COLs at 200 and 300 hPa from a regional perspective, the SH was divided in three regions: Australia-New Zealand (60 E-130 W), South America (130 degrees W-20 degrees W), and southern Africa (20 degrees W-60 degrees E). The common COLs features in these sectors for both reanalyses are a short lifetime (similar to 80.0% and similar to 70.0% of COLs at 200 and 300 hPa, respectively, persisting for up to 3 days), mobility (similar to 70.0% and similar to 50% of COLs at 200 and 300 hPa, respectively, traveling distances of up to 1200 km), and an eastward propagation.

Predicting spectral features in galaxy spectra from broad-band photometry

We explore the prospects of predicting emission-line features present in galaxy spectra given broad-band photometry alone. There is a general consent that colours, and spectral features, most notably the 4000 angstrom break, can predict many properties of galaxies, including star formation rates and hence they could infer some of the line properties. We argue that these techniques have great prospects in helping us understand line emission in extragalactic objects and might speed up future galaxy redshift surveys if they are to target emission-line objects only. We use two independent methods, Artificial Neural Networks (based on the ANNz code) and Locally Weighted Regression (LWR), to retrieve correlations present in the colour N-dimensional space and to predict the equivalent widths present in the corresponding spectra. We also investigate how well it is possible to separate galaxies with and without lines from broad-band photometry only. We find, unsurprisingly, that recombination lines can be well predicted by galaxy colours. However, among collisional lines some can and some cannot be predicted well from galaxy colours alone, without any further redshift information. We also use our techniques to estimate how much information contained in spectral diagnostic diagrams can be recovered from broad-band photometry alone. We find that it is possible to classify active galactic nuclei and star formation objects relatively well using colours only. We suggest that this technique could be used to considerably improve redshift surveys such as the upcoming Fibre Multi Object Spectrograph (FMOS) survey and the planned Wide Field Multi Object Spectrograph (WFMOS) survey.

Successional stage and geographic features determine floristic similarity among Atlantic Forest remnants, Sao Paulo State, Brazil

Analysis of floristic similarity relationships between plant communities can detect patterns of species occurrence and also explain conditioning factors. Searching for such patterns, floristic similarity relationships among Atlantic Forest sites situated at Ibiuna Plateau, Sao Paulo state, Brazil, were analyzed by multivariate techniques. Twenty one forest fragments and six sites within a continuous Forest Reserve were included in the analyses. Floristic composition and structure of the tree community (minimum dbh 5 cm) were assessed using the point centered quarter method. Two methods were used for multivariate analysis: Detrended Correspondence Analysis (DCA) and Two-Way Indicator Species Analysis (TWINSPAN). Similarity relationships among the study areas were based on the successional stage of the community and also on spatial proximity. The more similar the successional stage of the communities, the higher the floristic similarity between them, especially if the communities are geographically close. A floristic gradient from north to south was observed, suggesting a transition between biomes, since northern indicator species are mostly heliophytes, occurring also in cerrado vegetation and seasonal semideciduous forest, while southern indicator species are mostly typical ombrophilous and climax species from typical dense evergreen Atlantic Forest.

Genotyping, physiological features and proteolytic activities of a potentially pathogenic Acanthamoeba sp isolated from tap water in Brazil

Acanthamoeba spp., known to cause keratitis and granulomatous encephalitis in humans, are frequently isolated from a variety of water sources. Here we report for the first time the characterization of an Acanthamoeba sp. (ACC01) isolated from tap water in Brazil. This organism is currently being maintained in an axenic growth medium. Phylogenetic analysis based on SSU rRNA gene sequences positioned the new isolate in genotype T4, closest to the keratitis-causing isolate, A. polyphaga ATCC 30461 (similar to 99% similarity). Acanthamoeba ACC01 and A. polyphaga 30461 both grew at 37 degrees C and were osmotically resistant, multiplying in hyperosmolar medium. Both isolates secreted comparable amounts of proteolytic enzymes, including serine peptidases that were optimally active at a near neutral/alkaline pH and resolved identically in gelatin gels. Incubation of gels at pH 4.0 with 2 mM DTT also indicated the secretion of similar cysteine peptidases. Altogether, the results point to the pathogenic potential of Acanthamoeba ACC01. (C) 2009 Elsevier Inc. All rights reserved.

Beyond the average: Detecting global singular nodes from local features in complex networks

Deviations from the average can provide valuable insights about the organization of natural systems. The present article extends this important principle to the systematic identification and analysis of singular motifs in complex networks. Six measurements quantifying different and complementary features of the connectivity around each node of a network were calculated, and multivariate statistical methods applied to identify singular nodes. The potential of the presented concepts and methodology was illustrated with respect to different types of complex real-world networks, namely the US air transportation network, the protein-protein interactions of the yeast Saccharomyces cerevisiae and the Roget thesaurus networks. The obtained singular motifs possessed unique functional roles in the networks. Three classic theoretical network models were also investigated, with the Barabasi-Albert model resulting in singular motifs corresponding to hubs, confirming the potential of the approach. Interestingly, the number of different types of singular node motifs as well as the number of their instances were found to be considerably higher in the real-world networks than in any of the benchmark networks. Copyright (C) EPLA, 2009

Spectroscopic features of erbium-doped CaM2O6 (M = Nb, Ta) single crystal fibers grown by the laser-heated pedestal growth technique

Erbium-doped single crystal fibers, with low phonon energy and fairly high absorption and emission cross sections are interesting laser active media, for compact, near-infrared and/or upconversion lasers. In this work, high optical quality Er3+-doped CaNb2O6 and CaTa2O6 single crystal fibers were successfully grown by the versatile laser-heated pedestal growth technique, and characterized from the structural and spectroscopic points of view. The results indicate that these crystal fiber compositions, which had not been explored so far, offer potential applications, not only as laser active media, but also in other optical devices. (c) 2007 Elsevier B.V. All rights reserved.

Intermolecular Contacts Influencing the Conformational and Geometric Features of the Pharmaceutically Preferred Mebendazole Polymorph C

Mebendazole (MBZ) is a common benzimidazole anthelmintic that exists in three different polymorphic forms, A, B, and C. Polymorph C is the pharmaceutically preferred form due to its adequated aqueous solubility. No single crystal structure determinations depicting the nature of the crystal packing and molecular conformation and geometry have been performed on this compound. The crystal structure of mebendazole form C is resolved for the first time. Mebendazole form C crystallizes in the triclinic centrosymmetric space group and this drug is practically planar, since the least-squares methyl benzimidazolylcarbamate plane is much fitted on the forming atoms. However, the benzoyl group is twisted by 31(1)degrees from the benzimidazole ring, likewise the torsional angle between the benzene and carbonyl moieties is 27(1)degrees. The formerly described bends and other interesting intramolecular geometry features were viewed as consequence of the intermolecular contacts occurring within mebendazole C structure. Among these features, a conjugation decreasing through the imine nitrogen atom of the benzimidazole core and a further resonance path crossing the carbamate one were described. At last, the X-ray powder diffractogram of a form C rich mebendazole mixture was overlaid to the calculated one with the mebendazole crystal structure. (C) 2008 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 98:2336-2344, 2009

Cross-situational learning of object-word mapping using Neural Modeling Fields

The issue of how children learn the meaning of words is fundamental to developmental psychology. The recent attempts to develop or evolve efficient communication protocols among interacting robots or Virtual agents have brought that issue to a central place in more applied research fields, such as computational linguistics and neural networks, as well. An attractive approach to learning an object-word mapping is the so-called cross-situational learning. This learning scenario is based on the intuitive notion that a learner can determine the meaning of a word by finding something in common across all observed uses of that word. Here we show how the deterministic Neural Modeling Fields (NMF) categorization mechanism can be used by the learner as an efficient algorithm to infer the correct object-word mapping. To achieve that we first reduce the original on-line learning problem to a batch learning problem where the inputs to the NMF mechanism are all possible object-word associations that Could be inferred from the cross-situational learning scenario. Since many of those associations are incorrect, they are considered as clutter or noise and discarded automatically by a clutter detector model included in our NMF implementation. With these two key ingredients - batch learning and clutter detection - the NMF mechanism was capable to infer perfectly the correct object-word mapping. (C) 2009 Elsevier Ltd. All rights reserved.

Cross-situational and supervised learning in the emergence of communication

Scenarios for the emergence or bootstrap of a lexicon involve the repeated interaction between at least two agents who must reach a consensus on how to name N objects using H words. Here we consider minimal models of two types of learning algorithms: cross-situational learning, in which the individuals determine the meaning of a word by looking for something in common across all observed uses of that word, and supervised operant conditioning learning, in which there is strong feedback between individuals about the intended meaning of the words. Despite the stark differences between these learning schemes, we show that they yield the same communication accuracy in the limits of large N and H, which coincides with the result of the classical occupancy problem of randomly assigning N objects to H words.

Resilience of protein-protein interaction networks as determined by their large-scale topological features

The relationship between the structure and function of biological networks constitutes a fundamental issue in systems biology. Particularly, the structure of protein-protein interaction networks is related to important biological functions. In this work, we investigated how such a resilience is determined by the large scale features of the respective networks. Four species are taken into account, namely yeast Saccharomyces cerevisiae, worm Caenorhabditis elegans, fly Drosophila melanogaster and Homo sapiens. We adopted two entropy-related measurements (degree entropy and dynamic entropy) in order to quantify the overall degree of robustness of these networks. We verified that while they exhibit similar structural variations under random node removal, they differ significantly when subjected to intentional attacks (hub removal). As a matter of fact, more complex species tended to exhibit more robust networks. More specifically, we quantified how six important measurements of the networks topology (namely clustering coefficient, average degree of neighbors, average shortest path length, diameter, assortativity coefficient, and slope of the power law degree distribution) correlated with the two entropy measurements. Our results revealed that the fraction of hubs and the average neighbor degree contribute significantly for the resilience of networks. In addition, the topological analysis of the removed hubs indicated that the presence of alternative paths between the proteins connected to hubs tend to reinforce resilience. The performed analysis helps to understand how resilience is underlain in networks and can be applied to the development of protein network models.

Road Sign Recognition based onInvariant Features using SupportVector Machine

Since last two decades researches have been working on developing systems that can assistsdrivers in the best way possible and make driving safe. Computer vision has played a crucialpart in design of these systems. With the introduction of vision techniques variousautonomous and robust real-time traffic automation systems have been designed such asTraffic monitoring, Traffic related parameter estimation and intelligent vehicles. Among theseautomatic detection and recognition of road signs has became an interesting research topic.The system can assist drivers about signs they don’t recognize before passing them.Aim of this research project is to present an Intelligent Road Sign Recognition System basedon state-of-the-art technique, the Support Vector Machine. The project is an extension to thework done at ITS research Platform at Dalarna University [25]. Focus of this research work ison the recognition of road signs under analysis. When classifying an image its location, sizeand orientation in the image plane are its irrelevant features and one way to get rid of thisambiguity is to extract those features which are invariant under the above mentionedtransformation. These invariant features are then used in Support Vector Machine forclassification. Support Vector Machine is a supervised learning machine that solves problemin higher dimension with the help of Kernel functions and is best know for classificationproblems.

Gapet mellan en ledares självuppfattning och medarbetarnas uppfattningar

Leadership is about synchronizing people into doing actions together to reach a common goal. To be able to do such thing you have to be a good leader. A mutual feature between good leaders is a good self awareness in order to be able to lead others. By letting others evaluate the features of a leader it can give a good self image of the leaders’ leadership. This is for seeing if there is a gap between the leaders own and the co-workers opinions about the leaders’ leadership. The purpose is to analyze if there could be a gap between a leader and its co-workers opinions about the leaders’ leadership and also to analyze why such a gap could exist.The method that has been used for analyzing the leadership is a 360 degree evaluation. The 360 degree evaluation is used in such way that the chosen leader, beyond its self assessment, is getting evaluated by its co-workers closest to him or her. This was implemented by questionnaires and interviews. The questionnaires are made after Adizes leadership roles and Hersey and Blanchards’ situational leadership.A leader often has different features, these does not accentuate because of the organizational structure and the position of the leaders in the organization emphasizes different features.

Testing common nonlinear features in vector nonlinear autoregressive models

This paper studies a special class of vector smooth-transition autoregressive (VSTAR) models that contains common nonlinear features (CNFs), for which we proposed a triangular representation and developed a procedure of testing CNFs in a VSTAR model. We first test a unit root against a stable STAR process for each individual time series and then examine whether CNFs exist in the system by Lagrange Multiplier (LM) test if unit root is rejected in the first step. The LM test has standard Chi-squared asymptotic distribution. The critical values of our unit root tests and small-sample properties of the F form of our LM test are studied by Monte Carlo simulations. We illustrate how to test and model CNFs using the monthly growth of consumption and income data of United States (1985:1 to 2011:11).

Residual-based Inference for Common Nonlinear Features

This paper investigates common nonlinear features in multivariate nonlinear autore-gressive models via testing the estimated residuals. A Wald-type test is proposed and itis asymptotically Chi-squared distributed. Simulation studies are given to examine thefinite-sample properties of the proposed test.