Robust Hybrid Mechanisms for Scheduling of Grid Tasks

This paper proposes three new hybrid mechanisms for the scheduling of grid tasks, which integrate reactive and proactive approaches. They differ by the scheduler used to define the initial schedule of an application and by the scheduler used to reschedule the application. The mechanisms are compared to reactive and proactive mechanisms. Results show that hybrid approach produces performance close to that of the reactive mechanisms, but demanding less migrations.

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

Structure-semantics interplay in complex networks and its effects on the predictability of similarity in texts

The classification of texts has become a major endeavor with so much electronic material available, for it is an essential task in several applications, including search engines and information retrieval. There are different ways to define similarity for grouping similar texts into clusters, as the concept of similarity may depend on the purpose of the task. For instance, in topic extraction similar texts mean those within the same semantic field, whereas in author recognition stylistic features should be considered. In this study, we introduce ways to classify texts employing concepts of complex networks, which may be able to capture syntactic, semantic and even pragmatic features. The interplay between various metrics of the complex networks is analyzed with three applications, namely identification of machine translation (MT) systems, evaluation of quality of machine translated texts and authorship recognition. We shall show that topological features of the networks representing texts can enhance the ability to identify MT systems in particular cases. For evaluating the quality of MT texts, on the other hand, high correlation was obtained with methods capable of capturing the semantics. This was expected because the golden standards used are themselves based on word co-occurrence. Notwithstanding, the Katz similarity, which involves semantic and structure in the comparison of texts, achieved the highest correlation with the NIST measurement, indicating that in some cases the combination of both approaches can improve the ability to quantify quality in MT. In authorship recognition, again the topological features were relevant in some contexts, though for the books and authors analyzed good results were obtained with semantic features as well. Because hybrid approaches encompassing semantic and topological features have not been extensively used, we believe that the methodology proposed here may be useful to enhance text classification considerably, as it combines well-established strategies. (c) 2012 Elsevier B.V. All rights reserved.

New approach for power transformer protection based on intelligent hybrid systems

A power transformer needs continuous monitoring and fast protection as it is a very expensive piece of equipment and an essential element in an electrical power system. The most common protection technique used is the percentage differential logic, which provides discrimination between an internal fault and different operating conditions. Unfortunately, there are some operating conditions of power transformers that can mislead the conventional protection affecting the power system stability negatively. This study proposes the development of a new algorithm to improve the protection performance by using fuzzy logic, artificial neural networks and genetic algorithms. An electrical power system was modelled using Alternative Transients Program software to obtain the operational conditions and fault situations needed to test the algorithm developed, as well as a commercial differential relay. Results show improved reliability, as well as a fast response of the proposed technique when compared with conventional ones.

Terbium(III) and dysprosium(III) 8-connected 3D networks containing 2,5-thiophenedicarboxylate anion: Crystal structures and photoluminescence studies

Two novel coordination polymers with the formula {[Ln(2)(2,5-tdc)(3)(dmso)(2)].H2O}(n) (Ln = Tb(III) for (1) and Dy(III) for (2)), (2,5-tdc(2-) = 2,5-thiophenedicarboxylate and dmso = dimethylsulfoxide) have been synthesized by the diffusion method and characterized by thermal analysis, vibrational spectroscopy and single crystal X-ray diffraction analysis. Structure analysis reveals that 2,5-tdc(2-) play a versatile role toward different lanthanide ions to form three-dimensional metal-organic frameworks (MOFs) in which the lanthanides ions are heptacoordinated. Photophysical properties were studied using excitation and emission spectra, where the photoluminescence data show the high emission intensity of the characteristic transitions D-5(4 ->) F-7(J) (J= 6, 5, 4 and 3) for (1) and (F9/2 -> HJ)-F-4-H-6 (J = 15/2, 13/2 and 11/2) for (2), indicating that 2,5-tdc(2-) is a good sensitizer. (C) 2012 Elsevier Ltd. All rights reserved.

Fracture and resistance-curve behavior in hybrid natural fiber and polypropylene fiber reinforced composites

This article presents the results of a combined experimental and theoretical study of fracture and resistance-curve behavior of hybrid natural fiber- and synthetic polymer fiber-reinforced composites that are being developed for potential applications in affordable housing. Fracture and resistance-curve behavior are studied using single-edge notched bend specimens. The sisal fibers used were examined using atomic force microscopy for fiber bundle structures. The underlying crack/microstructure interactions and fracture mechanisms are elucidated via in situ optical microscopy and ex-situ environmental scanning microscopy techniques. The observed crack bridging mechanisms are modeled using small and large scale bridging concepts. The implications of the results are then discussed for the design of eco-friendly building materials that are reinforced with natural and polypropylene fibers.

Electrochromic Properties of a Metallo-supramolecular Polymer Derived from Tetra(2-pyridyl-1,4-pyrazine) Ligands Integrated in Thin Multilayer Films

The electrochromic behavior of iron complexes derived from tetra-2-pyridyl-1,4-pyrazine (TPPZ) and a hexacyanoferrate species in polyelectrolytic multilayer adsorbed films is described for the first time. This complex macromolecule was deposited onto indium-tin oxide (ITO) substrates via self-assembly, and the morphology of the modified electrodes was studied using atomic force microscopy (AFM), which indicated that the hybrid film containing the polyelectrolyte multilayer and the iron complex was highly homogeneous and was approximately 50 nm thick. The modified electrodes exhibited excellent electrochromic behavior with both intense and persistent coloration as well as a chromatic contrast of approximately 70%. In addition, this system achieved high electrochromic efficiency (over 70 cm(2) C-1 at 630 nm) and a response time that could be measured in milliseconds. The electrode was cycled more than 10(3) times, indicating excellent stability.

Hybrid Water Demand Forecasting Model Associating Artificial Neural Network with Fourier Series

This paper addressed the problem of water-demand forecasting for real-time operation of water supply systems. The present study was conducted to identify the best fit model using hourly consumption data from the water supply system of Araraquara, Sa approximate to o Paulo, Brazil. Artificial neural networks (ANNs) were used in view of their enhanced capability to match or even improve on the regression model forecasts. The ANNs used were the multilayer perceptron with the back-propagation algorithm (MLP-BP), the dynamic neural network (DAN2), and two hybrid ANNs. The hybrid models used the error produced by the Fourier series forecasting as input to the MLP-BP and DAN2, called ANN-H and DAN2-H, respectively. The tested inputs for the neural network were selected literature and correlation analysis. The results from the hybrid models were promising, DAN2 performing better than the tested MLP-BP models. DAN2-H, identified as the best model, produced a mean absolute error (MAE) of 3.3 L/s and 2.8 L/s for training and test set, respectively, for the prediction of the next hour, which represented about 12% of the average consumption. The best forecasting model for the next 24 hours was again DAN2-H, which outperformed other compared models, and produced a MAE of 3.1 L/s and 3.0 L/s for training and test set respectively, which represented about 12% of average consumption. DOI: 10.1061/(ASCE)WR.1943-5452.0000177. (C) 2012 American Society of Civil Engineers.

Hybrid heuristic-waterfilling game theory approach in MC-CDMA resource allocation

This paper discusses the power allocation with fixed rate constraint problem in multi-carrier code division multiple access (MC-CDMA) networks, that has been solved through game theoretic perspective by the use of an iterative water-filling algorithm (IWFA). The problem is analyzed under various interference density configurations, and its reliability is studied in terms of solution existence and uniqueness. Moreover, numerical results reveal the approach shortcoming, thus a new method combining swarm intelligence and IWFA is proposed to make practicable the use of game theoretic approaches in realistic MC-CDMA systems scenarios. The contribution of this paper is twofold: (i) provide a complete analysis for the existence and uniqueness of the game solution, from simple to more realist and complex interference scenarios; (ii) propose a hybrid power allocation optimization method combining swarm intelligence, game theory and IWFA. To corroborate the effectiveness of the proposed method, an outage probability analysis in realistic interference scenarios, and a complexity comparison with the classical IWFA are presented. (C) 2011 Elsevier B.V. All rights reserved.

Hybrid Scaffolds Built From PET and Collagen as a Model For Vascular Graft Architecture

A hybrid material with excellent mechanical and biological properties is produced by electrospinning a co-solution of PET and collagen. The fibers are mapped using SEM, confocal Raman microscopy and collagenase digestion assays. Fibers of different compositions and morphologies are intermingled within the same membrane, resulting in a heterogeneous scaffold. The collagen distribution and exposure are found to depend on the PET/collagen ratio. The materials are chemically and mechanically characterized and biologically tested with fibroblasts (3T3-L1) and a HUVEC culture in vitro. All of the hybrid scaffolds show better cell attachment and proliferation than PET. These materials are potential candidates to be used as vascular grafts.