A random dipole model for spontaneous brain activity

The statistical properties of the EEG and the MEG are described mathematically as the result of randomly distributed dipoles. These dipoles represent the interactions of cortical neurons. For certain dipole distributions, the first- and second-order moments of the electric and magnetic fields are derived analytically. If the dipoles are in a spherical volume conductor and have no preference for any direction, the variance of a differentially measured EEG-signal is only a function of the electrode distance. In this paper, the theoretically derived variance function will be compared with EEG- and MEG-measurements. It is shown that a dipole with a fixed position and a randomly fluctuating amplitude is an adequate model for the alpha-rhythm. An expression for the covariance between the magnetic field and a differentially measured EEG-signal is derived. This covariance is considered as a function of the magnetometer position, and is compared with the measurements of Chapman et al. [23]. The theory can be used to obtain a (spatial) covariance matrix of the background noise, which occurs in evoked potential measurements. Such a covariance matrix can be used to obtain a maximum likelihood estimator of the dipole parameters in evoked potential studies, to evaluate the merits of the so-called "Laplacian derivation," and for the interpolation of electromagnetic data.     

Color and COD retention by nanofiltration membranes

In the present study the application of the nanofiltration process was investigated mainly in the retention ofcolor and chemical oxygen demand (COD) present in textile industry wastewater. Nanofiltration experiments were carried out in a pilot unit, operating in crossflow. Three different types of spiral wound membranes, DK 1073, NF 45 and MPS 31 were used simultaneously in the same unit. The results of the tests showed that for color retention, the values were around 99% for the DK 1073 and NF 45 membranes and the 87% for COD retention for the DK 1073. The permeate flux for the different wastewaters varied from 30.5 to 70 L/h.m². Fouling was observed in all membranes due to the accumulation of molecular species close to the filtering surface. The process was efficient and promising for the reuse of wastewater from this type of industry.     

Effect of catalysts in the quality of syngas and by-products obtained by co-gasification of coal and wastes. 1. Tars and nitrogen compounds abatement

The aim of this work is to analyse the possibility of using co-gasification technology to process coal mixed with wastes to take profit of its energy content and at the same time to minimize the environmental impact associated with the use of wastes and to diminish the costs of flue gas treatment. The addition to coal of different types of materials, like: pine based waste, petcoke and polyethylene (PE), was not found to give rise to any operational problems, regarding both the feeding system and gasification process and led to higher energy conversions, however, the gas presented higher tars and hydrocarbons content. Several catalysts were tested, such as, dolomite, olivine, nickel and magnesium oxides, zinc oxides and cobalt and molybdenum oxides. Catalyst action was analysed in tars release and also in ammonia compounds reduction. The presence of catalysts allowed increasing hydrogen release, whilst there was a decrease in hydrocarbons and tars contents. A nickel-magnesium oxide was the catalyst that led to the highest reduction in hydrocarbons and tars. This catalyst also led to the lowest NH₃ content in the fuel gas produced, due to the catalyst efficiency in NH₃ destruction.     

Inductive Model Generation for Text Classification Using a Bipartite Heterogeneous Network

Algorithms for numeric data classification have been applied for text classification. Usually the vector space model is used to represent text collections. The characteristics of this representation such as sparsity and high dimensionality sometimes impair the quality of general-purpose classifiers. Networks can be used to represent text collections, avoiding the high sparsity and allowing to model relationships among different objects that compose a text collection. Such network- based representations can improve the quality of the classification results. One of the simplest ways to represent textual collections by a network is through a bipartite heterogeneous network, which is composed of objects that represent the documents connected to objects that represent the terms. Heterogeneous bipartite networks do not require computation of similarities or relations among the objects and can be used to model any type of text collection. Due to the advantages of representing text collections through bipartite heterogeneous networks, in this article we present a text classifier which builds a classification model using the structure of a bipartite heterogeneous network. Such an algorithm, referred to as IMBHN （Inductive Model Based on Bipartite Heterogeneous Network）, induces a classification model assigning weights to objects that represent the terms for each class of the text collection. An empirical evaluation using a large amount of text collections from different domains shows that the proposed IMBHN algorithm produces significantly better results than k-NN, C4.5, SVM, and Naive Bayes algorithms.     

Towards adaptive learning with improved convergence of deep belief networks on graphics processing units

In this paper we focus on two complementary approaches to significantly decrease pre-training time of a deep belief network (DBN). First, we propose an adaptive step size technique to enhance the convergence of the contrastive divergence (CD) algorithm, thereby reducing the number of epochs to train the restricted Boltzmann machine (RBM) that supports the DBN infrastructure. Second, we present a highly scalable graphics processing unit (GPU) parallel implementation of the CD-k algorithm, which boosts notably the training speed. Additionally, extensive experiments are conducted on the MNIST and the HHreco databases. The results suggest that the maximum useful depth of a DBN is related to the number and quality of the training samples. Moreover, it was found that the lower-level layer plays a fundamental role for building successful DBN models. Furthermore, the results contradict the pre-conceived idea that all the layers should be pre-trained. Finally, it is shown that by incorporating multiple back-propagation (MBP) layers, the DBNs generalization capability is remarkably improved.     

Synthesis, characterization and crystal structure of a novel thiocyanate–ruthenium(II) complex

The cis-[Ru(dppb)(Me-bipy)(NCS)₂], dppb = 1,4-bis (diphenylphosphino)butane, Me-bipy = 4,4′-dimethyl-2,2′-bipyridine, and NCS = thiocyanate, was synthesized and characterized by spectroscopic and electrochemical techniques and its structure was determined by crystal X-ray analysis. The crystal structure reveals that the coordination geometry around the Ru(II) center is distorted octahedron where two molecules of thiocyanate are bonded to the ruthenium through nitrogen atom in cis orientation. The half-wave formal potential value E_1/2 = 0.8 V (versus Ag/AgCl) observed is considerable higher than that for the cis-[RuCl₂(dppb)(Me-bipy)] complex, E_1/2 = 0.6 V (versus Ag/AgCl), well illustrating the strong π-acceptor effect the NCS ligand toward the backbonding interaction with the Ru(II) metal center. The MLCT absorption bands of the thiocyanate complex present a higher molar absorptivity (about 12%) compared with the cis-[RuCl₂(dppb)(Me-bipy)] complex, in the same experimental conditions. These properties make the complex potentially promising for the photosensitization process.     

OpenCOPI: middleware integration for Ubiquitous Computing

In this paper, we present OpenCOPI (Open COntext Platform Integration), a Service-Oriented Architecture-based middleware platform that supports the integration of services provided by distinct sources, ranging from services offered by simple systems to more complex services provided by context-provision middleware. OpenCOPI offers selection and composition mechanisms to, respectively, select and compose services provided by different sources, considering applications of both Quality of Service and Quality of Context requirements. It also offers an adaptation mechanism that enables to adapt the application execution due to service failures, service quality fluctuation and user mobility. OpenCOPI allows the definition of applications in a higher abstraction level by the specification of a semantic workflow that contains abstract activities. This paper illustrates the use of OpenCOPI in an application from the Gas & Oil Industry and it also shows the evaluation of the main mechanisms of OpenCOPI: the service selection, composition, adaptation and workflow execution.     

Master batch approach for developing PVDF/EVA/CNT nanocomposites with co-continuous morphology and improved electrical conductivity

Conducting polymer composites constituted by co-continuous poly (vinylidene fluoride) (PVDF)/ ethylene- vinyl acetate copolymer (EVA) blends with multiwalled carbon nanotube (CNT) were prepared by melt mixing using different procedures. The effect of the master batch approach on the conductivity, morphology, mechanical, thermal and rheological properties of PVDF/EVA/CNT nanocomposites was compared with that based on one step mixing strategy. The selective extraction experiments revealed that CNT was preferentially localized in the EVA phase in all situations, even when PVDF@CNT master batch was employed. Nanocomposites prepared with EVA@CNT master batch displayed higher conductivity, whose value reached around 10⁻¹ S m⁻¹ with the addition of 0.56 vol% of CNT. The better electrical performance was attributed to the better distribution of the filler, as indicated by transmission electron microscopy and rheological behavior. The electrical and rheological behavior were also investigated as a function of the CNT content.     

Spectroscopic Multicomponent Analysis Using Multi-objective Optimization for Variable Selection

The multiple determination tasks of chemical properties are a classical problem in analytical chemistry. The major problem is concerned in to find the best subset of variables that better represents the compounds. These variables are obtained by a spectrophotometer device. This device measures hundreds of correlated variables related with physicocbemical properties and that can be used to estimate the component of interest. The problem is the selection of a subset of informative and uncorrelated variables that help the minimization of prediction error. Classical algorithms select a subset of variables for each compound considered. In this work we propose the use of the SPEA-II （strength Pareto evolutionary algorithm II）. We would like to show that the variable selection algorithm can selected just one subset used for multiple determinations using multiple linear regressions. For the case study is used wheat data obtained by NIR （near-infrared spectroscopy） spectrometry where the objective is the determination of a variable subgroup with information about E protein content （%）, test weight （Kg/HI）, WKT （wheat kernel texture） （%） and farinograph water absorption （%）. The results of traditional techniques of multivariate calibration as the SPA （successive projections algorithm）, PLS （partial least square） and mono-objective genetic algorithm are presents for comparisons. For NIR spectral analysis of protein concentration on wheat, the number of variables selected from 775 spectral variables was reduced for just 10 in the SPEA-II algorithm. The prediction error decreased from 0.2 in the classical methods to 0.09 in proposed approach, a reduction of 37%. The model using variables selected by SPEA-II had better prediction performance than classical algorithms and full-spectrum partial least-squares.