Identification of Ultraviolet radiation induced Gallic acid and Caffeic acid formation in Palm juice (Borassus flabellifer) by HPLC & Mass Spectra Technique.

Palm juice, a common-cheap-antioxidants rich natural plant juice has been investigated for optimizing the effect of UV-radiation on the antioxidant activity using a DPPH free radical scavenging activity method. In this study separate set of samples of raw palm juice has been treated with 365 and 254 nm UV-lights (UVL) respectively for different exposure time. When exposed for 15 min with 365 nm UVL induces concentration factor of caffeic acid, whereas, 254 nm UVL induces gallic acid accumulation, but overall antioxidant activity was higher for 365 nm UVradiation. Caffeic acid and other polyphenol compounds are increased by 5.5 ± 0.5 % than normal palm juice, observed after irradiation with 365 nm UVL. Even after the exposure of UV irradiation for 15 min, did not affect on peptide bond modification of protein molecules present in palm juice, therefore a green effect of UVL is explored for the effective increase of antioxidant activity.

Non uniform embedding based on relevance analysis with reduced computational complexity: application to the detection of pathologies from biosignal recordings

Nonlinear analysis tools for studying and characterizing the dynamics of physiological signals have gained popularity, mainly because tracking sudden alterations of the inherent complexity of biological processes might be an indicator of altered physiological states. Typically, in order to perform an analysis with such tools, the physiological variables that describe the biological process under study are used to reconstruct the underlying dynamics of the biological processes. For that goal, a procedure called time-delay or uniform embedding is usually employed. Nonetheless, there is evidence of its inability for dealing with non-stationary signals, as those recorded from many physiological processes. To handle with such a drawback, this paper evaluates the utility of non-conventional time series reconstruction procedures based on non uniform embedding, applying them to automatic pattern recognition tasks. The paper compares a state of the art non uniform approach with a novel scheme which fuses embedding and feature selection at once, searching for better reconstructions of the dynamics of the system. Moreover, results are also compared with two classic uniform embedding techniques. Thus, the goal is comparing uniform and non uniform reconstruction techniques, including the one proposed in this work, for pattern recognition in biomedical signal processing tasks. Once the state space is reconstructed, the scheme followed characterizes with three classic nonlinear dynamic features (Largest Lyapunov Exponent, Correlation Dimension and Recurrence Period Density Entropy), while classification is carried out by means of a simple k-nn classifier. In order to test its generalization capabilities, the approach was tested with three different physiological databases (Speech Pathologies, Epilepsy and Heart Murmurs). In terms of the accuracy obtained to automatically detect the presence of pathologies, and for the three types of biosignals analyzed, the non uniform techniques used in this work lightly outperformed the results obtained using the uniform methods, suggesting their usefulness to characterize non-stationary biomedical signals in pattern recognition applications. On the other hand, in view of the results obtained and its low computational load, the proposed technique suggests its applicability for the applications under study.

Application of Independent Components Analysis with the JADE algorithm and NIR hyperspectral imaging for revealing food adulteration

In recent years, Independent Components Analysis (ICA) has proven itself to be a powerful signal-processing technique for solving the Blind-Source Separation (BSS) problems in different scientific domains. In the present work, an application of ICA for processing NIR hyperspectral images to detect traces of peanut in wheat flour is presented. Processing was performed without a priori knowledge of the chemical composition of the two food materials. The aim was to extract the source signals of the different chemical components from the initial data set and to use them in order to determine the distribution of peanut traces in the hyperspectral images. To determine the optimal number of independent component to be extracted, the Random ICA by blocks method was used. This method is based on the repeated calculation of several models using an increasing number of independent components after randomly segmenting the matrix data into two blocks and then calculating the correlations between the signals extracted from the two blocks. The extracted ICA signals were interpreted and their ability to classify peanut and wheat flour was studied. Finally, all the extracted ICs were used to construct a single synthetic signal that could be used directly with the hyperspectral images to enhance the contrast between the peanut and the wheat flours in a real multi-use industrial environment. Furthermore, feature extraction methods (connected components labelling algorithm followed by flood fill method to extract object contours) were applied in order to target the spatial location of the presence of peanut traces. A good visualization of the distributions of peanut traces was thus obtained

Systematizing requirements elicitation technique selection

Context: This research deals with requirements elicitation technique selection for software product requirements and the overselection of open interviews. Objectives: This paper proposes and validates a framework to help requirements engineers select the most adequate elicitation techniques at any time. Method: We have explored both the existing underlying theory and the results of empirical research to build the framework. Based on this, we have deduced and put together justified proposals about the framework components. We have also had to add information not found in theoretical or empirical sources. In these cases, we drew on our own experience and expertise. Results: A new validated approach for requirements technique selection. This new approach selects tech- niques other than open interview, offers a wider range of possible techniques and captures more require- ments information. Conclusions: The framework is easily extensible and changeable. Whenever any theoretical or empirical evidence for an attribute, technique or adequacy value is unearthed, the information can be easily added to the framework.

A systematic mapping study on testing technique experiments: has the situation changed since 2000?

Context: Empirical Software Engineering (ESE) replication researchers need to store and manipulate experimental data for several purposes, in particular analysis and reporting. Current research needs call for sharing and preservation of experimental data as well. In a previous work, we analyzed Replication Data Management (RDM) needs. A novel concept, called Experimental Ecosystem, was proposed to solve current deficiencies in RDM approaches. The empirical ecosystem provides replication researchers with a common framework that integrates transparently local heterogeneous data sources. A typical situation where the Empirical Ecosystem is applicable, is when several members of a research group, or several research groups collaborating together, need to share and access each other experimental results. However, to be able to apply the Empirical Ecosystem concept and deliver all promised benefits, it is necessary to analyze the software architectures and tools that can properly support it.

The Proper Generalized Decomposition With Application In Problems Of Dynamics

In this paper we address the new reduction method called Proper Generalized Decomposition (PGD) which is a discretization technique based on the use of separated representation of the unknown fields, specially well suited for solving multidimensional parametric equations. In this case, it is applied to the solution of dynamics problems. We will focus on the dynamic analysis of an one-dimensional rod with a unit harmonic load of frequency (ω) applied at a point of interest. In what follows, we will present the application of the methodology PGD to the problem in order to approximate the displacement field as the sum of the separated functions. We will consider as new variables of the problem, parameters models associated with the characteristic of the materials, in addition to the frequency. Finally, the quality of the results will be assessed based on an example.

Class-Conditional Probabilistic Principal Component Analysis: application to gender recognition

Este trabajo presenta una solución al problema del reconocimiento del género de un rostro humano a partir de una imagen. Adoptamos una aproximación que utiliza la cara completa a través de la textura de la cara normalizada y redimensionada como entrada a un clasificador Näive Bayes. Presentamos la técnica de Análisis de Componentes Principales Probabilístico Condicionado-a-la-Clase (CC-PPCA) para reducir la dimensionalidad de los vectores de características para la clasificación y asegurar la asunción de independencia para el clasificador. Esta nueva aproximación tiene la deseable propiedad de presentar un modelo paramétrico sencillo para las marginales. Además, este modelo puede estimarse con muy pocos datos. En los experimentos que hemos desarrollados mostramos que CC-PPCA obtiene un 90% de acierto en la clasificación, resultado muy similar al mejor presentado en la literatura---ABSTRACT---This paper presents a solution to the problem of recognizing the gender of a human face from an image. We adopt a holistic approach by using the cropped and normalized texture of the face as input to a Naïve Bayes classifier. First it is introduced the Class-Conditional Probabilistic Principal Component Analysis (CC-PPCA) technique to reduce the dimensionality of the classification attribute vector and enforce the independence assumption of the classifier. This new approach has the desirable property of a simple parametric model for the marginals. Moreover this model can be estimated with very few data. In the experiments conducted we show that using CCPPCA we get 90% classification accuracy, which is similar result to the best in the literature. The proposed method is very simple to train and implement.

Optical properties of ZnMgO films grown by spray pyrolysis and their application to UV photodetection

This work presents a comprehensive optical characterization of Zn1−xMgxO thin films grown by spray pyrolysis (SP). Absorption measurements show the high potential of this technique to tune the bandgap from 3.30 to 4.11 eV by changing the Mg acetate content in the precursor solution, leading to a change of the Mg-content ranging from 0 up to 35%, as measured by transmission electron microscopy-energy dispersive x-ray spectroscopy. The optical emission of the films obtained by cathodoluminescence and photoluminescence spectroscopy shows a blue shift of the peak position from 3.26 to 3.89 eV with increasing Mg incorporation, with a clear excitonic contribution even at high Mg contents. The linewidth broadening of the absorption and emission spectra as well as the magnitude of the observed Stokes shift are found to significantly increase with the Mg content. This is shown to be related to both potential fluctuations induced by pure statistical alloy disorder and the presence of a tail of band states, the latter dominating for medium Mg contents. Finally, metal–semiconductor–metal photodiodes were fabricated showing a high sensitivity and a blue shift in the cut-off energy from 3.32 to 4.02 eV, i.e., down to 308 nm. The photodiodes present large UV/dark contrast ratios (102 − 107), indicating the viability of SP as a growth technique to fabricate low cost (Zn, Mg)O-based UV photodetectors reaching short wavelengths.

A source reconstruction technique for planar arrays of wide slots

A method to reconstruct the excitation coefficients of wide-slot arrays from near-field data is presented. The plane wave spectrum (PWS) is used for reconstruction, and the shape of the field distribution on a wide slot is considered in the calculation of the PWS. The proposed algorithm is validated through the reconstruction of the excitation coefficients of a wide-slot array with element failures from the simulated nearfield data. The element failures are clearly located by the proposed algorithm

A clustering technique for partial discharge and noise sources identification in power cables by means of waveform parameters

On-line partial discharge (PD) measurements have become a common technique for assessing the insulation condition of installed high voltage (HV) insulated cables. When on-line tests are performed in noisy environments, or when more than one source of pulse-shaped signals are present in a cable system, it is difficult to perform accurate diagnoses. In these cases, an adequate selection of the non-conventional measuring technique and the implementation of effective signal processing tools are essential for a correct evaluation of the insulation degradation. Once a specific noise rejection filter is applied, many signals can be identified as potential PD pulses, therefore, a classification tool to discriminate the PD sources involved is required. This paper proposes an efficient method for the classification of PD signals and pulse-type noise interferences measured in power cables with HFCT sensors. By using a signal feature generation algorithm, representative parameters associated to the waveform of each pulse acquired are calculated so that they can be separated in different clusters. The efficiency of the clustering technique proposed is demonstrated through an example with three different PD sources and several pulse-shaped interferences measured simultaneously in a cable system with a high frequency current transformer (HFCT).

Application of the homogenization techniques to the determination of the effective flexure constants of plate structural systems

A Mindlin plate with periodically distributed ribs patterns is analyzed by using homogenization techniques based on asymptotic expansion methods. The stiffness matrix of the homogenized plate is found to be dependent on the geometrical characteristics of the periodical cell, i.e. its skewness, plan shape, thickness variation etc. and on the plate material elastic constants. The computation of this plate stiffness matrix is carried out by averaging over the cell domain some solutions of different periodical boundary value problems. These boundary value problems are defined in variational form by linear first order differential operators on the cell domain and the boundary conditions of the variational equation correspond to a periodic structural problem. The elements of the stiffness matrix of homogenized plate are obtained by linear combinations of the averaged solution functions of the above mentioned boundary value problems. Finally, an illustrative example of application of this homogenization technique to hollowed plates and plate structures with ribs patterns regularly arranged over its area is shown. The possibility of using in the profesional practice the present procedure to the actual analysis of floors of typical buildings is also emphasized.