Corporate governance : value creation and performance in credit unions

In this study, we examine the relationship between good corporate governance practices and the creation of value/performance of credit unions from 2010 to 2012. The objective was to create and validate a corporate governance index for credit unions, and to then analyse the relationship between good governance practices and the creation of value/performance. The problem question is: do good corporate governance practices provide value creation for credit unions? The research started by creating indices from factor analysis to identify latent dependent variables related to value creation and performance; next indices were created from the principal component analysis for the creation of independent latent variables related to corporate governance. Finally, based on panel data from regression models, the influence of the variables and indices related to corporate governance on the indices of value creation and performance was verified. Based on the research, it became evident that the Corporate Governance Index (IGC) is mainly impacted by Executive Management, with 40.31% of the IGC value, followed by the Representation and Participation dimension, with 34.07% of the IGC value. The contribution for academics was the creation of the Corporate Governance Index (IGC) applied for credit unions. As for the contribution to the system of credit unions, the highlight was the effectiveness of the mechanisms for economic-financial and asset management adopted by BACEN, credit unions and OCEMG.

The Associations Between the Maladaptive Personality Dimensions of Neediness and Self-Criticism, Defense Styles, Selfobject Needs, and Attachment Styles in an Iranian Sample

The present study extends previous findings by examining whether defense styles, selfobject needs, attachment styles relate to Neediness and Self-Criticism, as maladaptive personality dimensions focused, respectively, on relatedness and self-definition in an Iranian sample. Three hundred and fifty two participants completed a socio-demographic questionnaire as well as the Persian forms of the Depressive Experiences Questionnaire, Experience of Close Relationships-Revised, Defense Style Questionnaire, Beck Depression Inventory–II and Selfobject Needs Inventory. Two Multiple Linear Regression Analyses, entering Self-criticism and Neediness as criterion variables, were computed. According to the results high Attachment anxiety, high Immature defenses, high depressive symptoms, and high need for idealization were related to self-criticism, and explained 47% of its variance. In addition, high attachment anxiety, low mature defenses, high neurotic defenses, high avoidance of mirroring, and low avoidance of idealization/twinship were related to neediness, and explained 40% of its variance. A Principal Component Analysis was performed, entering all the studied variables. Three factors emerged; one describing a maladaptive form of psychological functioning and two describing more mature modes of psychological functioning. The results are discussed in their implications for the understanding of neediness and self-criticism as maladaptive personality dimensions focused, respectively, on relatedness and self-definition.

The Virome of two Fusarium spp. Collections and its Potential for Controlling Fusarium Disease

This research aims to discover the virome diversity and composition in Fusarium poae and Fusarium proliferatum collections, characterize the mycovirus that may have an effect on host pathogenicity to provide potential materials for the biological control of Fusarium spp. pathogens. Next-Generation Sequencing (NGS) analysis of 30 F. poae isolates revealed an extreme diversity of mycoviruses. Bioinformatic analysis shows that contigs associated with viral genome belong to the families: Hypoviridae, Mitoviridae, Partitiviridae, Polymycoviridae, proposed Alternaviridae, proposed Fusagraviridae, proposed Fusariviridae, proposed Yadokariviridae, and Totiviridae. The complete genomes of 12 viruses were obtained by assembling contigs and overlapping cloning sequences. Moreover, all the F. poae isolates analyzed are multi-infected. Fusarium poae partitivirus 1 appears in all the 30 strains, followed by Fusarium poae fusagravirus 1 (22), Fusarium poae mitovirus 2 (18), Fusarium poae partitivirus 3 (16), and Fusarium poae mitovirus 2 and 3 (11). Using the same approach, the virome of F. proliferatum collections resulted in lower diversity and abundance. The identified mycoviruses belong to the family Mitoviridae and Mymonaviridae. Interestingly, most F. proliferatum isolates are not multi-infected. The complete genomes of four viruses were obtained by assembling contigs and overlapping cloning sequences. By multiple liner regression of the virome composition and growth rate of 30 F. poae, Fusarium poae mitovirus 3 is significantly correlated with the growth rate among F. poae collection. Furthermore, the principal component analysis of the virome composition from 30 F. poae showed that the presence of Fusarium poae mitovirus 3 and other two viruses could increase the F. poae growth rate. The curing experiment and pathogenicity test in Petri indicated that Fusarium poae hypovirus 1 might be associated with the host hypovirulence phenotype, while Fusarium poae fusagravirus 1 and Fusarium poae partitivirus 3 may have some beneficial effect on host pathogenicity.

Statistical learning of random probability measures

The study of random probability measures is a lively research topic that has attracted interest from different fields in recent years. In this thesis, we consider random probability measures in the context of Bayesian nonparametrics, where the law of a random probability measure is used as prior distribution, and in the context of distributional data analysis, where the goal is to perform inference given avsample from the law of a random probability measure. The contributions contained in this thesis can be subdivided according to three different topics: (i) the use of almost surely discrete repulsive random measures (i.e., whose support points are well separated) for Bayesian model-based clustering, (ii) the proposal of new laws for collections of random probability measures for Bayesian density estimation of partially exchangeable data subdivided into different groups, and (iii) the study of principal component analysis and regression models for probability distributions seen as elements of the 2-Wasserstein space. Specifically, for point (i) above we propose an efficient Markov chain Monte Carlo algorithm for posterior inference, which sidesteps the need of split-merge reversible jump moves typically associated with poor performance, we propose a model for clustering high-dimensional data by introducing a novel class of anisotropic determinantal point processes, and study the distributional properties of the repulsive measures, shedding light on important theoretical results which enable more principled prior elicitation and more efficient posterior simulation algorithms. For point (ii) above, we consider several models suitable for clustering homogeneous populations, inducing spatial dependence across groups of data, extracting the characteristic traits common to all the data-groups, and propose a novel vector autoregressive model to study of growth curves of Singaporean kids. Finally, for point (iii), we propose a novel class of projected statistical methods for distributional data analysis for measures on the real line and on the unit-circle.

Efficient and low-computational predictive models for spectral sensors

Spectral sensors are a wide class of devices that are extremely useful for detecting essential information of the environment and materials with high degree of selectivity. Recently, they have achieved high degrees of integration and low implementation cost to be suited for fast, small, and non-invasive monitoring systems. However, the useful information is hidden in spectra and it is difficult to decode. So, mathematical algorithms are needed to infer the value of the variables of interest from the acquired data. Between the different families of predictive modeling, Principal Component Analysis and the techniques stemmed from it can provide very good performances, as well as small computational and memory requirements. For these reasons, they allow the implementation of the prediction even in embedded and autonomous devices. In this thesis, I will present 4 practical applications of these algorithms to the prediction of different variables: moisture of soil, moisture of concrete, freshness of anchovies/sardines, and concentration of gasses. In all of these cases, the workflow will be the same. Initially, an acquisition campaign was performed to acquire both spectra and the variables of interest from samples. Then these data are used as input for the creation of the prediction models, to solve both classification and regression problems. From these models, an array of calibration coefficients is derived and used for the implementation of the prediction in an embedded system. The presented results will show that this workflow was successfully applied to very different scientific fields, obtaining autonomous and non-invasive devices able to predict the value of physical parameters of choice from new spectral acquisitions.

Synthetic data generation for the assessment of antimicrobial resistance through machine learning

As a consequence of the diffusion of next generation sequencing techniques, metagenomics databases have become one of the most promising repositories of information about features and behavior of microorganisms. One of the subjects that can be studied from those data are bacteria populations. Next generation sequencing techniques allow to study the bacteria population within an environment by sampling genetic material directly from it, without the needing of culturing a similar population in vitro and observing its behavior. As a drawback, it is quite complex to extract information from those data and usually there is more than one way to do that; AMR is no exception. In this study we will discuss how the quantified AMR, which regards the genotype of the bacteria, can be related to the bacteria phenotype and its actual level of resistance against the specific substance. In order to have a quantitative information about bacteria genotype, we will evaluate the resistome from the read libraries, aligning them against CARD database. With those data, we will test various machine learning algorithms for predicting the bacteria phenotype. The samples that we exploit should resemble those that could be obtained from a natural context, but are actually produced by a read libraries simulation tool. In this way we are able to design the populations with bacteria of known genotype, so that we can relay on a secure ground truth for training and testing our algorithms.

Antioxidant Activity Of Hydrophilic And Lipophilic Extracts Of Brazilian Blueberries.

Hydrophilic and lipophilic extracts of ten cultivars of Highbush and Rabbiteye Brazilian blueberries (Vaccinium corymbosum L. and Vacciniumashei Reade, respectively) that are used for commercial production were analysed for antioxidant activity by the FRAP, ORAC, ABTS and β-carotene-linoleate methods. Results were correlated to the amounts of carotenoids, total phenolics and anthocyanins. Brazilian blueberries had relatively high concentration of total phenolics (1,622-3,457 mg gallic acid equivalents per 100 g DW) and total anthocyanins (140-318 mg cyanidin-3-glucoside equivalents per 100 g DW), as well as being a good source of carotenoids. There was a higher positive correlation between the amounts of these compounds and the antioxidant activity of hydrophilic compared to lipophilic extracts. There were also significant differences in the level of bioactive compounds and antioxidant activities between different cultivars, production location and year of cultivation.

Determination Of Flavanones In Orange Juices Obtained From Different Sources By Hplc/dad.

Flavanones (hesperidin, naringenin, naringin, and poncirin) in industrial, hand-squeezed orange juices and from fresh-in-squeeze machines orange juices were determined by HPLC/DAD analysis using a previously described liquid-liquid extraction method. Method validation including the accuracy was performed by using recovery tests. Samples (36) collected from different Brazilian locations and brands were analyzed. Concentrations were determined using an external standard curve. The limits of detection (LOD) and the limits of quantification (LOQ) calculated were 0.0037, 1.87, 0.0147, and 0.0066 mg 100 g(-1) and 0.0089, 7.84, 0.0302, and 0.0200 mg 100 g(-1) for naringin, hesperidin, poncirin, and naringenin, respectively. The results demonstrated that hesperidin was present at the highest concentration levels, especially in the industrial orange juices. Its average content and concentration range were 69.85 and 18.80-139.00 mg 100 g(-1). The other flavanones showed the lowest concentration levels. The average contents and concentration ranges found were 0.019, 0.01-0.30, and 0.12 and 0.1-0.17, 0.13, and 0.01-0.36 mg 100 g(-1), respectively. The results were also evaluated using the principal component analysis (PCA) multivariate analysis technique which showed that poncirin, naringenin, and naringin were the principal elements that contributed to the variability in the sample concentrations.

In Vivo Determination Of The Volatile Metabolites Of Saprotroph Fungi By Comprehensive Two-dimensional Gas Chromatography.

In this work, we discuss the use of multi-way principal component analysis combined with comprehensive two-dimensional gas chromatography to study the volatile metabolites of the saprophytic fungus Memnoniella sp. isolated in vivo by headspace solid-phase microextraction. This fungus has been identified as having the ability to induce plant resistance against pathogens, possibly through its volatile metabolites. Adequate culture media was inoculated, and its headspace was then sampled with a solid-phase microextraction fiber and chromatographed every 24 h over seven days. The raw chromatogram processing using multi-way principal component analysis allowed the determination of the inoculation period, during which the concentration of volatile metabolites was maximized, as well as the discrimination of the appropriate peaks from the complex culture media background. Several volatile metabolites not previously described in the literature on biocontrol fungi were observed, as well as sesquiterpenes and aliphatic alcohols. These results stress that, due to the complexity of multidimensional chromatographic data, multivariate tools might be mandatory even for apparently trivial tasks, such as the determination of the temporal profile of metabolite production and extinction. However, when compared with conventional gas chromatography, the complex data processing yields a considerable improvement in the information obtained from the samples. This article is protected by copyright. All rights reserved.

Profile Of Phenolic Compounds Of Brazilian Virgin Olive Oils By Rapid Resolution Liquid Chromatography Coupled To Electrospray Ionisation Time-of-flight Mass Spectrometry (rrlc-esi-tof-ms).

In recent years, agronomical researchers began to cultivate several olive varieties in different regions of Brazil to produce virgin olive oil (VOO). Because there has been no reported data regarding the phenolic profile of the first Brazilian VOO, the aim of this work was to determine phenolic contents of these samples using rapid-resolution liquid chromatography coupled to electrospray ionisation time-of-flight mass spectrometry. 25 VOO samples from Arbequina, Koroneiki, Arbosana, Grappolo, Manzanilla, Coratina, Frantoio and MGS Mariense varieties from three different Brazilian states and two crops were analysed. It was possible to quantify 19 phenolic compounds belonging to different classes. The results indicated that Brazilian VOOs have high total phenolic content because the values were comparable with those from high-quality VOOs produced in other countries. VOOs from Coratina, Arbosana and Grappolo presented the highest total phenolic content. These data will be useful in the development and improvement of Brazilian VOO.

Classificação de água de coco processada e natural por meio de HCA, PCA e teores de íons metálicos determinados por ICP OES

Inductively Coupled Plasma Optical Emission Spectrometry was used to determine Ca, Mg, Mn, Fe, Zn and Cu in samples of processed and natural coconut water. The sample preparation consisted in a filtration step followed by a dilution. The analysis was made employing optimized instrumental parameters and the results were evaluated using methods of Pattern Recognition. The data showed common concentration values for the analytes present in processed and natural samples. Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) indicated that the samples of different kinds were statistically different when the concentrations of all the analytes were considered simultaneously.

Uso de perfis cromatográficos de voláteis de cafés arábicas torrados para a diferenciação das amostras segundo o sabor, o aroma e a qualidade global da bebida

In this work, the volatile chromatographic profiles of roasted Arabica coffees, previously analyzed for their sensorial attributes, were explored by principal component analysis. The volatile extraction technique used was the solid phase microextraction. The correlation optimized warping algorithm was used to align the gas chromatographic profiles. Fifty four compounds were found to be related to the sensorial attributes investigated. The volatiles pyrrole, 1-methyl-pyrrole, cyclopentanone, dihydro-2-methyl-3-furanone, furfural, 2-ethyl-5-methyl-pyrazine, 2-etenyl-n-methyl-pyrazine, 5-methyl-2-propionyl-furan compounds were important for the differentiation of coffee beverage according to the flavour, cleanliness and overall quality. Two figures of merit, sensitivity and specificity (or selectivity), were used to interpret the sensory attributes studied.

Experimento didático de quimiometria para análise exploratória de óleos vegetais comestíveis por espectroscopia no infravermelho médio e análise de componentes principais: um tutorial, parte I

This manuscript aims to show the basic concepts and practical application of Principal Component Analysis (PCA) as a tutorial, using Matlab or Octave computing environment for beginners, undergraduate and graduate students. As a practical example it is shown the exploratory analysis of edible vegetable oils by mid infrared spectroscopy.

Perfil sensorial de ovos de Páscoa

Easter egg is a popular chocolate-candy in egg form commercialized in Brazil during Easter time. In this research, Quantitative Descriptive Analysis was applied to select sensory attributes which best define the modifications in appearance, aroma, flavor and texture when cocoa butter equivalent (CBE) is added to Easter eggs. Samples with and without CBE were evaluated by a selected panel and fourteen attributes best describing similarities and differences between them, were defined. Terms definition, reference materials and a consensus ballot were developed. After a training period, panelists evaluated the samples in a Complete Block Design using a 9 cm unstructured scale. Principal Component Analysis, ANOVA and Tukey test (p<0.05) were applied to the data in order to select attributes which best discriminated and characterized the samples. Samples showed significant differences (p<0.05) in all attributes. Easter egg without CBE showed higher intensities (p<0.05) in relation to the following descriptors: brown color, characteristic aroma, cocoa mass aroma, cocoa butter aroma, characteristic flavor, cocoa mass flavor, hardness and brittleness.

Perfil sensorial de vinhos brancos varietais brasileiros através de análise descritiva quantitativa

Descriptive terminology and sensory profile of three varieties of brazilian varietal white wines (cultivars Riesling, Gewürztraminer and Chardonnay) were developed by a methodology based on the Quantitative Descriptive Analysis (QDA). The sensory panel consensually defined the sensory descriptors, their respective reference materials and the descriptive evaluation ballot. Ten individuals were selected as judges based on their discrimination, reproducibility and individual consensus with the sensory panel. Twelve descriptors were generated showing similarities and differences among the wine samples. Each descriptor was evaluated using a nine-centimeters non-structured scale with the intensity terms anchored at its ends. The collected data were analysed by ANOVA, Tukey test and Principal Component Analysis (PCA). The results showed a great difference within the sensory profile of Riesling and Gewürztraminer wines, whereas Chardonnay wines showed a lesser variation. PCA separated samples into two groups: a first group formed by wines higher in sweetness and fruitty flavor and aroma; and a second group of wines higher in sourness, adstringency, bitterness, alcoholic and fermented flavors.