Application of Doehlert experimental design in the optimization of experimental variables for the Pseudozyma sp. (CCMB 306) and Pseudozyma sp. (CCMB 300) cell lysis

This study aimed to verify the influence of pH and temperature on the lysis of yeast using experimental design. In this study, the enzymatic extract containing β-1,3-glucanase and chitinase, obtained from the micro-organism Moniliophthora perniciosa, was used. The experiment showed that the best conditions for lysis of Pseudozyma sp. (CCMB 306) and Pseudozyma sp. (CCMB 300) by lytic enzyme were pH 4.9 at 37 ºC and pH 3.9 at 26.7 ºC, respectively. The lytic enzyme may be used for obtaining various biotechnology products from yeast.

Optimization of extrusion variables for the production of snacks from by-products of rice and soybean

This study aimed to define the process conditions to obtain snacks from the by-products of rice and soybean with physical characteristics suitable for marketing. Therefore, the effects of moisture and extrusion temperature on the expansion and color of the products obtained experimentally obtained were evaluated, and the proximate composition of the by-products and that of the snack with greater desirability were determined. Response surface methodology and rotational central composite design were used, and desirability test based on the regression models adjusted was applied. The most desirable snack, with the highest expansion index (3.39), specific volume (13.5 mL.g-1), and the chromaticity coordinate a* (2.79), was obtained under 12 g.100 g-1 moisture and 85ºC of temperature in the third zone of the extruder. The snack produced under these conditions attained content of protein and lipid content 41 and 64% higher than that of the traditional corn snack. It can be concluded that producing extruded snack made form a mixture of broken grains, rice bran, and soybean okara (81:9:10) is technologically feasible, enabling the development of a new product with good nutritional value that can improve the diet of children, the main consumers of this type of food.

Interdependence of Macroeconomics Variables, Stock Market Returns and Stock Market Volatility in Latin America

This thesis examines the interdependence of macroeconomic variables, stock market returns and stock market volatility in Latin America between 2000 and 2015. Argentina, Brazil, Chile, Colombia, Mexico and Peru were chosen as the sample markets, while inflation, interest rate, exchange rate, money supply, oil and gold were chosen as the sample macroeconomic variables. Bivariate VAR (1) model was applied to examine the mean return spillovers between the variables, whereas GARCH (1, 1) – BEKK model was applied to capture the volatility spillovers. The sample was divided into two smaller sub-periods, where the first sub-period covers from 2000 to 2007, and the second sub-period covers from 2007 to 2015. The empirical results report significant shock transmissions and volatility spillovers between inflation, interest rate, exchange rate, money supply, gold, oil and the selected markets, which suggests interdependence between the variables.

Effects of orange winemaking variables on antioxidant activity and bioactive compounds

AbstractAscorbic acid, carotenoids and polyphenols stand out among the orange juice natural antioxidants. The winemaking process affects their bioavailability and bioactivity. Antioxidant activities (AA) were estimated in different process conditions to asses those properties. The AA and their correlation with ascorbic acid, total phenolics and carotenoids content were calculated. The variables and levels analyzed were: pasteurized and natural must (PJ and NJ), pH 3.5 and 4.0 and fermentation temperatures at 10°C and 20°C. Statistically significant differences (α=0.05) were found among bioactive compounds concentrations. Antioxidant compounds concentration was higher in raw material than in orange wine. Juice pasteurization caused the major losses while subsequent vinification affects them to a lesser extent. Highest antioxidants retention was measured in wines from JN fermented at pH 3.5 and 10 °C (JN-3.5-10) followed by wines from JP and fermented at the same conditions (JP-3.5-10). AA determined by DPPH showed a positive and close correlation with FRAP, while ABTS showed a low correlation with former assays. Juice pasteurization process and fermentation temperature influenced bioactive compound reduction which correlates with the AA variation.

Visual Customer Segmentation and Data Exploration with the Self-Organizing Map

Advancements in information technology have made it possible for organizations to gather and store vast amounts of data of their customers. Information stored in databases can be highly valuable for organizations. However, analyzing large databases has proven to be difficult in practice. For companies in the retail industry, customer intelligence can be used to identify profitable customers, their characteristics, and behavior. By clustering customers into homogeneous groups, companies can more effectively manage their customer base and target profitable customer segments. This thesis will study the use of the self-organizing map (SOM) as a method for analyzing large customer datasets, clustering customers, and discovering information about customer behavior. Aim of the thesis is to find out whether the SOM could be a practical tool for retail companies to analyze their customer data.

Wood species identification by visual characterization of fibers

Kandidaatintyö tehtiin osana PulpVision-tutkimusprojektia, jonka tarkoituksena on kehittää kuvapohjaisia laskenta- ja luokittelumetodeja sellun laaduntarkkailuun paperin valmistuksessa. Tämän tutkimusprojektin osana on aiemmin kehitetty metodi, jolla etsittiin kaarevia rakenteita kuvista, ja tätä metodia hyödynnettiin kuitujen etsintään kuvista. Tätä metodia käytettiin lähtökohtana kandidaatintyölle. Työn tarkoituksena oli tutkia, voidaanko erilaisista kuitukuvista laskettujen piirteiden avulla tunnistaa kuvassa olevien kuitujen laji. Näissä kuitukuvissa oli kuituja neljästä eri puulajista ja yhdestä kasvista. Nämä lajit olivat akasia, koivu, mänty, eukalyptus ja vehnä. Jokaisesta lajista valittiin 100 kuitukuvaa ja nämä kuvat jaettiin kahteen ryhmään, joista ensimmäistä käytettiin opetusryhmänä ja toista testausryhmänä. Opetusryhmän avulla jokaiselle kuitulajille laskettiin näitä kuvaavia piirteitä, joiden avulla pyrittiin tunnistamaan testausryhmän kuvissa olevat kuitulajit. Nämä kuvat oli tuottanut CEMIS-Oulu (Center for Measurement and Information Systems), joka on mittaustekniikkaan keskittynyt yksikkö Oulun yliopistossa. Yksittäiselle opetusryhmän kuitukuvalle laskettiin keskiarvot ja keskihajonnat kolmesta eri piirteestä, jotka olivat pituus, leveys ja kaarevuus. Lisäksi laskettiin, kuinka monta kuitua kuvasta löydettiin. Näiden piirteiden eri yhdistelmien avulla testattiin tunnistamisen tarkkuutta käyttämällä k:n lähimmän naapurin menetelmää ja Naiivi Bayes -luokitinta testausryhmän kuville. Testeistä saatiin lupaavia tuloksia muun muassa pituuden ja leveyden keskiarvoja käytettäessä saavutettiin jopa noin 98 %:n tarkkuus molemmilla algoritmeilla. Tunnistuksessa kuitujen keskimäärinen pituus vaikutti olevan kuitukuvia parhaiten kuvaava piirre. Käytettyjen algoritmien välillä ei ollut suurta vaihtelua tarkkuudessa. Testeissä saatujen tulosten perusteella voidaan todeta, että kuitukuvien tunnistaminen on mahdollista. Testien perusteella kuitukuvista tarvitsee laskea vain kaksi piirrettä, joilla kuidut voidaan tunnistaa tarkasti. Käytetyt lajittelualgoritmit olivat hyvin yksinkertaisia, mutta ne toimivat testeissä hyvin.

Pretraining Convolutional Neural Networks for Visual Recognition

Convolutional Neural Networks (CNN) have become the state-of-the-art methods on many large scale visual recognition tasks. For a lot of practical applications, CNN architectures have a restrictive requirement: A huge amount of labeled data are needed for training. The idea of generative pretraining is to obtain initial weights of the network by training the network in a completely unsupervised way and then fine-tune the weights for the task at hand using supervised learning. In this thesis, a general introduction to Deep Neural Networks and algorithms are given and these methods are applied to classification tasks of handwritten digits and natural images for developing unsupervised feature learning. The goal of this thesis is to find out if the effect of pretraining is damped by recent practical advances in optimization and regularization of CNN. The experimental results show that pretraining is still a substantial regularizer, however, not a necessary step in training Convolutional Neural Networks with rectified activations. On handwritten digits, the proposed pretraining model achieved a classification accuracy comparable to the state-of-the-art methods.

Paramé mondain : Echo des plages de Paramé et de Rothéneuf, programme hebdomadaire du casino de Paramé, "puis sous-titre variables"

1912/06/16 (A1,N3).

Paramé mondain : Echo des plages de Paramé et de Rothéneuf, programme hebdomadaire du casino de Paramé, "puis sous-titre variables"

1913/02/02 (A2,N36).

Paramé mondain : Echo des plages de Paramé et de Rothéneuf, programme hebdomadaire du casino de Paramé, "puis sous-titre variables"

1894/07/22 (A1,N3).

Paramé mondain : Echo des plages de Paramé et de Rothéneuf, programme hebdomadaire du casino de Paramé, "puis sous-titre variables"

1912/10/06 (A1,N19).

Paramé mondain : Echo des plages de Paramé et de Rothéneuf, programme hebdomadaire du casino de Paramé, "puis sous-titre variables"

1895/09/15 (A2,N23).

Paramé mondain : Echo des plages de Paramé et de Rothéneuf, programme hebdomadaire du casino de Paramé, "puis sous-titre variables"

1912/09/01 (A1,N14).

Paramé mondain : Echo des plages de Paramé et de Rothéneuf, programme hebdomadaire du casino de Paramé, "puis sous-titre variables"

1912/08/11 (A1,N11).

Paramé mondain : Echo des plages de Paramé et de Rothéneuf, programme hebdomadaire du casino de Paramé, "puis sous-titre variables"

1912/09/08 (A1,N15).