Fractionation of hydro-ethanolic extracts from grape pomace through membrane processing: the effect of membrane and extracting media on process performance

The EM3E Master is an Education Programme supported by the European Commission, the European Membrane Society (EMS), the European Membrane House (EMH), and a large international network of industrial companies, research centers and universities

State detection in a financial portfolio: a self-organizing maps approach for financial time series

This study analyses financial data using the result characterization of a self-organized neural network model. The goal was prototyping a tool that may help an economist or a market analyst to analyse stock market series. To reach this goal, the tool shows economic dependencies and statistics measures over stock market series. The neural network SOM (self-organizing maps) model was used to ex-tract behavioural patterns of the data analysed. Based on this model, it was de-veloped an application to analyse financial data. This application uses a portfo-lio of correlated markets or inverse-correlated markets as input. After the anal-ysis with SOM, the result is represented by micro clusters that are organized by its behaviour tendency. During the study appeared the need of a better analysis for SOM algo-rithm results. This problem was solved with a cluster solution technique, which groups the micro clusters from SOM U-Matrix analyses. The study showed that the correlation and inverse-correlation markets projects multiple clusters of data. These clusters represent multiple trend states that may be useful for technical professionals.

Can the Yield Spread predict economic growth? An assessment for European countries

Double Degree. A Work Project presented as part of the requirements for the Award of a Masters Degree in Economics from the NOVA- School of Business and Economics and a Masters Degree in Management from Louvain School of Management

Search-for-yield in Portuguese fixed-income mutual funds and monetary policy

This paper studies the effects of monetary policy on mutual fund risk taking using a sample of Portuguese fixed-income mutual funds in the 2000-2012 period. Firstly I estimate time-varying measures of risk exposure (betas) for the individual funds, for the benchmark portfolio, as well as for a representative equally-weighted portfolio, through 24-month rolling regressions of a two-factor model with two systematic risk factors: interest rate risk (TERM) and default risk (DEF). Next, in the second phase, using the estimated betas, I try to understand what portion of the risk exposure is in excess of the benchmark (active risk) and how it relates to monetary policy proxies (one-month rate, Taylor residual, real rate and first principal component of a cross-section of government yields and rates). Using this methodology, I provide empirical evidence that Portuguese fixed-income mutual funds respond to accommodative monetary policy by significantly increasing exposure, in excess of their benchmarks, to default risk rate and slightly to interest risk rate as well. I also find that the increase in funds’ risk exposure to gain a boost in return (search-for-yield) is more pronounced following the 2007-2009 global financial crisis, indicating that the current historic low interest rates may incentivize excessive risk taking. My results suggest that monetary policy affects the risk appetite of non-bank financial intermediaries.

Saeta Yield utilities

Equity research report

Where@UM - Dependable organic radio maps

In the past decade, the research community has been dedicating considerable effort into indoor positioning systems based on Wi-Fi fingerprinting techniques, mainly due to their capability to exploit existing infrastructures. Crowdsourcing approaches, also known as organic, have been proposed recently to address the problem of creating and maintaining the corresponding radio maps. In these organic systems, the users of the system build the radio map themselves while using it to estimate their own position/location. However, most of these collaborative methods, proposed by several authors, assume that all the users are honest and committed to contribute to a good quality radio map. In this paper we assess the quality of a radio map built collaboratively and propose a method to classify the credibility of individual contributions and the reputation of individual users. Experimental results are presented for an organic indoor location system that has been used by more than one hundred users over a period of around 12 months.

Biomass production and essential oil yield from leaves, fine stems and resprouts using pruning the crown of Aniba canelilla (H.B.K.) (Lauraceae) in the Central Amazon

Aniba canelilla (H.B.K.) Mez. is a tree species from Amazon that produces essential oil. The oil extraction from its leaves and stems can be an alternative way to avoid the tree cutting for production of essential oil. The aim of this study was to analyse factors that may influence the essential oil production and the biomass of resprouts after pruning the leaves and stems of A. canelilla trees. The tree crowns were pruned in the wet season and after nine months the leaves and stems of the remaining crown and the resprouts were collected, in the dry season. The results showed that the essential oil yield and chemical composition differed among the stems, leaves and resprouts. The stems' essential oil production differed between the seasons and had a higher production in the resprouting stems than the old stems of the remaining crown. The production of essential oil and leaf biomass of resprouts were differently related to the canopy openness, indicating that light increases the production of the essential oil and decreases the biomass of resprouting leaves. This study revealed that plant organs differ in their essential oil production and that the canopy openness must be taken into account when pruning the A. canelilla tree crown in order to achieve higher oil productivity.

Rotary peeling yield of Schizolobium amazonicum (Leguminosae - Caesalpinioideae)

The tropical tree Schizolobium amazonicum is native from the Amazonian forest, naturally occurring in Brazil, Peru and Colombia. This work aimed to study the veneer yield made from this species. For this purpose, 50 logs from S. amazonicum were rotary peeled in a plywood industry installed in Brazilian Amazon region. The results indicated that S. amazonicum had a peeling yield similar or even higher than those usually obtained for species traditionally used for this purpose in Brazil, like those of Pinus and Eucalyptus. It was also observed that the dendrometric parameters of the log can be used to estimate the peeling yield in this species.

Yield of essential oil and safrole content based on fresh and dry biomass of long pepper in the Brazilian Amazon

Long pepper (Piper hispidinervum) is an Amazonian species of commercial interest due to the production of safrole. Drying long pepper biomass to extract safrole is a time consuming and costly process that can also result in the contamination of the material by microorganisms. The objective of this study was to analyze the yield of essential oil and safrole content of fresh and dried biomass of long pepper accessions maintained in the Active Germoplasm Bank of Embrapa Acre, in the state of Acre, Brazil, aiming at selecting genotypes with best performance on fresh biomass to recommend to the breeding program of the species. Yield of essential oil and safrole content were assessed in 15 long pepper accessions. The essential oil extraction was performed by hydrodistillation and analyzed by gas chromatography. A joint analysis of experiments was performed and the means of essential oil yield and safrole content for each biomass were compared by Student's t-test. There was variability in the essential oil yield and safrole content. There was no difference between the types of biomass for oil yield; however to the safrole content there was difference. Populations 9, 10, 12 and 15 had values of oil yield between 4.1 and 5.3%, and safrole content between 87.2 and 94.3%. The drying process does not interfere in oil productivity. These populations have potential for selection to the long pepper breeding program using oil extraction in the fresh biomass

The influence of kaolin application on key metabolic pathways associated with grape berry quality: a molecular and biochemical analysis

Dissertação de mestrado em Biologia Molecular, Biotecnologia e Bioempreendedorismo em Plantas

Biotechnological approaches for yield improvement of anticancer alkaloids in the plant Catharanthus roseus

Dissertação de mestrado em Biologia Molecular, Biotecnologia e Bioempreendedorismo em Plantas

An algorithmic approach to estimating the minimal number of periodic points for smooth self-maps of simply-connected manifolds

For a given self-map f of M, a closed smooth connected and simply-connected manifold of dimension m ≥ 4, we provide an algorithm for estimating the values of the topological invariant Dm r [f], which equals the minimal number of r-periodic points in the smooth homotopy class of f. Our results are based on the combinatorial scheme for computing Dm r [f] introduced by G. Graff and J. Jezierski [J. Fixed Point Theory Appl. 13 (2013), 63–84]. An open-source implementation of the algorithm programmed in C++ is publicly available at http://www.pawelpilarczyk.com/combtop/.

Improving diagnostic yield in obscure gastrointestinal bleeding - how virtual chromoendoscopy may be the answer

Background and aim: A significant proportion of patients presenting with obscure gastrointestinal bleeding (OGIB) have negative small bowel capsule endoscopy (SBCE) examinations, and yet remain at risk of rebleeding. We aimed to evaluate whether a second-look review of SBCE images using flexible spectral color enhancement (FICE) may improve the detection of potentially bleeding lesions. Materials and methods: This was a retrospective, single-center study including consecutive patients with OGIB subjected to SBCE, whose standard white light examination was nondiagnostic. Each SBCE was reviewed using FICE 1. New findings were labeled as either P1 or P2 lesions according to bleeding potential. Patients were followed up to assess the incidence of rebleeding. Results: A total of 42 consecutive patients were included. Sixteen patients (38%) experienced rebleeding after a mean follow-up of 26 months. Review of SBCE images using FICE 1 enabled the identification of previously unrecognized P2 lesions, mainly angioectasias, in nine patients (21%) and P1 lesions, mainly erosions, in 26 patients (62%). Among patients who experienced rebleeding, 13/16 (81%) were diagnosed with P1 lesions with FICE 1 (P=0.043), whereas 3/16 (19%) had confirmed nondiagnostic SBCE and only 1/16 (6%) had newly diagnosed P2 (plus P1) lesions. An alternative source of bleeding outside the small bowel was found in only 3/16 (19%) patients with rebleeding during the follow-up. Conclusion: In a significant proportion of patients with OGIB, FICE 1 may detect potentially bleeding lesions previously missed under conventional white light SBCE. Review of nondiagnostic SBCE with FICE 1 may be a valuable strategy to obviate the need for further investigations in patients with OGIB, particularly for those who experience rebleeding.

DESARROLLO DE APLICACIONES ESTADÍSTICAS PARA LA AGRICULTURA DE PRECISIÓN. STATISTICAL APPLICATIONS FOR PRECISION AGRICULTURE

A partir de las últimas décadas se ha impulsado el desarrollo y la utilización de los Sistemas de Información Geográficos (SIG) y los Sistemas de Posicionamiento Satelital (GPS) orientados a mejorar la eficiencia productiva de distintos sistemas de cultivos extensivos en términos agronómicos, económicos y ambientales. Estas nuevas tecnologías permiten medir variabilidad espacial de propiedades del sitio como conductividad eléctrica aparente y otros atributos del terreno así como el efecto de las mismas sobre la distribución espacial de los rendimientos. Luego, es posible aplicar el manejo sitio-específico en los lotes para mejorar la eficiencia en el uso de los insumos agroquímicos, la protección del medio ambiente y la sustentabilidad de la vida rural. En la actualidad, existe una oferta amplia de recursos tecnológicos propios de la agricultura de precisión para capturar variación espacial a través de los sitios dentro del terreno. El óptimo uso del gran volumen de datos derivado de maquinarias de agricultura de precisión depende fuertemente de las capacidades para explorar la información relativa a las complejas interacciones que subyacen los resultados productivos. La covariación espacial de las propiedades del sitio y el rendimiento de los cultivos ha sido estudiada a través de modelos geoestadísticos clásicos que se basan en la teoría de variables regionalizadas. Nuevos desarrollos de modelos estadísticos contemporáneos, entre los que se destacan los modelos lineales mixtos, constituyen herramientas prometedoras para el tratamiento de datos correlacionados espacialmente. Más aún, debido a la naturaleza multivariada de las múltiples variables registradas en cada sitio, las técnicas de análisis multivariado podrían aportar valiosa información para la visualización y explotación de datos georreferenciados. La comprensión de las bases agronómicas de las complejas interacciones que se producen a la escala de lotes en producción, es hoy posible con el uso de éstas nuevas tecnologías. Los objetivos del presente proyecto son: (l) desarrollar estrategias metodológicas basadas en la complementación de técnicas de análisis multivariados y geoestadísticas, para la clasificación de sitios intralotes y el estudio de interdependencias entre variables de sitio y rendimiento; (ll) proponer modelos mixtos alternativos, basados en funciones de correlación espacial de los términos de error que permitan explorar patrones de correlación espacial de los rendimientos intralotes y las propiedades del suelo en los sitios delimitados. From the last decades the use and development of Geographical Information Systems (GIS) and Satellite Positioning Systems (GPS) is highly promoted in cropping systems. Such technologies allow measuring spatial variability of site properties including electrical conductivity and others soil features as well as their impact on the spatial variability of yields. Therefore, site-specific management could be applied to improve the efficiency in the use of agrochemicals, the environmental protection, and the sustainability of the rural life. Currently, there is a wide offer of technological resources to capture spatial variation across sites within field. However, the optimum use of data coming from the precision agriculture machineries strongly depends on the capabilities to explore the information about the complex interactions underlying the productive outputs. The covariation between spatial soil properties and yields from georeferenced data has been treated in a graphical manner or with standard geostatistical approaches. New statistical modeling capabilities from the Mixed Linear Model framework are promising to deal with correlated data such those produced by the precision agriculture. Moreover, rescuing the multivariate nature of the multiple data collected at each site, several multivariate statistical approaches could be crucial tools for data analysis with georeferenced data. Understanding the basis of complex interactions at the scale of production field is now within reach the use of these new techniques. Our main objectives are: (1) to develop new statistical strategies, based on the complementarities of geostatistics and multivariate methods, useful to classify sites within field grown with grain crops and analyze the interrelationships of several soil and yield variables, (2) to propose mixed linear models to predict yield according spatial soil variability and to build contour maps to promote a more sustainable agriculture.