Behavior-based price discrimination in a multi-dimensional preferences market

NIPE WP 04/ 2016

Optimização do processo de recolha de resíduos: desenvolvimento de ferramentas de investigação operacional para o problema de orientação de equipas com multi-restrições

Tese de Doutoramento em Engenharia Industrial e de Sistemas.

Parallelization of kinetic Monte Carlo algorithm to simulate AL3Sc precipitation

The present paper reports the precipitation process of Al3Sc structures in an aluminum scandium alloy, which has been simulated with a synchronous parallel kinetic Monte Carlo (spkMC) algorithm. The spkMC implementation is based on the vacancy diffusion mechanism. To filter the raw data generated by the spkMC simulations, the density-based clustering with noise (DBSCAN) method has been employed. spkMC and DBSCAN algorithms were implemented in the C language and using MPI library. The simulations were conducted in the SeARCH cluster located at the University of Minho. The Al3Sc precipitation was successfully simulated at the atomistic scale with the spkMC. DBSCAN proved to be a valuable aid to identify the precipitates by performing a cluster analysis of the simulation results. The achieved simulations results are in good agreement with those reported in the literature under sequential kinetic Monte Carlo simulations (kMC). The parallel implementation of kMC has provided a 4x speedup over the sequential version.

An alternative educational indicator for classifying secondary schools in Portugal

The purpose of this paper aims at carrying out a study in the area of Statistics for classifying Portuguese Secondary Schools (both mainland and islands: “Azores” and “Madeira”), taking into account the results achieved by their students in both national examinations and internal assessment. The main according consists of identifying groups of schools with different performance levels by considering the sub-national public and private education systems’ as well as their respective geographic location. For this, we developed an alternative educational indicator for the so-called Secondary Education indicator rankings released since 2001 by the Portuguese media.

A new perspective of students allocation satisfaction in engineering courses in Portugal

Publicado em "AIP Conference Proceedings" Vol. 1648

The Data Mining applied for the prediction of highway roughness due to overloaded trucks

Currently, the quality of the Indonesian national road network is inadequate due to several constraints, including overcapacity and overloaded trucks. The high deterioration rate of the road infrastructure in developing countries along with major budgetary restrictions and high growth in traffic have led to an emerging need for improving the performance of the highway maintenance system. However, the high number of intervening factors and their complex effects require advanced tools to successfully solve this problem. The high learning capabilities of Data Mining (DM) are a powerful solution to this problem. In the past, these tools have been successfully applied to solve complex and multi-dimensional problems in various scientific fields. Therefore, it is expected that DM can be used to analyze the large amount of data regarding the pavement and traffic, identify the relationship between variables, and provide information regarding the prediction of the data. In this paper, we present a new approach to predict the International Roughness Index (IRI) of pavement based on DM techniques. DM was used to analyze the initial IRI data, including age, Equivalent Single Axle Load (ESAL), crack, potholes, rutting, and long cracks. This model was developed and verified using data from an Integrated Indonesia Road Management System (IIRMS) that was measured with the National Association of Australian State Road Authorities (NAASRA) roughness meter. The results of the proposed approach are compared with the IIRMS analytical model adapted to the IRI, and the advantages of the new approach are highlighted. We show that the novel data-driven model is able to learn (with high accuracy) the complex relationships between the IRI and the contributing factors of overloaded trucks

All different or all the same? Exploring the diversity of professional practices in Portuguese school psychology

"Published online: 29 March 2016"

Typologies of post-divorce coparenting and parental well-being, parenting quality and children’s psychological adjustment

First published online: 30 October 2015

Perfis de vinculação, coparentalidade e ajustamento familiar em pais recém-divorciados: diferenças no ajustamento psicológico

O objectivo deste estudo foi identificar, através de uma análise de cluster, perfis de regulação individualdiádica-sistémica em pais récem-divorciados (N=81) com base na vinculação, coparentalidade e ajustamentofamiliar. Três padrões de regulação foram identificados: regulado-seguro (RS), desregulado-ansioso (DA) e desregulado-evitante (DE). O grupo RS mostrou níveis mais elevados de segurança na vinculação, maior qualidade na coparentalidade e no ajustamento familiar que os grupos DA e DE. A coparentalidade e o funcionamento familiar foram as dimensões que melhor diferenciaram os clusters. Avaliando o ajustamento psicológico dos pais recém-divorciados em função dos perfis encontrados, o grupo RS apresentou maiores níveis de ajustamento psicológico do que os restantes dois grupos. Não foram encontradas diferenças no ajustamento psicológico entre os grupos DA e DE.

New integrative computational approaches unveil the Saccharomyces cerevisiae pheno-metabolomic fermentative profile and allow strain selection for winemaking

During must fermentation by Saccharomyces cerevisiae strains thousands of volatile aroma compounds are formed. The objective of the present work was to adapt computational approaches to analyze pheno-metabolomic diversity of a S. cerevisiae strain collection with different origins. Phenotypic and genetic characterization together with individual must fermentations were performed, and metabolites relevant to aromatic profiles were determined. Experimental results were projected onto a common coordinates system, revealing 17 statistical-relevant multi-dimensional modules, combining sets of most-correlated features of noteworthy biological importance. The present method allowed, as a breakthrough, to combine genetic, phenotypic and metabolomic data, which has not been possible so far due to difficulties in comparing different types of data. Therefore, the proposed computational approach revealed as successful to shed light into the holistic characterization of S. cerevisiae pheno-metabolome in must fermentative conditions. This will allow the identification of combined relevant features with application in selection of good winemaking strains.