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.

Discrimination of Brazilian propolis according to the seasoning using chemometrics and machine learning based on UV-Vis scanning data

Propolis is a chemically complex biomass produced by honeybees (Apis mellifera) from plant resins added of salivary enzymes, beeswax, and pollen. The biological activities described for propolis were also identified for donor plants resin, but a big challenge for the standardization of the chemical composition and biological effects of propolis remains on a better understanding of the influence of seasonality on the chemical constituents of that raw material. Since propolis quality depends, among other variables, on the local flora which is strongly influenced by (a)biotic factors over the seasons, to unravel the harvest season effect on the propolis chemical profile is an issue of recognized importance. For that, fast, cheap, and robust analytical techniques seem to be the best choice for large scale quality control processes in the most demanding markets, e.g., human health applications. For that, UV-Visible (UV-Vis) scanning spectrophotometry of hydroalcoholic extracts (HE) of seventy-three propolis samples, collected over the seasons in 2014 (summer, spring, autumn, and winter) and 2015 (summer and autumn) in Southern Brazil was adopted. Further machine learning and chemometrics techniques were applied to the UV-Vis dataset aiming to gain insights as to the seasonality effect on the claimed chemical heterogeneity of propolis samples determined by changes in the flora of the geographic region under study. Descriptive and classification models were built following a chemometric approach, i.e. principal component analysis (PCA) and hierarchical clustering analysis (HCA) supported by scripts written in the R language. The UV-Vis profiles associated with chemometric analysis allowed identifying a typical pattern in propolis samples collected in the summer. Importantly, the discrimination based on PCA could be improved by using the dataset of the fingerprint region of phenolic compounds ( = 280-400m), suggesting that besides the biological activities of those secondary metabolites, they also play a relevant role for the discrimination and classification of that complex matrix through bioinformatics tools. Finally, a series of machine learning approaches, e.g., partial least square-discriminant analysis (PLS-DA), k-Nearest Neighbors (kNN), and Decision Trees showed to be complementary to PCA and HCA, allowing to obtain relevant information as to the sample discrimination.

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

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.