Teacher placement report: investigation: action research project investigating methods for promoting creativity in double bass students attending a portuguese music school

The investigation which employed the action research method (qualitative analysis)was divided into four fases. In phases 1-3 the participants were six double bass students at Nossa Senhora do Cabo Music School. Pilot exercises in creativity were followed by broader and more ambitious projects. In phase 4 the techniques were tested and amplified during a summer course for twelve double bass students at Santa Cecilia College.

Probabilistic models for mechanical properties of prestressing strands

This study focus on the probabilistic modelling of mechanical properties of prestressing strands based on data collected from tensile tests carried out in Laboratório Nacional de Engenharia Civil (LNEC), Portugal, for certification purposes, and covers a period of about 9 years of production. The strands studied were produced by six manufacturers from four countries, namely Portugal, Spain, Italy and Thailand. Variability of the most important mechanical properties is examined and the results are compared with the recommendations of the Probabilistic Model Code, as well as the Eurocodes and earlier studies. The obtained results show a very low variability which, of course, benefits structural safety. Based on those results, probabilistic models for the most important mechanical properties of prestressing strands are proposed.

Probabilistic tsunami hazard in the Northeast Atlantic from near- and far-field tectonic sources

In this article, we present the first study on probabilistic tsunami hazard assessment for the Northeast (NE) Atlantic region related to earthquake sources. The methodology combines the probabilistic seismic hazard assessment, tsunami numerical modeling, and statistical approaches. We consider three main tsunamigenic areas, namely the Southwest Iberian Margin, the Gloria, and the Caribbean. For each tsunamigenic zone, we derive the annual recurrence rate for each magnitude range, from Mw 8.0 up to Mw 9.0, with a regular interval, using the Bayesian method, which incorporates seismic information from historical and instrumental catalogs. A numerical code, solving the shallow water equations, is employed to simulate the tsunami propagation and compute near shore wave heights. The probability of exceeding a specific tsunami hazard level during a given time period is calculated using the Poisson distribution. The results are presented in terms of the probability of exceedance of a given tsunami amplitude for 100- and 500-year return periods. The hazard level varies along the NE Atlantic coast, being maximum along the northern segment of the Morocco Atlantic coast, the southern Portuguese coast, and the Spanish coast of the Gulf of Cadiz. We find that the probability that a maximum wave height exceeds 1 m somewhere in the NE Atlantic region reaches 60 and 100 % for 100- and 500-year return periods, respectively. These probability values decrease, respectively, to about 15 and 50 % when considering the exceedance threshold of 5 m for the same return periods of 100 and 500 years.

Determinação dos valores de referência da excursão sistólica do plano do anel tricúspide (TAPSE) em crianças saudáveis

Mestrado em Tecnologia de Diagnóstico e Intervenção Cardiovascular - Área de especialização: Ultrassonografia cardiovascular

A MAP approach to evidence accumulation clustering

The Evidence Accumulation Clustering (EAC) paradigm is a clustering ensemble method which derives a consensus partition from a collection of base clusterings obtained using different algorithms. It collects from the partitions in the ensemble a set of pairwise observations about the co-occurrence of objects in a same cluster and it uses these co-occurrence statistics to derive a similarity matrix, referred to as co-association matrix. The Probabilistic Evidence Accumulation for Clustering Ensembles (PEACE) algorithm is a principled approach for the extraction of a consensus clustering from the observations encoded in the co-association matrix based on a probabilistic model for the co-association matrix parameterized by the unknown assignments of objects to clusters. In this paper we extend the PEACE algorithm by deriving a consensus solution according to a MAP approach with Dirichlet priors defined for the unknown probabilistic cluster assignments. In particular, we study the positive regularization effect of Dirichlet priors on the final consensus solution with both synthetic and real benchmark data.