Agostinho da Silva: a "vida conversável" como fundamento da educação

Tese de Doutoramento em Ciências da Educação (área de especialização em Filosofia da Educação).

Do pré-escolar para o 1º ano do ensino básico: "A Voz da Criança"

Dissertação de mestrado em Educação da Infância (área de especialização em Supervisão e Pedagogia da Infância)

Equations for defining the mismatch between students and school furniture: A systematic review

The present study reviews the scientific literature that describes the criteria equations for defining the mismatch between students and school furniture. This mismatch may negatively affect students' performance and comfort. Seventeen studies met the criteria of this review and twenty-one equations to test six furniture dimensions were identified. There was substantial mismatch between the relative heights of chairs and tables. Some systematic errors have been found during the application of the different equations, such as the assumption that students are sitting on chairs with a proper seat height. Only one study considered the cumulative fit. Finally, some equations are based on contradictory criteria and need to develop and evaluate new equations for these cases. Relevance to industry: Ultimately, the present work is a contribution toward improving the evaluation of school furniture and could be used to design ergonomic-oriented classroom furniture.

Solving large 0–1 multidimensional knapsack problems by a new simplified binary artificial fish swarm algorithm

The artificial fish swarm algorithm has recently been emerged in continuous global optimization. It uses points of a population in space to identify the position of fish in the school. Many real-world optimization problems are described by 0-1 multidimensional knapsack problems that are NP-hard. In the last decades several exact as well as heuristic methods have been proposed for solving these problems. In this paper, a new simpli ed binary version of the artificial fish swarm algorithm is presented, where a point/ fish is represented by a binary string of 0/1 bits. Trial points are created by using crossover and mutation in the different fi sh behavior that are randomly selected by using two user de ned probability values. In order to make the points feasible the presented algorithm uses a random heuristic drop item procedure followed by an add item procedure aiming to increase the profit throughout the adding of more items in the knapsack. A cyclic reinitialization of 50% of the population, and a simple local search that allows the progress of a small percentage of points towards optimality and after that refines the best point in the population greatly improve the quality of the solutions. The presented method is tested on a set of benchmark instances and a comparison with other methods available in literature is shown. The comparison shows that the proposed method can be an alternative method for solving these problems.