Higher order model for analysis of magneto-electro-elastic plates

In this paper is presented an higher-order model for static and free vibration analyses of magneto-electro-elastic plates, wich allows the analysis of thin and thick plates, which allows the analysis of thin and thick plates. The finite element model is a single layer triangular plate/shell element with 24 degrees of fredom for the generalized mechanical displacements. Two degrees on freedom are introduced per each element layer, one corresponding to the electrical potential and the other for magnetic potential. Solutions are obtained for different laminations of the magneto-electro-elastic plate, as well as for the purely elastic plate as a special case.

Effects of vitamin K deficiency and its mechanisms in vertebrate's early development: zebrafish as a model

Disertação de mestrado, Ciências Biomédicas, Departamento de Ciências Biomédicas e Medicina, Universidade do Algarve, 2015

Population model of the recent subtidal invasion of Sargassum muticum in southern Portugal

Dissertação de Mestrado, Biologia Marinha, Especialização em Ecologia e Conservação, Faculdade de Ciências do Mar e do Ambiente, Universidade do Algarve, 2007

Assessing the effects of pseudo-absences on predictive distribution model performance

Modelling species distributions with presence data from atlases, museum collections and databases is challenging. In this paper, we compare seven procedures to generate pseudoabsence data, which in turn are used to generate GLM-logistic regressed models when reliable absence data are not available. We use pseudo-absences selected randomly or by means of presence-only methods (ENFA and MDE) to model the distribution of a threatened endemic Iberian moth species (Graellsia isabelae). The results show that the pseudo-absence selection method greatly influences the percentage of explained variability, the scores of the accuracy measures and, most importantly, the degree of constraint in the distribution estimated. As we extract pseudo-absences from environmental regions further from the optimum established by presence data, the models generated obtain better accuracy scores, and over-prediction increases. When variables other than environmental ones influence the distribution of the species (i.e., non-equilibrium state) and precise information on absences is non-existent, the random selection of pseudo-absences or their selection from environmental localities similar to those of species presence data generates the most constrained predictive distribution maps, because pseudo-absences can be located within environmentally suitable areas. This study showsthat ifwe do not have reliable absence data, the method of pseudo-absence selection strongly conditions the obtained model, generating different model predictions in the gradient between potential and realized distributions.