Abstract
In this paper we introduce a bilinear repetitive process and present an iterative subspace algorithm for its identification. The advantage of the proposed approach is that it overcomes the "curse of dimensionality", a hurdle commonly encountered with classical bilinear subspace identification algorithms. Simulation results show that the algorithm converges quickly and provides new alternatives for modeling/identifying nonlinear repetitive processes.

Abstract
In this paper the nonparametric identification of state-space linear parameter-varying models with dynamic mapping between the scheduling signal and the model matrices is considered. Indeed, we are particularly interested on the problem of estimating a model using data generated from an LPV system with static dependence, which is however represented on a different state-basis from the one considered by the estimator.

Abstract
In this paper an identification method for statespace LPV models is presented. The method is based on a particular parameterization that can be written in linear regression form and enables model estimation to be handled using Least-Squares Support Vector Machine (LS-SVM). The regression form has a set of design variables that act as filter poles to the underlying basis functions. In order to preserve the meaning of the Kernel functions (crucial in the LS-SVM context), these are filtered by a 2D-system with the predictor dynamics. A data-driven, direct optimization based approach for tuning this filter is proposed. The method is assessed using a simulated example and the results obtained are twofold. First, in spite of the difficult nonlinearities involved, the nonparametric algorithm was able to learn the underlying dependencies on the scheduling signal. Second, a significant improvement in the performance of the proposed method is registered, if compared with the one achieved by placing the predictor poles at the origin of the complex plane, which is equivalent to considering an estimator based on an LPV auto-regressive structure.

Abstract
O recurso à modelação como metodologia de ensino é relevante no processo de recriação e simulação de fenómenos naturais, nomeadamente fenómenos geológicos e ambientais. Pretendendo-se verificar se o recurso à modelação é uma metodologia significativa no sucesso da aprendizagem dos estudantes de riscos naturais do ensino superior português, foram analisadas as fichas das unidades curriculares que abordam temáticas de riscos naturais nas universidades públicas portuguesas, tendo-se optado por selecionar apenas unidades curriculares do primeiro ciclo de estudos – licenciatura. No total foram analisadas, com recurso a uma grade de análise, oito fichas de unidades curriculares, ministradas em sete universidades públicas portuguesas. Asevidências encontradas indicam que nenhum dos documentos em análise contempla a utilização da modelação como metodologia de ensino de temáticas de riscos naturais, predominando um ensino essencialmente tradicional, sobressaindo a necessidade de intervenção ao nível das metodologias de ensino valorizando-se a manipulação e a exploração de modelos no ensino superior.

Abstract
Modern and historical buildings may show some degree of subsidence resulting from foundation deterioration and local geological conditions. Hence, buildings stability can be affected and restoration plans must be envisaged. Resistivity methods have been used to investigate local conditions, providing 3D images of the soil under man made structures and hence contributing to the delimitation of hazardous areas and pathologies. However these techniques require the deployment of a grid of electrodes, which can be difficult to accomplish because of physical limitations and of the buildings nature that cannot be damaged. To overcome these problems special arrays have been used (L, Corner, Square arrays, etc). Here in it is proposed to use the "Odd-Even Pole-Pole Array" to study the ground under a contemporary building and under a high historical value XIV century Abbey, both showing evidence of subsidence. Field data quality is also addressed and it is proposed to identify low quality data to be expunged so that modelling is improved. It is also shown how to estimate resistivity values from data quality tests, to carry out further zonation, locate hazardous areas and to enhance modelling.

Abstract
Model-based learning is a teaching methodology that facilitates the learning process through the construction of models, which represent the conceptual models taught in geosciences lessons, promoting the construction of students’ scientific knowledge and the development of a meaningful learning. It is crucial that teachers know how to apply it in schools in order to support students’ learning process, but also because models are important tools for dissemination of science concepts. Having this in mind, it becomes relevant, beyond the analysis of its importance for both teaching and disseminating geosciences in Portuguese high schools, to provide some guidelines and recommendations about the use of models in geosciences teaching, based on the literature, seeking to prepare teachers to apply the methodology in science lessons and for making them more informed about the importance of dissemination of science. To achieve this purpose, the attitudes of Portuguese high school students towards the importance of model-based learning in teaching and disseminating the dependence of earthquakes effects on soils and buildings were analysed. The data were collected through a scale for model evaluation named Seismological Models’ Evaluation Scale (SMES), applied to 126 students who participated in Faculty of Sciences’ Open Days to Schools. This instrument was validated by two experts in geosciences teaching, and its fidelity was also determined. © Springer International Publishing Switzerland 2016.