Abstract
This work describes a preliminary methodological framework for the assessment of coastal volcanic rocky platforms in Pico Island (Azores). This study also deals with the importance of GIS-based mapping and unmanned aerial vehicles (UAVs) whichwere tested in two studied areas (Lajes and Madalena sites). This research gives an overview about the UAVs work flow and its application for photogrammetric assessment of coastal volcanic rocky platforms. The main purpose of this study is to explore the influence of basaltic lava coastal platforms and associated boulder strewn, which are well differentiated in Lajes and Madalena sites. In general, this research describes the image acquisition process and the UAV LiDAR technology and the aerial surveys to acquire the geodatabase which will enable the design of geoscience and geotechnical maps on coastal volcanic rock platforms. The images analysis will allow: (i) boulder geotechnical description and evaluation; (ii) shore and coastal platform assessment; (iii) propose monitoring coastal plans (short to long-term); (iv) rock boulders (basaltic blocks) mobility, movement trend, imbrication, indicating flow and source direction; (v) obtain DTM and contour lines to generate 3D models of the coastal area. All these data is crucial to understand the coastal dynamics of the sites and develop an applied mapping which couples GIS technologies and UAV based spatial platform. It was also developed a GNSS (Global Navigation Satellite System) datasheet which incorporates the entire database of ground control points, using high resolution GPS system for the georeferencing and differential correction process. This research presents the preliminary results of the coastal geotechnics mapping for volcanic environments. Furthermore, high resolution image acquisition, georeferencing and differential correction of the ground control points using high accuracy GPS, were also thorough analysed to improve the general methodology presented herein. Finally, it was proposed a preliminary integrated coastal engineering study for the rocky platform zoning and short to long-term monitoring in selected sites on Pico Island. The study was partially financed by FEDEREU COMPETE Funds and FCT (GeoBioTec|UA: UID/GEO/04035/2013) and LABCARGA|ISEP reequipment program: IPP-ISEP|PAD’2007/08. © 2016 Taylor & Francis Group, London.

Abstract
This work focus on the simulation of gas pipeline dynamic models in view to develop a leakage detection tool. The gas dynamics in the pipes is represented by a system of nonlinear partial differential equations. The linear partial differential equations is reduced to a transfer function model. Taking advantage of an electrical analogy, a pipeline can be represented by a two port network where gas mass flows behave like electrical currents and pressures like voltages. Thence, four transfer functions quadripole models are found to describe the gas pipeline dynamics, depending on the variable of interest at the boundaries. These models are simple enough to be used in the control and management of the network. These models have been validated using operational data and used to simulate a leakage. © Copyright 2016, PSIG, Inc.

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.