Learning roadmaps for Higher Education

An integrated platform for the support of teaching activities as been developed and deployed at the Aveiro Norte Polytechnic School of the University of Aveiro. In this paper we present an approach to Learning Roadmaps for Higher Education based on this platform. The aprend.e platform – Electronic Integrated System for Learning and Training - has at its core a Learning Management System with a number of plugins. It represents a new challenge for the University of Aveiro for higher education and is already being at its core is the concept of learning roadmaps that act upon two fundamental axes: education and learning. For the teachers, it aims at becoming a self-supporting tool that stimulates the organization and management of the course materials (lectures, presentations, multimedia content, and evaluation materials, amongst others). For the students, the learning roadmap aims at promoting self-study and supervised study, endowing the pupil with the capabilities to find the relevant information and to capture the concepts in the study materials. The outcome will be a stimulating learning process together with an organized management of those materials

La guerra en las sociedades primitivas. El caso de Irlanda céltica a través de sus mitos

En este artículo, analizaremos las causas que originan la guerra entre las sociedades de jefatura y las intentaremos aplicar sobre la sociedad celta y más concretamente en la sociedad de la Irlanda antigua. Para este tipo de análisis y su aplicación utilizaremos uno de tantos personajes heroicos: Cuchulain y sus ciclo mitológico e intentaremos saber sí un mito nos da la suficiente información para aplicar el estudio sobre los fundamentos de la guerra primitiva

eABC: scientific publications bibliografic archive

The paper intends to present eABC – Scientific Publications Bibliographic Archive of University of Aveiro’. It describes the motivation that induced its implementation, advantages for users and for all those whom the system serves. Some of the systems functionalities will be presented, along with a description on how to use them. Finally, the current status of the system - as it is being used by the University of Aveiro - will be presented, with the addition of an explanation on how this system helps in the creation of mechanisms that enable the adaptability and flexibility of systems to improve the scientific community dynamics

Effects of the Tax on Retail Sales of Some Fuels on a regional economy: a computable general equilibrium approach

This paper simulates the effects on the economy of Extremadura that are produced by a new tax on retail sales of some fuels. A computable general equilibrium model involving various labour market scenarios is employed as a modelling framework. Model parameters are obtained by calibration, using a social accounting matrix for Extremadura updated to the year 2000. Further, we also include an additional simulation in which a hypothetical regional tax rate, to finance environmental policies, is considered. This second simulation assumes constant fiscal revenues. The results of the first simulation show that the effects of this tax are modest. The simulation shows household welfare losses, decreasing activity levels and generalised price reductions, except in production sectors more directly linked to the oil products sector. In addition, we also observe that this hypothetical additional regional fuel tax rate would reinforce the effects produced by the national tax rate.Tax on retail sales of some fuels, computable general equilibrium models, social accounting matrices, fiscal policy.

An enhanced classifier system for autonomous robot navigation in dynamic environments

In many cases, a real robot application requires the navigation in dynamic environments. The navigation problem involves two main tasks: to avoid obstacles and to reach a goal. Generally, this problem could be faced considering reactions and sequences of actions. For solving the navigation problem a complete controller, including actions and reactions, is needed. Machine learning techniques has been applied to learn these controllers. Classifier Systems (CS) have proven their ability of continuos learning in these domains. However, CS have some problems in reactive systems. In this paper, a modified CS is proposed to overcome these problems. Two special mechanisms are included in the developed CS to allow the learning of both reactions and sequences of actions. The learning process has been divided in two main tasks: first, the discrimination between a predefined set of rules and second, the discovery of new rules to obtain a successful operation in dynamic environments. Different experiments have been carried out using a mini-robot Khepera to find a generalised solution. The results show the ability of the system to continuous learning and adaptation to new situations.Publicad

Hierarchical genetic algorithms for composite laminate panels stress optimisation

IEEE International Conference on Systems, Man, and Cybernetics. Tokyo, 12-15 October 1999.Genetic algorithms (GAs) have demonstrated to be a powerful technique for solving optimisation problems. In this article, the problem of optimising the number of plies and their stacking sequence in the design of laminated composite panels is considered. This problem has special features that makes it different from traditional problems in which GAs have been applied, which make the problem a multiobjective optimisation one. Symmetry and equilibrium constraints have also been included in the solution. A modification of the canonical GA is needed and a new perspective for solving this problem by using GA techniques is introduced

Hydroelectric power plant management relying on neural networks and expert system integration

The use of Neural Networks (NN) is a novel approach that can help in taking decisions when integrated in a more general system, in particular with expert systems. In this paper, an architecture for the management of hydroelectric power plants is introduced. This relies on monitoring a large number of signals, representing the technical parameters of the real plant. The general architecture is composed of an Expert System and two NN modules: Acoustic Prediction (NNAP) and Predictive Maintenance (NNPM). The NNAP is based on Kohonen Learning Vector Quantization (LVQ) Networks in order to distinguish the sounds emitted by electricity-generating machine groups. The NNPM uses an ART-MAP to identify different situations from the plant state variables, in order to prevent future malfunctions. In addition, a special process to generate a complete training set has been designed for the ART-MAP module. This process has been developed to deal with the absence of data about abnormal plant situations, and is based on neural nets trained with the backpropagation algorithm.Publicad