Abstract
Hybridisation is a systematic process along which the characteristic features of hybrid logic, both at the syntactic and the semantic levels, are developed on top of an arbitrary logic framed as an institution. It also captures the construction of first-order encodings of such hybridised institutions into theories in first-order logic. The method was originally developed to build suitable logics for the specification of reconfigurable software systems on top of whatever logic is used to describe local requirements of each system’s configuration. Hybridisation has, however, a broader scope, providing a fresh example of yet another development in combining and reusing logics driven by a problem from Computer Science. This paper offers an overview of this method, proposes some new extensions, namely the introduction of full quantification leading to the specification of dynamic modalities, and exemplifies its potential through a didactical application. It is discussed how hybridisation can be successfully used in a formal specification course in which students progress from equational to hybrid specifications in a uniform setting, integrating paradigms, combining data and behaviour, and dealing appropriately with systems evolution and reconfiguration. © Springer International Publishing Switzerland 2016.

Abstract
This paper presents how the ICT infrastructure developed in the European ENCOURAGE project, centered around a message oriented middleware, enabled energy savings in buildings and households. The components of the middleware, as well as the supervisory control strategy, are overviewed, to support the presentation of the results and how they could be achieved. The main results are presented on three of the pilots of the project, a first one consisting of a single household, a second one of a residential neighborhood, and a third one in a university campus. © 2016 IEEE.

Abstract
Artificial Intelligence (AI) methods contribute to the construction of systems where there is a need to automate the tasks. They are typically used for problems that have a large response time, or when a mathematical method cannot be used to solve the problem. However, the application of AI brings an added complexity to the development of such applications. AI has been frequently applied in the power systems field, namely in Electricity Markets (EM). In this area, AI applications are essentially used to forecast /estimate the prices of electricity or to search for the best opportunity to sell the product. This paper proposes a clustering methodology that is combined with fuzzy logic in order to perform the estimation of EM prices. The proposed method is based on the application of a clustering methodology that groups historic energy contracts according to their prices' similarity. The optimal number of groups is automatically calculated taking into account the preference for the balance between the estimation error and the number of groups. The centroids of each cluster are used to define a dynamic fuzzy variable that approximates the tendency of contracts' history. The resulting fuzzy variable allows estimating expected prices for contracts instantaneously and approximating missing values in the historic contracts.

Abstract
Optics is probably one on the most exciting topics in physics. However, it also contains some of the less understood phenomena by students - the light spectra obtained from the diffraction of light. The experimental study of light spectra for studying radiating bodies, usually requests sophisticated and expensive equipment that is not normaly affordable for schools, and only a few teachers know how to measure the wavelength of light in a spectrum. In this work we present a simple and inexpensive setup, with enough accuracy for measuring light spectra to be used both in physics and chemistry classes. We show how freeware software Tracker, commonly used for teaching mechanics, can serve to measure wavelengths with about 2 nm of resolution. Several approaches to the calibration of different setups are also provided, depending on the degree of accuracy demanded. © 2016 IOP Publishing Ltd.

Abstract
This paper addresses the problem of decision making in Unit Commitment in systems with a significant penetration of wind power. Traditional approaches to Unit Commitment are inadequate to fully deal with the uncertainties associated to wind, represented by scenarios of forecasted wind power qualified by probabilities. Departing from a critique of planning paradigms, the paper argues that a stochastic programming approach, while a step in the good direction, is insufficient to model all aspects of the decision process and therefore proposes the adoption of models based on a Risk Analysis paradigm. A case study is worked out reinforcing this perspective. In a multi-objective context, the properties of the cost vs. risk Pareto-optimal fronts are analyzed, where risk may be represented by aversion to a worst scenario or a worst event. It is shown that the Pareto-optimal front may not be convex, which precludes a simplistic use of tradeoff concepts. It is also shown that decisions based on stochastic programming may in fact put the system at risk. An evaluation of risk levels and cost of hedging against undesired events is proposed as the paradigm to be followed in Unit Commitment decision making. (C) 2016 Published by Elsevier Ltd.

Abstract