Abstract
In the last decades, dynamic logics have been used in different domains as a suitable formalism to reason about and specify a wide range of systems. On the other hand, logics with many-valued semantics are emerging as an interesting tool to handle devices and scenarios where uncertainty is a prime concern. This paper contributes towards the combination of these two aspects through the development of a method for the systematic construction of many-valued dynamic logics. Technically, the method is parameterised by an action lattice that defines both the computational paradigm and the truth space (corresponding to the underlying Kleene algebra and residuated lattices, respectively).

Abstract
The increasing penetration of renewable generation brings a significant escalation of intermittency to the power and energy system. This variability requires a new degree of flexibility from the whole system. The active participation of small and medium players becomes essential in this context. This is only possible by using adequate forecasting techniques applied both to the consumption and to generation. However, the large number of incontrollable factors, such as the presence of consumers in the building, the luminosity, or external temperature, makes the forecasting of energy consumption an arduous task. This paper addresses the electrical energy consumption forecasting problem, by studying the correlation between the solar radiation and the electrical consumption of lights. This study is performed by means of three forecasting methods, namely a multi-layer perceptron artificial neural network, a support vector regression method, and a linear regression method. The performed studies are analyzed using data gathered from a real installation - campus of the Polytechnic of Porto, in real time. © 2016 IEEE.

Abstract
Information and Communication Technologies (ICT) are part of the day to day activities of a large part of world population, however its use involves a growing generation of electronic waste (ewaste). Due to the increasing technological innovation, it occurs that in a short time, the products become obsolete and have their life cycle reduced. The article aims to present the development, verification and validation of models of computational simulation for assessment of environmental and financial impacts caused by the extension of the life cycle of personal computers (PC) through their remanufacturing. For the system modeling the System Dynamics theory was used. Results generated by the simulation model, show that the remanufacturing is a viable alternative for the reutilization of discarded computers and that it is possible, in advance, to discuss, assess and decide necessary measures for a better financial and environmental performance in the acquisition and use of ICT.

Abstract
Determining valuable data among large volumes of data is one of the main challenges in Big Data. We aim to extract knowledge from these sources using a Hierarchical Multi-Label Classification process called Semantic HMC. This process automatically learns a label hierarchy and classifies items from very large data sources. Five steps compose the Semantic HMC process: Indexation, Vectorization, Hierarchization, Resolution and Realization. The first three steps construct automatically the label hierarchy from data sources. The last two steps classify new items according to the label hierarchy. This paper focuses in the last two steps and presents a new highly scalable process to classify items from huge sets of unstructured text by using ontologies and rule-based reasoning. The process is implemented in a scalable and distributed platform to process Big Data and some results are discussed. © 2016 ACM.

Abstract
This paper focuses on the design of high quality spiral resonators for maximising wireless power transfer efficiency between an AUV and an underwater docking station. By using 3D electromagnetic simulations and numerical analysis, the relevant parameters for quality factor computation are extracted. The impact of different variables on a spiral resonator's quality factor is assessed, allowing to conclude on the optimum design parameters to achieve optimum efficiency on the power transmission through magnetic coupling. This work will contribute to enable the development future AUV wireless charging systems, which will allow for an improvement of AUV's range and endurance while ensuring lower operational costs. © 2016 IEEE.

Abstract
The design, manufacture and operational characteristics (e.g., yield, performance, and reliability) of modern electronic integrated systems exhibit extreme levels of complexity that cannot be easily modelled or predicted. Different mathematical methodologies have been explored to address this issue. Monte Carlo simulation is the most widely employed and straightforward approach to evaluate the circuits' performance statistics. However, the high number of trial cases and the long simulations times required to obtain results for complex circuits with a ppm resolution, lead to very long analysis times. The present work addresses the evaluation of alternative statistical inference methodologies which allow obtaining similar results departing from a smaller dimension data set of Monte Carlo simulations from which the overall population is estimated. These methodologies include the use of Bayesian inference, Expectation-inimization, and Kolmogorov-Smirnov tests. Results are presented which show the validity of these approaches.