Abstract
We present and evaluate two different methods for building spatio-temporal features: a propositional method and a method based on propositionalisation of relational clauses. Our motivating application, a regression problem, requires the prediction of the fraction of each Portuguese parish burnt yearly by wildfires - a problem with a strong socio-economic and environmental impact in the country. We evaluate and compare how these methods perform individually and combined together. We successfully use under-sampling to deal with the high skew in the data set. We find that combining the approaches significantly improves the similar results obtained by each method individually.

Abstract
A considerable number of classic and modern control applications in distribution systems are developed aiming at make the most of the existing infrastructure. Under this perspective, investments postponement in grid upgrade appears as a good expectable result. The advent of smart grids enhances this expectation since it promises ubiquitous system monitoring, secure and trustworthy communication technologies and advanced control schemes. These features will allow the implementation of innovative algorithms for better operation and management of system assets. New possibilities arise for DSM, microgeneration control, electric vehicle charging and intelligent operation of energy storage devices. The scientific literature has countless examples that illustrate the benefits of such tools. A frequent outcome of these studies highlights the advantage of investments deferral in network reinforcement. This paper analyzes the combined effects of these control actions with the investments required for network upgrade, namely in lines and transformers reinforcement. Contrary to other research papers, our results demonstrate that investments deferral could lead frequently to higher costs in a long-term perspective.

Abstract

Abstract
The manufacturing industry is facing a technology paradigm change, as also captured in the Industrie 4.0 vision as the fourth industrial revolution. Future smart industries will require to optimize not only their own manufacturing processes but also the use of products and manufacturing resources, their maintenance and their recycling. In this context the strengths and weaknesses of two key concepts, namely Cyber-Physical Systems (CPS) and Intelligent Product (IP) are discussed, and it is suggested that an integration of these two approaches to meet the introduced emergent requirements is beneficial. The integration of CPS and IP is shown via two real-world industrial cases, covering different phases of the product life-cycle, namely the production, use and maintenance phases.

Abstract

Abstract
Earthworks are part of the construction of any type of ground transport infrastructure. In many road and railway infrastructures earthworks represent up to 30 to 50% of total cost of the construction. Moreover, earthworks involve the use of heavy mechanical equipment (e.g., excavators, dumper trucks, bulldozers and rollers) and repetitive activities that are responsible for large amounts of carbon emissions with negative impact to the environment. In this context, the optimization of earthworks construction activities is becoming increasingly important in recent years, while effective and practical integrated solutions have not been established so far. As such, this work introduces a novel optimization integrated system for earthwork tasks. In this integrated system, the optimization is carried out on various fronts, namely minimization of execution cost and duration, while attempting to reduce environmental impacts, such as carbon emissions. In order to achieve this, the integration of a wide array of technologies is required, so as to allow for a proper adjustment to reality. These range from evolutionary computation and data mining (i.e., soft computing), to geographic information systems and linear programming. The former are used firstly to provide realistic estimates of the productivity of available resources (i.e., equipment), and secondly to perform their optimal allocation throughout the construction site. Concurrently, the latter are employed for supporting the optimization of resource and material management, as well as of the trajectories associated with transportation of material from excavation to embankment fronts. The system has been validated using real-world data stemming from a Portuguese road construction site. Results show that the proposed system is very competitive when compared with the manual allocation methodologies currently used for the design and construction of earthworks. In fact, the system can output several different resource distribution solutions, which comprehend a trade-off between the referred optimization objectives, enhancing the flexibility of design by allowing the user to select the solution that best fits the project restrictions (e.g., deadline, budget). As such, the system is capable of allocating the available equipment in a way that maximizes its potential and productivity, while indirectly guaranteeing minimum carbon emissions in each possible solution. These results emphasize the importance of using this kind of decision support/optimization tools in the design and construction of earthworks. (C) 2016 The Authors. Published by Elsevier B.V.