Abstract
The assessment of contaminant dispersion in Controlled Dumps (CD) of Municipal Solid Waste (MSW) is possible through the combination of geophysical, geochemical and statistical methods. The methodology applied in this study will contribute to evaluate the environmental consequences of the Matosinhos CD (N Portugal), which is set in a granitic crystalline geological context with a permeability controlled by differential weathering which, in turn, is associated with fracturing. The statistical methods that we will describe are usually used in geochemical anomaly determination and were adapted to be used with resistivity data obtained from twenty-two 2D profiles performed around the CD. The data set was inverted and the results were processed and visualized by a 3D software application that we are currently developing. Groundwater samples were also collected in piezometers, upstream and downstream of the CD, with the aim of directly confirming the presence of contaminants indirectly detected by the geophysical and geostatistical methods described before. The combination of these different approaches allows, in our opinion, an improved approach towards the detection and delineation of contaminant plumes from these deposits.

Abstract
In this paper, we propose a multiobjective local search metaheuristic for a mean-risk multistage capacity investment problem with process flexibility, irreversibility, lumpiness and economies of scale in capacity costs. In each period, discrete decisions concerning the investment in capacity expansion, and continuous decisions concerning the utilization of the available capacity to satisfy demand are considered. We solve the capacity utilization problems with linear programming, in order to find the minimum capacity for each resource with the other resources remaining unchanged, this way providing information on the feasibility of the discrete investment decisions. Conditional value-at-risk is considered as a risk measure. Results of a computational study are presented, that show the approach is capable of obtaining high-quality approximations to the efficient sets, with a modest computational effort.

Abstract
This paper presents a new approach to incipient fault diagnosis in power transformers, based on the results of dissolved gas analysis. A set of autoassociative neural networks or autoencoders is trained, so that each becomes tuned with a particular fault mode or no fault condition. The scarce data available forms clusters that are densified using an Information Theoretic Mean Shift algorithm, allowing all real data to be used in the validation process. Then, a parallel model is built where the autoencoders compete with one another when a new input vector is entered and the closest recognition is taken as the diagnosis sought. A remarkable accuracy of 100% is achieved with this architecture, in a validation data set using all real information available.

Abstract
Power systems have been through deep changes in recent years, namely with the operation of competitive electricity markets in the scope and the increasingly intensive use of renewable energy sources and distributed generation. This requires new business models able to cope with the new opportunities that have emerged. Virtual Power Players (VPPs) are a new player type which allows aggregating a diversity of players (Distributed Generation (DG), Storage Agents (SA), Electrical Vehicles (V2G) and consumers), to facilitate their participation in the electricity markets and to provide a set of new services promoting generation and consumption efficiency, while improving players` benefits. A major task of VPPs is the remuneration of generation and services (maintenance, market operation costs and energy reserves), as well as charging energy consumption. This paper proposes a model to implement fair and strategic remuneration and tariff methodologies, able to allow efficient VPP operation and VPP goals accomplishment in the scope of electricity markets.

Abstract

Abstract
The Portuguese coastline, like many other worldwide coastlines, is often submitted to several types of extreme events resulting in erosion, thus, acquisition of high quality field measurements has become a common concern. The nearshore survey systems have been traditionally based on in situ measurements or in the use of satellite or aircraft mounted remote sensing systems. As an alternative, video-monitoring systems proved to be an economic and efficient way to collect useful and continuous data, and to document extreme events. In this context, is under development the project MoZCo (Advanced Methodologies and Techniques Development for Coastal Zone Monitoring), which intends to develop and implement monitoring techniques for the coastal zone based on a low cost video monitoring system. The pilot study area is Ofir beach (north of Portugal), a critical coastal area. In the beginning of this project (2010) a monitoring video station was developed, collecting snapshots and 10 minutes videos every hour. In order to process the data, several video image processing algorithms were implemented in Matlab (R), allowing achieve the main video-monitoring system products, such as, the shoreline detection. An algorithm based on image processing techniques was developed, using the HSV color space, the idea is to select a study and a sample area, containing pixels associated with dry and wet regions, over which a thresholding and some morphological operators are applied. After comparing the results with manual digitalization, promising results were achieved despite the method's simplicity, which is in continuous development in order to optimize the results.