Abstract
Electricity markets are becoming more competitive, to some extent due to the increasing number of players that have moved from other sectors to the power industry. This is essentially resulting from incentives provided to distributed generation. Relevant changes in this domain are still occurring, such as the extension of national and regional markets to continental scales. Decision support tools have thereby become essential to help electricity market players in their negotiation process. This paper presents a metalearner to support electricity market players in bidding definition. The proposed metalearner uses a dynamic artificial neural network to create its own output, taking advantage on several learning algorithms already implemented in ALBidS (Adaptive Learning strategic Bidding System). The proposed metalearner considers different weights for each strategy, based on their individual performance. The metalearner's performance is analysed in scenarios based on real electricity markets data using MASCEM (Multi-Agent Simulator for Competitive Electricity Markets). Results show that the proposed metalearner is able to provide higher profits to market players when compared to other current methodologies and that results improve over time, as consequence of its learning process.

Abstract
Patient-specific surgical predictions of Breast Conserving Therapy, through mechano-biological simulations, could inform the shared decision making process between clinicians and patients by enabling the impact of different surgical options to be visualised. We present an overview of our processing workflow that integrates MR images and three dimensional optical surface scans into a personalised model. Utilising an interactively generated surgical plan, a multi-scale open source finite element solver is employed to simulate breast deformity based on interrelated physiological and biomechanical processes that occur post surgery. Our outcome predictions, based on the pre-surgical imaging, were validated by comparing the simulated outcome with follow-up surface scans of four patients acquired 6 to 12 months post-surgery. A mean absolute surface distance of 3.3mm between the follow-up scan and the simulation was obtained.

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Abstract
This paper introduces the Disturbance Description Language (DDL), an XML dialect intended to describe a number of anomalous elements that can occur in a given scenario (including people, vehicles, fire or focus of pollution) and their respective properties, such as temporal availability, location, motion pattern and details for individual components, such as growth pattern and detectability. This dialect is part of a framework to support the execution of cooperative missions by a group of vehicles, in a simulated, augmented or real environment. An interface was incorporated into the framework, for creating and editing XML files following the defined schema. Once the information is correctly specified, it can be used in the framework, thus facilitating the process of environment disturbances specification and deployment. A survey answered by both practitioners and researchers shows that the degree of satisfaction with DDL is elevated (the overall evaluation of DDL achieved a 4.14 score (out of 5), with 81.1% of the answers being equal to or above 4); also, the usability of the interface was evaluated, having achieved a score of 83.6 in the SUS scale. These results imply that DDL is flexible enough to represent several types of disturbances, through a user-friendly interface.

Abstract
The overwhelming amount of news content published online every day has made it increasingly difficult to perform macro-level analysis of the news landscape. Visual exploration tools harness both computing power and human perception to assist in making sense of large data collections. In this paper, we employed three visualization tools to explore a dataset comprising one million articles published by news organizations and blogs. The visual analysis of the dataset revealed that 1) news and blog sources evaluate very differently the importance of similar events, granting them distinct amounts of coverage, 2) there are both dissimilarities and overlaps in the publication patterns of the two source types, and 3) the content's direction and diversity behave differently over time. Copyright © 2016 for the individual papers by the paper's authors.