Abstract
Modern real-time embedded applications present high computation requirements which need to be realized within strict time constraints. The current trend towards parallel processing in the embedded domain allows providing higher processing power. However, in some embedded applications, the use of powerful enough multi-core processors, may not be possible due to energy, space or cost constraints. A solution for this problem is to extend the parallel execution of the applications, allowing them to distribute their workload among networked nodes, on peak situations, to remote neighbour nodes in the system. In this context, we present the Partitioned-Distributed- Deadline Monotonic Scheduling algorithm for fork-join parallel/distributed fixed-priority tasks. We study the problem of scheduling fork-join tasks that execute in a distributed system, where the inherent transmission delay of tasks must be considered and cannot be deemed negligible, as in the case of multicore systems. Our scheduling algorithm is shown to have a resource augmentation bound of 4, which implies that any task set that is feasible on m unit-speed processors and a single shared real-time network, can be scheduled by our algorithm on m processors and a single real-time network that are 4 times faster. We confirm through simulations our analytical results.

Abstract
Due to the recent developments in smart grid area, demand response (DR) based load pattern evaluations have gained more attention in the literature. The elasticity of load pattern related to the consumer preferences, the ratio of employing DR activities and the types of controllable loads affecting load pattern are prior topics to be evaluated in terms of better and more effective market regulation, especially in day-ahead and real time periods. In this study, an Adaptive Neuro-Fuzzy Inference System (ANFIS) based method combined with a General Algebraic Modeling System (GAMS)-based training pattern creation is presented in order to assess the effect of demand elasticity driven by DR activities in a day-ahead pool.

Abstract
Metabolomics aims at gathering the maximum amount of metabolic information for a total interpretation of biological systems. A process analytical technology pipeline, combining gas chromatography-mass spectrometry data preprocessing with multivariate analysis, was applied to a Port wine "forced ageing" process under different oxygen saturation regimes at 60 degrees C. It was found that extreme "forced ageing" conditions promote the occurrence of undesirable chemical reactions by production of dioxane and dioxolane isomers, furfural and 5-hydroxymethylfurfural, which affect the quality of the final product through the degradation of the wine aromatic profile, colour and taste. Also, were found high kinetical correlations between these key metabolites with benzaldehyde, sotolon, and many other metabolites that contribute for the final aromatic profile of the Port wine. The use of the kinetical correlations in time-dependent processes as wine ageing can further contribute to biological or chemical systems monitoring, new biomarkers discovery and metabolic network investigations.

Abstract
In this paper we present the overall study that includes the model developed (VS-Virtual Sign Model) and the experiences performed, with an automatic bidirectional sign language translator, between written and sign language, which is being supervised by the research group GILT (Graphics, interaction and learning technologies) under the frame of a national project called Virtual Sign (VS project). This project aims to develop and evaluate a model that facilitates access for the deaf and hearing impaired to digital content in particular the educational content and learning objects creating the conditions for greater social inclusion of deaf and hearing impaired people. Access to digital content will be supported by an automatic translator between Portuguese Writing (LEP) and Portuguese Sign Language (LGP) supported by an interaction model.

Abstract
Pattern Based GUI Testing (PBGT) is a new methodology that aims at systematizing and automating the GUI testing process. It is supported by a Tool (PBGT Tool) which provides an integrated modeling and testing environment that supports the crafting of test models based on UI Test Patterns, using a GUI modeling DSL called PARADIGM. The tool is freely available as an Eclipse plugin, developed on top of the Eclipse Modeling Framework. This paper presents PBGT Tool, which has been successfully used in several projects, and more recently at industry level.

Abstract
Massive scale data stores, which exhibit highly desirable scalability and availability properties are becoming pivotal systems in nowadays infrastructures. Scalability achieved by these data stores is anchored on data independence; there is no clear relationship between data, and atomic inter-node operations are not a concern. Such assumption over data allows aggressive data partitioning. In particular, data tables are horizontally partitioned and spread across nodes for load balancing. However, in current versions of these data stores, partitioning is either a manual process or automated but simply based on table size. We argue that size based partitioning does not lead to acceptable load balancing as it ignores data access patterns, namely data hotspots. Moreover, manual data partitioning is cumbersome and typically infeasible in large scale scenarios. In this paper we propose an automated table splitting mechanism that takes into account the system workload. We evaluate such mechanism showing that it simple, non-intrusive and effective. Copyright 2014 ACM.