Abstract
This paper presents the timing analysis methodology developed in the European project P-SOCRATES (Parallel Software Framework for Time-Critical Many-core Systems). This timing analysis methodology is defined for parallel applications that must satisfy both performance and real-time requirements and are executed on modern many-core processor architectures. We discuss the motivation and objectives of the project, the timing analysis flow that we proposed, the tool that has been developed to automatize it, and finally we report on some of the preliminary results that we have obtained when applying this methodology to the three application use-cases of the project.

Abstract
The aim of this study is twofold: first, to present a survey of the actual and most advanced methods related to the use of unmanned aerial systems (UASs) that emerged in the past few years due to the technological advancements that allowed the miniaturization of components, leading to the availability of small-sized unmanned aerial vehicles (UAVs) equipped with Global Navigation Satellite Systems (GNSS) and high quality and cost-effective sensors; second, to advice the target audience - mostly farmers and foresters - how to choose the appropriate UAV and imaging sensor, as well as suitable approaches to get the expected and needed results of using technological tools to extract valuable information about agroforestry systems and its dynamics, according to their parcels' size and crop's types. Following this goal, this work goes beyond a survey regarding UAS and their applications, already made by several authors. It also provides recommendations on how to choose both the best sensor and UAV, in according with the required application. Moreover, it presents what can be done with the acquired sensors' data through theuse of methods, procedures, algorithms and arithmetic operations. Finally, some recent applications in the agroforestry research area are presented, regarding the main goal of each analysed studies, the used UAV, sensors, and the data processing stage to reach conclusions.

Abstract
In this work, we present a method to extract features from heart sound signals in order to enhance segmentation performance. The approach is data-driven, since the way features are extracted from the recorded signals is adapted to the data itself. The proposed method is based on the extraction of delay vectors, which are modeled with Gaussian mixture model priors, and an information-theoretic dimensionality reduction step which aims to maximize discrimination between delay vectors in different segments of the heart sound signal. We test our approach with heart sounds from the publicly available PhysioNet dataset showing an average F-1 score of 92.6% in detecting S-1 and S-2 sounds.

Abstract
This paper presents a context analysis methodology to improve the management of residential energy resources by making the decision making process adaptive to different contexts. A context analysis model is proposed and described, using a clustering process to group similar situations. Several clustering quality assessment indices, which support the decisions on how many clusters should be created in each run, are also considered, namely: the Calinski Harabasz, Davies Bouldin, Gap Value and Silhouette. Results show that the application of the proposed model allows to identify different contexts by finding patterns of devices' use and also to compare different optimal k criteria. The data used in this case study represents the energy consumption of a generic home during one year (2014) and features the measurements of several devices' consumption as well as of several contextual variables. The proposed method enhances the energy resource management through adaptation to different contexts. © 2017 IEEE.

Abstract
A Virtual Enterprise (VE) can be viewed as a project, with a lifecycle corresponding to the period between its creation and integration of the constituting elements until its dissolution, comprehending its operation and including its reconfigurations. The authors propose that the VE lifecycle is aligned and can be managed using the frameworks provided by several bodies of knowledge, such as the PMBoK Guide. In this paper the authors propose an alignment referential between the Project Management phases defined by PMBoK and management processes during the VE lifecycle. (C) 2017 The Authors. Published by Elsevier B.V.

Abstract
A cost-effective, accurate, and robust leak detection method is essential in gas network management in order to reduce inspection time and to increase reliability in the system. This work presents a model-based leakage detection method; the gas dynamics are described by a linearized system of partial differential equations that is further reduced to a one-dimensional spatial model. By using an electrical analogy, a pipeline can be represented by a two-port network, where mass flow behaves like current and pressure like voltage. Four transfer function quadripole models are then established to describe the gas pipeline dynamics, depending on the variables of interest at the pipeline boundaries. A leak detection method is devised by employing mass flow data at boundaries and pressure data at some point of the pipeline, as well as by assessing the effects of the leakage on the pressure and mass flow along the pipeline. A case study has been built from operational data supplied by REN Gasodutos (the Portuguese gas company) to show the advantages of the proposed models. © Springer International Publishing AG 2017.