Abstract
Operation of distributed energy resources is taking importance nowadays. This paper proposes an optimal planning and operation model of distributed energy resources in a district taking into account the mobility of consumers using conventional fuel vehicles (FV) or electric vehicles (EV). The stochastic model considers the uncertainty of the type of vehicle, availability and distance traveled, and then it manages the available resources to obtain the maximum benefit from the grid. Results show that the EVs assist to achieve greater benefits of the distributed resources. Moreover, the costs per driven km are mainly independent of the type of vehicle considered. © 2015 IEEE.

Abstract
The main goal of this work is to produce machine learning models that predict the outcome of a mammography from a reduced set of annotated mammography findings. In the study we used a dataset consisting of 348 consecutive breast masses that underwent image guided core biopsy performed between October 2005 and December 2007 on 328 female subjects. We applied various algorithms with parameter variation to learn from the data. The tasks were to predict mass density and to predict malignancy. The best classifier that predicts mass density is based on a support vector machine and has accuracy of 81.3%. The expert correctly annotated 70% of the mass densities. The best classifier that predicts malignancy is also based on a support vector machine and has accuracy of 85.6%, with a positive predictive value of 85%. One important contribution of this work is that our model can predict malignancy in the absence of the mass density attribute, since we can fill up this attribute using our mass density predictor.

Abstract
Learning through games is increasingly gaining acceptance as a valuable training tool within the education and training community due to its simplicity, cost-effectiveness and essentially because most people prefer playing over learning. However, the use of games by students brings additional challenges regarding the design of games and their adoption in different learning, academic and interdisciplinary contexts, where issues such as planning, teachers and students participation have an important role in the success of contextual learning initiatives. This paper introduces a novel development framework and a learning process called WalkAbout for contextual learning mobile game systems that enables learners to practice and enhance 21st century skills, while generating and playing mobile contextual games. In our research, we investigate the main issues regarding the process of creating contextual mobile games and we detail the adopted methodology used in the design and implementation process of the WalkAbout framework. Finally, in order to preliminarily validate the platform and adopted methodology, we present and discuss the main results obtained after the games development, by looking at the potential of our design approach, the software framework and the learning experience that was offered to the students.

Abstract
Energy resource scheduling is becoming increasingly important, as the use of distributed resources is intensified and of massive electric vehicle is envisaged. The present paper proposes a methodology for day-ahead energy resource scheduling for smart grids considering the intensive use of distributed generation and Vehicle-to-Grid (V2G). This method considers that the energy resources are managed by a Virtual Power Player (VPP) which established contracts with their owners. It takes into account these contracts, the users' requirements subjected to the VPP, and several discharge price steps. The full AC power flow calculation included in the model takes into account network constraints. The influence of the successive day requirements on the day-ahead optimal solution is discussed and considered in the proposed model. A case study with a 33-bus distribution network and V2G is used to illustrate the good performance of the proposed method. © 2015 IEEE.

Abstract
Objectives/Hypothesis. This article aims to establish correlations between acoustic and audio-perceptual measures using the GRBAS scale with respect to four different voice analysis software programs. Study Design. Exploratory, transversal. Methods. A total of 90 voice records were collected and analyzed with the Dr. Speech (Tiger Electronics, Seattle, WA), Multidimensional Voice Program (Kay Elemetrics, NJ, USA), PRAAT (University of Amsterdam, The Netherlands), and Voice Studio (Seegnal, Oporto, Portugal) software programs. The acoustic measures were correlated to the audio-perceptual parameters of the GRBAS and rated by 10 experts. Results. The predictive value of the acoustic measurements related to the audio-perceptual parameters exhibited magnitudes ranging from weak (R-a(2) = 0.17) to moderate (R-a(2) = 0.71). The parameter exhibiting the highest correlation magnitude is B (Breathiness), whereas the weaker correlation magnitudes were found to be for A (Asthenia) and S (Strain). The acoustic measures with stronger predictive values were local Shimmer, harmonics-to-noise ratio, APQ5 shimmer, and PPQ5 jitter, with different magnitudes for each one of the studied software programs. Conclusions. Some acoustic measures are pointed as significant predictors of GRBAS parameters, but they differ among software programs. B (Breathiness) was the parameter exhibiting the highest correlation magnitude.

Abstract
CRDTs are distributed data types that make eventual consistency of a distributed object possible and non ad-hoc. Specifically, state-based CRDTs ensure convergence through disseminating the entire state, that may be large, and merging it to other replicas; whereas operation-based CRDTs disseminate operations (i.e., small states) assuming an exactly-once reliable dissemination layer. We introduce Delta State Conflict-Free Replicated Datatypes (d-CRDT) that can achieve the best of both worlds: small messages with an incremental nature, disseminated over unreliable communication channels. This is achieved by defining d-mutators to return a delta-state, typically with a much smaller size than the full state, that is joined to both: local and remote states. We introduce the d-CRDT framework, and we explain it through establishing a correspondence to current state-based CRDTs. In addition, we present an anti-entropy algorithm that ensures causal consistency, and two d-CRDT specifications of well-known replicated datatypes.