Abstract
This paper proposes a linear programming problem to find the optimal planning and operation of aggregated distributed energy resources (DER), managed by an aggregator that participates in the day-ahead wholesale electricity market as a price-maker agent. The proposed model analyzes the impact of the size of the aggregated resources and gives the optimal planning and management of DER systems, and the corresponding energy transactions in the wholesale day-ahead market. The results suggest that when the aggregated resources are large enough, DER systems can achieve up to 32% extra economic benefits depending on the market share, compared with a business-as-usual approach (not implementing DER systems). © 2016 IEEE.

Abstract

Abstract
Information and knowledge can be seen as key resources for improving the internationalisation processes of small and medium-sized enterprises (SMEs). Collaboration has also been considered as an important facilitator of these processes, particularly by nurturing information and knowledge sharing. However, the current literature is unclear about the way SMEs can access information and assimilate knowledge in a collaborative network context, to support decision-making. This paper systematically reviews the literature, examining the role of information, knowledge and collaboration in internationalisation decisions of SMEs. To this end, 38 relevant journal articles were analysed, with the identification of some important issues, as well as gaps in the existing empirical knowledge. This analysis provided valuable input for the development of research suggestions and directions for future work in this area.

Abstract
Parkinson's disease (PD) is often responsible for difficulties in interacting with smartphones; however, research has not yet addressed these issues and how these challenge people with Parkinson's (PwP). This paper specifically investigates the symptoms and characteristics of PD that may influence the interaction with smartphones to then contribute in this direction. The research was based on a literature review of PD symptoms, eight semi-structured interviews with healthcare professionals and observations of PwP, and usability experiments with 39 PwP. Contributions include a list of PD symptoms that may influence the interaction with smartphones, a set of experimental results that evaluated the performance of four gestures tap, swipe, multiple-tap, and drag and 12 user interface design guidelines for creating smartphone user interfaces for PwP. Findings contribute to the work of researchers and practitioners' alike engaged in designing user interfaces for PwP or the broader area of inclusive design.

Abstract
Cardiotocography is a diagnostic exam performed from the 28th week of pregnancy that registers the fetus cardiac frequency and uterine contractions. From this exam results a cardiotocogram whose reading and observation of the patterns contained in it allow an evaluation of the baby's condition and the fetal vitality in the maternal womb. This work aims the creation of a classification model using Learning Algorithms/Data Mining using the tool Rapid Miner. The subject of study was a Data Set with information registered from a total of 2126 cardiotograms, with 23 attributes, properly classified by 3 specialized obstetricians as to the baby status, in three possible states, namely: N = Normal; S = Suspect; P = Pathologic. All models tested showed an overall accuracy greater than 80%. Therefore the usefulness of creating predictive models for the classification of this type of diagnosis is great.

Abstract
Stochastic search algorithms are black-box optimizer of an objective function. They have recently gained a lot of attention in operations research, machine learning and policy search of robot motor skills due to their ease of use and their generality. Yet, many stochastic search algorithms require relearning if the task or objective function changes slightly to adapt the solution to the new situation or the new context. In this paper, we consider the contextual stochastic search setup. Here, we want to find multiple good parameter vectors for multiple related tasks, where each task is described by a continuous context vector. Hence, the objective function might change slightly for each parameter vector evaluation of a task or context. Contextual algorithms have been investigated in the field of policy search, however, the search distribution typically uses a parametric model that is linear in the some hand-defined context features. Finding good context features is a challenging task, and hence, non-parametric methods are often preferred over their parametric counter-parts. In this paper, we propose a non-parametric contextual stochastic search algorithm that can learn a non-parametric search distribution for multiple tasks simultaneously. In difference to existing methods, our method can also learn a context dependent covariance matrix that guides the exploration of the search process. We illustrate its performance on several non-linear contextual tasks.