Abstract
The use of agile methodologies like SCRUM is seen by companies in the software engineering field as a strategic necessity for their competitiveness, which makes them more reactive and dynamic in an increasingly demanding and competitive international market. One of the critical factors in the implementation of a SCRUM environment is the set-up of teams that are simultaneously homogeneous and composed of the best collaborators for each SCRUM role. In this sense, this study describes the modeling process and presents the implementation of a decision support system that can contribute to improving the process of assigning an agile team simultaneously considering the technical and social skills of employees. The results of the study allowed testing the application considering different competencies associated to each Agile position, the impact that the attribution process suffers from oscillations in the process of evaluation and self-evaluation, and the impact in terms of the performance of the inclusion of new collaborators and criteria comparison.

Abstract
If done properly, physical exercise can help maintain fitness and health. The benefits of physical exercise could be increased with real time monitoring by measuring physical exercise intensity, which refers to how hard it is for a person to perform a specific task. This parameter can be estimated using various sensors, including contactless technology. Physical exercise intensity is usually synchronous to heart rate; therefore, if we measure heart rate, we can define a particular level of physical exercise. In this paper, we proposed a Convolutional Neural Network (CNN) to classify physical exercise intensity based on the analysis of facial images extracted from a video collected during sub-maximal exercises in a stationary bicycle, according to standard protocol. The time slots of the video used to extract the frames were determined by heart rate. We tested different CNN models using as input parameters the individual color components and grayscale images. The experiments were carried out separately with various numbers of classes. The ground truth level for each class was defined by the heart rate. The dataset was prepared to classify the physical exercise intensity into two, three, and four classes. For each color model a CNN was trained and tested. The model performance was presented using confusion matrix as metrics for each case. The most significant color channel in terms of accuracy was Green. The average model accuracy was 100%, 99% and 96%, for two, three and four classes classification, respectively. © 2019, Springer Nature Switzerland AG.

Abstract
Vertical load is the power flow between electrical transmission and distribution networks. In the past, large-scale generators connected to transmission systems supplied consumers connected to lower voltage levels across distribution grids. Thus, vertical loads tended to be downward-oriented. This paper presents a spatiotemporal distributed energy resources (DER) diffusion model to analyze vertical load uncertainty resulting from different DER diffusion process representations currently used in the industry and academia. Network planners and operators can use such model to understand the long-term evolution of load at the T/D boundary. The proposal is applied to the Portuguese power system, combining, as first of its kind, highly granulated population census with georeferenced transmission and distribution network datasets. This application analyzes the 20-year evolution of such vertical load flows at the transmission-distribution boundary under a strong uptake of DER embodied in lower voltage levels in Portugal. © 2019 IEEE.

Abstract

Abstract
Citation analysis has been used to compare researchers, fields, institutions and countries. However, not much has been done to compare citations of papers belonging to different databases and published in different years. This comparison could play a relevant role in many systematic literature reviews concerned with the growth, development, and changes of a particular scientific subject. This study aims to examine whether we can use the percentile approach to compare the number of citations from papers in different databases. We argue that this method can convert citations from different databases when there are same articles belonging to more than one database. We apply the method on Thomson Reuters' Web of Science and Elsevier's Scopus databases because they are the leading databases of scholarly impact. In this study we use two different Scopus subject area: Engineering - Industrial and Manufacturing Engineering; and Arts and Humanities -Archaeology. The analysis comprises articles published for the time period 1987-2017, of journals in the Scopus top 10%, corresponding to approximately 152,000 papers.

Abstract
The present study aims to translate and validate the Temple Presence Inventory (TPI) for the Portuguese context, respecting the maintenance of an equivalent semantics as well as the validity of its contents and concepts. This study also aims to verify the psychometric properties of the instrument (factor validity and internal consistency). The sample consisted of 455 individuals (male = 271, female = 184). The fidelity of the factors varied between 0.5 and 0.84. The confirmatory factor analysis produced a theoretical model with 38 items distributed among eight factors. The covariance between some residual errors of instrument items was considered, and the following fit indices were observed: chi 2/df = 2.073; GFI = 0.858; CFI = 0.887; RSMEA = 0.049; AIC = 1527. The results confirm the appropriateness of the version adapted to the Portuguese language of the TPI and that it can be used in research projects aiming to evaluate Presence in the Portuguese-speaking population (Europe).