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Abstract
This paper proposes an innovative composite indicator to evaluate Corporate Social Responsibility (CSR). The methodology proposed involves two stages. The first stage specifies an optimization model, based on Directional Distance Functions, to obtain a relative measure of CSR at the firm level that can guide performance improvements. This model allows distinguishing the firms with best practices from those with potential for improvement and can suggest targets for future achievements. In the second stage, a goal programming model is proposed to identify a common set of weights for the key performance indicators, enabling the evaluation of all firms on common grounds. These weights are used to construct an industry ranking, which is based on the distance of firms to a common frontier of technology that respects the trade-offs determined for the industry. An illustrative application of the method proposed is presented at the end of the paper. The indicators considered in the evaluation were selected according to international standards and guidelines applicable to mining firms. All dimensions of the Triple Bottom Line (economic, environmental and social) were taken into account. The results and their managerial implications are discussed with the objective of promoting the awareness of CSR levels in the mining activity, supporting the sustainable development of industrial activities.

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.

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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