Abstract
Interest in robotics field as a teaching tool to promote the STEM areas has grown in the past years. The search for solutions to promote robotics is a major challenge and the use of real robots always increases costs. An alternative is the use of a simulator. The construction of a simulator related with the Portuguese Autonomous Driving Competition using Gazebo as 3D simulator and ROS as a middleware connection to promote, attract, and enthusiasm university students to the mobile robotics challenges is presented. It is intended to take advantage of a competitive mindset to overcome some obstacles that appear to students when designing a real system. The proposed simulator focus on the autonomous driving competition task, such as semaphore recognition, localization, and motion control. An evaluation of the simulator is also performed, leading to an absolute error of 5.11% and a relative error of 2.76% on best case scenarios relating to the odometry tests, an accuracy of 99.37% regarding to the semaphore recognition tests, and an average error of 1.8 pixels for the FOV tests performed.

Abstract
The Brazilian Power System is mostly supplied by hydro-generation. In this context there is a strong connection between rain-fall regimes and the Electricity Prices in the short term market. This work describes the main features, developments and functioning of a System Dynamics model that simulates the four Brazilian short term electricity submarkets. Based on studies reporting the change in rainfall regimes in Brazil due to Climate Change, we analyze the impacts of these changes in each specific region and in the electricity markets as a whole. The results provide good insights on the impacts of Global Warming in the Brazilian Power System, indicating for instance that the Southeast/Center-West Electricity Submarket is the one that will be most affected by the global issue in terms of rise in the electricity prices.

Abstract

Abstract
In this work we address the problem of generating electricity through Kite Power Systems. We solve an optimal control problem which devises the trajectories and controls for the kite that maximize the total energy produced in a given interval. This is a highly nonlinear problem for which the optimization is challenging. We use a continuous time model of the kite and implement time mesh refinement strategies to solve the problem. We report results that show that with an adaptive mesh refinement strategy the problem can be solved with a high level of accuracy and (in simplified versions) much faster. (C) 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the scientific committee of the 4th International Conference on Energy and Environment Research.

Abstract
Multi-objective optimization evolutionary techniques provide solutions for a specific problem using optimally concepts taking into consideration all the design criteria. In the last years, several multi-objective algorithms were proposed but usually the performance is measured at the end neglecting, therefore, the solution diversity along the interactions. In order to understand the evolution of the solutions this work studies the dynamic of the successive iterations. The analysis adopts the fractional entropy for measuring the statistical behavior of the population. The results show that the entropy is a good tool to monitor and capture phenomena such as the diversity and convergence during the algorithm execution. © Springer International Publishing AG 2017.

Abstract
In the last issue of 2015, the editorial was titled “Winter is coming: The dawn of Innovation?“ As almost 2 years have gone by, this topic was revisited. So why not wonder how innovation was going around this same time of the year? In trying to answer this question, a visit to Google© Trends revealed an interesting and possibly unexpected chart that is illustrated in figure 1. (...)