Abstract
Power transformers have an imperative role in the future developments of the electrical grids. Treated as crucial assets for transportation and distribution of electrical energy, transformers are currently being studied regarding to the integration of technologies aiming to diagnose problems and monitoring data of electrical power grid. Furthermore, environmental noise pollution has gained importance, especially in active units of the power grid, located near consumers, such as transformers. Transformers noise can be classified according to its source: core, windings and cooling. This study addresses an experimental characterization of one of the main causes of transformers core noise-magnetostriction of electrical steel. An evaluation of magnetostriction properties of electrical steel, including resistive strain gauges and Fiber Bragg Gratings (FBGs) measurements with an Epstein frame, are presented and discussed. The magnetic flux density influence on hysteretic strain behavior of magnetostriction was evaluated, as well as the effect of a clamping load on core joints. Nowadays, optical interrogators for Bragg gratings have a high acquisition frequencies and wavelength sensitivity when compared to former optical interrogation systems, allowing to evaluate physical phenomena without electromagnetic interference and with equivalent resolution of conventional strain gauges. © 2019 Cassiano C. Linhares et al.

Abstract
The implementation of routine breast cancer screening and better treatment strategies made possible to offer to the majority of women the option of breast conservation instead of a mastectomy. The most important aim of breast cancer conservative treatment (BCCT) is to try to optimize aesthetic outcome and implicitly, quality of life (QoL) without jeopardizing local cancer control and overall survival. As a consequence of the impact aesthetic outcome has on QoL, there has been an effort to try to define an optimal tool capable of performing this type of evaluation. Starting from the classical subjective aesthetic evaluation of BCCT (either by the patient herself or by a group of clinicians through questionnaires) to an objective aesthetic evaluation (where machine learning and computer vision methods are employed), leads to less variability and increasing reproducibility of results. Currently, there are some offline software applications available such as BAT(c) and BCCT.core, which perform the assessment based on asymmetry measurements that are computed based on semi-automatically annotated keypoints. In the literature, one can find algorithms that attempt to do the completely automatic keypoint annotation with reasonable success. However, these algorithms are very time-consuming. As the course of research goes more and more into the development of web software applications, these time-consuming tasks are not desirable. In this work, we propose a novel approach to the keypoints detection task treating the problem in part as image segmentation. This novel approach can improve both execution-time and results.

Abstract
This paper provides an overview of the development of a Sail Car within the European Project Semester (EPS), the international multidisciplinary engineering capstone programme offered by the Instituto Superior de Engenharia do Porto (ISEP). The main goal of EPS@ISEP is to offer a project-based educational experience to develop teamwork, communication, interpersonal and problem-solving skills in an international and multidisciplinary set up. The Sail Car team consisted of six Erasmus students, who participated in EPS@ISEP during the spring of 2019. The objective of the project was to design and develop a wind-powered, easy to drive land sailing vehicle. First, the team researched existing commercial solutions and considered the marketing, ethics and sustainability dimensions of the project. Next, based on these studies, specified the full set of requirements, designed the Sailo solution and procured the components and materials required to build a real size proof-of-concept prototype. Finally, the team assembled and tested successfully the prototype. At the end of the semester, the team considered EPS@ISEP a mind-opening opportunity.

Abstract
Industry 4.0 is a result of technological evolution and is intended to promote technological transformations in industry at different levels. The impact in human employment has been perceived as a major threat and is a matter of concern. Some authors argue that automation will bring unimaginable changes as soon as computers get more intelligence and as machines become able to perform complex tasks more efficiently than humans. However, technological progress is also pointed out as a stimulus for human-beings to develop the competencies that differentiate them from the machines. In this context, this study aims to explore the impacts of adopting Industry 4.0 technologies on work. The results of a comprehensive literature review provide an integrated perspective to identify and understand such impacts, analysing them in four categories: evolution of employment and creation of new jobs, human-machine interaction, new competencies creation/ development, and, organizational and professional changes. © 2020 IEEE.

Abstract
Simple and easy to use methods are of great practical demand in the design of Proportional, Integral, and Derivative (PID) controllers. Controller design criteria are to achieve a good set-point tracking and disturbance rejection with minimal actuator variation. Achieving satisfactory trade-offs between these performance criteria is not easily accomplished with classical tuning methods. A particle swarm optimization technique is proposed to design PID controllers. The design method minimizes a compromise cost function based on both the integral absolute error and control signal total variation criteria. The proposed technique is tested on an Arduino-based Temperature Control Laboratory (TCLab) and compared with the Grey Wolf Optimization algorithm. Both TCLab simulation and physical data show that satisfactory trade-offs between the performance and control effort are enabled with the proposed technique.

Abstract
In this paper it is described a low-cost robotic manipulator that was developed to be applied in the study of over-sensored systems and state estimation. The prototype was developed to be a teaching aid in advanced courses, such as a Robotics Doctoral Program, although it is also suitable to be applied in some other educational contexts, to support experiments, for graduate and undergraduate students. Its features and software implementation are described, as well as two possible approaches to the problem of estimating its pose, based on a wide variety of sensors use. © 2020 ACM.