Abstract
This paper provides an overview of the development of a solar dehydrator, a project undertaken by a team of six Erasmus students from different countries during the European Project Semester at the Instituto Superior de Engenharia do Porto in the spring of 2019. The main objective of the European Project Semester is to develop teamwork, communication and problem-solving skills through team work and project-based learning. The purpose of the project was to design a sustainable solution to dehydrate and preserve food, build and test the corresponding proof-of-concept prototype, while respecting requirements such as the budget, the use of reusable materials and components or European Union directives. To achieve this goal, the team considered the technological, ethical and deontological, economic and environmental perspectives in the design of the Dryfoo prototype. This paper describes, after a short introduction, the performed research, the development and the testing of the proof-of-concept prototype, as well as the personal outcomes of this learning experience.

Abstract
Among conventional generation technologies in Spain, Combined Cycle Gas Turbines (CCGT) is the one that has experienced the largest development over the first decade of the 21st century. However, despite its promising future, multiple factors (such as the renewable generation increase, demand decline, adverse regulatory policies, etc.) have compromised their competitive position, reducing their capacity factor and undermining their financial viability. Because of those issues, electricity companies are giving up on new CCGTs investments, or even considering closing or mothballing some of their recently built plants. However, many still claim for the necessity of maintaining flexible backup technologies to cope with the variability of renewable energies, as a transition technology until energy storage or other future technologies emerge. This paper makes a profitability analysis of CCGTs in the Spanish electric power sector under different scenarios of RES penetration, carbon plants decommissioning, CO2 emission costs and EV penetration.

Abstract
Deep learning models have as of late risen as popular function approximators for single-agent reinforcement learning challenges, by accurately estimating the value function of complex environments and being able to generalize to new unseen states. For multi-agent fields, agents must cope with the non-stationarity of the environment, due to the presence of other agents, and can take advantage of information sharing techniques for improved coordination. We propose an neural-based actor-critic algorithm, which learns communication protocols between agents and implicitly shares information during the learning phase. Large numbers of agents communicate with a self-learned protocol during distributed execution, and reliably learn complex strategies and protocols for partially observable multi-agent environments. © Springer Nature Switzerland AG 2019.

Abstract
Design complexity and its relation to Design for Manufacturing continues to be a challenging topic, namely when the objective is to address these aspects in a straightforward manner to young or less experienced design teams. The availability of practical methodologies that induce rules to support novel design creation, in the environment of manufacturing processes, continues to be scarce. This work presents a novel approach named - 0-3D Design Method - that aims to define, in a systematic and geometrical manner, practical rules that allows mechanical designers to relate, in a codified manner, the design geometry of a given mechanical component to the typical manufacturing processes that permit its physical embodiment. The method proposes the definition of types and sub-types of parts, based on the number of spatial dimensions that need to be modified for a given design construction, and its relation to the manufacturing phase of the product life-cycle. A practical mechanical design example is presented to demonstrate the methodology, mapping the parts necessary for the design of a cutting head of a machine-tool. (C) 2019 The Authors. Published by Elsevier B.V.

Abstract
For Programming teachers it is of utter most importance to understand the factors that impact on students' motivation to improve their ability to become good computer programmers. To understand a problem, to develop an algorithm for its solution, and to write the corresponding program is a challenging and arduous task, demanding time and self-confidence. In previous work we studied computer based technics to engage students in the learning activity; visualization, animation, automatic program assessment were some approaches that we combined. To support that work we studied carefully students' motivation and complemented that study with an inquiry to a group of students of Algorithm and Programming course of the first year of an Engineering degree. In this paper we show how Association Rules can be used to mine the data gathered in the inquiry to discover relationships among factors influencing extrinsic motivation.

Abstract
Distribution system operators (DSO) are currently moving towards active distribution grid management. One goal is the development of tools for operational planning of flexibility from distributed energy resources (DER) in order to solve potential (predicted) congestion and voltage problems. This work proposes an innovative flexibility management function based on stochastic and chance-constrained optimization that copes with forecast uncertainty from renewable energy sources (RES). Furthermore, the model allows the decision-maker to integrate its attitude towards risk by considering a trade-off between operating costs and system reliability. RES forecast uncertainty is modeled through spatial-temporal trajectories or ensembles. An AC-OPF linearization that approximates the actual behavior of the system is included, ensuring complete convexity of the problem. McCormick and big-M relaxation methods are compared to reformulate the chance-constrained optimization problem. The discussion and comparison of the proposed models is carried out through a case study based on actual generation data, where operating costs, system reliability and computer performance are evaluated.