Abstract
This paper reports the collaborative learning experience of a team of five Erasmus students who participated in EPS@ISEP—the European Project Semester (EPS) at Instituto Superior de Engenharia do Porto (ISEP)—during the spring of 2018. EPS@ISEP is a project-based learning capstone programme for third and fourth year engineering, product design and business students, focussing on teamwork and multidisciplinary problem solving as well as on the development of sustainable and ethical practices. In this context, the Team developed a drifting intelligent buoy to monitor the water quality of urban water spaces. Motivated by the desire to build an intelligent buoy for urban water bodies, the Team conducted several scientific, technical, sustainability, marketing, ethical and deontological analyses. Based on the findings, it has derived the requirements, designed the structure and functional system, selected the list of components and providers and assembled a proof of concept prototype. The result is Aquality, an intelligent drifting buoy prototype, designed for private sustainable pools. Aquality monitors the quality of the pool water by measuring its temperature and turbidity, while interfacing with the user through a mobile application. Considering the EPS@ISEP learning experience, the Team valued the knowledge and skills acquired, and, particularly, the collaborative learning and working component of the project, i.e., working together towards one goal while maintaining high motivation and cohesion. © 2019, Springer Nature Switzerland AG.

Abstract
In this demo, we present a tool that allows to automatically generate temporal summarization of news collections. Conta-me Histórias (Tell me stories) is a friendly user interface that enables users to explore and revisit events in the past. To select relevant stories and temporal periods, we rely on a key-phrase extraction algorithm developed by our research team, and event detection methods made available by the research community. Additionally, we offer the engine as an open source package that can be extended to support different datasets or languages. The work described here stems from our participation at the Arquivo.pt 2018 competition, where we have been awarded the first prize. © Springer Nature Switzerland AG 2019.

Abstract

Abstract
This study presents an interval based robust chance constrained (IBRCC) optimisation model for allocating demand response program to effective buses of the power systems considering wind uncertainty and equipment failures. In the proposed formulation, an interval based robust approach is applied to evaluate the highest uncertainty spectrum of the wind power generation that the power system can tolerate. Accordingly, to cope with the uncertainty sources, chance-based constraints are implemented. In the proposed IBRCC optimisation framework, the level of the optimal solution robustness is probabilistically maximised subject to a set of operational constraints. Besides, to facilitate the massive integration of uncertain wind generation and to mitigate congestion in the transmission grid, an efficient allocation, and scheduling scheme of demand response programs is proposed. The proposed model is evaluated on the IEEE 24 bus system.

Abstract
This work demonstrates the potential of combining a microsphere with a tip for the functionality of the contact sensor. This sensor consists of a tip aligned with the fiber core and a microsphere, which appears during tip formation. This new structure was produced using the electric arc machine. The sensor operation consists of the variation of the tip curvature, which causes a variation of the optical paths and, consequently, a change in the output signal. The study of this micro-cantilever consisted of an exploration of the contact mode. In addition, the sensor was characterized by temperature, which shows very low sensitivity and vibration. This last characterization was performed with two configurations parallel and perpendicular to the oscillating surface. The perpendicular case showed higher sensitivity and has an operating band of 0 Hz to 20 kHz. In this configuration, for frequencies up to 2 Hz, the intensity varies linearly with the frequencies and with a sensitivity of 0.032 +/- 0.001 (Hz(-1)). For the parallel case, the operating band was from 1.5 kHz to 7 kHz.

Abstract
The integration of renewable generation in electricity networks is one of the most widespread strategies to improve sustainability and to deal with the energy supply problem. Typically, the reinforcement of the generation fleet of an existing network requires the assessment and minimization of the installation and operating costs of all the energy resources in the network. Such analyses are usually conducted using peak demand and generation data. This paper proposes a method to optimize the location and size of different types of generation resources in a network, taking into account the typical evolution of demand and generation. The importance of considering this evolution is analyzed and the methodology is applied to two standard networks, namely the Institute of Electrical and Electronics Engineers (IEEE) 30-bus and the IEEE 118-bus. The proposed algorithm is based on the use of particle swarm optimization (PSO). In addition, the use of an initialization process based on the cross entropy (CE) method to accelerate convergence in problems of high computational cost is explored. The results of the case studies highlight the importance of considering dynamic demand and generation profiles to reach an effective integration of renewable resources (RRs) towards a sustainable development of electric systems.