Abstract
This paper reports the research and design of a foldable disaster shelter for people left homeless due to natural disasters, by a multinational team composed of six students, from six different countries. The team was enrolled in the European Project Semester (EPS), a project-based capstone programme offered by Instituto Superior de Engenharia do Porto (ISEP), to students who have completed at least two years of undergraduate studies. The main objective of the project was to design, simulate and test an ethics and sustainability driven foldable shelter. This goal was pursued by conducting a series of studies to derive the solution requirements, involving a survey on shelter concepts and solutions, a review on worldwide natural disasters, as well as an analysis of the shelter market. The latter led to the definition of a business plan, a marketing strategy, a logo and a brand name. The solution comes with a Web application to help rescue organisations to follow the scheduled maintenance plan and keep track of the deployed units.

Abstract
Deep Learning achieves state-of-the-art results in many domains, yet its black-box nature limits its application to real-world contexts. An intuitive way to improve the interpretability of Deep Learning models is by explaining their decisions with similar cases. However, case-based explanations cannot be used in contexts where the data exposes personal identity, as they may compromise the privacy of individuals. In this work, we identify the main limitations and challenges in the anonymization of case-based explanations of image data through a survey on case-based interpretability and image anonymization methods. We empirically analyze the anonymization methods in regards to their capacity to remove personally identifiable information while preserving relevant semantic properties of the data. Through this analysis, we conclude that most privacy-preserving methods are not sufficiently good to be applied to case-based explanations. To promote research on this topic, we formalize the privacy protection of visual case-based explanations as a multi-objective problem to preserve privacy, intelligibility, and relevant explanatory evidence regarding a predictive task. We empirically verify the potential of interpretability saliency maps as qualitative evaluation tools for anonymization. Finally, we identify and propose new lines of research to guide future work in the generation of privacy-preserving case-based explanations.

Abstract
The increasing complexity of recent Wi-Fi amendments is making optimal Rate Adaptation (RA) a challenge. The use of classic algorithms or heuristic models to address RA is becoming unfeasible due to the large combination of configuration parameters along with the variability of the wireless channel. We propose a simple Deep Reinforcement Learning approach for the automatic RA in Wi-Fi networks, named Data-driven Algorithm for Rate Adaptation (DARA). DARA is standard-compliant. It dynamically adjusts the Wi-Fi Modulation and Coding Scheme (MCS) solely based on the observation of the Signal-to-Noise Ratio (SNR) of the received frames at the transmitter. Our simulation results show that DARA achieves higher throughput when compared with Minstrel High Throughput (HT)

Abstract
Digitalization is undoubtedly a major challenge for companies in the coming years. Applying a Design Science methodology this paper aims to describe the process for the development of a solution for obtaining an overview of the Digital Maturity in the manufacturing industry of the region of Tamega e Sousa (an industrial region located in the north of Portugal). The evaluation process consisted of a sample of 53 companies that allowed to get a first picture of the region. Summing up, it is possible to say that a digital strategy is in the companies' plans with a focus on processes digitalization. In general, an overall digital strategy for the companies is in line with the marketing and human resources, in a middle position, with a few companies taking the lead, the majority following, and some others still now awakening to this reality.

Abstract
The rapid developments in Internet-of-Things (IoT), cloud computing, and big data technologies have increased the popularity of machine learning (ML) techniques. As a result, of all ML techniques, deep learning (DL) is at the forefront of innovation, outperforming all other techniques in many application domains. DL has made breakthroughs in speech recognition, image processing, forecasting, natural language processing, fault detection, power disturbance classification, energy trading, and much more. DL is a complex ML approach composed of multiple processing layers, which allows pattern and structure recognition on huge datasets. This chapter takes an in-depth look at the most recent and promising DL works in the literature for intelligent power and energy systems (PES). Several types of problems are explored, including regression, classification, and decision-making problems. The presented works show an increasing trend of new DL techniques that outperform traditional approaches, either through novel architectures or hybrid systems. © 2023 The Institute of Electrical and Electronics Engineers, Inc.

Abstract