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The coronavirus disease 2019 (COVID-19) pandemic has impacted healthcare systems across the world. Chest radiography (CXR) can be used as a complementary method for diagnosing/following COVID-19 patients. However, experience level and workload of technicians and radiologists may affect the decision process. Recent studies suggest that deep learning can be used to assess CXRs, providing an important second opinion for radiologists and technicians in the decision process, and super-human performance in detection of COVID-19 has been reported in multiple studies. In this study, the clinical applicability of deep learning systems for COVID-19 screening was assessed by testing the performance of deep learning systems for the detection of COVID-19. Specifically, four datasets were used: (1) a collection of multiple public datasets (284.793 CXRs); (2) BIMCV dataset (16.631 CXRs); (3) COVIDGR (852 CXRs) and 4) a private dataset (6.361 CXRs). All datasets were collected retrospectively and consist of only frontal CXR views. A ResNet-18 was trained on each of the datasets for the detection of COVID-19. It is shown that a high dataset bias was present, leading to high performance in intradataset train-test scenarios (area under the curve 0.55-0.84 on the collection of public datasets). Significantly lower performances were obtained in interdataset train-test scenarios however (area under the curve > 0.98). A subset of the data was then assessed by radiologists for comparison to the automatic systems. Finetuning with radiologist annotations significantly increased performance across datasets (area under the curve 0.61-0.88) and improved the attention on clinical findings in positive COVID-19 CXRs. Nevertheless, tests on CXRs from different hospital services indicate that the screening performance of CXR and automatic systems is limited (area under the curve < 0.6 on emergency service CXRs). However, COVID-19 manifestations can be accurately detected when present, motivating the use of these tools for evaluating disease progression on mild to severe COVID-19 patients.

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Despite the important advances observed, nowadays, the Engineering programmes keep being challenged to better prepare their students to work on complex and multidisciplinary projects while demonstrating awareness of environmental and socio-economic issues and other soft skills as communication and teamwork. Recently, to meet these challenges, the ISEP' Informatics Engineering programme (LEI) successfully adopted a project-based learning approach. In this approach, throughout the entire semester, students develop a real-world project that allows the application and assessment of the competencies taught by all course units of the semester in an integrated, multidisciplinary, and transversal way. In this paper, the authors (i) present this approach as well as the main challenges faced in implementing it; (ii) report the major findings and the perceived benefits and drawbacks; and (iii) discuss the ongoing adaptations and/or others seen as required to improve the approach and its results. © CDIO 2022.All rights reserved.

Abstract
Web 2.0 has allowed collaboration, interaction and sharing of information online, such as online review platforms. Consequently, these short and straightforward opinions have increasingly proved to be essential sources of information not only for consumers but also for companies, as they represent the consumer's sincere evaluation, free from any kind of bias. In this sense, there should be an interest in the analysis and monitoring of online reviews by companies, as the result of these actions may provide guidelines to readjust their strategy, support decision-making and ensure the satisfaction of their consumers. To generate useful information to assist decision-making and strategies' implementation by retailers in the Municipality of Porto, online reviews from the GoogleMyBusiness platform were organised, classified, and analysed. 9945 online reviews were extracted, directed to 246 retail adaptations of the Municipality of Porto, from 2017 to 2020, which were later classified by the polarity of sentiment (positive, negative, neutral, or mixed). Sentiment analysis was conducted, combined with statistical tests and frequency distribution tables to discover relevant information for retailers. With sentiment analysis, retailers can understand their consumers and their behaviour to adapt their strategies and make the right decisions to ensure their customers' satisfaction. With the results obtained, this study proves that it is possible to extract useful information from online reviews and reveals that it is still an area of little interest for retailers in the Municipality of Porto.