The single-layer technique appeared at the beginning of the 1980s, with the ACCROPODE™ unit, and is thus entering its third decade. At the time, this solution was a real innovation, reducing the amount of concrete and steepening armour facing slopes, hence reducing the volume of materials required. After three decades in use and more than 200 projects to date, it was important to summarize the lessons learned during this period and to inspect (above and below water) some of these structures in order to assess their behaviour and particularly to confirm the validity of the unit placing rules. In addition to the aspects related to armour stability, the focus has been given to the colonization by marine life of the structures, including the bedding layers, toe berms, underlayer, armour units. The purpose of this paper is to share the experience gained throughout the inspections undertaken since 2010 on structures built more than 10 years ago. A large panel of structures has been inspected, of different ages and at various locations worldwide.

Background: The SARS-CoV-2 virus has demonstrated the weakness of many health systems worldwide, creating a saturation and lack of access to treatments. A bottleneck to fight this pandemic relates to the lack of diagnostic infrastructure for early detection of positive cases, particularly in rural and impoverished areas of developing countries. In this context, less costly and fast machine learning (ML) diagnosis-based systems are helpful. However, most of the research has focused on deep-learning techniques for diagnosis, which are computationally and technologically expensive. ML models have been mainly used as a benchmark and are not entirely explored in the existing literature on the topic of this paper. Objective: To analyze the capabilities of ML techniques (compared to deep learning) to diagnose COVID-19 cases based on X-ray images, assessing the performance of these techniques and using their predictive power for such a diagnosis. Methods: A factorial experiment was designed to establish this power with X-ray chest images of healthy, pneumonia, and COVID-19 infected patients. This design considers data-balancing methods, feature extraction approaches, different algorithms, and hyper-parameter optimization. The ML techniques were evaluated based on classification metrics, including accuracy, the area under the receiver operating characteristic curve (AUROC), F1-score, sensitivity, and specificity. Results: The design of experiment provided the mean and its confidence intervals for the predictive capability of different ML techniques, which reached AUROC values as high as 90% with suitable sensitivity and specificity. Among the learning algorithms, support vector machines and random forest performed best. The down-sampling method for unbalanced data improved the predictive power significantly for the images used in this study. Conclusions: Our investigation demonstrated that ML techniques are able to identify COVID-19 infected patients. The results provided suitable values of sensitivity and specificity, minimizing the false-positive or false-negative rates. The models were trained with significantly low computational resources, which helps to provide access and deployment in rural and impoverished areas.

Este artigo apresenta um estudo sobre o impacto da pandemia de COVID-19, ocorrida no primeiro semestre de 2020, sobre a avaliação da aprendizagem de estudantes de graduação. Questionamos um conjunto de professores, de diferentes universidades brasileiras, sobre mudanças nas suas práticas de avaliação, introduzidas no contexto do ensino remoto e das adaptações pedagógicas necessárias frente à pandemia. A pesquisa, de natureza qualitativa e exploratória, foi realizada por meio de entrevistas realizadas on-line. Os dados obtidos foram submetidos a uma abordagem de análise de conteúdo e interpretação teórica. A pesquisa identificou mudanças nas estratégias didáticas, nos critérios avaliativos e nos significados das práticas de avaliação.

RésuméLa construction de la digue Torres, digue d'agrandissement du port de Gij6n, requiert une étude préalable des répercussions de l'ouvrage sur la morphodynamique des plages alentour. Cet article traite de la morpho dynamique de la plage de San Lorenzo, directement affectée par la présence de la nouvelle digue : les changements induits dans la propagation de la houle, principalement une réduction de l'énergie incidente et la diffraction des fronts, se manifestent sur la plage par une augmentation de la pente du profil et une rotation de la ligne de côte dans le sens horaire. Un rechargement de sable permet d'exploiter ce changement de configuration pour augmenter la largeur de plage sèche, actuellement déficitaire dans la zone ouest, et d'améliorer ainsi la fonctionnalité de la plage. AbstractThe construction of the Torres breakwater, as part of the expansion plan of the industrial port of Gij6n, requires a previous evaluation of the morphodynamic impact of the structure on the beaches in the vicinity of the port. This paper contains the specifie study of the San Lorenzo beach morphodynamic behaviour, directly affected by the presence of the new breakwater. The changes on the wave propagation, namely a reduction of the incident energy and the diffraction of the wave fronts, induce a beach profile steepening and a c10ckwise rotation of the coast line. This change of the beach configuration, coupled with a sand nourishment, would permit to overcome the CUITentlack of dry beach, improving its functionality.

No abstract