Abstract
Treatments for breast cancer have continued to evolve and improve in recent years, resulting in a substantial increase in survival rates, with approximately 80% of patients having a 10-year survival period. Given the serious that impact breast cancer treatments can have on a patient's body image, consequently affecting her self-confidence and sexual and intimate relationships, it is paramount to ensure that women receive the treatment that optimizes both survival and aesthetic outcomes. Currently, there is no gold standard for evaluating the aesthetic outcome of breast cancer treatment. In addition, there is no standard way to show patients the potential outcome of surgery. The presentation of similar cases from the past would be extremely important to manage women's expectations of the possible outcome. In this work, we propose a deep neural network to perform the aesthetic evaluation. As a proof-of-concept, we focus on a binary aesthetic evaluation. Besides its use for classification, this deep neural network can also be used to find the most similar past cases by searching for nearest neighbours in the high-semantic space before classification. We performed the experiments on a dataset consisting of 143 photos of women after conservative treatment for breast cancer. The results for accuracy and balanced accuracy showed the superior performance of our proposed model compared to the state of the art in aesthetic evaluation of breast cancer treatments. In addition, the model showed a good ability to retrieve similar previous cases, with the retrieved cases having the same or adjacent class (in the 4-class setting) and having similar types of asymmetry. Finally, a qualitative interpretability assessment was also performed to analyse the robustness and trustworthiness of the model.

Abstract

Abstract
The digitalization of companies and the implantation of Industry 4.0 concepts are emerging and challenging for micro and Small and Medium Enterprises (SME). The benefits are evident for the companies, as their business processes become simplified, and the internationalization and global market penetration turn out to be improved. However, information security and cybersecurity concerns have been raised on SME, as best practices and regulations compliance should be applied. The wide set of these regulations and their broad scope has put constraints on their overall direct mapping and adoption into SME. This paper describes an original methodology to map the Roadmap for Minimum Cybersecurity Capabilities (RMCSC) delivered by the Portuguese Cybersecurity Centre, into the well-Adopted international information security ISO 27001:2013 standard. The proposed mapping is oriented toward the characteristics of SME and allows these companies to assess their cybersecurity risk to further mitigate potential identified flaws. The main deliverable of this paper is the developed methodology, which correlates the actions of the cybersecurity capabilities roadmap and the security controls enclosed in the ISO 27001:2013 standard. A questionnaire was developed to support the cybersecurity risk self-diagnosis, and the actions were justified and detailed in this paper. Further developments include the submission of the questionnaire to a case study of SME in the centre region of Portugal.

Abstract
Renewable energy communities (REC) are bound to play a crucial role in the energy transition, as their role, activities, and legal forms become clearer, and their dissemination becomes larger. Even though their mass grid integration, is regarded with high expectations, their diffusion, however, has not been an easy task. Its legal form and success, entail responsibilities, prospects, trust, and synergies to be explored between its members, whose collective dynamics should aim for optimal operation. In this regard, the pairing methodology of potential participants ahead of asset dimensioning seems to have been overlooked. This article presents a methodology for pairing consumers, based on their georeferenced load consumptions. A case study in an area of Porto (Asprela) was used to test the methodology. QGIS is used as a geo-representation tool and its PlanHeat plugin for district characterization support. A supervised statistical learning approach is used to identify the feature importance of an overall district energy consumption profile. With the main variables identified, the methodology applies standard K-means and Dynamic Time Warping clustering, from which, users from different clusters should be paired to explore PV as the main generation asset. To validate the assumption that this complementarity of load diagrams could decrease the total surplus of a typical PV generation, 18 pairings were tested. Results show that, even though it is not true that all pairings from different clusters lead to lower surplus, on average, this seems to be the trend. From the sample analyzed a maximum of 36% and an average of 12% less PV surplus generation is observed.

Abstract
The need for meeting energy needs but at the same time reduce greenhouse gas emissions (GHG) produced by primary energy sources has raised a wide range of concerns for different for industrial activities. Nonetheless, the development of the green marketing strategies over the year has drawn attention to a new phenomenon, namely the Greenwashing. This research attempts to contribute to analyze the use of greenwashing practices in companies' operating in the regulated energy market. Results from the content analysis showed that practices of greenwashing are not generalizable to the sample observed. However, for some companies these practices were evidenced.

Abstract
A pandemia Covid-19 teve um profundo impacto nos processos pedagógicos das universidades de ensino presencial. O confinamento obrigatório da população universitária implicou a transposição do ensino presencial em sala para as aulas realizadas online, levando a um aumento considerável do uso de sistemas de videoconferência, estabelecendo novas (ou reforçando as existentes) comunidades de aprendizagem online. Estas práticas de aprendizagem online tendem a se manter após o fim da pandemia na medida em que proporcionem alternativas complementares de contacto, partilha e colaboração em rede, determinando formas não estruturadas de modelos pedagógicos híbridos de ensino a distância. Neste artigo descrevemos práticas concretas de ensino online implementadas durante a pandemia em duas universidades de ensino presencial, a Universidade de São José em Macau; e a Universidade de São Paulo, Brasil, procedendo à sua análise crítica à luz da práxis de ensino a distância online em voga na Universidade Aberta, Portugal, uma universidade virtual de ensino a distância, tendo em vista a definição de um modelo pedagógico virtual de cariz geral que possa proporcionar princípios e linhas mestras para o planeamento, organização e implementação de oferta educativa de nível universitário administrável online para os tempos pós-pandémicos suportada nas evidências da pesquisa e tendências emergentes.