Abstract
Purpose - The World State of Quality (WSQ) Project aims to evaluate, analyse, rank and categorise countries according to their performance in quality as a multidimensional concept. The Project involves the computation of an overall score for each country, obtained as a weighted average of ranking positions of 16 metrics, with weights determined by a panel of experts. Methodology-This work proposes an alternative strategy for that procedure, using a Benefit-of-the-Doubt (BoD) Composite Indicator approach under the framework of Data Envelopment Analysis (DEA). This strategy avoids the need of using subjective weights and normalising data by rank positions, using a more objective procedure to obtain the countries’ ranking. A new overall score of the World State of Quality is proposed, which allows the categorisation of countries’ performance. The novel insights resulting from the use of this methodology are discussed, including the identification of strengths and weaknesses of the various countries, and the peers that can be used for facilitating continuous improvements policies. Findings - The results show that the BoD approach and the original method used by the WSQ Project present comparable results. Countries’ strengths and weaknesses and their suitable peers and targets for benchmarking are presented with illustrative examples. Originality/value – A novel frontier approach for countries’ benchmarking regarding their performance in quality is proposed, incorporating new insights into the current method. © 2022 Universidade do Minho. All rights reserved.

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.

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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.