Abstract
Background Forgiveness is linked with well-being, and social and health research has focused on the role and aspects of forgiveness that has been recently suggested as a phenomenon of public health importance. The Heartland Forgiveness Scale (HFS) was developed gathering three subscales to assess the forgiveness of others, forgiveness of self, and forgiveness of situation. The present study aimed to adapt the HFS into European Portuguese, and investigate its reliability and validity. Methods Translation and cross-cultural adaptation were conducted using a multistep forward-back translation process. Internal consistency was assessed by Cronbach's alpha. Confirmatory factor analysis was conducted to verify that the factor structure is the same as in the original HFS. The short version of the Ruminative Response Scale (RRS) and the Satisfaction with Life Scale (SWLS) were used to examine convergent validity. Results A sample of 222 university students, selected through convenience sampling, was used to access the validity of the European Portuguese version of the HFS (EPHFS). Cronbach's alpha for the European Portuguese HFS subscales were 0.777, 0.814 and 0.816 for Self, Others and Situation, respectively, indicating acceptable reliability. The 3-factor model of the original HFS was replicated in confirmatory factor analysis. As expected by evidence in the literature, positive and statistically significant correlations were found between SWLS and HFS and subscales. RRS showed negative and statistically significant correlations with HFS and subscales. Conclusions The European Portuguese version of the HFS presented acceptable internal consistency, construct validity and confirmed the three-factor structure of the original HFS.

Abstract
Quantum annealing provides a method to solve combinatorial optimization problems in complex energy landscapes by exploiting thermal fluctuations that exist in a physical system. This work introduces the mapping of a graph coloring problem based on pseudo-Boolean constraints to a working graph of the D-Wave Systems Inc. We start from the problem formulated as a set of constraints represented in propositional logic. We use the SATyrus approach to transform this set of constraints to an energy minimization problem. We convert the formulation to a quadratic unconstrained binary optimization problem (QUBO), applying polynomial reduction when needed, and solve the problem using different approaches: (a) classical QUBO using simulated annealing in a von Neumann machine; (b) QUBO in a simulated quantum environment; (c) actual quantum 1, QUBO using the D-Wave quantum machine and reducing polynomial degree using a D-Wave library; and (d) actual quantum 2, QUBO using the D-Wave quantum machine and reducing polynomial degree using our own implementation. We study how the implementations using these approaches vary in terms of the impact on the number of solutions found (a) when varying the penalties associated with the constraints and (b) when varying the annealing approach, simulated (SA) versus quantum (QA). Results show that both SA and QA produce good heuristics for this specific problem, although we found more solutions through the QA approach.

Abstract
Purpose The purpose of this paper is to conduct a systematic literature review (SLR) to identify the main firm-level performance indicators and group them in dimensions that support decision-making in the wine industry. Design/methodology/approach To achieve this goal, an SLR approach was conducted in the Scopus database from 2009 to 2019. From a set of 607 articles, only 25 studies related to firm-level performance indicators were considered and, following an inductive thematic analysis and an interpretative synthesis, separated into different specific foci that include social, economic and environmental dimensions. Findings There is a limited number of papers identifying indicators regarding the firm-level performance of wine firms, and even fewer studies including indicators on an integrated approach to measure the different dimensions of firm performance. This paper documents that economic and environmental indicators cover 78.2% of all SLR indicators analyzed. As this group of indicators is limited to a set of sub-dimensions, this paper found that several groups of indicators are misrepresented, such as product portfolio or certifications related to marketing activities and indicators covering purchasing and supply chain activities, which play a crucial role in the competitiveness of the wine industry. Practical implications For practitioners, it discloses the most pertinent indicators they need to improve to craft their business strategies. This framework is of added value for policymakers to customize their support programs for specific producers to develop their competitive strategies. It could be deployed in teaching programs as a tool to address the importance of aligning different types of indicators to achieve firm-level performance in the wine industry. Originality/value This study contributes to the literature identifying a framework of analysis that includes indicators of four dimensions, namely, economic, social, territorial and environmental. This framework aims to relate performance measures to corporate strategy as a management control tool. The framework intends to improve the fit between firms' activities and their competitive context and to be flexibly adapted to various products/firms in the wine industry.

Abstract
In an evolving European power system, with increasing shares of renewable energy sources – a high percentage of which connected to the distribution network – an accurate, reliable and up-to-date representation of the distribution network becomes a key tool for transmission and distribution system operators’ coordination. The Flexibility Hub, under development by INESC TEC and EDP, and in the scope of the European Union-funded project EU-SysFlex, offers a service that delivers an enhanced dynamic equivalent model of the distribution network to the transmission system operator. It is a useful tool for planning purposes to enable a better understanding of how the distribution network will behave under large voltage and frequency disturbances at the transmission level. This study describes the overall concept and the methodology, provides an overview of the data management model adopted to interface the involved agents and depicts some relevant scenarios under consideration for testing.

Abstract
Purpose The paper aims to explore the relationship between gamification and design thinking approach to innovation in the context of the early stage of innovation process (ESoIP). Design thinking is conceptually appropriate to support innovative, complex and uncertain business environments. Still, its practices have demonstrated some difficulties in managing the ESoIP, such as lack of structure and clarity around goals. This paper argues that gamification can enhance and complement design thinking in the management of firms' ESoIP. Design/methodology/approach Given the need to achieve a deeper understanding of the linkages between gamification and design thinking, the paper follows an exploratory theory building approach for this complex reality of innovation. The case study research method was conducted in three firms (Trivalor, Novartis and Microsoft) that applied a gamification approach to the ESoIP. Findings The results demonstrate that gamification has the power to enhance and complement design thinking practices by getting tasks more organized and improving coordination and employees' engagement in the innovation process. Practical implications The paper provides critical managerial contributions on how firms can use gamification to improve design thinking approaches to ESoIP. Its consequences are also crucial to innovation, R&D, and product/service development managers interested in using gamification to support the ideation and concept development of new solutions complementing traditional design thinking approaches. Originality/value Merging the gamification and design thinking approaches is novel, particularly on firms' ESoIP. The paper provides a comprehensive discussion of design thinking shortcomings and the role that gamification can play in overcoming them.

Abstract
Breast cancer is a highly mutable and rapidly evolving disease, with a large worldwide incidence. Even though, it is estimated that approximately 90% of the cases are treatable and curable if detected on early staging and given the best treatment. Nowadays, with the existence of breast cancer routine screening habits, better clinical treatment plans and proper management of the disease, it is possible to treat most cancers with conservative approaches, also known as breast cancer conservative treatments (BCCT). With such a treatment methodology, it is possible to focus on the aesthetic results of the surgery and the patient's Quality of Life, which may influence BCCT outcomes. In the past, this assessment would be done through subjective methods, where a panel of experts would be needed to perform the assessment; however, with the development of computer vision techniques, objective methods, such as BAT (c) and BCCT.core, which perform the assessment based on asymmetry measurements, have been used. On the other hand, they still require information given by the user and none of them has been considered the gold standard for this task. Recently, with the advent of deep learning techniques, algorithms capable of improving the performance of traditional methods on the detection of breast fiducial points (required for asymmetry measurements) have been proposed and showed promising results. There is still, however, a large margin for investigation on how to integrate such algorithms in a complete application, capable of performing an end-to-end classification of the BCCT outcomes. Taking this into account, this thesis shows a comparative study between deep convolutional networks for image segmentation and two different quality-driven keypoint detection architectures for the detection of the breast contour. One that uses a deep learning model that has learned to predict the quality (given by the mean squared error) of an array of keypoints, and, based on this quality, applies the backpropagation algorithm, with gradient descent, to improve them; another which uses a deep learning model which was trained with the quality as a regularization method and that used iterative refinement, in each training step, to improve the quality of the keypoints that were fed into the network. Although none of the methods surpasses the current state of the art, they present promising results for the creation of alternative methodologies to address other regression problems in which the learning of the quality metric may be easier. Following the current trend in the field of web development and with the objective of transferring BCCT.core to an online format, a prototype of a web application for the automatic keypoint detection was developed and is presented in this document. Currently, the user may upload an image and automatically detect and/or manipulate its keypoints. This prototype is completely scalable and can be upgraded with new functionalities according to the user's needs.