Abstract
AbstractThe correction of artifacts in Magnetic Resonance Imaging (MRI) is crucial due to physiological phenomena and technical issues affecting diagnostic quality. Reverting from corrupted to artifact-free images is a complex task. Deep Learning (DL) models have been employed to preserve data characteristics and to identify and correct those artifacts. We proposeMAC, a novel DL-based solution to correct artifacts in multi-contrast brain MRI scans.MACoffers two models: the simulation and the correction models. The simulation model introduces perturbations similar to those occurring in an exam while preserving the original image as ground truth; this is required as publicly available datasets rarely have motion-corrupted images. It allows the addition of three types of artifacts with different degrees of severity. The DL-based correction model adds a fourth contrast to state-of-the-art solutions while improving the overall performance of the models.MACachieved the highest results in the FLAIR contrast, with a Structural Similarity Index Measure (SSIM) of 0.9803 and a Normalized Mutual Information (NMI) of 0.8030. Moreover, the model reduced training time by 63% compared to its predecessor.MACmodel can correct large volumes of images faster and adapt to different levels of artifact severity than current state-ofthe-art models, allowing for better diagnosis.

Abstract
Lung cancer represents a significant health concern necessitating diligent monitoring of individuals at risk. While the detection of pulmonary nodules warrants clinical attention, not all cases require immediate surgical intervention, often calling for a strategic approach to follow-up decisions. The Lung-RADS guideline serves as a cornerstone in clinical practice, furnishing structured recommendations based on various nodule characteristics, including size, calcification, and texture, outlined within established reference tables. However, the reliance on labor-intensive manual measurements underscores the potential advantages of integrating decision support systems into this process. Herein, we propose a feature-based methodology aimed at enhancing clinical decision-making by automating the assessment of nodules in computed tomography scans. Leveraging algorithms tailored for nodule calcification, texture analysis, and segmentation, our approach facilitates the automated classification of follow-up recommendations aligned with Lung-RADS criteria. Comparison with a previously reported end-to-end image-based classification method revealed competitive performance, with the feature-based approach achieving an accuracy of 0.701 +/- 0.026, while the end-to-end method attained 0.727 +/- 0.020. The inherent explainability of the feature-based approach offers distinct advantages, allowing clinicians to scrutinize and modify individual features to address disagreements or rectify inaccuracies, thereby tailoring follow-up recommendations to patient profiles.

Abstract

Abstract
PurposeRefugee entrepreneurship is increasingly viewed as a silver bullet being able to promote host countries' economic performance and enable the successful integration of refugees. This study aims to identify the main determinants of entrepreneurial intentions of refugees in Portugal based on the underdog theory.Design/methodology/approachIn this study, the authors scrutinize the entrepreneurial intentions of refugees living in Portugal, an overlooked context, using a purpose-built inquiry responded to by 41 refugees and resorting to fuzzy-set qualitative comparative analysis, complemented with partial least squares path modeling.FindingsSome important results are worth highlighting: the entrepreneurial intentions of the respondent sample of refugees living in Portugal are high; the theoretical arguments underlying the underdog or challenge-based entrepreneurship theory are validated in the context of the respondent sample; and psychological related factors associated with the more standard explanations of entrepreneurial intentions constitute necessary conditions for high refugee entrepreneurial intentions.Originality/valueEntrepreneurial intentions to launch a business have been discussed in the entrepreneurship literature vastly, but it has not yet received much attention when focusing on refugees, often identified as underdogs (potential) entrepreneurs. This study contributes to the literature by testing the challenge-based entrepreneurship theory to identify the primary factors influencing refugee entrepreneurial intentions.

Abstract
Microgrids are poised to become the building blocks of the future control architecture of electric power systems. As the number of controllable points in the system grows exponentially, traditional control and optimization algorithms become inappropriate for the required operation time frameworks. Reinforcement learning has emerged as a potential alternative to carry out the real-time dispatching of distributed energy resources. This paper applies one of the continuous action-space algorithms, proximal policy optimization, to the optimal dispatch of a battery in a grid-connected microgrid. Our simulations show that, though suboptimal, RL presents some advantages over traditional optimization setups. Firstly, it can avoid the use of forecast data and presents a lower computational burden, therefore allowing for implementation in distributed control devices.

Abstract
Background Engaging in advance care planning can be emotionally challenging, but gamification and technology are suggested as a potential solution.Objective Present the development stages of a mobile app prototype to improve quality of life for patients in palliative care.Design The study started with a comprehensive literature review to establish a foundation. Subsequently, interviews were conducted to validate the proposed features of the mobile application. Following the development phase, usability tests were conducted to evaluate the overall usability of the mobile application. Furthermore, an oral questionnaire was administered to understand user satisfaction about the implemented features.Results A three-phase testing approach was employed based on the chosen user-centred design methodology to obtain the results. Three iterations were conducted, with improvements being made based on feedback and tested in subsequent phases. Despite the added complexity arising from the health status of patients in palliative care, the usability tests and implemented features received positive feedback from both patients and healthcare providers.Conclusion The research findings have demonstrated the potential of digitisation in enhancing the quality of life for patients in palliative care. This was achieved through the implementation of patient-centred design, personalised care, the inclusion of social chatrooms and facilitating end-of-life discussions.