Abstract
uebe.Q is a managing software for solid referential information systems, such as ISO 9000 (for quality) and ISO 1400 (for environment). This is a long-term developed software, encompassing extensive and solid business logic with a long and successful record of deployments. A recent business model change imposed that the evolution and configuration of the software, shifts from the company (and especially the development team) to consultants and other business partners, along with the fact that different systems and respective data/information need to be integrated with minimal intervention of the development team. The so far acceptable rigidity, fragility, immobility and opacity of the software became a problem. Especially, the system was prepared to deal with a specific database respecting a specific schema and code-defined semantics. This paper describes the approach taken to overcome the problems derived form the previous architecture, by adopting (i) ontologies for the specification of business concepts and (ii) an information-integration Decision Support System (DSS) for mapping the domain specific ontologies to the database schemas. © 2020, Springer Nature Switzerland AG.

Abstract
Hereditary amyloidosis associated with transthyretin V30M (ATTRv V30M) is a rare and inherited multisystemic disease, with a variable presentation and a challenging diagnosis, follow-up and treatment. This condition entails a definitive and progressive motor impairment that compromises walking ability from near onset. The detection of the latter is key for the disease's diagnosis. The aim of this work is to perform quantitative 3-D gait analysis in ATTRv V30M patients, at different disease stages, and explore the potential of the obtained gait information for supporting early diagnosis and/or stage distinction during follow-up. Sixty-six subjects (25 healthy controls, 14 asymptomatic ATTRv V30M carriers, and 27 symptomatic patients) were included in this case-control study. All subjects were asked to walk back and forth for 2 min, in front of a Kinect v2 camera prepared for body motion tracking. We then used our own software to extract gait-related parameters from the camera's 3-D body data. For each parameter, the main subject groups and symptomatic patient subgroups were statistically compared. Most of the explored gait parameters can potentially be used to distinguish between the considered group pairs. Despite of statistically significant differences being found, most of them were undetected to the naked eye. Our Kinect camera-based system is easy to use in clinical settings and provides quantitative gait information that can be useful for supporting clinical assessment during ATTRv V30M onset detection and follow-up, as well as developing more objective and fine-grained rating scales to further support the clinical decisions.

Abstract
The COVID-19 outbreak has proven to be a challenge for most communities, requiring them to adapt to a newfound reality. Cities need now to accommodate the circulation of their populations in a safe manner, dealing with economic repercussions, and avoiding to oversaturate the countries' healthcare facilities. So far, the latter has happened with dramatic consequences in terms of loss of human lives. In this context, we propose a social simulation meta-model suitable to represent the complex socio-technical system of a campaign hospital, created to support existing healthcare facilities as a response to the demands created by the coronavirus pandemic. With this model we intend to support the analysis of social coordination policies towards the improvement of a given set of characteristics of the system. By considering both technical and social dimensions, we expect to gain insights into how certain aspects such as the collaborativeness of patients or the nature of staff might affect the healing speed of patients and, similarly, the efficiency of the campaign hospital. Ultimately, all emergent behaviour should provide useful insights allowing for the identification of key social practices influencing its performance. © 2020 IEEE.

Abstract
This paper presents a model of a system capable of addressing the training needs identified for small universities, using the University of Tras-os-Montes e Alto Douro (UTAD) as a case study. In addition to supporting the typical needs of distance learning/education (e.g., e-learning), it is also intended that the proposed system complements the traditional classroom-based teaching. This model will have two modules: the physical/infrastructural module and the policies/practices module. While the physical module will have all the infrastructure services associated with educational practices, such as the e-learning platform, the policy module will include institutional policies and rules in the creation, development, practice and management of courses, equipment and physical spaces, such as exam rooms. In line with these, UTAD has come to recognize that e-learning should be part of its strategy for its training offer and, consequently, is being adopting new policies, namely through the signing of protocols with other institutions with more experience using e-learning. As such, a review of other models and systems that have been successfully implemented in other international reference universities will also be briefly presented here. The courses implemented so far, and the results achieved, are also presented and commented.

Abstract

Abstract
The main aim of breast cancer conservative treatment is the optimisation of the aesthetic outcome and, implicitly, women’s quality of life, without jeopardising local cancer control and overall survival. Moreover, there has been an effort to try to define an optimal tool capable of performing the aesthetic evaluation of breast cancer conservative treatment outcomes. Recently, a deep learning algorithm that integrates the learning of keypoints’ probability maps in the loss function as a regularisation term for the robust learning of the keypoint localisation has been proposed. However, it achieves the best results when used in cooperation with a shortest-path algorithm that models images as graphs. In this work, we analysed a novel algorithm based on the interaction of deep image segmentation and deep keypoint detection models capable of improving both state-of-the-art performance and execution-time on the breast keypoint detection task.