Abstract

Abstract
We are going through perhaps the greatest crisis of our lives, where the pace of decision making, and the adoption of new public policies may indelibly condition individual and collective futures. Among other megatrends, Covid-19's impact on the digital world will facilitate the trend towards osmosis between real and virtual, human, machine and nature, public and private. The migration of all economic activities to digital, for safety and business survival reasons, will require adaptation and transition models to the new digital reality. The acceleration of digital transformation in hardware and software infrastructures will also lead to remodelling and innovate in all socio-economic, labour, and educational activities. The trend towards skills and qualifications will become more imperative. Higher Education Institutions can have a central role in developing the needed skills with their students, providing digital skills as well as pedagogical policies that stimulates them, specially focused on leadership, critical thinking, and creativity. This research aims to understand what the perceptions of the demand for digital workforce competencies are. It also intends to comprehend how those competencies are linked with entrepreneurship and leadership. After a literature review, data are presented and discussed, as well as conclusions and future potential research directions.

Abstract
To gather hydrological measurements is a difficult task for Autonomous Surface Vessels. It is necessary for precise navigation considering underwater obstacles, shallow and fast water flows, and also mitigate misreadings due to disturbs caused by their propulsion system. To deal with those problems, this paper presents a new topology of an Autonomous Surface Vessel (ASV) based on a catamaran boat with an aerial propulsion system with azimuth control. This set generates an over-actuated 3 Degree of Freedom (DoF) ASV, highly maneuverable and able of operating over the above-mentioned situations. To deal with the high computational cost of the over-actuated control allocation (CA) problem, this paper also proposes a Fast CA (FCA) approach. The FCA breaks the initial nonlinear system into partially-dependent linear subsystems. This approach generates smaller connected systems with overlapping solution spaces, generating fast and robust convergence, especially attractive for embedded control devices. Both proposals, i.e., ASV and FCA, are assessed through mathematical simulations and real scenarios.

Abstract
Artificial intelligence (AI)-based solutions have revolutionized our world, using extensive datasets and computational resources to create automatic tools for complex tasks that, until now, have been performed by humans. Massive data is a fundamental aspect of the most powerful AI-based algorithms. However, for AI-based healthcare solutions, there are several socioeconomic, technical/infrastructural, and most importantly, legal restrictions, which limit the large collection and access of biomedical data, especially medical imaging. To overcome this important limitation, several alternative solutions have been suggested, including transfer learning approaches, generation of artificial data, adoption of blockchain technology, and creation of an infrastructure composed of anonymous and abstract data. However, none of these strategies is currently able to completely solve this challenge. The need to build large datasets that can be used to develop healthcare solutions deserves special attention from the scientific community, clinicians, all the healthcare players, engineers, ethicists, legislators, and society in general. This paper offers an overview of the data limitation in medical predictive models; its impact on the development of healthcare solutions; benefits and barriers of sharing data; and finally, suggests future directions to overcome data limitations in the medical field and enable AI to enhance healthcare. This perspective is dedicated to the technical requirements of the learning models, and it explains the limitation that comes from poor and small datasets in the medical domain and the technical options that try or can solve the problem related to the lack of massive healthcare data.

Abstract
Finding effective ways to evaluate the process and a team in software engineering is not a trivial task. Therefore, it is pertinent to study metrics that can be used in Scrum environments to monitor and evaluate the team's progress and support the implementation of improvements. This study explores the relevance of 12 specific metrics applied in a Scrum environment and explores their importance considering multiple dimensions through the adoption of a quantitative methodology based on a survey that received 137 valid answers from Portuguese Scrum professionals. The results allowed us to conclude that the metrics related to the business value delivered and sprint goal success are the most relevant. Furthermore, factors like the number of years of experience of individuals, their role in the Scrum team, and the size of the organisation are factors that influence the perception of the importance of these metrics.

Abstract
Background & aims: The Mini Nutritional Assessment (MNA) is the most used tool to assess malnutrition and/or its risk among older adults. Its Screening section was proposed as a short form (MNA-SF) but studies comparing the two forms present controversial results. Our main aims were to study the agreement between MNA-SF and its full form (MNA-FF) among Portuguese older adults living in the community and to develop a more sensible version of the MNA-SF. Material and methods: This cross-sectional study used a convenience sample of 456 older adults (54.2% females) aged 65-92 years (mean = 73; SD = 6). Data analyzed included: nutritional status (MNA), social support (Fillenbaum's Social Network Index), level of independency in daily activities (Lawton e Brody's scale) and eating-related quality of life. Both MNA-FF and MNA-SF classify participants as malnourished, at risk of malnutrition or with normal nutrition status. Anthropometric assessments (weight, height, arm and calf perimeters) were carried out and BMI was computed. Results: The agreement between the two classifications is 82.7%, but Cohen's k shows a weak agreement (weighted Cohen's k = 0.497; p < 0,001), and the sensitivity of the MNA-SF to detect malnutrition or its risk (as assessed by the MNA-FF) was 42.6% (despite a specificity of 98.8%). Participants classified as normal using the MNA-SF despite at risk using the MNA-FF present lower scores in two items from the Assessment section (number of full meals eaten daily and amount of fluid consumed per day). These were included in MNA-SF to obtain an extended short-version (MNA-5F8). The difference between the ROC curves for MNA-SF and MNA-5F8 justifies the preferential use of the MNA-5F8 with an estimated cut-off of 14 points, which showed high sensitivity (91.8%) and specificity (79.9%). Conclusions: The addition of two items to the MNA-SF provides a more sensible tool to detect the risk of malnutrition among older adults. General eating-related questions seem relevant to assess malnutrition in this age group.