Abstract

Abstract
Higher Education Institutions (HEI) play a central role in shaping the future through their ability to transmit, innovate and generate knowledge, near their students and community. They also establish strong relationship within society and the environment. Higher Education plays an important role in laying the foundations for the development of competencies for sustainable entrepreneurship, competencies that go beyond disciplinary knowledge and encompass skills, knowledge, and attitudes focused towards a holistic and sustainability-oriented approach. By preparing their students for the labour market, HEIs are proactively responding to the wide range of challenges that the dynamic and uncertain environment of the 21st-century presents. However, the demand is great and the road to be travelled is long. The literature review is extensive about the expected competencies, all indicating that they are critical success factors for individuals to ensure and sustain their career progression. Today's students are tomorrow leaders, players who can shape the world, make it a better place to live and work. Based on personal characteristics (attitudes and personality) and leadership, this study aims to contribute a better understanding of the relationships between these factors, with a particular focus on entrepreneurship. Using a quantitative approach, a questionnaire was given to undergraduate students of the Polytechnic Institute of Cavado and Ave (IPCA), in Portugal. The data were analysed and discussed according to the possible impact of the entrepreneurial leader's behaviour and the performance of the HEI where he/she is inserted.

Abstract
Several approaches with interesting results have been proposed over the years for robot grasp planning. However, the industry suffers from the lack of an intuitive and reliable system able to automatically estimate grasp poses while also allowing the integration of grasp information from the accumulated knowledge of the end user. In the presented paper it is proposed a non-object-agnostic grasping pipeline motivated by picking use cases from the aerospace industry. The planning system extends the functionality of the simulated annealing optimization algorithm for allowing its application within an industrial use case. Therefore, this paper addresses the first step of the design of a reconfigurable and modular grasping pipeline. The key idea is the creation of an intuitive and functional grasping framework for being used by factory floor operators according to the task demands. This software pipeline is capable of generating grasp solutions in an offline phase, and later on, in the robot operation phase, can choose the best grasp pose by taking into consideration a set of heuristics that try to achieve a successful grasp while also requiring the least effort for the robotic arm. The results are presented in a simulated and a real factory environment, relying on a mobile platform developed for intralogistic tasks. With this architecture, new state-of-art methodologies can be integrated in the future for growing the grasping pipeline and make it more robust and applicable to a wider range of use cases.

Abstract
The increasing use of sensor networks has led to an ever larger number of available spatiotemporal datasets. Forecasting applications using this type of data are frequently motivated by important domains such as environmental monitoring. Being able to properly assess the performance of different forecasting approaches is fundamental to achieve progress. However, traditional performance estimation procedures, such as cross-validation, face challenges due to the implicit dependence between observations in spatiotemporal datasets. In this paper, we empirically compare several variants of cross-validation (CV) and out-of-sample (OOS) performance estimation procedures, using both artificially generated and real-world spatiotemporal datasets. Our results show both CV and OOS reporting useful estimates, but they suggest that blocking data in space and/or in time may be useful in mitigating CV's bias to underestimate error. Overall, our study shows the importance of considering data dependencies when estimating the performance of spatiotemporal forecasting models.

Abstract
Introduction: Disease-related undernutrition is highly prevalent and requires timely intervention. However, identifying undernutrition often relies on physician judgment. As Internal Medicine wards are the backbone of the hospital setting, insight into the prevalence of nutritional risk in this population is essential. We aimed to determine the prevalence of nutritional risk in Internal Medicine wards, to identify its correlates, and to assess the agreement between the physicians' impression of nutritional risk and evaluation by Nutritional Risk Screening 2002. Material and Methods: A cross-sectional multicentre study was performed in Internal Medicine wards of 24 Portuguese hospitals during 2017. Data on demographics, previous hospital admissions, primary diagnosis, and Charlson comorbidity index score were collected. Nutritional risk at admission was assessed using Nutritional Risk Screening 2002. Agreement between physicians' impression of nutritional risk and Nutritional Risk Screening 2002 was tested by Cohen's kappa. Results: The study included 729 participants (mean age 74 +/- 14.6 years, 51% male). The main reason for admission was respiratory disease. Mean Charlson comorbidity index score was 5.8 +/- 2.8. Prevalence of nutritional risk was 51%. Nutritional risk was associated with admission during the previous year (odds ratio = 1.65, 95% confidence interval: 1.22 - 2.24), solid tumour with metastasis (odds ratio = 4.73, 95% confidence interval: 2.06 - 10.87), any tumour without metastasis (odds ratio = 2.04, 95% confidence interval:1.24 - 3.34), kidney disease (odds ratio = 1.83, 95% confidence interval: 1.21 - 2.75), peptic ulcer (odds ratio = 2.17, 95% confidence interval: 1.10 - 4.25), heart failure (odds ratio = 1.51, 95% confidence interval: 1.11 - 2.04), dementia (odds ratio = 3.02, 95% confidence interval: 1.96 - 4.64), and cerebrovascular disease (odds ratio = 1.62, 95% confidence interval: 1.12 - 2.35). Agreement between physicians' evaluation of nutritional status and Nutritional Risk Screening 2002 was weak (Cohen's kappa = 0.415, p < 0.001). Discussion: Prevalence of nutritional risk in the Internal Medicine population is very high. Admission during the previous year and multiple comorbidities increase the odds of being at-risk. Subjective physician evaluation is not appropriate for nutritional screening. Conclusion: The high prevalence of at-risk patients and poor subjective physician evaluation suggest the need to implement mandatory nutritional screening.

Abstract
The algebraic-group model (AGM), which lies between the generic group model and the standard model of computation, provides a means by which to analyze the security of cryptosystems against so-called algebraic adversaries. We formalize the AGM within the framework of universal composability, providing formal definitions for this setting and proving an appropriate composition theorem. This extends the applicability of the AGM to more-complex protocols, and lays the foundations for analyzing algebraic adversaries in a composable fashion. Our results also clarify the meaning of composing proofs in the AGM with other proofs and they highlight a natural form of independence between idealized groups that seems inherent to the AGM and has not been made formal before—these insights also apply to the composition of game-based proofs in the AGM. We show the utility of our model by proving several important protocols universally composable for algebraic adversaries, specifically: (1) the Chou-Orlandi protocol for oblivious transfer, and (2) the SPAKE2 and CPace protocols for password-based authenticated key exchange.