Abstract
Background Stratifying patients with sepsis was the basis of the predisposition, infection, response and organ dysfunction (PIRO) concept, an attempt to resolve the heterogeneity in treatment response. The purpose of this study is to perform an independent validation of the PIRO staging system in an international cohort and explore its utility in the identification of patients in whom time to antibiotic treatment is particularly important. Methods Prospective international cohort study, conducted over a 6-month period in five Portuguese hospitals and one Australian institution. All consecutive adult patients admitted to selected wards or the intensive care, with infections that met the CDC criteria for lower respiratory tract, urinary, intra-abdominal and bloodstream infections were included. Results There were 1638 patients included in the study. Patients who died in hospital presented with a higher PIRO score (10 +/- 3 vs 8 +/- 4, p < 0.001). The observed mortality was 3%, 15%, 24% and 34% in stage I, II, III and IV, respectively, which was within the predicted intervals of the original model, except for stage IV patients that presented a lower mortality. The hospital survival rate was 84%. The application of the PIRO staging system to the validation cohort resulted in a positive predictive value of 97% for stage I, 91% for stage II, 85% for stage III and 66% for stage IV. The area under the receiver operating characteristics curve (AUROC) was 0.75 for the all cohort and 0.70 if only patients with bacteremia were considered. Patients in stage III and IV who did not have antibiotic therapy administered within the desired time frame had higher mortality rate than those who have timely administration of antibiotic. Conclusions To our knowledge, this is the first external validation of this PIRO staging system and it performed well on different patient wards within the hospital and in different types of hospitals. Future studies could apply the PIRO system to decision-making about specific therapeutic interventions and enrollment in clinical trials based on disease stage.

Abstract
As e-Learning systems have become gradually prevalent, forcing a (sometimes needed) physical distance between lecturers and their students, new methods need to emerge to fill this enlarging gap. Educators need, more than ever, systems capable of warning them (and the students) of situations that might create future problems for the learning process. The capacity to give and get feedback is naturally the best way to overcome this problem. However, in e-learning contexts, with dozens or hundreds of students, the solution becomes less simple. In this work we propose a system capable of continuously giving feedback on the performance of the students based on the interaction sequences they undertake with the LMS. This work innovates in what concerns the sequences of activity accesses together with the computation of the duration of these online learning activities, which are then encoded and fed into machine learning algorithms. We used a longitudinal experiment from five academic years. From our set of classifiers, the Random Forest obtained the best results for preventing low grades, with an accuracy of nearly 87%.

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
The growing adoption of photovoltaic systems as a result of government incentives and the cost-effectiveness of the technology will bring significant environmental benefits and help countries meeting their international commitments in terms of renewables share. Nevertheless, an unsuitable site location could compromise its production and lead to a poor integration. An optimal location of photovoltaic systems must account for factors such as land use restrictions, orography, environmental, climatic limitations, and proximity to infrastructure. A key aspect that needs to be further researched is the influence of the electric demand requirement and its spatial distribution on the enhancement of photovoltaic integration. This paper proposes a novel approach to define optimal sites for photovoltaic plants, connected to the medium-voltage level, using a geographic information system based multi-criteria decision making and spatial overlay with electric load. The main feature of this work is the use of high-resolution information to spatially characterize the demand and make a density analysis. The performance of the proposed method is assessed in the service area of an Ecuadorian power utility. Scenarios considering solar potential and the massive penetration of a new type of load are assessed to define the photovoltaic sites that enhance the integration of renewable sources in the case study.

Abstract
The robotics field is widely used in the industrial domain, but nowadays several other domains could also take advantage of it. This interdisciplinary branch of engineering requires the use of human interfaces, efficient communication systems, high storage and processing capabilities, among other issues, to perform complex tasks. This paper aims to propose a cloud-based framework platform for robot operation in a hospital environment, addressing some challenges, such as communications security and processing/storage features. The recent developments in the artificial intelligence field and cloud resources sharing are allowing the penetration of robots in unstructured environments. However, some new challenges and solutions need to be tested in real environments. Our main contribution is to decrease the time-consumption related to processing and storage costs, associated with the physical processing resources of the robots. Also, the proposed methods provide an increase of the processing variables that are not yet present in the physical resources, such as in the case of robots with limited processing time or storage capabilities. This paper presents a platform based on Cloud Computing with services to support processing, storage and analytics applied to hospital environments. The proposed platform enables to achieve a decrease in the time-consumption, especially when it is intended to retrieve information about all robot activities. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

Abstract
Geological disasters are responsible for the loss of human lives and for significant economic and financial damage every year. Considering that these disasters may occur anywhere-both in remote and/or in highly populated areas-and anytime, continuously monitoring areas known to be more prone to geohazards can help to determine preventive or alert actions to safeguard human life, property and businesses. Remote sensing technology-especially satellite-based-can be of help due to its high spatial and temporal coverage. Indeed, data acquired from the most recent satellite missions is considered suitable for a detailed reconstruction of past events but also to continuously monitor sensitive areas on the lookout for potential geohazards. This work aims to apply different techniques and methods for extensive exploitation and analysis of remote sensing data, with special emphasis given to landslide hazard, risk management and disaster prevention. Multi-temporal SAR (Synthetic Aperture Radar) interferometry, SAR tomography, high-resolution image matching and data modelling are used to map out landslides and other geohazards and to also monitor possible hazardous geological activity, addressing different study areas: (i) surface deformation of mountain slopes and glaciers; (ii) land surface displacement; and (iii) subsidence, landslides and ground fissure. Results from both the processing and analysis of a dataset of earth observation (EO) multi-source data support the conclusion that geohazards can be identified, studied and monitored in an effective way using new techniques applied to multi-source EO data. As future work, the aim is threefold: extend this study to sensitive areas located in different countries; monitor structures that have strategic, cultural and/or economical relevance; and resort to artificial intelligence (AI) techniques to be able to analyse the huge amount of data generated by satellite missions and extract useful information in due course.