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 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.

Abstract
The widespread adoption of digital technology tied with the 4th industrial revolution means the complete reinvention of how business is done. Digital Twin (DT) technologies are now a key technology trend that is already being developed and commercialized to optimize numerous manufacturing processes. In this paper, and from an Information Systems (IS) discipline viewpoint, we take stock of the different technological visions of the DT in manufacturing. We leverage this summary as a stepping stone for discussing the DT's sociotechnical design implications by pointing how this approach is essential for the design of DT software that is specific to its environment and users and co-evolves with it. Furthermore, we present our vision for a DT-based Digital Platform that can support product design and life-cycle management while generating value through an ecosystem of twin-driven product-service systems. Lastly, we show how the Transformer 4.0 project will demonstrate the main principle of our vision by placing the DT of the power transformer with the dual role of virtual counterpart of the physical product and as the architectural framework for (i) managing and processing the historical data collected from the multiple working instances of DTs, and (ii) managing and integrating design information (models, specifications, design data, among others). Copyright (C) 2021 The Authors.

Abstract
There are significant difficulties in deploying and reusing application code within the robotics community. Container technology proves to be a viable solution for such problems, as containers isolate application code and all its dependencies from the surrounding computational environment. They are also light, fast and performant. Manual generation of configuration files required by orchestration tools such as Docker Compose is very time-consuming, especially for more complex scenarios. In this paper a solution is presented to ease the development and deployment of Robot Operating System (ROS) packages using containers, by automatically generating all files used by Docker Compose to both containerize and orchestrate multiple ROS workspaces, supporting multiple ROS distributions and multi-robot scenarios. The proposed solution also generates Dockerfiles and is capable of building new Docker images at run-time, given a list of desired ROS packages to be containerized. Integration with existing Docker images is supported, even if non-ROS-related. After an analysis of existing solutions and techniques for containerizing ROS nodes, the multi-stage pipeline adopted by the proposed solution for file generation is detailed. Then, a real usage example of the proposed tool is presented, showcasing how it an aid both the development and deployment of new ROS packages and features.