Abstract
Nowadays, the technical feasibility and willingness to use blockchain-based electricity trading are taken for granted. However, in the not-too-distant past, the concept was no more than a mere vision. In this chapter, we investigate the sequence of events leading to this rapid paradigm shift over the past 10 years. The development stages of peer-to-peer electricity trading within the context of the fourth industrial revolution and the success of decentralized cryptocurrency markets are first presented. Afterward, the earliest applications of blockchain are demonstrated, along with key milestones in legislation and scientific research to reach the current situation.

Abstract
The Information and Communication Technologies (ICT) plays a major role in tourism, travel and hospitality industry. The Integration of ICT in the tourism industry is essential for success of tourism enterprise, as such it is necessary to integrate ICT in higher education curricula. This paper analyzes the relevance given by the various Portuguese and Spanish institutions of higher education to ICT in their degrees. The analysis carried out was done in degree courses operating in the academic year 2018/2019, in Portuguese and Spanish universities and polytechnics. A comparison was also made with the reality of 2012/2013. Copyright © 2020. Elisabete PAULO MORAIS, Carlos R. CUNHA and João Pedro GOMES. Distributed under Creative Commons Attribution 4.0 International CC-BY 4.0.

Abstract
Common beans (Phaseolus vulgaris L.), represent the most consumed legume worldwide and constitute an important source of protein, being also known to contain antinutritional compounds, which compromise nutrients' bioavailability. However, the standard methodologies to assess these constituents are time-consuming and complex. Therefore, the present study evaluated the suitability of near-infrared (NIR) and mid-infrared (MIR) spectroscopies for the development of simple and reliable methods to assess protein, lipids, tannins and phytic acid contents, besides specific amino acids, in whole bean flours. Partial least squares (PLS) regression was used to develop analytical models, and external validation was performed. NIR displayed better performance for the evaluation of protein, lipids, tannins and phytic acid contents, and MIR, for the assessment of specific amino acids. In both techniques, the use of the 1st derivative was the best data treatment. Overall, both techniques represent reliable methods to evaluate the proximate and antinutritional composition of bean flours.

Abstract
Diabetic Retinopathy (DR) is the most common cause of avoidable vision loss, predominantly affecting the working-age population across the globe. Screening for DR, coupled with timely consultation and treatment, is a globally trusted policy to avoid vision loss. However, implementation of DR screening programs is challenging due to the scarcity of medical professionals able to screen a growing global diabetic population at risk for DR. Computer-aided disease diagnosis in retinal image analysis could provide a sustainable approach for such large-scale screening effort. The recent scientific advances in computing capacity and machine learning approaches provide an avenue for biomedical scientists to reach this goal. Aiming to advance the state-of-the-art in automatic DR diagnosis, a grand challenge on "Diabetic Retinopathy - Segmentation and Grading" was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI-2018). In this paper, we report the set-up and results of this challenge that is primarily based on Indian Diabetic Retinopathy Image Dataset (IDRiD). There were three principal subchallenges: lesion segmentation, disease severity grading, and localization of retinal landmarks and segmentation. These multiple tasks in this challenge allow to test the generalizability of algorithms, and this is what makes it different from existing ones. It received a positive response from the scientific community with 148 submissions from 495 registrations effectively entered in this challenge. This paper outlines the challenge, its organization, the dataset used, evaluation methods and results of top-performing participating solutions. The top-performing approaches utilized a blend of clinical information, data augmentation, and an ensemble of models. These findings have the potential to enable new developments in retinal image analysis and image-based DR screening in particular.

Abstract
This study proposes two predictive models of classification that allow to identify, at the end of the 1st and 2nd semesters, the undergraduate students of a higher education institution more prone to academic dropout. The proposed methodology, which combines 3 popular data mining algorithms, such as random forest, support vector machines and artificial neural networks, in addition to contributing to predictive performance, allows to identify the main factors behind academic dropout. The empirical results show that it is possible to reduce to about 1/4 the 4 tens potential predictors of dropout, and show that there are essentially two predictors, concerning student’s curriculum context, that explain this propensity. This knowledge is useful for decision-makers to adopt the most appropriate strategic measures and decisions in order to reduce student dropout rates.

Abstract
Thin-film sensors surfaces are becoming popular to collect data in several specific and complex processes, namely plastic injection or metal stamping, allowing the digitization of such processes through the use of Internet of Things technologies. A particular challenge in such thin-film sensors surfaces is the data acquisition and signal conditioning system, which implementation is complex due to the characteristics of these sensors (e.g., low amplitude and noisy signals), but even more complex when implemented in real industrial processes, which are subject to harsh conditions, namely noise, dirt and aggressive elements. This work describes a modular data acquisition and signals conditioning system for thin-film sensors surfaces, meeting the requirements of scalability, robustness and low-cost, meaning that it can be easily expanded according to the number of sensors required for the application scenario.