Abstract
COVID pandemic has changed the way in which we carry out our daily life and also have affected educational processes. Teaching and learning have changed from a most common face to face context to a blended or online context. This implies changes in the way to carry out the activates and have an impact in research projects such as RoboSTEAM. Such project, that applies Challenge Based Learning methodologies with application of Robotics and Mechatronics, requires to change its approach to show how it is possible to succeed in the new situation. This paper describes how the project has evolved, how it has been affected by COVID and the possible changes to carry out. Regarding this last issue remote labs and online tools are presented as solutions to support changes in the application of challenge-based learning methodology. © 2020 ACM.

Abstract
Digital marketing is gaining predominance in marketing strategies across the globe. Digitization is becoming more and more present in business, not just changes in consumer behavior, but the adoption of new technologies, tools, and applications is highly disruptive, with immediate impact on the business of all companies. The common link between digital marketing definitions is the use of tools, namely online tools. One of the biggest changes in human interaction is the recent proliferation of online social networks. Rapid growth of web-based platforms that facilitate online social behavior has significantly modified the nature of human activities, habitats, and interactions. Real-world social relationships have been migrated to the virtual world, resulting in online communities that bring people together from across the globe. This study aims to identify and describe the various digital marketing tools and which of these can be used to increase the SMEs competitiveness. © 2021, IGI Global.

Abstract
Conventionally, vital sign monitoring for neonatal infants inside the Neonatal Intensive Care Unit is performed via probes affixed to their skin. However, such instruments may cause damage to the epidermis and increase the risk of infection as well as promote discomfort to the infant. As an alternative to traditional means of monitoring heart rate, remote Photoplethysmography techniques have been surging among the scientific community. These techniques have been vastly explored for adult subjects but not for neonatal infants, who would greatly benefit from such applications. This study aims at developing a regular consumer camera-based framework for continuous and contactless extraction of the heart rate in adult subjects in challenging conditions and investigating the tool's ability to adapt to a new domain which consists of newborn subjects and the real-world conditions of a Neonatal Intensive Care Unit.

Abstract
AbstractPurposePredicting outcomes after resection of primary melanoma remains crude, primarily based on tumour thickness. We explored gene expression signatures for their ability to better predict outcomes.MethodsDifferential expression analysis of 194 primary melanomas resected from patients who either developed distant metastasis (n=89) or did not (n=105) was performed. We identified 121 metastasis-associated genes that were included in our prognostic signature, “Cam_121”. Several machine learning classification models were trained using nested leave- one-out cross validation (LOOCV) to test the signature’s capacity to predict metastases, as well as regression models to predict survival. The prognostic accuracy was externally validated in two independent datasets.ResultsCam_121 performed significantly better in predicting distant metastases than any of the models trained with the clinical covariates alone (pAccuracy=4.92×10-3), as well as those trained with two published prognostic signatures. Cam_121 expression score was strongly associated with progression-free survival (HR=1.7, p=3.44×10-6), overall survival (HR=1.73, p=7.71×10-6) and melanoma-specific survival (HR=1.59, p=0.02). Cam_121 expression score also negatively correlated with measures of immune cell infiltration (?=-0.73, p<2.2×10-16), with a higher score representing reduced tumour lymphocytic infiltration and a higher absolute 5-year risk of death in stage II melanoma.ConclusionsThe Cam_121 primary melanoma gene expression signature outperformed currently available alternatives in predicting the risk of distant recurrence. The signature confirmed (using unbiased approaches) the central prognostic importance of immune cell infiltration in long-term patient outcomes and could be used to identify stage II melanoma patients at highest risk of metastases and poor survival who might benefit most from adjuvant therapies.Translational relevancePredicting outcomes after resection of primary melanoma is currently based on traditional histopathological staging, however survival outcomes within these disease stages varies markedly. Since adjuvant systemic therapies are now being used routinely, accurate prognostic information is needed to better risk stratify patients and avoid unnecessary use of high cost, potentially harmful drugs, as well as to inform future adjuvant strategies. The Cam_121 gene expression signature appears to have this capability and warrants evaluation in prospective clinical trials.

Abstract
The requirements of Machine Learning applications are changing rapidly. Machine Learning models need to deal with increasing volumes of data, and need to do so quicker as responses are expected more than ever in real-time. Plus, sources of data are becoming more and more dynamic, with patterns that change more frequently. This calls for new approaches and algorithms, that are able to efficiently deal with these challenges. In this paper we propose the use of a Genetic Algorithm to Optimize a Stacking Ensemble specifically developed for streaming scenarios. A pool of solutions is maintained in which each solution represents a distribution of weights in the ensemble. The Genetic Algorithm continuously optimizes these weights to minimize the cost function. Moreover, new models are added at regular intervals, trained on more recent data. These models eventually replace older and less accurate ones, making the ensemble adapt continuously do changes in the distribution of the data.

Abstract
The heart-rate dynamics are one of the most analyzed physiological interactions. Many mathematical methods were proposed to evaluate heart-rate variability. These methods have been successfully applied in research to expand knowledge concerning the cardiovascular dynamics in healthy as well as in pathological conditions. Notwithstanding, they are still far from clinical practice. In this paper, we aim to review the nonlinear methods most used to assess heart-rate dynamics. We focused on methods based on concepts of chaos, fractality, and complexity: Poincare plot, recurrence plot analysis, fractal dimension (and the correlation dimension), detrended fluctuation analysis, Hurst exponent, Lyapunov exponent entropies (Shannon, conditional, approximate, sample entropy, and multiscale entropy), and symbolic dynamics. We present the description of the methods along with their most notable applications.