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The production and management of urban waste is a growing challenge and a consequence of our day-to-day resources and activities. According to the Portuguese Environment Agency, in 2019, Portugal produced 1% more tons compared to 2018. The proper management of this waste can be co-substantiated by existing policies, namely, national legislation and the Strategic Plan for Urban Waste. Those policies assess and support the amount of waste processed, allowing the recovery of materials. Among the solutions for waste management is the selective collection of waste. We improve the possibility of manage the smart waste collection of Paper, Plastic, and Glass packaging from corporate customers who joined a recycling program. We have data collected since 2017 until 2020. The main objective of this work is to increase the system's predictive performance, without any loss for citizens, but with improvement in the collection management. We analyze two types of problems: (i) the presence or absence of containers; and (ii) the prediction of the number of containers by type of waste. To carry out the analysis, we applied three machine learning algorithms: XGBoost, Random Forest, and Rpart. Additionally, we also use AutoML for XGBoost and Random Forest algorithms. The results show that with AutoML, generally, it is possible to obtain better results for classifying the presence or absence of containers by type of waste and predict the number of containers.

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The growing digitization of healthcare institutions and its increasing dependence on Internet infrastructure has boosted the concerns related to data privacy and confidentiality. These institutions have been challenged with specific issues, namely the sensitivity of data, the specificity of networked equipment, the heterogeneity of healthcare professionals (nurses, doctors, administrative staff and other) and the IT skills they have. In this paper we present the results obtained with a study made with healthcare professionals on evaluating their awareness level with the information security, namely by assessing their attitudes and behaviours in cybersecurity. The methodology consisted in translating, adjusting and applying two previously validated and already published Likert-type response scales, in a healthcare institution in Portugal, namely "Centro Hospitalar Barreiro Montijo" (CHBM). The scales used were cybersecurity risky behaviour (RScB) and cybersecurity and cybercrime in business attitudes (ATC-IB). Although there were no significant statistical differences between the sociodemographic factors and the scores obtained on both scales, the results showed a relationship between acquired behaviours and the attitudes of involvement with work and organizational commitment, establishing a bridge for the quantification in awareness.(C) 2021 The Authors. Published by Elsevier B. V.

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Objectives To determine the prevalence of atrial fibrillation (AF) and to assess how these patients are being cared for: what anticoagulants are being prescribed and are they being prescribed as recommended? Design Retrospective longitudinal study. Setting This study was conducted in the Regional Health Administration of Northern Portugal. Participants This study used a database that included 63526 patients with code K78 of the International Classification of Primary Care between January 2016 and December 2018. Results The prevalence of AF among adults over 40 years in the northern region of Portugal was 2.3% in 2016, 2.8% in 2017 and 3% in 2018. From a total of 63 526 patients, 95.8% had an indication to receive anticoagulation therapy. Of these, 44 326 (72.9%) are being treated with anticoagulants: 17 936 (40.5%) were prescribed vitamin K antagonists (VKAs) and 26 390 (59.5%) were prescribed non-VKA anticoagulants. On the other hand, 2688 patients of the total (4.2%) had no indication to receive anticoagulation therapy. Of these 2688 patients, 1100 (40.9%) were receiving anticoagulants. Conclusions The prevalence of AF is 3%. Here, we report evidence of both undertreatment and overtreatment. Although having an indication, a considerable proportion of patients (27.1%) are not anticoagulated, and among patients with AF without an indication to receive anticoagulation therapy, a considerable proportion (40.9%) are receiving anticoagulants. The AF-React study brings extremely relevant conclusions to Portugal and follows real-world studies in patients with AF in Europe, presenting some data not yet studied.

Abstract
Gait disabilities empowered intensive research on the field of human-robot interaction to promote effective gait rehabilitation. Assist-as-needed strategies are becoming prominent, appealing to the users' participation in their rehabilitation therapy. This study proposes and assesses the biomechanical effects of an adaptive impedance control strategy that innovatively allows adaptability in interaction-based stiffness and gait trajectory towards a fully assist-as-needed therapy. By modulating the interaction-based stiffness per gait phase, we hypothesize that the strategy appeals to a symbiotic human-orthotic cooperation, augmenting the user's muscular activity. The interaction stiffness was estimated by modelling the human-orthosis interaction torque vs angle curve with a linear regression model. The strategy also allows for real-time trajectory adaptations at different gait phases to fulfil the users' needs. The biomechanical assessment of the impedance-controlled ankle orthosis involved eight healthy volunteers walking at 1.0 and 1.6 km/h. The results revealed a stronger muscular activation regarding the non-assisted leg for the gastrocnemius lateralis (increment ratio >= 1.0 for both gait speeds) and for the tibialis anterior muscle (increment ratio >= 1.0 for 1.6 km/h). The strategy guided users successfully on a healthy gait pattern while allowing deviations (median error < 5.0 degrees) given the users' intention weighted by interaction stiffness. Findings showed the relevance for adapting gait trajectory as users prefer higher trajectories as the speed increases. No significant temporal variations or neither knee angular compensations were observed (p value >= 0.11). Overall results support that this strategy may be applied for intensity-adapted gait training, allowing different human-robot compliant levels.

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