Abstract
Internet voting has been trialed or introduced for several countries, including Norway, Portugal, United States, United Kingdom and Switzerland as an additional voting channel to increase voter turnout and, also to modernize the electoral process. However, only Estonia has successful introduced internet voting, deploying e-enabled elections in general governmental levels. This paper aims to provide an exploratory study on the Estonian internet voting model to identify pre-conditions for internet voting introduction in Portugal, addressing legal, technical and technological considerations. For doing so, it includes a cross-country comparative analysis in two perspectives. Firstly, an analysis in the Estonian electoral framework, highlighting the most important legal adaptations that make possible internet voting introduction to identify potential transformation for the Portuguese context. Secondly, to provide a technological overview towards the Portuguese e-government ecosystem to seek similar conditions that can make internet voting possible in Estonia. Copyright ©2021 for this paper by its authors.

Abstract
Industry 4.0 is changing the manufacturing paradigms across industries. However, many repetitive processes still rely heavily on human workers, as in the case of the automotive industry, where the final quality inspection of assembled vehicles is still performed using a paper-based conformity list. We instead propose a hybrid solution where a deep learning-based hierarchical autonomous detection system identifies the non-conforming parts and informs the operator via a wearable device, trained exclusively with simulated data. This scalable and cost-effective system achieved a 65.7% accuracy score, which, considering the experimental nature of this work, further confirms the potential of this approach.

Abstract
Robotic soccer simulation is a challenging area, where the development of new techniques is paramount to remain competitive. Robotic skill evolution has accelerated with recent developments in deep learning algorithms, leading to improvements in behavior number and complexity. Shooting a ball towards a defined target is one of the most basic yet indispensable skills in soccer. However, fast and accurate kicks pose several challenges. In order to reach that target, the skill is highly dependent on the ability of the agent to self-locate and self-orient, in order to better position itself before the kick. To tackle these issues, a 6D localization technique was devised. To optimize the kick behavior, two scenarios were proposed. In the first, the robot walks to the ball, stops, and then kicks. In the second, it kicks the ball while moving. We used state-of-the-art algorithms - Proximal Policy Optimization and Soft Actor Critic - to solve these complex problems and show their applicability in the context of RoboCup. Obtained results have shown very significant improvements over previously used behaviors by FC Portugal 3D team. The new kick in motion executes 5 times faster than the previous kick, and the new 6D pose estimator has an average error of just 6.3mm, a reduction of more than 97%.

Abstract
Objectives High-quality data are crucial for guiding decision-making and practising evidence-based healthcare, especially if previous knowledge is lacking. Nevertheless, data quality frailties have been exposed worldwide during the current COVID-19 pandemic. Focusing on a major Portuguese epidemiological surveillance dataset, our study aims to assess COVID-19 data quality issues and suggest possible solutions. Settings On 27 April 2020, the Portuguese Directorate-General of Health (DGS) made available a dataset (DGSApril) for researchers, upon request. On 4 August, an updated dataset (DGSAugust) was also obtained. Participants All COVID-19-confirmed cases notified through the medical component of National System for Epidemiological Surveillance until end of June. Primary and secondary outcome measures Data completeness and consistency. Results DGSAugust has not followed the data format and variables as DGSApril and a significant number of missing data and inconsistencies were found (eg, 4075 cases from the DGSApril were apparently not included in DGSAugust). Several variables also showed a low degree of completeness and/or changed their values from one dataset to another (eg, the variable 'underlying conditions' had more than half of cases showing different information between datasets). There were also significant inconsistencies between the number of cases and deaths due to COVID-19 shown in DGSAugust and by the DGS reports publicly provided daily. Conclusions Important quality issues of the Portuguese COVID-19 surveillance datasets were described. These issues can limit surveillance data usability to inform good decisions and perform useful research. Major improvements in surveillance datasets are therefore urgently needed-for example, simplification of data entry processes, constant monitoring of data, and increased training and awareness of healthcare providers-as low data quality may lead to a deficient pandemic control.

Abstract
This paper presents a nine-leg (9L) multilevel inverter to drive a six-phase induction machine in an open-end winding (OEW) configuration. The system is based on three conventional two-level three-phase voltage source inverters (VSIs) and three, two, or one isolated dc links. A machine with two sets of 30 degrees shifted three-phase stator windings is considered. The inverter operating principles are discussed and a simple space-vector pulse-width modulation (SV-PWM) is proposed. The 9L-OEW system is compared with two conventional drives: the six-leg two-level (6L), and the 12-leg OEW (12L-OEW) systems. The number of components, the required dc-link voltage, voltage and current harmonic distortions, torque ripple, and semiconductor losses are considered as figures of merit. Simulations and experiments were also performed, showing steady-state results for a 1 HP machine operating at constant torque.

Abstract
Background: Ovar was the first Portuguese municipality to declare active community transmission of SARS-CoV-2, with total lockdown decreed on March 17, 2020. This context provided conditions for a large-scale testing strategy, allowing a referral system considering other symptoms besides the ones that were part of the case definition (fever, cough, and dyspnea). This study aims to identify other symptoms associated with COVID-19 since it may clarify the pre-test probability of the occurrence of the disease. Methods: This case-control study uses primary care registers between March 29 and May 10, 2020 in Ovar municipality. Pre-test clinical and exposure-risk characteristics, reported by physicians, were collected through a form, and linked with their laboratory result. Results: The study population included a total of 919 patients, of whom 226 (24.6%) were COVID-19 cases and 693 were negative for SARS-CoV-2. Only 27.1% of the patients reporting contact with a confirmed or suspected case tested positive. In the multivariate analysis, statistical significance was obtained for headaches (OR 0.558), odynophagia (OR 0.273), anosmia (OR 2.360), and other symptoms (OR 2.157). The interaction of anosmia and odynophagia appeared as possibly relevant with a borderline statistically significant OR of 3.375. Conclusion: COVID-19 has a wide range of symptoms. Of the myriad described, the present study highlights anosmia itself and calls for additional studies on the interaction between anosmia and odynophagia. Headaches and odynophagia by themselves are not associated with an increased risk for the disease. These findings may help clinicians in deciding when to test, especially when other diseases with similar symptoms are more prevalent, namely in winter.