Abstract
Background: During the global pandemic crisis, various detection methods of COVID-19-positive cases based on self-reported information were introduced to provide quick diagnosis tools for effectively planning and managing healthcare resources. These methods typically identify positive cases based on a particular combination of symptoms, and they have been evaluated using different datasets.Purpose: This paper presents a comprehensive comparison of various COVID-19 detection methods based on self-reported information using the University of Maryland Global COVID-19 Trends and Impact Survey (UMD-CTIS), a large health surveillance platform, which was launched in partnership with Facebook.Methods: Detection methods were implemented to identify COVID-19-positive cases among UMD-CTIS participants reporting at least one symptom and a recent antigen test result (positive or negative) for six countries and two periods. Multiple detection methods were implemented for three different categories: rule-based approaches, logistic regression techniques, and tree-based machine-learning models. These methods were evaluated using different metrics including F1-score, sensitivity, specificity, and precision. An explainability analysis has also been conducted to compare methods.Results: Fifteen methods were evaluated for six countries and two periods. We identify the best method for each category: rule-based methods (F1-score: 51.48% -71.11%), logistic regression techniques (F1-score: 39.91% -71.13%), and tree-based machine learning models (F1-score: 45.07% -73.72%). According to the explainability analysis, the relevance of the reported symptoms in COVID-19 detection varies between countries and years. However, there are two variables consistently relevant across approaches: stuffy or runny nose, and aches or muscle pain.Conclusions: Regarding the categories of detection methods, evaluating detection methods using homogeneous data across countries and years provides a solid and consistent comparison. An explainability analysis of a tree-based machine-learning model can assist in identifying infected individuals specifically based on their relevant symptoms. This study is limited by the self-report nature of data, which cannot replace clinical diagnosis.

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Abstract
Hydrogen is a promising commodity, a renewable secondary energy source, and feedstock alike, to meet greenhouse gas emissions targets and promote economic decarbonization. A common goal pursued by many countries, the hydrogen economy receives a blending of public and private capital. After European Green Deal, state members created national policies focused on green hydrogen. This paper presents a study of energy transition considering green hydrogen production to identify Portugal's current state and prospects. The analysis uses energy generation data, hydrogen production aspects, CO2 emissions indicators and based costs. A comprehensive simulation estimates the total production of green hydrogen related to the ratio of renewable generation in two different scenarios. Then a comparison between EGP goals and Portugal's transport and energy generation prospects is made. Portugal has an essential renewable energy matrix that supports green hydrogen production and allows for meeting European green hydrogen 2030-2050 goals. Results suggest that promoting the conversion of buses and trucks into H2-based fuel is better for CO2 reduction. On the other hand, given energy security, thermoelectric plants fueled by H2 are the best option. The aggressive scenario implies at least 5% more costs than the moderate scenario, considering economic aspects.

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Abstract
Developing distributed and scalable Cyber-Physical Systems (CPS) that can handle large amounts of data at high data rates at the edge, remains a challenging task. Also, the limited availability of open-source solutions makes it difficult for developers and researchers to experiment with and deploy CPSs on a larger scale. This work introduces Edge4CPS, an open-source multi-architecture solution built over Kubernetes that aims to enable an easy to use, efficient and scalable solution for the deployment of applications on edge-like distributed computing clusters. To verify the successful real-world implementation of the introduced architecture, the system was tested in a railway scenario, derived from the Ferrovia 4.0 project, which highlights its functionalities.

Abstract
The increasing number of vehicle to grid (V2G) charging stations connected to the electrical grid changes the characteristics of electrical distribution grids. Active power electronics introduces additional capacitance and inductance to the electrical grid and affects the frequency dependent grid impedance. This study shows the impact of a V2G charging station to the frequency dependent grid impedance up to 500 kHz. The LCL filter, the DC link capacitor and inductors cause parallel and series resonances. Resonance frequencies appear in a wide frequency range starting from 500 Hz up to 30 kHz. It is shown that the V2G charger can represent a source of supraharmonic emissions and the importance to consider supraharmonic emissions and the frequency dependent grid impedance to determine the impact of V2G chargers (active power electronics) to the electrical grid is outlined.