ESTiG - Artigos em Revistas Indexados à WoS/Scopus
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- The artificial intelligence act: insights regarding its application and implicationsPublication . Cabrera, Beatriz M.; Luiz, Luiz E.; Teixeira, João PauloThis paper deals with Europe's Artificial Intelligence Act, the first regulation on the subject and is considered an international milestone. Therefore, the introduction provides a historical overview of legislative developments on Artificial Intelligence in Europe until the current milestone, namely the approval of the AI Act text, which is currently being amended and translated into its official publication. Afterwards, the regulation is dealt with in detail; its nuances are presented, along with the conceptualisation of artificial intelligence in the regulation and the classification of artificial intelligence systems, which is based on risks to users, together with the mechanisms created to make the regulation more efficient, specifically the Artificial Intelligence Office. Ultimately, considering the great innovation on the subject, this work presents different opinions regarding the application of the regulation, its risk-based analysis and classification, expectations and views on the possible impacts of the act on the market, thereby seeking to expose society's receptiveness to the regulation created. Therefore, based on the discussion points, it can be concluded that the regulation, which will soon be in effect, brings different feelings to citizens and members of the European market, who are still insecure about the risk-based approach used, harbouring feelings of fear about the limitation of innovation. However, at the same time, there is hope, given that regulation is necessary to guarantee safe innovation in line with the fundamental rights set out by the European Union. It can also be concluded that the approval and forthcoming publication of the act is a small step towards the challenges that will arise. It is certain that, regardless of the different opinions that exist, it is necessary to start implementing the act to analyse its effects on the market, society, politics, the economy and, above all, on innovations in artificial intelligence systems.
- A machine learning approach for enhanced glucose prediction in biosensorsPublication . Abreu, António; Oliveira, Daniela dos Santos; Vinagre, Inês; Cavouras, Dionisios; Alves, Joaquim A.; Pereira, Ana I.; Moreira, Felismina T. C.The detection of glucose is crucial for diagnosing diseases such as diabetes and enables timely medical intervention. In this study, a disposable enzymatic screen-printed electrode electrochemical biosensor enhanced with machine learning (ML) for quantifying glucose in serum is presented. The platinum working surface was modified by chemical adsorption with biographene (BGr) and glucose oxidase, and the enzyme was encapsulated in polydopamine (PDP) by electropolymerisation. Electrochemical characterisation and morphological analysis (scanning and transmission electron microscopy) confirmed the modifications. Calibration curves in Cormay serum (CS) and selectivity tests with chronoamperometry were used to evaluate the biosensor’s performance. Non-linear ML regression algorithms for modelling glucose concentration and calibration parameters were tested to find the best-fit model for accurate predictions. The biosensor with BGr and enzyme encapsulation showed excellent performance with a linear range of 0.75–40 mM, a correlation of 0.988, and a detection limit of 0.078 mM. Of the algorithms tested, the decision tree accurately predicted calibration parameters and achieved a coefficient of determination above 0.9 for most metrics. Multilayer perceptron models effectively predicted glucose concentration with a coefficient of determination of 0.828, demonstrating the synergy of biosensor technology and ML for reliable glucose detection.
- The role of sleep on physical and cognitive performance of ultra-endurance athletes: a systematic reviewPublication . Guilherme, Larissa; Rodrigues, Bruno; Rosa, Carla; Leite, Luciano; Scheer, Volker; Forte, Pedro; Hermsdorff, Helen; Kravchychyn, Ana; Souza, Helton de SáSleep is an important factor for recovery and performance in endurance sports, yet its role in ultra-endurance events remains unclear due to extreme physical and cognitive demands and disrupted sleep patterns. This systematic review aimed to analyze the role of sleep in physical and cognitive performance in ultra-endurance athletes. This systematic review followed PRISMA guidelines. A comprehensive search was conducted in May 2025 across PubMed/Medline, Embase, SPORTDiscus, and Web of Science. Two researchers independently screened, selected, extracted, and assessed data quality using the JBI tools (PROSPERO ID: CRD420251042220). Of 424 articles, 16 met inclusion criteria, totaling data from 1389 athletes. Regarding physical performance, better outcomes were associated with no or less sleep during competition (TST), extended sleep the night before, and increased time in light sleep. In contrast, longer wake time, lower sleep quality, greater sleepiness during competition, and higher sleep efficiency were linked to poorer performance. Cognitive performance was positively associated with pre-race sleep quality and mid-race naps. Conversely, greater accumulated sleep before testing was linked to worse cognitive outcomes. Sleep, particularly total sleep time (TST), plays an important role in ultra-endurance performance, although this relationship may be non-linear and influenced by race context and individual strategies. Pre-race and intra-race sleep strategies such as napping and extended sleep may benefit performance. Further rigorous and longitudinal studies are needed to clarify sleep’s impact on performance and recovery in ultra-endurance contexts.