Abstract
Portugal's electricity generation relies heavily on renewable sources, which accounted for over half of the country's production in recent years. The Portuguese government has set ambitious renewable energy targets for 2030. The R3EA project (https://r3ea.web.ua.pt/pt/projeto) evaluates the impact of new investments in solar and wind energy capacity in the Centro Region of Portugal, focusing on the costs and benefits of externalities. This study examines Portugal's electricity market outcomes in terms of prices, generation mix, and emissions for different wind and solar capacities, using the National Energy and Climate Plans (NECP) of Portugal and Spain as the reference scenario. The electricity markets of both countries are modelled together, reflecting the integrated Iberian market with significant interconnections. The NECP scenario results in lower market prices and emissions, but less significantly than scenarios with lower demand and higher renewable energy share. In all scenarios, increasing renewable energy sources drives market prices down from over 200/MWh in 2022 to under 100/MWh during peak hours in 2030. Demand is the main driver of emissions, as higher demand leads to more reliance on fossil fuel plants. Lower demand scenarios in 2030 show 20 % fewer CO2 emissions per TWh than higher demand ones.

Abstract
Background and Objectives: Early detection of cognitive impairment is crucial for timely clinical interventions aimed at delaying progression to dementia. However, existing screening tools are not ideal for wide population screening. This study explores the potential of combining machine learning, specifically, one-class classification, with simpler and quicker motor-cognitive tasks to improve the early detection of cognitive impairment. Methods: We gathered data on gait, fingertapping, cognitive, and dual tasks from older adults with mild cognitive impairment and healthy controls. Using one-class classification, we modeled the behavior of the majority group (healthy controls), identifying deviations from this behavior as abnormal. To account for confounding effects, we integrated confound regression into the classification pipeline. We evaluated the performance of individual tasks, as well as the combination of features (early fusion) and models (late fusion). Additionally, we compared the results with those from two-class classification and a standard cognitive screening test. Results: We analyzed data from 37 healthy controls and 16 individuals with mild cognitive impairment. Results revealed that one-class classification had higher predictive accuracy for mild cognitive impairment, whereas two-class classification performed better in identifying healthy controls. Gait features yielded the best results for one-class classification. Combining individual models led to better performance than combining features from the different tasks. Notably, the one-class majority voting approach exhibited a sensitivity of 87.5% and a specificity of 75.7%, suggesting it may serve as a potential alternative to the standard cognitive screening test. In contrast, the two-class majority voting failed to improve the low sensitivities achieved by the individual models due to the underrepresentation of the impaired group. Conclusion: Our preliminary results support the use of one-class classification with confound control to detect abnormal patterns of gait, fingertapping, cognitive, and dual tasks, to improve the early detection of cognitive impairment. Further research is necessary to substantiate the method's effectiveness in broader clinical settings.

Abstract
Parkinson's Disease (PD) is a neurodegenerative disease characterized by severe motor symptoms, with no cure to date, and deep brain stimulation (DBS) is one of the most effective therapies to reduce the symptoms. However, not all patients benefit equally of this therapy due to adverse effects caused by the stimulation of unwanted areas, making it essential to use diverse technologies when analysing the effect of the DBS in PD. Wearable devices, such as the iHandU System that analyses motor data combined with the Percept TM PC neurostimulator, capable of recording brain signals in real time, allow a more precise, personalized, and adaptive approach to treatment. While each method alone provides valuable but limited insights, combining and synchronizing data from the different sources enables a more comprehensive and dynamic understanding of the effects of stimulation on the patient. A study with 6 DBS-implanted participants from Centro Hospitalar Universitario de São João was conducted to test a synchronization protocol using both the Percept TM PC and the iHandU System. The protocol combined the Network Time Protocol and the Artifact-Based Synchronization Techniques, with an average of less than 500 ms of delay between signals. The results obtained show that this combination improves signal synchronization accuracy and consistency across subjects, minimizing delays and reducing reliance on visible peaks in cases of low signal quality or inconsistent artifact detection. © 2025 IEEE.

Abstract
The Internet of Vehicles (IoV) presents complex cybersecurity challenges, particularly against Denial-of-Service (DoS) and spoofing attacks targeting the Controller Area Network (CAN) bus. This study leverages the CICIoV2024 dataset, comprising six distinct classes of benign traffic and various types of attacks, to evaluate advanced machine learning techniques for instrusion detection systems (IDS). The models XGBoost, Random Forest, AdaBoost, Extra Trees, Logistic Regression, and Deep Neural Network were tested under realistic, imbalanced data conditions, ensuring that the evaluation reflects real-world scenarios where benign traffic dominates. Using hyperparameter optimization with Optuna, we achieved significant improvements in detection accuracy and robustness. Ensemble methods such as XGBoost and Random Forest consistently demonstrated superior performance, achieving perfect accuracy and macro-average F1-scores, even when detecting minority attack classes, in contrast to previous results for the CICIoV2024 dataset. The integration of optimized hyperparameter tuning and a broader methodological scope culminated in an IDS framework capable of addressing diverse attack scenarios with exceptional precision.

Abstract
The growing use of technology and social media has resulted in the emergence of digital influencers, a new profession capable of changing the mentalities and behaviours of those who follow them. This study arises to better understand the potential impact digital influencers might have on the Portuguese population's purchase behaviour and patterns, and for this purpose, seven hypotheses were formulated. An online questionnaire was conducted to respond to these theoretical assumptions and collected data from 175 respondents. A total of 129 valid answers were considered. It was possible to conclude that purchase intention does not necessarily translate into a purchase action. It was also concluded that the relationship between social network use and the purchase of products/services recommended by influencers is only statistically significant for Instagram. Furthermore, the individuals' generation is not statistically significant / linked with purchasing a product/service recommended by influencers. Yet further, a small percentage of respondents have also identified themselves as impulsive shoppers and perceived Instagram as their favourite social network. With the results of this study, it is also possible to state that the influencer's opinion was classified as the last factor considered in the purchase decision process. Additionally, there is a weak negative association between purchasing a product/service recommended by influencers with sponsorship disclosure and remunerated partnership, which decreases credibility and discourages purchasing, in Portugal, a feminine culture which dislikes materialism.

Abstract
In pursuing Industry 5.0's vision, which emphasises human well-being and the seamless integration of robots into manufacturing processes, understanding the role of anthropomorphic design is crucial. Anthropomorphic design, where robots exhibit human-like, animal-like, or even entirely novel traits (e.g. a display scrolling text), aims to improve human-robot interaction (HRI) and enhance human acceptance within manufacturing contexts. Understanding the optimal degree of human-readable characteristics in robots is essential for further advancements in this domain. This systematic literature review aims to identify anthropomorphic mechanisms in HRI and their effect on human acceptance in manufacturing. Using the PRISMA methodology, a systematic literature review was conducted across the WOS, EBSCO, and SCOPUS databases, resulting in the selection of four articles for final analysis. A quality assessment of the articles was conducted. On a scale of 0 to 16, article scores ranged from 10 to 15, with an average score of 13. The findings indicate that while current research provides valuable insights, it has predominantly focused on conventional anthropomorphic mechanisms from social robotics, such as basic human-like features (e.g., facial expressions, gestures), without exploring more advanced or novel traits. This highlights significant room for further exploration and innovation in industrial settings to enhance user acceptance and interaction. The study underscores the necessity for continued research and development to leverage advanced anthropomorphic designs that can better fulfil the goals of Industry 5.0.