Abstract
Objectives: Social isolation and loneliness among older people are widespread, with an impact on physical and mental health. Cycling Without Age (CWA) is an international cycling programme developed to minimise social isolation and loneliness in older people. It involves trishaw (electric bicycle) rides in the open air, led by volunteer riders. This study aimed to analyse the effects of CWA intervention on loneliness and social isolation among older people living in Porto, Portugal. Methods: Older adults (aged 55 years or older) living in the community or a nursing home were included. The intervention comprised at least four bicycle rides, with a duration between 30 and 60 min. A research protocol was applied before and after the intervention, which included the UCLA Loneliness Scale and the Abbreviated Lubben Social Network Scale. Results: A total of 47 participants (median age = 85 years) completed the intervention. Participants were mostly female (81%), widowed (66%) and living in nursing homes (72%). A statistically significant decrease in loneliness was found after the intervention (Median [IQR]_after = 24.0 [16.0] vs. before = 17.0 [6.0]; p < 0.05). Discussion: This preliminary work highlights the positive effect the CWA intervention may have on loneliness among older adults, which is consistent with other CWA programme studies. However, future research is required to evaluate whether these effects persist over time.

Abstract
Riding a motorcycle involves risks that can be minimized through advanced sensing and response systems to assist the rider. The use of camera-collected images to monitor road conditions can aid in the development of tools designed to enhance rider safety and prevent accidents. This paper proposes a method for developing deep learning models designed to operate efficiently on embedded systems like the Raspberry Pi, facilitating real-time decisions that consider the road condition. Our research tests and compares several state-of-the-art convolutional neural network architectures, including EfficientNet and Inception, to determine which offers the best balance between inference time and accuracy. Specifically, we measured top-1 accuracy and inference time on a Raspberry Pi, identifying EfficientNetV2 as the most suitable model due to its optimal trade-off between performance and computational demand. The model's top-1 accuracy significantly outperformed other models while maintaining competitive inference speeds, making it ideal for real-time applications in traffic-dense urban settings.

Abstract
The global transition to a low-carbon energy system requires innovative solutions that integrate renewable energy production with storage and utilization technologies. The growth in energy demand, combined with the intermittency of these sources, highlights the need for advanced management models capable of ensuring system stability and efficiency. This paper presents the development of an optimized energy management system integrating renewable sources, with a focus on green hydrogen production via electrolysis, storage, and use through a fuel cell. The system aims to promote energy autonomy and support the transition to a low-carbon economy by reducing dependence on the conventional electricity grid. The proposed model enables flexible hourly energy flow optimization, considering solar availability, local consumption, hydrogen storage capacity, and grid interactions. Formulated as a Mixed-Integer Linear Programming (MILP) model, it supports strategic decision-making regarding hydrogen production, storage, and utilization, as well as energy trading with the grid. Simulations using production and consumption profiles assessed the effects of hydrogen storage capacity and electricity price variations. Results confirm the effectiveness of the model in optimizing system performance under different operational scenarios.

Abstract
O género textual de divulgação científica é fundamental para a disseminação do conhecimento científico de forma acessível e compreensível junto do público não especializado, apresentando estrutura e características diferentes das dos artigos científicos (e.g., Garces-Conejos & Sanchez-Macarro, 1998; Zamboni, 1998). Os estudos sobre as propriedades linguísticas do texto de divulgação científica em português europeu não abundam, sendo a exceção o projeto Promoção da Literacia Científica (Gonçalves & Jorge, 2018). Por outro lado, no âmbito da produção de conteúdo, os grandes modelos de linguagem (LLM), nomeadamente os modelos GPT da OpenAI, ganharam, em pouco tempo, atenção generalizada do público. Sendo recentes, a avaliação da qualidade linguística dos textos produzidos é ainda muito reduzida. Tendo estas premissas em consideração, o presente estudo tem como objetivo avaliar a qualidade linguística das respostas geradas pelo ChatGPT (GPT-3.5) no domínio da cosmetologia, no que respeita às categorias de produtos cosméticos, ingredientes, segurança e eficácia e regulamentação, visando identificar padrões que permitam compreender as diferenças e/ou semelhanças entre o conteúdo gerado pelo LLM e aquele produzido por especialistas humanos no Portal infoCosméticos. Para isso, foram selecionadas vinte questões previamente respondidas e publicadas no portal e, posteriormente, criados quatro prompts distintos com diferentes graus de complexidade, que deram origem a oitenta respostas geradas pelo ChatGPT. As respostas foram, de seguida, analisadas, de acordo com os resultados conduzidos por uma grelha de avaliação linguística composta por 11 perguntas. A análise produziu resultados de diferentes tipos: em termos globais, as respostas escritas pelos especialistas produzem resultados ligeiramente superiores às do ChatGPT; quanto à coesão interfrásica, constatou-se que os textos produzidos por especialistas usam um número reduzido de conectores, contrastando com o uso recorrentemente de marcadores discursivos nos textos do ChatGPT; verifica-se o uso de jargão científico não explicado e uma macroestrutura com ausência do parágrafo da conclusão, nos textos publicados no portal; os textos gerados pelo ChatGPT apresentam uma frequência elevada de repetições e/ou tautologias.

Abstract
We present PolyNarrative, a new multilingual dataset of news articles, annotated for narratives. Narratives are overt or implicit claims, recurring across articles and languages, promoting a specific interpretation or viewpoint on an ongoing topic, often propagating mis/disinformation. We developed two-level taxonomies with coarse- and fine-grained narrative labels for two domains: (i) climate change and (ii) the military conflict between Ukraine and Russia. We collected news articles in four languages (Bulgarian, English, Portuguese, and Russian) related to the two domains and manually annotated them at the paragraph level. We make the dataset publicly available, along with experimental results of several strong baselines that assign narrative labels to news articles at the paragraph or the document level. We believe that this dataset will foster research in narrative detection and enable new research directions towards more multi-domain and highly granular narrative related tasks.

Abstract
This work introduces a web application for extracting, processing, and visualizing data from sleep studies reports. Using Optical Character Recognition (OCR) and Natural Language Processing (NLP), the pipeline extracts over 75 key data points from four types of sleep reports. The web application offers an intuitive interface to view individual reports' details and aggregate data from multiple reports. The pipeline demonstrated 100% accuracy in extracting targeted information from a test set of 40 reports, even in cases with missing data or formatting inconsistencies. The developed tool streamlines the analysis of OSA reports, reducing the need for technical expertise and enabling healthcare providers and researchers to utilize sleep study data efficiently. Future work aims to expand the dataset for more complex analyses and imputation techniques.