Abstract
Narratives have been the subject of extensive research across various scientific fields such as linguistics and computer science. However, the scarcity of freely available datasets, essential for studying this genre, remains a significant obstacle. Furthermore, datasets annotated with narratives components and their morphosyntactic and semantic information are even scarcer. To address this gap, we developed the Text2Story Lusa datasets, which consist of a collection of news articles in European Portuguese. The first datasets consists of 357 news articles and the second dataset comprises a subset of 117 manually densely annotated articles, totaling over 50 thousand individual annotations. By focusing on texts with substantial narrative elements, we aim to provide a valuable resource for studying narrative structures in European Portuguese news articles. On the one hand, the first dataset provides researchers with data to study narratives from various perspectives. On the other hand, the annotated dataset facilitates research in information extraction and related tasks, particularly in the context of narrative extraction pipelines. Both datasets are made available adhering to FAIR principles, thereby enhancing their utility within the research community.

Abstract
Identifying the influential spreaders in complex networks has emerged as an important research challenge to control the spread of (mis)information or infectious diseases. Researchers have proposed many centrality measures to identify the influential nodes (spreaders) in the past few years. Still, most of them have not considered the importance of the edges in unweighted networks. To address this issue, we propose a novel centrality measure to identify the spreading ability of the Influential Spreaders using the Potential Edge Weight method (IS-PEW). Considering the connectivity structure, the ability of information exchange, and the importance of neighbouring nodes, we measure the potential edge weight. The ranking similarity of spreaders identified by IS-PEW and the baseline centrality methods are compared with the Susceptible-Infectious-Recovered (SIR) epidemic simulator using Kendall's rank correlation. The spreading ability of the top-ranking spreaders is also compared for five different percentages of top-ranking node sets using six different real networks.

Abstract
Faced with the current unsustainability and recognizing the importance of engineering (and technology) in the Capitalocene, it is important to develop educational approaches that facilitate the awareness and training of engineering students to the sustainable future's construction. The main objective of the study is the evaluation of the educational approach developed (educational board game). It was used an action-research methodology and a quasi-experimental method. These results show that the developed game can be an important contribution in the engineers training to change the role of engineering to an ethical and responsible construction of a sustainable and fair future.

Abstract
Although the classification of chest radiographs has long been an extensively researched topic, interest increased significantly with the onset of the COVID-19 pandemic. Existing results are promising; however, the radiological similarities between COVID-19 and other types of respiratory diseases limit the success of conventional image classification approaches that focus on single instances. This study proposes a novel perspective that conceptualizes COVID-19 pneumonia as a deviation from a normative distribution of typical pneumonia patterns. Using a population- based approach, our approach utilizes distributional anomaly detection. This method diverges from traditional instance-wise approaches by focusing on sets of scans instead of individual images. Using an autoencoder to extract feature representations, we present instance-based and distribution-based assessments of the separability between COVID-positive and COVIDnegative pneumonia radiographs. The results demonstrate that the proposed distribution-based methodology outperforms conventional instance-based techniques in identifying radiographic changes associated with COVID-positive cases. This underscores its potential as an early warning system capable of detecting significant distributional shifts in radiographic data. By continuously monitoring these changes, this approach offers a mechanism for early identification of emerging health trends, potentially signaling the onset of new pandemics and enabling prompt public health responses.

Abstract

Abstract
Context. A low-mass companion potentially in the brown dwarf mass regime was discovered on a similar to 12 yr orbit (similar to 5.5 au) around HD 167665 using radial velocity (RV) monitoring. Joint RV-astrometry analyses confirmed that HD 167665B is a brown dwarf with precisions on the measured mass of similar to 4-9%. Brown dwarf companions with measured mass and luminosity are valuable for testing formation and evolutionary models. However, its atmospheric properties and luminosity are still unconstrained, preventing detailed tests of evolutionary models. Aims. We further characterize the HD 167665 system by measuring the luminosity and refining the mass of its companion and reassessing the stellar age. Methods. We present new high-contrast imaging data of the star and of its close-in environment from SPHERE and GRAVITY, which we combined with RV data from CORALIE and HIRES and astrometry from HIPPARCOS and Gaia. Results. The analysis of the host star properties indicates an age of 6.20 +/- 1.13 Gyr. GRAVITY reveals a point source near the position predicted from a joint fit of RV data and HIPPARCOS-Gaia proper motion anomalies. Subsequent SPHERE imaging confirms the detection and reveals a faint point source of contrast of Delta H2 = 10.95 +/- 0.33 mag at a projected angular separation of similar to 180 mas. A joint fit of the high-contrast imaging, RV, and HIPPARCOS intermediate astrometric data together with the Gaia astrometric parameters constrains the mass of HD 167665B to similar to 1.2%, 60.3 +/- 0.7 M-J. The SPHERE colors and spectrum point to an early or mid-T brown dwarf of spectral type T4(-2)(+1). Fitting the SPHERE spectrophotometry and GRAVITY spectrum with synthetic spectra suggests an effective temperature of similar to 1000-1150 K, a surface gravity of similar to 5.0-5.4 dex, and a bolometric luminosity log(L/L-circle dot)=-4.892(-0.028)(+0.024) dex. The mass, luminosity, and age of the companion can only be reproduced within 3 sigma by the hybrid cloudy evolutionary models of Saumon & Marley (2008, ApJ, 689, 1327), whereas cloudless evolutionary models underpredict its luminosity.