Abstract
Introduction/Objective: the objective of this article is to analyze the current academic literature on smart cities in Brazil with evidence of the application of Digital Twin or Digital Shadow technology. Method: Integrative Literature Review was used as the research instrument, analyzing in the articles: a) objective; b) research method; c) study subject (location); d) application of Digital Twin or Digital Shadow; e) Results and conclusions. Results: portfolio with 25 articles on the topic and qualitative analysis regarding objective, method, study location, Digital Twin technology, Digital Shadow, and results. Studies with elements of Digital Shadow are perceived timidly in two cases of smart cities in Brazil. Conclusions: smart city technologies should be centered on the interests of users to not lose their humanity. It is worth adding that people's needs change and, therefore, smart technologies should have a forward-looking vision to anticipate the needs of future generations. Digital Twin technology is a model that can contribute in this sense, monitoring and providing readings of future scenarios for smart cities.

Abstract
The Adaptive Optics Telemetry (AOT) format has recently been proposed to standardize the telemetry data generated by adaptive optics systems. Yet its usability remains limited by the user's programming expertise and familiarity with the accompanying Python package. There is an opportunity for substantial improvement in data accessibility by offering users an alternative tool for conducting exploratory data analysis in a visual and intuitive manner. We aim to design and develop an open-source Python visualization tool for exploring AOT data. This tool should support researchers and users by offering a broad set of interactive features for the analysis and exploration of the data. We designed a prototype dashboard and performed user testing to validate its usability. We compared the prototype with existing data visualization and exploration tools to ensure we provided the necessary functionality. We made publicly available a user-friendly dashboard for analyzing and exploring AOT data.

Abstract
This paper addresses a critical gap in applying semantic enrichment for online news text classification using large language models (LLMs) in fast-paced newsroom environments. While LLMs excel in static text classification tasks, they struggle in real-time scenarios where news topics and narratives evolve rapidly. The dynamic nature of news, with frequent introductions of new concepts and events, challenges pre-trained models, which often fail to adapt quickly to changes. Additionally, the potential of ontology-based semantic enrichment to enhance model adaptability in these contexts has been underexplored. To address these challenges, we propose a novel supervised news classification system that incorporates semantic enrichment to enhance real-time adaptability. This approach bridges the gap between static language models and the dynamic nature of modern newsrooms. The system operates on an adaptive prequential learning framework, continuously assessing model performance on incoming data streams to simulate real-time newsroom decision-making. It supports diverse content formats - text, images, audio, and video - and multiple languages, aligning with the demands of digital journalism. We explore three strategies for deploying LLMs in this dynamic environment: using pre-trained models directly, fine-tuning classifier layers while freezing the initial layers to accommodate new data, and continuously fine-tuning the entire model using real-time feedback combined with data selected based on specified criteria to enhance adaptability and learning over time. These approaches are evaluated incrementally as new data is introduced, reflecting real-time news cycles. Our findings demonstrate that ontology-based semantic enrichment consistently improves classification performance, enabling models to adapt effectively to emerging topics and evolving contexts. This study highlights the critical role of semantic enrichment, prequential evaluation, and continuous learning in building robust and adaptive news classification systems capable of thriving in the rapidly evolving digital news landscape. By augmenting news content with third-party ontology-based knowledge, our system provides deeper contextual understanding, enabling LLMs to navigate emerging topics and shifting narratives more effectively. Copyright © 2024 for this paper by its authors.

Abstract

Abstract
This paper reports on the development and validation of a formal model for an automotive adaptive exterior lights system (ELS) with multiple variants in Alloy 6, which is the most recent version of the Alloy lightweight formal specification language that supports mutable relations and temporal logic. We explore different strategies to address variability, one in pure Alloy and another through an annotative language extension. We then show how Alloy and its Analyzer can be used to validate systems of this nature, namely by checking that the reference scenarios are admissible, and to automatically verify whether the established requirements hold. A prototype was developed to translate the provided validation sequences into Alloy and back to further automate the validation process. The resulting ELS model was validated against the provided validation sequences and verified for most of requirements for all variants.

Abstract
Data description is a fundamental step in Research Data Management (RDM). When it comes to images, the challenge is increased, as they have characteristics that differentiate them from other typologies. We conducted a study in which we obtained a set of 27 images described according to their content, by researchers of the projects where they are inserted. After obtaining the ground-truth that would support the analysis, we proceeded to two more stages of description, one through an automatic processing tool (Vision AI) and the other through researchers with no knowledge of the images. We concluded that the human description is more elucidative of the images' content, namely at a semantic level. In turn, the automatic tools enhance a more literal description. This study allowed us to reflect on the description of images in a research context and to discuss the potential of formal analysis and analysis of the semantic expression of images.