Abstract
RESUMO A cidadania digital é a cidadania do mundo algoritmizado, datificado, conectado, sensorizado, dos metaversos, multiversos, big data. É a expressão de um novo tipo de arquitetura do social, constituído na reticularidade pela contínua conectividade entre humanos e não humanos. Uma conexão AtoBit, simbiótica, que potencializa a emergência de uma nova ecologia feita de pessoas, dados, algoritmos, sensores, florestas, clima, vírus, cidades. Essa conexão nos desafia a uma nova política cognitiva no campo da Educação. O artigo apresenta o conceito de Cidadania Digital e problematiza formas de conhecer e produzir conhecimento relacionado ao desenvolvimento do pensamento computacional na Educação Básica. Tem por objetivo compreender como o pensamento computacional é potencializado e produzido na cidade. As experiências se desenvolvem a partir de práticas pedagógicas inventivas, simpoiéticas, intervencionistas e gamificadas. Como método de pesquisa, se apropria do método cartográfico de pesquisa-intervenção para produção e análise de dados. Os resultados baseiam-se em elementos presentes nas epistemologias reticulares e conectivas, na cognição inventiva e nos conceitos de ato conectivo transorgânico e habitar atópico. Tais resultados indicam o pensamento computacional sendo potencializado no coengendramento entre entidades humanas e não humanas, numa perspectiva de Educação OnLIFE cidadã, contribuindo para a sua compreensão interdisciplinar e transversal, bem como para a valorização da comunidade. Esses resultados apontam para a emergência de uma política cognitiva ecológica em educação, o que implica repensar o currículo e a formação docente.

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Abstract
Background: We assessed the pertinence of yearly updating the International Working Group on the Diabetic Foot (IWGDF) risk classification in people with diabetes by quantifying the changes in the risk group and its accuracy in identifying those developing an ulcer (DFU) in a primary care setting. Methods: In our retrospective cohort study, we included all people with diabetes with a foot as-sessment registry between January 2016 and December 2018 in the Baixo Alentejo Local Health Unit. Foot-related data was collected at baseline after one and two years. DFU and/or death until December 2019 were registered. The proportion of people changing their risk status each year was calculated. Accuracy measures of the IWGDF classification to predict DFU occurrence at one, two, and three years were calculated. Results: A total of 2097 people were followed for three years, during which 0.1% died, and 12.4% developed a DFU. After two years, 3.6% of the participants had progressed to a higher-risk group. The IWGDF classification presented specificity values superior to 90% and negative predictive values superior to 99%. Conclusion: Foot risk status can be safely updated every two years instead of yearly. The IWGDF classification can accurately identify those not at risk of DFU.

Abstract
In the healthcare context, artificial intelligence (AI) has the potential to power decision support systems and help health professionals in their clinical decisions. However, given its complexity, AI is usually seen as a black box that receives data and outputs a prediction. This behavior may jeopardize the adoption of this technology by the healthcare community, which values the existence of explanations to justify a clinical decision. Besides, the developers must have a strategy to assess and audit these systems to ensure their reproducibility and quality in production. The field of interpretable artificial intelligence emerged to study how these algorithms work and clarify their behavior. This chapter reviews several interpretability of AI algorithms for medical imaging, discussing their functioning, limitations, benefits, applications, and evaluation strategies. The chapter concludes with considerations that might contribute to bringing these methods closer to the daily routine of healthcare professionals. © 2025 Elsevier Inc. All rights reserved.

Abstract
Fault tolerance (FT) is a critical aspect of industry, where systems are susceptible to disturbance and faults. Traditional FT models, based on the centralization of information to handle fault episodes, no longer meet the current industrial models based on Cyber-physical Systems (CPS). Self-healing is a promising approach for FT in CPS, consisting of the individual competence of each component in detect, diagnose and recover from failures. With this in mind, this paper discusses the engineering of self-healing fault-tolerance in industrial CPS, analyzing the maturation process of this paradigm from the local model through collaboration models and later to self-organization features. The paper also discusses the main research challenges that self-healing FT faces during this process.

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The main objective of this study is to contribute to multilingual discourse research by employing ISO-24617 Part 8 (Semantic Relations in Discourse, Core Annotation Schema – DR-core) for annotating discourse relations. Centering around a parallel discourse relations corpus that includes English, Polish, and European Portuguese, we initiate one of the few ISO-based comparative analyses through a multilingual corpus that aligns discourse relations across these languages. In this paper, we discuss the project’s contributions, including the annotated corpus, research findings, and statistics related to the use of discourse relations. The paper further discusses the challenges encountered in complying with the ISO standard, such as defining the scope of arguments and annotating specific relation types like Expansion. Our findings highlight the necessity for clearer definitions of certain discourse relations and more precise guidelines for argument spans, especially concerning the inclusion of connectives. Additionally, the study underscores the importance of ongoing collaborative efforts to broaden the inclusion of languages and more comprehensive datasets, with the objective of widening the reach of ISO-guided multilingual discourse research. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.