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II. Componente Politécnica

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http://hdl.handle.net/10400.1/2

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Percorrer II. Componente Politécnica por Objetivos de Desenvolvimento Sustentável (ODS) "09:Indústria, Inovação e Infraestruturas"

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  • Innovation and competitiveness in the territorial brand of the Algarve: a comparative analysis of its social media communication and web content
    Publication . Cristòfol, Francisco J.; Berraquero-Rodríguez, Diego; Zamarreño-Aramendia, Gorka; Alves, Paulo
    In an increasingly competitive global tourism context, territorial branding plays a key role in enhancing the visibility, identity, and resilience of regions. This study focuses on the Algarve, a region in southern Portugal, and investigates how innovation and competitiveness are reflected in its digital communication strategy. Using a mixed-methods approach, this research combines the quantitative analysis of 689 social media posts published in 2024 on Facebook, Instagram, and YouTube with the qualitative content analysis of 38 documents and the official website of Algarve. The findings reveal a coherent and visually appealing brand narrative centred on the coastal identity of the Algarve, complemented by content related to nature, gastronomy, and cultural heritage. Instagram stands out as the most engaging platform, particularly when posts adopt a participatory tone, emotional storytelling, and references to specific locations. However, only 6.4% of the content surpassed the 1% engagement threshold, suggesting limited audience connection. The website presents a broader thematic range but under-represents intangible heritage and local products. The Algarve brand successfully projects an aspirational image based on landscape and leisure but would benefit from greater content diversification, enhanced stakeholder integration, and expanded narrative strategies to strengthen digital engagement and destination competitiveness.
    2025-05-17Artigo científico Acesso aberto Ver mais
  • PneumoNet: artificial intelligence assistance for pneumonia detection on X-rays
    Publication . Antunes, Carlos; Rodrigues, Joao; Cunha, António
    Pneumonia is a respiratory condition caused by various microorganisms, including bacteria, viruses, fungi, and parasites. It manifests with symptoms such as coughing, chest pain, fever, breathing difficulties, and fatigue. Early and accurate detection is crucial for effective treatment, yet traditional diagnostic methods often fall short in reliability and speed. Chest X-rays have become widely used for detecting pneumonia; however, current approaches still struggle with achieving high accuracy and interpretability, leaving room for improvement. PneumoNet, an artificial intelligence assistant for X-ray pneumonia detection, is proposed in this work. The framework comprises (a) a new deep learning-based classification model for the detection of pneumonia, which expands on the AlexNet backbone for feature extraction in X-ray images and a new head in its final layers that is tailored for (X-ray) pneumonia classification. (b) GPT-Neo, a large language model, which is used to integrate the results and produce medical reports. The classification model is trained and evaluated on three publicly available datasets to ensure robustness and generalisability. Using multiple datasets mitigates biases from single-source data, addresses variations in patient demographics, and allows for meaningful performance comparisons with prior research. PneumoNet classifier achieves accuracy rates between 96.70% and 98.70% in those datasets.
    2025-07-07Artigo científico Acesso aberto Ver mais