FCT (DCEA) - Artigos em Revistas Científicas Internacionais com Arbitragem Científica
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Browsing FCT (DCEA) - Artigos em Revistas Científicas Internacionais com Arbitragem Científica by Sustainable Development Goals (SDG) "13:Ação Climática"
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- The 15-minute city in Porto, Portugal: accessibility for the elderlyPublication . Guerreiro, Maria; Dinis, Maria Alzira Pimenta; Sucena, Sara; Pereira, Madalena Sofia Araujo; Silva, Isabel; Ferreira, Diogo; Silva Moreira, RuiThe concept of the 15-Minute City aims to enhance urban accessibility by ensuring that essential services are within a short walking distance. This study evaluates the accessibility of Porto, Portugal, particularly for the elderly, by assessing urban density, permeability, and walkability, with a specific focus on crossings and ramps. A five-step methodology was employed, including spatial analysis using QGIS and Place Syntax Tool, proximity assessments, and an in-situ survey of crossings and ramps in the CHP. The results indicate that while the city of Porto offers a dense and walkable urban environment, significant accessibility challenges remain due to inadequate ramp distribution. The data collection identified 80 crossings, of which only 60 were listed in OpenStreetMap, highlighting data inconsistencies. Additionally, 18 crossings lacked curb ramps, posing mobility barriers for elderly residents. These findings highlight the need of infrastructure improvements to support inclusive urban mobility. The study also proposes an automated method to enhance ramp data collection for broader applications. Addressing these gaps is crucial for achieving the equity and sustainability goals of the 15-Minute City model, ensuring that aging populations can navigate urban spaces safely and efficiently.
- Artificial intelligence and climate change: the potential roles of foundation modelsPublication . Leal Filho, Walter; Kovaleva, Marina; Ng, Artie; Nagy, Gustavo; Luetz, Johannes; Dinis, Maria Alzira PimentaArtificial intelligence (AI) is being developed fast and applied in several areas including education and healthcare with excellent potential for use in fields that require complex analytics, particularly in the case of climate change. Recent developments in AI, such as ChatGPT and OpenAI, machine vision technologies and deep learning, among others, may be deployed in various contexts, including climate change. Of specific interest is the role played by foundation models (FMs), which may help to augment intelligence on climate change and reduce the social risks of adaptation and mitigation initiatives. This article discusses the potential applications of FMs in climate change research and management and illustrates the need for further studies. FMs, built on large unlabelled data sets and enabled by transfer learning, offer versatility in handling complex tasks. Specifically, FMs can aid in climate data analysis, modelling future scenarios, assessing risks, and supporting decision-making processes. Despite their potential, challenges such as data privacy, algorithm bias, and energy consumption require careful consideration. The article emphasizes the importance of interdisciplinary efforts to address these challenges and maximize the positive impact of FMs in mitigation and adaptation. AI, including advanced models like FMs, holds significant promise for addressing climate change challenges.