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- Development of a rehabilitation action plan to promote urban sustainability in existing neighbourhoodsPublication . Reyes Nieto, Jocelyn Erandi; Rigueiro, Constança; Silva, Luís Simões da; Murtinho, Vítor; Dinis, Maria Alzira Pimenta; Farinha, Luis; Raposo, Daniel; Ferreira, João J.; Gaspar, Marcelo Calvete; Alves, Maria Leopoldina; Serrano, LuisUrban degradation in existing neighbourhoods has become a pressing global issue, driven by demographic pressure, environmental degradation, and inefficient urban management practices. This chapter proposes a strategic framework for a sustainable rehabilitation action plan aimed at reversing these trends and promoting urban sustainability in already urbanised contexts. The framework addresses both the physical transformation of the built environment and the social dynamics required to foster long-term community engagement and resilience. Grounded in theories of sustainable urban development and participatory planning, the research employs a mixed-methods approach that integrates spatial analysis, evaluation, and policy review. This methodology supports the identification of key components necessary for effective urban rehabilitation, including diagnostic tools to assess neighbourhood conditions, guidelines for sustainable construction practices, and participatory mechanisms to ensure local ownership of interventions. The chapter’s core contribution lies in outlining a structured rehabilitation process that bridges regulatory planning frameworks with residents’ demands and aspirations. It emphasises the need for local governance to take a proactive role in enabling sustainable change through legislation that is responsive to community needs. Aligning environmental, social, and economic objectives, the proposed action plan aims to not only improve the physical environment but also to enhance social cohesion, mitigate urban desertification, and foster a stronger sense of place and responsibility among residents. Ultimately, this work offers policymakers, urban planners, and researchers a practical and theoretically grounded proposal for urban regeneration that prioritises sustainability and inclusion in the rehabilitation of existing urban neighbourhoods.
- iGenTrivia: intelligent content generation system for trivia gamesPublication . Gonçalves, Manuel José Silva; Torres, José; Moreira, Rui SilvaThe digital migration of trivia games significantly expanded their reach and accelerated development; however, the demand for high-quality, diverse, and accurate content remains a critical bottleneck. Traditional human-generated content is time-consuming, while early attempts at automated generation often suffer from quality issues. Although Large Language Models (LLMs) offer a promising solution for content scaling, they are prone to hallucinations and factual inconsistencies errors that are unacceptable in a trivia context. This thesis addresses these limitations by proposing a novel, automated content generation system that integrates Retrieval-Augmented Generation (RAG) with a multi-agent validation pipeline. The system employs distinct LLM roles for generation, fact-checking, and answer verification, utilising a consensusbased mechanism to filter low-confidence outputs. By leveraging vector embeddings for semantic duplicate detection and cross-referencing answers across multiple models, the pipeline ensures high factual integrity. Evaluated across English (United States) and European Portuguese, the system achieved 100% factual correctness in both languages, eliminating the residual hallucinations present in the single-model baseline, and reduced the semantic duplicate rate by up to 15 percentage points (from 21.74% to 6.76% in English, and from 23.87% to 14.40% in Portuguese). On the efficiency dimension, the pipeline improved the question yield rate by over 30 percentage points in both languages 88% versus 53% for English, and 80% versus 49% for Portuguese requiring 44% fewer generated candidates and half the number of API calls to reach the same output target. The result is a scalable, language-agnostic framework capable of producing high-quality trivia content with minimal human intervention.
- Volleyball performance statistics based on video analysis and deep learningPublication . Ferreira, Bruno Filipe Baptista; Torres, José; Soares, ChristopheThis dissertation presents the development and evaluation of a semi-automatic video-based system for extracting volleyball performance statistics using computer vision and deep learning techniques. Performance analysis plays a critical role in modern volleyball, supporting coaches and analysts in understanding match dynamics, technical execution, and tactical behaviour. However, the collection of such statistics is often performed manually or semimanually, making the process time-consuming, difficult to scale, and prone to inconsistency. To address these limitations, this work proposes a modular analysis pipeline centred on ball detection, tracking, scoreboard reading, court calibration, rally management, and event interpretation. The system employs a YOLObased model for volleyball detection, combined with temporal tracking strategies that exploit spatial continuity, motion constraints, foreground validation, and outlier rejection. Manual initialisation is still required for court calibration and scoreboard region selection, but the subsequent analysis stage operates automatically over the selected video segment. The work integrates these components into a unified prototype for volleyball match analysis. The system produces structured outputs describing rallies and event categories such as attacks, blocks, balls out, balls on the net, aces, and errors. The implemented pipeline was evaluated on two recorded volleyball sets from different venues. The system achieved a manual-review agreement rate of 81.82% on the first evaluated set and 72.50% on the second, corresponding to an overall agreement rate of 77.38% across 84 reviewed points. The evaluation shows that the proposed approach can extract useful volleyball event information from fixed-camera match footage, while also highlighting important limitations. The main sources of disagreement were related to fast rallies, trajectory ambiguity near the net and court boundaries, visual interference from additional balls, and venue-specific background complexity. The prototype therefore offers a concrete starting point for future work on semi-automatic volleyball performance analysis, particularly through improved ball tracking, more robust scoreboard recognition, and broader evaluation across additional venues.