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- Exploring the user-generated content data to improve quality managementPublication . Maia, Sara; Domingues, José Pedro Teixeira; Varela, Maria Leonilde R. Rocha; Fonseca, Luís MiguelThe focus of this research is to investigate if user-generated content (UGC) generated in the Booking platformcan support qualitymanagement improvement within the hospitality industry by increasing customer satisfaction and eliminating defects more efficiently. Hence, it contributes to understanding how data-driven companies can rely on customer data to focus on innovation and performance improvement to meet customer requirements, eliminate defects and increase customer satisfaction.
- A review of applied artificial intelligence in manufacturing: Emergent AI models in cyber–physical systems for manufacturingPublication . Rocha Varela, Maria Leonilde; Manupati, Vijaya Kumar; Pinheiro, Pedro; Putnik, Goran; Ferreira, Luís; Alves, Cátia; Ávila, Paulo; Castro, HelioThe integration of artificial intelligence (AI) is a cornerstone of Industry 4.0, driving significant gains in automation, efficiency, and adaptability. In parallel, manufacturing environments are evolving into cyber–physical systems (CPS), where physical processes are deeply integrated with computational intelligence. While machine learning and deep learning techniques have become standard practice in manufacturing CPS, the emergence of advanced and foundation AI models—such as reinforcement learning, agent-based AI systems, large language models, and neuro-symbolic approaches—brings fresh opportunities and challenges that are not fully understandable. This paper offers a comprehensive systematic literature review (SLR) on AI applications in manufacturing cyber–physical systems, with a particular focus on the role, maturity, and industrial readiness of emergent AI models. Following the PRISMA 2020 guidelines, a structured search was carried out in Scopus andWeb of Science, producing over 4200 publications, out of which a final set of 172 publications were retained following a rigorous multi-stage screening and eligibility process. We analysed the selected literature through complementary descriptive, longitudinal, and mapping syntheses to identify publication trends, paradigm evolution, and relationships between AI paradigms and manufacturing functions. Our findings show a clear transition from rule-based and conventional machine learning approaches toward more adaptive, decentralized, and learning-driven AI paradigms. However, despite their conceptual suitability for complex and dynamic manufacturing environments, emergent AI models are mostly limited to experimental, hybrid, or decision-support contexts, with limited integration into core manufacturing operations. Critical research gaps regarding the industrial readiness of these models—specifically concerning integration frameworks, empirical validation, safety, and trust—are identified. Furthermore, the study outlines future research directions for advancing the next generation of intelligent and autonomous manufacturing CPS. Overall, this review underscores the rapid growth and current fragmentation of the field, highlighting the need for more integrative and production-ready AI frameworks in the evolution of manufacturing CPS.