Panos Fitsilis

Ioanna Chatzopoulou, Paraskevi Tsoutsa, Panos Fitsilis

The integration of metaverse technologies within Smart Cities is transforming urban governance and citizen engagement. Despite the increasing academic and industry interest, research on the practical applications of the metaverse in SCs remains fragmented. This study addresses this gap through a systematic literature review on how metaverse-driven solutions impact economic transformation, governance, mobility, sustainability, and social interactions in urban environments. The study synthesizes findings from existing applications and case studies, such as Metaverse Seoul, Dubai's Metaverse Strategy, Virtual Helsinki, and Tampere's CitiVerse initiative, to illustrate the diverse ways in which cities are leveraging metaverse technologies. These applications demonstrate the metaverse's potential in digital governance, Artificial Intelligence (AI)-driven urban planning, e-participation, transportation optimization, and climate resilience strategies. This research contributes to the field by providing a comprehensive framework for understanding the benefits and challenges of metaverse-driven SC models. The findings suggest that while metaverse adoption in SCs presents significant advantages in efficiency, participation, and innovation, it also entails challenges related to technological accessibility, governance frameworks, and security measures that must be addressed for broad uptake. The study's impact extends to policymakers, urban planners, and technology developers by offering strategic insights for responsible and inclusive metaverse adoption. Ultimately, this study provides a structured roadmap for integrating metaverse technologies into smart urban ecosystems, ensuring their long-term viability, accessibility, and effectiveness in shaping the cities of the future.

Panos Fitsilis, Paraskevi Tsoutsa, Vyron Damasiotis et al.

This article analyzes around 200 online articles to identify trends within Industry 5.0 using artificial intelligence techniques. Specifically, it applies algorithms such as LDA, BERTopic, LSA, and K-means, in various configurations, to extract and compare the central themes present in the literature. The results reveal a convergence around a core set of themes while also highlighting that Industry 5.0 spans a wide range of topics. The study concludes that Industry 5.0, as an evolution of Industry 4.0, is a broad concept that lacks a clear definition, making it difficult to focus on and apply effectively. Therefore, for Industry 5.0 to be useful, it needs to be refined and more clearly defined. Furthermore, the findings demonstrate that well-known AI techniques can be effectively utilized for trend identification, particularly when the available literature is extensive and the subject matter lacks precise boundaries. This study showcases the potential of AI in extracting meaningful insights from large and diverse datasets, even in cases where the thematic structure of the domain is not clearly delineated.