The main purpose of this article is to provide an up-to-date understanding of the utilization and deployment of emerging technologies in the public sector, as this is reflected through 19 recently funded Horizon 2020 research projects. For the needs of this study, we have adopted a well-known literature review method that enables a concept-centric analysis of the accumulated knowledge in the field under consideration, and accordingly proposed a conceptual framework that facilitates such an analysis. Through a detailed consideration of these projects and their pilot case implementations, a series of insights about recent research development and applications in the public sector are extracted and discussed. To the best of our knowledge, this is the first attempt to gain such insights from a research projects perspective, which may reveal useful information about the utilization and deployment of these technologies in real-life pilots. The findings of this study are also justified or challenged by referring to recent review articles that investigate the use of emerging technologies in the public sector.

Sports-related injuries remain a major challenge in team sports, with important consequences for athlete health, performance, and team success. Recent advances in artificial intelligence (AI) and sensor-based monitoring technologies have enabled the integration of large volumes of training, competition, and physiological data to support injury prediction and risk modelling. However, the literature is characterised by substantial methodological diversity, limiting the ability to draw consistent conclusions. Hence, this scoping review aimed to map the existing evidence on the use of AI and sensor-based monitoring technologies for injury prediction and risk modelling in team sports, and to identify key methodological trends and research gaps. The scoping review was conducted in accordance with the PRISMA-ScR guidelines. Systematic searches were performed in PubMed and Scopus. Eligible studies included team-sport athletes and applied AI or machine learning approaches to predict injury occurrence, injury risk, or related outcomes using data derived from wearable or monitoring systems. Data were charted on study characteristics, sports and competition level, data sources, modelling techniques, validation strategies, and performance metrics. The database search yielded 123 records (PubMed: n = 37; Scopus: n = 86). After screening and eligibility assessment, 11 studies met the inclusion criteria. Most studies focused on football and rugby and relied primarily on wearable-derived data, particularly GPS and inertial sensor outputs. Common predictors included external workload variables, training exposure, previous injury history, and, in some studies, wellness or physiological markers. A wide range of models was reported, including logistic regression, decision trees, random forests, support vector machines, and neural networks. Validation strategies and reported performance varied markedly, and external validation was rarely undertaken. Across the included studies, injury risk was most consistently associated with external workload metrics, previous injury history, and internal or physiological indicators of recovery and readiness. However, current models remain limited by heterogeneous methodologies, single-team datasets, and the lack of external validation. Future research should emphasise multimodal data integration and multi-centre validation to develop reliable, interpretable, and practically applicable AI-based injury prediction systems.

The chapter presents the process behind the development of the ORBIS toolkit as a set of software components and services de-signed to facilitate large-scale online deliberation through the combination of human (collective intelligence) and artificial intelligence technologies. The platform is built on a modular and flexible architecture that combines different AI heterogeneous components. The main layers of the toolkit are: (i) Enabling layer: Provides core services such as authentication, authorisation, and resources management. (ii) Data Layer: Manages deliberation content, participant profiles, AI models, and connects to external knowledge bases, (iii) Service Layer: Contains the key AI-powered deliberation functionalities such as Argument Mining, Explanations Generator, Feedback Aggregation, Conflict Detection and Policy Recommendation, and (iv) User Interface Layer: Composed by existing well-adopted deliberation services designed to consume in a seamless to the end-user and orchestrated way, the external deliberation augmentation services. The chapter points out the key innovations of ORBIS toolkit, which include: (i) argument visualisation and clustering to organise lengthy discussions, (ii) AI-assisted explanation and interpretation of complex issues, (iii) summarisation of key ideas and points in long deliberations, and (iv) reporting mechanisms to assist policy-makers in their decision making procedures. By combining collective and artificial intelligence, the chapter ultimately describes how ORBIS aims to achieve scaling of online deliberations while maintaining discussion quality. Ultimately, it is discussed how the further promising research that this open architecture invites, and how it can inform future development and advances in the emerging field of AI-augmented collective intelligence.