Crisis management increasingly relies on geospatial information systems that integrate diverse datasets and support coordination across multiple agencies operating under time pressure. However, fragmentation across existing platforms continues to limit cross-hazard analysis and coordinated decision-making. This paper introduces the Rapid Intelligent Geospatial Integrated Disaster Management (RIGID) platform, a web-based geospatial environment that brings together multi-hazard forecast indicators and regional and municipality-level operational data within persistent, role-sensitive workspaces. The platform was evaluated through a structured workshop with civil protection stakeholders, focusing on usability and perceived operational value. The findings provide exploratory indications that participants perceived the platform as helping to reduce data fragmentation, supporting shared situational awareness, and facilitating coordinated interpretation across governance levels. By emphasising operational workspaces as shared reference points, the study highlights how integrated geospatial platforms may contribute to collaborative decision-making in complex emergency management contexts.

This article overviews methods and technologies underpinning impact-based early warning systems for weather and geo-hazards, with the objective of evaluating the feasibility and principal challenges of their effective implementation within future multi-risk frameworks, with attention to the European context. Adopting an expert-driven overview approach, the paper synthesizes representative methods, operational practices, and technological solutions implemented or under development across weather hazards, including fluvial and flash floods, windstorms, storm surges, heatwaves, droughts, and wildfires, and geo-hazards, including earthquakes, volcanoes, tsunamis, and landslides. For each hazard domain, the review considers forecasting or rapid-response methods, observational and modelling requirements, warning dissemination practices, technological maturity, and end users served. The analysis highlights heterogeneity in lead times, spatial scales, data availability, uncertainty treatment, and institutional organization, ranging from seconds for earthquake early warning to weeks or months for drought monitoring and outlooks. At the same time, common methodological foundations emerge across hazard communities, particularly the coupling of hazard information with exposure and vulnerability data, the increasing use of probabilistic forecasting and machine-learning tools, and the central role of user-oriented communication and decision support. Within Europe, these shared aims coexist with fragmented governance arrangements, uneven operational maturity, and challenges in transnational coordination across meteorological, hydrological, geological, and civil protection communities. Building on this comparative overview, the paper discusses the main barriers to the implementation of integrated impact-based and multi-risk warning capabilities, including fragmented data environments, limited interoperability among monitoring and modelling chains, uneven practices in uncertainty communication, and the complexity of compound and cascading events. The main conclusions indicate that the scientific and technological basis for multi-risk impact-based early warning is already available in many sectors, but that effective implementation in Europe will require progressive alignment of data standards, workflows, uncertainty representation, institutional coordination, and user-centred decision-support practices across weather and geo-hazard communities.

After three years of progress, the beAWARE project has achieved a significant impact in the fields in which it is involved. To achieve this, the platform and the mobile application had to be tested against real-life conditions, which had been demonstrated through the three pilots, heatwave, flood, and fire and evaluated based on the interaction with the technology that the end-users experienced during each pilot differently. beAWARE project proposed a novel approach for disaster management, which manages the different events and information retrieved all along the disaster management process. The economic impact of the project is not easy to measure, since it can be derived only indirectly from the outcomes and the benefits of the project and without properly set and test the beAWARE system for a well-accepted period of time. Safety impact is very important, from decision-makers to citizens.

This paper presents a short technical perspective on how Building Information Modelling (BIM) could support the immediate implementation of the recently announced Critical Raw Materials (CRM) Act that will ensure EU access to a secure and sustainable supply of CRMs towards meeting Europe's 2030 climate and digital objectives. By leveraging the digitalisation offered in the construction sector via the use of BIM, it will be possible to effectively monitor the use of CRMs in a BIM-enabled construction project. BIM covers the entire life cycle of a building, including the monitoring of quantities and related processes of CRMs. This extends from preparatory and construction phases to maintenance, demolition, and end-of-life stages. Extending further, and towards monitoring in a more holistic approach the material and waste journey before or after the BIM coverage, the integration of a Life Cycle Assessment could cover the entire CRM journey in the construction sector and beyond (mining, manufacturing, recycling, etc.). This direct access to information will allow for (i) a better understanding of the use of CRMs in the construction sector, (ii) improved material management and collection of critical raw material-rich waste, (iii) more informed decision-making across all involved actors, and (iv) evidence-based policy-making regarding primary and secondary CRMs in the construction sector and beyond.

As far as Water is concerned, a lot of new directives and world concerns highlight among others the need for using ICT technologies, the global healthcare issues, the demand for fresh water, the food/beverage quality and safety, the environmental protection and the security strategies to reduce intentional contamination, all of the above having worldwide massive economic, natural and social impacts. However, despite an increasing demand for adaptability, compacity and performances at ever decreasing costs, the vast majority of water network monitoring systems remains based on sensor nodes with predefined and vertical applicative goals hindering interoperability and increasing costs (OPEX and CAPEX) for deploying new and added value services. Innovative technological products could answer the following acute needs in the field. This chapter introduce advance research works in sensing within two H2020 EU projects: the aqua3S project addressing sensors for Water Security purposes and LOTUS addressing low-cost multiparameter sensors for water quality.

Infrastructure monitoring and rapid quality diagnosis comprise the key solutions to achieve zero-defect smart manufacturing. The most fundamental systems in manufacturing industries are computer numerical controlled (CNC) tools. Automating and optimizing their functionality is a highly challenging task because complex dynamics and non-linear relationships govern the overall machining operations. Recent scientific advances in machining processes, incorporate intelligence in CNC tools to improve both the reliability and the productivity of the real-time cutting operations, while reducing waste and cost. This study extensively reviews these advances focusing on three fundamental aspects: Surface roughness prediction, tool wear prediction, and chatter detection in CNC cutting processes.

Semantic Web technologies are increasingly being deployed in various e-health scenarios, prominently due to their inherent capacity to harmonize heterogeneous information from diverse sources and devices, as well as their capability to provide meaningful interpretations and higher-level insights. This paper reports on ongoing work in the recently started EU-funded project ALAMEDA towards a semantic toolkit for bridging the gap between early diagnosis and treatment in a variety of brain diseases. The toolkit comprises (a) a semantic model serving as the underlying knowledge base for the toolkit; (b) a flexible semantic data integration framework; (c) a semantics-enabled conversational agent for interacting with human users and other components of the ALAMEDA system.

Manufacturing companies increasingly become "smarter" as a result of the Industry 4.0 revolution. Multiple sensors are used for industrial monitoring of machines and workers in order to detect events and consequently improve the manufacturing processes, lower the respective costs, and increase safety. Multisensor systems produce big amounts of heterogeneous data. Data fusion techniques address the issue of multimodality by combining data from different sources and improving the results of monitoring systems. The current paper presents a detailed review of state-of-the-art data fusion solutions, on data storage and indexing from various types of sensors, feature engineering, and multimodal data integration. The review aims to serve as a guide for the early stages of an analytic pipeline of manufacturing prognosis. The reviewed literature showed that in fusion and in preprocessing, the methods chosen to be applied in this sector are beyond the state-of-the-art. Existing weaknesses and gaps that lead to future research goals were also identified.

AbstractThe specification of deployment topologies for complex applications distributed across multiple heterogeneous infrastructures is a difficult process that encompasses multiple modeling tasks, engaging several actors, including application ops experts, resource experts on the specification of the target infrastructure resources, quality experts on the application optimization, and application administrators on the deployment governance. SODALITE proposes a novel infrastructure as a code (IaC) modeling framework that provides a model driven engineering approach for the authoring of application- and infrastructure-level specifications, realizing an instantiation of an infrastructure as a code (IaC) modeling framework. This chapter introduces the SODALITE IDE and the IaC services. The IDE enables SODALITE expert roles to model (conforming to the SODALITE DSMLs) and generate IaC artefacts facilitating the app deployment. Experts are assisted in the modeling phase by the semantic knowledge inference and validation capabilities of a Knowledge Base (KB), which is populated with IaC descriptions for resources semi-automatically discovered from target heterogeneous infrastructures. The IDE leverages the SODALITE IaC services for automatic target image preparation and IaC artifacts generation upon deployment.

Participation in the labor market is seen as the most important factor favoring long-term integration of migrants and refugees into society. This paper describes the job recommendation framework of the Integration of Migrants MatchER SErvice (IMMERSE). The proposed framework acts as a matching tool that enables the contexts of individual migrants and refugees, including their expectations, languages, educational background, previous job experience and skills, to be captured in the ontology and facilitate their matching with the job opportunities available in their host country. Profile information and job listings are processed in real time in the back-end, and matches are revealed in the front-end. Moreover, the matching tool considers the activity of the users on the platform to provide recommendations based on the similarity among existing jobs that they already showed interest in and new jobs posted on the platform. Finally, the framework takes into account the location of the users to rank the results and only shows the most relevant location-based recommendations.

Most language learning applications are aimed at students or people who already know a language and want to improve their skills, or want to learn a new language. These applications, while seeking to be interactive, are not aimed at immigrants, refugees or asylum seekers, since the latter have different needs and interests from casual learners, opting for language skills that will allow them to function independently in the host society. This research is part of the European project WELCOME, which seeks to use state-of-the-art technologies, such as Virtual Reality (VR) apps and dialogue agents, to support the reception and integration of Third Country Nationals (TCNs) in Europe. The platform will be tested in three languages (Catalan, German and Greek), in real situations that the TCNs, mostly immigrants, refugees and asylum seekers, face, combining linguistic activities, where aspects related to language and culture are worked on. Furthermore, a Visual Analytics Component (VAC) leverages authority (NGOs/State institutions) users’ perceptual and cognitive abilities by employing interactive visualisations as interfaces between users and learning analytics outcomes generated by amassed data. The goal is to find patterns within the characteristics of TCNs, and thus help language teachers adapt the content and tools to TCNs, contributing to greater personalisation in learning.

Flooding is one of the most destructive natural phenomena that happen worldwide, leading to the damage of property and infrastructure or even the loss of lives. The escalation in the intensity and number of flooding events as a result of the combination of climate change and anthropogenic factors motivates the need to adopt real-time solutions for mapping flood hazards and risks. In this study, a methodological framework is proposed that enables the assessment of flood hazard and risk levels of severity dynamically by fusing optical remote sensing (Sentinel-1) and GIS-based data from the region of the Trieste, Monfalcone and Muggia Municipalities. Explainable machine learning techniques were utilised, aiming to interpret the results for the assessment of flood hazard. The flood inventory was randomly divided into 70%, used for training, and 30%, employed for testing. Various combinations of the models were evaluated for the assessment of flood hazard. The results revealed that the Random Forest model achieved the highest F1-score (approx. 0.99), among others utilised for generating flood hazard maps. Furthermore, the estimation of the flood risk was achieved by a combination of a rule-based approach to estimate the exposure and vulnerability with the dynamic assessment of flood hazard.

Nowadays, one of the most critical challenges is the ongoing climate change, which has multiple and significant impacts on human life in financial and environmental levels. As the adverse effects of unexpected destructive natural extreme events, such as loss of human lives and property, will become more frequent and intensive in the future, especially in developing countries, the efficient confront is required in a holistic manner. Hence, there is an urgent need to develop novelty tools to enhance awareness and preparedness, assess risks and support decision-making, aiming to increase social resilience to climate changes. This work suggests a unified multilayer framework that encapsulates machine learning techniques in the risk assessment process for analysing and fusing dynamically heterogeneous information obtained from the field.

New opportunities for improved personalized healthcare have emerged due to the recent advances in the development of modern methods which reinforce personalized early risk prediction, prevention and intervention. Using semantic techniques for data integration has become pivotal as it can deliver different ways to represent data, automating the process of data integration, and providing the ability to query semantically. In this paper, we propose a new semantic data model in which health information derived from Parkinson’s, Multiple Sclerosis, and Stroke (PMSS) patients is systematically analyzed to generate and improve knowledge that will be transferred to patient care in order to design and develop innovative health risk prediction and intervention tools. Furthermore, this project focuses on providing new opportunities for improved personalized healthcare and prevention that have been created by new designs and developments of innovative health risk prediction and intervention tools. A core ontology is currently being designed within the ALAMEDA project to deal with the semantic interoperability across heterogeneous datasets along with a semantic framework to concrete the generated heterogeneous data through a shared ontology. The ontology model development and the requirement elicitation will be done based on the components’ capabilities and use case requirements. The heterogeneous and dynamic data will be subjected to annotation through the development of semantic models for data sharing and usage apart from being interpretable.

We present a knowledge-driven multilingual conversational agent (referred to as "MyWelcome Agent") that acts as personal assistant for migrants in the contexts of their reception and integration. In order to also account for tasks that go beyond communication and require advanced service coordination skills, the architecture of the proposed platform separates the dialogue management service from the agent behavior including the service selection and planning. The involvement of genuine agent planning strategies in the design of personal assistants, which tend to be limited to dialogue management tasks, makes the proposed agent significantly more versatile and intelligent. To ensure high quality verbal interaction, we draw upon state-of-the-art multilingual spoken language understanding and generation technologies.

This paper presents a new innovative framework to support smart manufacturing quality assurance. More specifically, the i4Q framework provides an IoT-based Reliable Industrial Data Services (RIDS), a complete suite consisting of 22 innovative Solutions, able to manage the huge amount of industrial data coming from cheap cost-effective, smart, and small size interconnected factory devices for supporting manufacturing online monitoring and control. The i4Q Framework guarantees data reliability with functions grouped into five basic capabilities around the data cycle: sensing, communication, computing infrastructure, storage, and analysis-optimization. i4Q RIDS includes simulation and optimization tools for manufacturing line continuous process qualification, quality diagnosis, reconfiguration and certification for ensuring high manufacturing efficiency, leading to an integrated approach to zero-defect manufacturing. This paper presents the main principles of the i4Q framework and the relevant industrial case studies on which it will be evaluated.

In this paper we describe the xR4DRAMA system, a solution that makes use of XR capabilities to support professionals who deal with disasters, man-made crises or media productions. The key contribution of this work in progress is the increase of situation awareness, which is achieved by the innovative combination of data collection, multimedia and sensor analysis, linking data, GIS and interactive XR technologies. The proposed platform is designed to facilitate the creation of immersive environments using semantically enriched content and comprises a powerful tool that is applicable to multiple real use case scenarios.