WATSON provides a methodological framework combined with a set of tools and systems that can detect and prevent fraudulent activities throughout the whole food chain thus accelerating the deployment of transparency solutions in the EU food systems. The proposed framework will improve sustainability of food chains by increasing food safety and reducing food fraud through systemic innovations that a) increase transparency in food supply chains through improved track-and-trace mechanisms containing accurate, time-relevant and untampered information for the food product throughout its whole journey, b) equip authorities and policy makers with data, knowledge and insights in order to have the complete situational awareness of the food chain and c) raise the consumer awareness on food safety and value, leading to the adoption of healthier lifestyles and the development of sustainable food ecosystems. WATSON implements an intelligence-based risk calculation approach to address the phenomenon of food fraud in a holistic way. The project includes three distinct pillars, namely, a) the identification of data gaps in the food chain, b) the provision of methods, processes and tools to detect and counter food fraud and c) the effective cross border collaboration of public authorities through accurate and trustworthy information sharing. WATSON will rely upon emerging technologies (AI, IoT, DLT, etc.) enabling transparency within supply chains through the development of a rigorous, traceability regime, and novel tools for rapid, non-invasive, on-the-spot analysis of food products. The results will be demonstrated in 6 use cases: a) prevention of counterfeit alcoholic beverages, b) preservation of the authenticity of PGI honey, c) on-site authenticity check and traceability of olive oil, d) the identification of possible manipulations at all stages of the meat chain, e) the improved traceability of high-value products in cereal and dairy chain, f) combat of salmon counterfeiting.

EU customs authorities are a cornerstone in the international trade landscape, playing a critical role in preventing the entry of illegal goods, safeguarding revenue, & ensuring the seamless flow of goods. Balancing these responsibilities is particularly challenging in today’s environment, where the volume of global trade continues to expand rapidly. Mitigating the entrance of illegal goods is becoming increasingly difficult, especially with limited human resources. Aiming to address these challenges, the CustomAI consortium has united its expertise and competences to develop an AI-toolkit that will reduce the number of false positives (situations where the cargoes like shipping containers or parcels have been selected for inspection despite not containing contraband). The proposed AI-toolkit will revolutionise customs operations by involving non-intrusive and robust AI-enhanced technologies for predicting, detecting, and selecting high-risk cargoes for inspection. The VCCO concept is adopted for managing all processes in the customs control of artefacts (e.g. container, parcel). Key components of the AI toolkit include: * AI-based risk anticipation relying on AI-analysis of internal knowledge in compilation with external multilingual data, including manifest and declarations. Only relevant cargos will be sent for inspection. * AI-enhanced vapour-based detectors implied only on the containers selected in the previous step. * AI-based x-ray for threat detection in containers applied on output of step two (the human inspection takes place only after this step). * Multimodal LLM Continual Learning model, which will have as input, x-ray and camera images, and will be trained on threat dataset composed of threat samples (x-ray and visual images of threat parcels) updated by customs. * Blockchain technology for secure data sharing & supply chain traceability. By adopting these cutting-edge technologies, the CustomAI toolkit is set to revolutionise customs operations.

Europe is facing unprecedented challenges, such as the health, migration, economic, climate, energy, and political crises, leading to a sharp increase in emergency public spending and relaxation of due diligence checks. This has resulted in a rise in corruption and fraudulent activities, which have significant negative impacts on the European economy, society, environment, and democracy. Despite emerging technology’s potential to become a powerful tool in the fight against corruption and fraud, the public sector has been slow to adopt digitalization, resulting in data NOT being shared, harmonized, or properly analysed, making evidence-based decision-making almost impossible. Governments are slowly adopting new approaches to ensure a more data-driven, transparent, and accountable public governance, but several fundamental data-related issues remain unresolved. With a team of 9 excellent research institutions and universities, 12 technology, business, and standards, developing companies, 7 public end users, and 3 domain-relevant, industry-exposed NGOs, CEDAR will: (1) Identify, collect, fuse, harmonise, and protect complex data sources to generate and share 10+ high-quality, high-value datasets relevant for a more transparent and accountable public governance in Europe. (2) Develop interoperable and secure connectors and APIs to utilise and enrich 6+ Common European Data Spaces. (3) Develop innovative and scalable technologies for effective big data management and Machine Learning (ML) operations. (4) Deliver robust big data analytics and ML to facilitate human-centric and evidence-based decision-making in public administration. (4) Validate the new datasets and technologies (TRL5) in the context of fighting corruption, thus aligning with the EU strategic priorities: digitalisation, economy, democracy. (5) Actively promote results across Europe to ensure their adoption and longevity, and to generate positive, direct, tangible, and immediate impacts.

The BAG-INTEL project will provide robust AI based information utilization and decision support tools, within the context of advanced detection systems to support customs for increased effectiveness and efficiency of the customs control of air traveller baggage in inland border airports, while minimizing the human customs resources needed. This aim addresses the challenge of maintaining effective and efficient customs control of passenger baggage in the situation of the substantial growth of the volume of air travellers arriving in inland border airports with the limited human customs resources available. For this aim, the project will develop an integrated system solution comprising: (1) new AI powered functionality for enhanced detection of contraband in x-ray scanning of luggage, (2) AI camera based end-to-end reidentification of luggage, (3) digital twin for system visualisation and performance optimization for the operational context of an airport, (4) use case for test demonstration and evaluation in 3 European airports, a small, a medium sized, and a big airport, and (5) wide dissemination and elaboration of easy-to-use training material for end users. For the customs, BAG-INTEL solution aims to: increase the successful detection of contraband in luggage by at least 20%; demonstrate the possibility and utility in automatically to derive risk indicators from external data such as the Advanced Passenger Information; demonstrate the effectivity of AI camera based reidentification of luggage, when the traveller carries it into the customs space at the exit of the carousel area; increase the fluidity of passenger flow and control by at least 20%; decrease the customs personal resources mobilisation by at least 20%; derive data useful in flights risk assessment; derive data useful in flights risk assessment; demonstrate the autolearning capacity of this smart risk engine.

URBREATH vision is to develop, implement, demonstrate, validate and replicate a comprehensive, community participation and NBS-driven urban revitalisation, resilience and climate neutrality paradigm that will ultimately radically enhance the social interactions, inclusion, equitability and liveability in cities. Specifically, the aim of the URBREATH project is to implement hybrid/Natural Base Solutions putting at the heart of the decision-making process the communities within a city. Advanced techniques, particularly Local Digital Twins and AI, and social innovation will facilitate the achievement of its vision. The project will have four phases: 1. Inception, 2. Development, 3. Piloting, 4. Transition. The preliminary results of a single Phase are evaluated within the following Phase so to allow for feedback before releasing the final version. The Inception phase will define the methodology to be followed for the project development and will deliver the project functional and technical requirements. The second phase will release the URBREATH technical framework, consisting of tools to manage the whole data value chain and to support end-users to collaborate on the design and creation of NBS to be used in the city/district. It will be used to monitor and take decisions on the NBS to be implemented/deployed in the Piloting phase (evidence-based decision making), that involves 4 Front Runner Cities in 4 different climatic zones: Cluj-Napoca (RO - Continental), Leuven (BE – Atlantic), Madrid (ES – Mediterranean), and Tallin (EE – Boreal). During the Transition phase, all the information, results and lessons learnt from the previous steps will be collected and analysed to provide recommendations and foster replication activities and the uptake of project outputs at the end of its lifespan. To this aim, 5 Follower Cities are involved: Aarhus (DK), Athens (EL), Kajaani (FI), Parma (IT), Pilsen (CZ), linked to the Front Runners for climatic zone and/or dimension.