PACK’s objective is to reduce customs congestion by drastically decreasing the number of trucks required to stop for physical inspection. This goal is achieved by developing and utilizing an integrated system of sensors, artificial intelligence, and advanced software, which enables automatic risk assessment and fully automated customs inspections without compromising security.

Brief Project description: 

The PACK project aims to drastically reduce the number of trucks required to stop for customs inspection through reliable and automated risk assessment during transport. To this end, the system integrates and upgrades advanced detection, monitoring, and data analysis capabilities, utilizing artificial intelligence algorithms. 

The PACK approach is based on a smart customs seal with Internet of Things capabilities to prevent tampering, combined with a smart sensor array equipped with artificial intelligence for continuous and autonomous cargo monitoring. With this setup, the system can independently assess risk and decide to issue alerts to the relevant supervisory authorities, significantly simplifying on-site and on-the-go checks.

Implementing PACK at EU level could significantly reduce truck waiting times at external borders. With only 10% market penetration, it is estimated to generate savings of €241 million and 2.35 million man-hours annually, reduce fuel consumption by about 70.5 million liters, and cut CO₂ emissions by approximately 190,000 tons per year. 

Role of ICCS: 

ICCS contributes to the project by developing and adapting a chemical detection device that constitutes a core component of the sensor array for cargo monitoring. The device is capable of real-time detection of the potential presence of target compounds, leveraging a field-deployable, state-of-the-art mass spectrometry-based analytical technique with fast response times. 

This approach provides chemical measurements with high accuracy, sensitivity, and minimum false alarms. Within the project, ICCS will investigate the application of artificial intelligence and advanced data analytics to further enhance detection performance.

Project website: https://pack-project.com/

LinkedIn page: PACK Horizon Europe project: Overview | LinkedIn

Facebook page

This project has received funding from the European Union’s Horizon Europe research and innovation programme under the Grant Agreement No. 101225875.

The need to implement complex physics systems is critical across various scientific and engineering domains. However, traditional numerical models for simulating these systems are computationally expensive, requiring significant time, resources, and cost. Recent advancements in AI present a promising alternative, with AI models demonstrating the ability to capture the dynamics of complex physical systems. Despite these successes, AI models suffer from key limitations, including challenges with generalization, vulnerability to bias, ethical concerns, and accuracy, particularly when applied to unseen tasks or variable-range predictions. These limitations are collectively viewed as issues of robustness.

The TURING project aims to address these shortcomings by developing robust AI-driven solutions. It integrates multidisciplinary advancements from Machine Learning, Computer Engineering, Physics, and SSH to pre-train generative, multimodal foundation models capable of capturing the physics of dynamic systems that share common properties. Starting with a cautious approach, the models will incorporate representations of increasingly complex physical systems as robustness is ensured.

Once pre-trained, these foundation models will be fine-tuned for specific tasks, enhancing their domain-specific robustness. The tasks will target critical engineering and physics problems in nuclear energy, particle physics, and meteorology, which are of high priority for the EU. The task-specific and foundation models, collectively termed “TURING models”, will be developed in collaboration with partners from India, Canada, and Switzerland.

To maximize the impact of TURING models, the project will ensure compliance of its activities with regulations such as the EU AI Act and then publicly release those models, along with the TURING Framework (MLOps SW tools and web-based app with conversational capabilities), enabling developers and end users to leverage this technology for their applications.

Website: https://turing-project.eu
Linkedin: https://www.linkedin.com/company/turing-project/
X: https://x.com/Turing_project/
Zenodo community: https://zenodo.org/communities/turing-heu/

This project has received funding from the Horizon Europe Framework Programme (2021-2027) under the grant agreement No 101215032. 

The Castilla y León Hydrogen Valley (CyLH2Valley) is a pioneering strategic initiative funded by the European Union’s Horizon Europe program and the Clean Hydrogen Partnership, designed to transform the Castilla y León region into a premier European hub for renewable energy innovation. By integrating the entire hydrogen value chain—from large-scale production using wind and solar power to distribution and multi-sectoral end-use—the project aims to produce over 1,000 tonnes of green hydrogen annually during its initial stages, with a long-term roadmap to reach over 16,000 tonnes. The initiative encompasses eleven diverse pilot projects that demonstrate the versatility of green hydrogen, including its application in heavy-duty road transport, zero-emission maritime and aviation fuels, industrial decarbonization, and the production of green methanol and ammonia. 

Beyond its technical objectives, CyLH2Valley is a catalyst for regional economic growth, projected to create approximately 2,000 “green” jobs and attract significant sustainable investment, while utilizing advanced digital tools like a dedicated Data Space for transparent energy management. Ultimately, the project serves as a replicable blueprint for the European Green Deal, proving that regional energy autonomy and industrial decarbonization can be achieved through a coordinated, large-scale ecosystem approach.

This project receives funding from the HORIZON program, through the CLEAN HYDROGEN PARTNERSHIP, an organization with which the Region, through the Regional Energy Agency, has signed a Collaboration Agreement for the development of various activities.

This interconnected network of smart modular parts form a decentralized system, the Internet of Modularity (IoM²), that facilitates self-aware and adaptive building environments. Le Colaz also prioritizes environmental and economic impact through Life Cycle Assessment (LCA) and Life Cycle Costing (LCC), while validation is done in contrasting climatic zones, Athens and Trondheim, via actual Living Labs, as well as simulation testing.

Le Colaz’s long-term vision is to minimize construction waste by promoting reusability, reinnovation, and recycling, thereby facilitating the transition towards circular and sustainable living communities. Therefore, it offers a scalable solution for future climate-adaptive, resilient, and smart neighbourhoods.

LinkedIn page

This project has received funding from the European Union’s Horizon Europe programme under grant agreement No. 101236003.

The project develops five pilots that showcase AI across the energy landscape:
1️⃣ Smart Grids in Portugal: Advancing fault detection and resilience with AI-driven digital twins of the transmission system.
2️⃣ Microgrids in Spain: Predictive maintenance to keep renewable energy equipment reliable and efficient.
3️⃣ Hydrogen & Hybrid Energy Systems in France, Spain, and Italy: Optimizing electrolysers and hybrid plants through AI-powered digital twins.
4️⃣ Buildings in Latvia: Enhancing energy efficiency in residential complexes with AI-driven building digital twins.
5️⃣ Energy Communities in Italy: Improving the performance of local renewable energy communities through AI experimentation.

DSS Lab assumes the technical coordination of the Latvian pilot 4, where AI-powered digital twins are applied to multi-apartment residential buildings in Riga. By combining real consumption data with advanced modelling, the pilot explores how to optimize heating, cooling, and overall energy use in everyday living spaces. The aim is to demonstrate measurable efficiency gains and cost savings for residents, while contributing directly to Europe’s climate and digital transition goals. EFI-DSS tool developed by DSS Lab will play a key role in this process. More information on EFI-DSS can be found here.

Also, DSS Lab coordinates the Portuguese Pilot 1, where a digital twin of the country’s transmission system will be used along with forecasts of energy production and consumption. These forecasts will be provided by DeepTSF, which is an automated codeless forecasting application based on AI / ML algorithms, and developed by DSS Lab. The goal is to use these forecasts to validate energy market results while increasing efficiency and decreasing the imbalances of the grid. More information on DeepTSF can be found here.

The EnergyGuard project is co-funded by the Horizon Europe Programme of the European Union under grant agreement No. 101172705. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Climate, Infrastructure and Environment Executive Agency (CINEA). Neither the European Union nor the granting authority can be held responsible for them.

EnergyGuard brings together five large-scale laboratories and Meluxina (Europe’s greenest high-performance computer) into a unified service. The result is a one-stop platform where startups, SMEs, and researchers can develop, test, and certify AI solutions in realistic conditions without real-world risks.

As coordinator, DSS Lab steers a pan-European consortium into a single, service-oriented facility. The network spans Portugal, Spain, France, Italy, and Latvia, with each site offering unique assets — from grid-scale digital twins and hydrogen production lines to microgrids and Soviet-era apartment blocks. A curated catalogue of digital twins, datasets, models, and APIs makes these resources accessible through a common cloud portal.

Complementing the physical labs is the Meluxina supercomputer in Luxembourg, powered entirely by renewable energy. Developers can scale simulations across thousands of CPU and GPU cores while maintaining a transparent carbon footprint. Preconfigured container workspaces with open-source libraries give even small teams access to petascale computing, with results delivered in minutes.

For innovators, the benefits are clear: dramatically shorter development cycles, lower capital expenditure and a structured path to compliance with the forthcoming EU AI Act. The TEF’s Acceptance Environment measures algorithms against a comprehensive risk database, covering cybersecurity threats, data bias, functional safety and more. Passing solutions receive a digital badge that signals trustworthiness to investors, customers and regulators alike.

Looking ahead, DSS Lab will use its role as coordinator to seed pilot projects, open new datasets, and mentor early-stage companies. By combining world-class infrastructure with transparent governance, EnergyGuard aims to position Europe as the go-to market for responsible, high-impact energy AI.

👉 Call to innovators: AI startups, researchers, and technology providers can request access or subscribe to updates at EnergyGuard TEF Project.

The EnergyGuard project is co-funded by the Horizon Europe Programme of the European Union under grant agreement No. 101172705. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Climate, Infrastructure and Environment Executive Agency (CINEA). Neither the European Union nor the granting authority can be held responsible for them.

The project will integrate five significant European large-scale testing and experimentation facilities that cover the full energy value chain, supported by European’s greenest HPC infrastructure (Meluxina). This includes a digital twin (DT) of the Portuguese Transmission Network (RDN), the CEDER-CIEMAT Microgrid with its Distributed Energy Resources (DERs), the Hydrogen testing platforms at CEA LITEN, CARTIF, BER and CIEMAT, a high-fidelity local DT of Riga’s multi-apartment residential buildings and the Antrodoco Renewable Energy Community. The process requires a wide range of elements to cover diverse AI test needs, including wind power, photovoltaic systems, hydropower plant, AEM, PEM and SO eletrolyzers, fuel cells, EV charging stations, electric and public buses and battery storage systems. The facilities will be accessible to EnergyGuard end-users through a set of properly configured Digital Twins (DTs) and curated assets, including data, models, inference APIs, services, and applications through an AI development Testing environment.

EnergyGuard will enable easy seamless access to assets from the EU ecosystem including AIOD, Data Spaces, DIHs and other TEFs. Moreover, the project facilitates users to validate their products with an Acceptance Environment and a common open AI risks database for a wide range of cybersecurity and trustworthy AI assessments.

The TEF will serve as full infrastructure to support national AI regulatory sandbox initiatives and deliver 5 pilot cases for the private and public sector. EnergyGuard will build upon a long-term, self-sustainable business model driven by a new entity, incorporating market-ready features early in the design, such as a subscription/plan framework, billing, and professional support.

The EnergyGuard project is co-funded by the Horizon Europe Programme of the European Union under grant agreement No. 101172705.

Gas sensors are crucial in the personal and industrial monitoring to analyze personal exposure to air pollutants or to critical gases, to control product quality such as in the food industry, in health care by analyzing gases from human body and using breath analysis combined with wearable sensors for personal stress estimation. These applications require miniaturized low power and low-cost gas sensors with good gas selectivity to be integrated in personal devices, in product packaging or in widely distributed sensor networks.

AMUSENS aims at developing a gas sensor platform with flexible selectivity to different gas environments by combining a multi-pixel approach and artificial intelligence to adapt the data analysis to the targeted applications. It is based on metal oxide sensing materials on micro-hotplate platform, which are already available on the market for low power applications, but suffer from a lack of selectivity. Gas-selective multi-pixel sensors based on different metal oxide materials have been demonstrated, but their industrialization is limited to few industrially available materials. By using original additive manufacturing approaches for local liquid-phase and gas-phase depositions, we aim at extending the choice of available materials and demonstrate their sustainability in wafer-scale processing. Artificial intelligence will be used to accelerate the choice of materials, for data fusion to determine specific patterns in the gas analysis and to optimize sensor calibration through calibration transfer models. Three specific applications targeting personal exposure and health care will demonstrate the adaptability of the platform, based on an analysis of the users’ requirements. AI interpretation will be facilitated using model-agnostic interpretability analysis of the AI-based models for calibration transfer and on the use-case models.

The project brings together 11 partners from across Europe, with a total budget of over €8.5 million, and a duration of 48 months.

I-SENSE Group/ICCS proudly joins AMUSENS from May 2025 and will give a new boost to the project through:

• The development of interpretable AI models for calibration transfer and signal analysis (WP15 & WP16),
• Participation in the co-design process to ensure user-aligned tools,
• And real-life testing with human participants in the stress estimation use case, combining breath analysis with wearable devices (WP10).

Dissemination & Communication Material:

This project has received funding from the European Union’s Horizon Europe programme under grant agreement No. 101130159.

The Connected and Adaptive Maintenance for Safer Urban and Secondary Roads project (‘CAMBER’) aims to develop and demonstrate improved safety monitoring across urban and secondary rural road networks through realtime data feedback into road maintenance systems and proven low-cost interventions. Performance metrics based on new-generation data sources will provide road managers up-to-date information on safety issues, damage, and routine maintenance and upgrade needs. Data collated from a range of sources, such as telematics, vehicle and smartphone sensors, and road user feedback, will feed into safety assessment models to flag what measures are required to ensure a safe road environment for all road users, including road-user minority groups with varying design needs, such as powered two-wheelers (PTW).

CAMBER will support this through much-need research and testing of low-cost road safety interventions and low-impact maintenance techniques, including those for vehicles with advanced driver-assistance systems (ADAS). The approaches will be demonstrated on urban and road networks in five European countries. CAMBER’s economically-sound solutions and new knowledge will be communicated through established networks to European road managers, policymakers and industry to support the decision-making and investment needed for more efficient maintenance for safer urban and secondary roads.

ICCS is being represented by I-SENSE Group research group, leading WP5 Pilot testing and evaluation and the project’s 2^nd Pilot held in Trikala, Greece.

LinkedIn

This project has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement No 101076963. UK participant IRAP is supported by UKRI grant number 10139277. UK participant Agilysis is supported by UKRI grant number 10157029.

The SHIELD project seeks to revolutionise early detection of pancreatic cancer, focusing on individuals with high heritable genetic risk. Pancreatic ductal adenocarcinoma (PDAC) has a 5-year survival rate of less than 10%, primarily due to late-stage diagnosis. Consequently, 85% of PDAC cases are identified too late for curative treatment. However, early detection can significantly improve outcomes, increasing the survival rate to 42% with surgical intervention. SHIELD aims to validate a new blood-based diagnostic test designed for early PDAC detection in high-risk individuals and pilot an early detection programme in seven EU countries, targeting individuals with familial or genetic predispositions. It will also identify new protein biomarkers for other high-risk indications, such as new-onset diabetes (NOD). Collaboration with national screening authorities will help integrate this test into existing programs, and partnerships with patient organizations will enhance recruitment. SHIELD envisions transforming pancreatic cancer diagnostics by increasing the 5-year survival rate to 30% by 2035 in Europe.

Press Release 1 – May 2025

Funded by the European Union (European Commission, HORIZON-IA – HORIZON Innovation Actions)