Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Strengthening of existing masonry buildings with Fibre-Reinforced Mortar: calibration of a reliable numerical model to assess the structural performances (conFiRMa)

Framework: Horizon 2020 — Spreading excellence and widening participation
Programme: Widening participation and spreading excellence
Theme: Other Widening
Action type: Marie Skłodowska-Curie actions
Panel: Information Science and Engineering
Countries involved: Czechia
Call: Widening Fellowships (H2020-WF-02-2019, 2019)
Start date: 2020-11-01
End date: 2022-10-31
Duration (months): 24.0
Total project cost (EUR): 144980.64
EU funding (EUR): 144980.64
Participants: 1
Project status: CLOSED

The conFiRMa project is aimed at develop a suited, original numerical method, specifically formulated and validated in the open source finite element code ooFEM, for the assessment of the actual structural performances of historic masonry buildings strengthened with FRM (Fibre-Reinforced Mortar). This modern, effective and compatible reinforcement strategy, consists in plastering the walls by means of mortars with fibre-based elements embedded (meshes or textiles). The safe usability and preservation of historic masonry buildings, which constitute a significant part of the European architectural heritage, is an urgent issue, due to the intrinsic vulnerability of masonry related to durability and to accidental destructive actions (earthquakes, crushing, blasts, fracking...). However, while the study on unreinforced masonry has reached dvanced levels of definition, a specific numerical model for FRM strengthened masonry is still missing and a lack of standards concerning FRM interventions is nowadays detected. The innovative numerical model will thus be significantly useful for investigating on the actual performances of FRM strengthened masonry structures, for evaluating the applicability of the simplified modelling strategies currently adopted for unreinforced masonry in professional practice also to FRM strengthened masonry and for deriving important considerations for the standardization of the FRM design strategies. The methodology will be based on multiple-level analysis, starting with a detailed modelling (the masonry, the mortar and the reinforcement), followed by a computationally efficient intermediate level (e.g. layered macro-elements), until attain to simplified models (lumped plasticity). A significant impact is thus expected either on the scientific community, on the professional sectors (designers, fiber-based materials manufacturers) and, indirectly, on the whole society, dealing with the safety of people and the preservation of cultural heritage.

Keywords: structural refurbishment,structural vulnerability,masonry structures,fibre based materials,composite,materials,fibre reinforced mortar,numerical analysis,computational mechanics
INTERSECTIONS (INTERSECTIONS)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: European researchers' Night
Action type: Marie Skłodowska-Curie actions
Countries involved: United Kingdom,United Kingdom
Call: European Researchers' Night (H2020-MSCA-NIGHT-2020bis, 2020)
Start date: 2021-05-01
End date: 2022-02-28
Duration (months): 10.0
Total project cost (EUR): 123978.0
EU funding (EUR): 103815.0
Participants: 2
Project status: CLOSED

This edition of European Researcher's Night, run independently of all other NIGHTs, will provide a platform from which researchers and publics across Europe can participate to create a truly pan-national series of events showcasing the international and intersectional nature of research as well as the relevance of, and contribution to research that the public can make. INTERSECTIONS is developed from past success in international delivery of a group of seven NIGHTs across Europe. This experience has shown international delivery can be meaningful and rewarding, and demonstrated the barriers and drivers to a successful international and intersectional delivery. We propose now to expand on this and provide dedicated support for a pan-European delivery. In 2021 INTERSECTIONS will run a modest group of activities that we are confident are able to run on an international basis with significant impact and meaningful outcomes for all stakeholders from researchers to audiences. This truly pan-European research showcase event, connecting researchers, audiences, and NIGHTs, will showcase research and its relevance to all European citizens.

Keywords: science communication,public engagement,intersectionality
THE ROLE OF MUCINS AND MUCUS IN THE DISEASES CYSTIC FIBROSIS, CELIAC DISEASE, AND ULCERATIVE COLITIS (MUC2GO)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Advanced grants
Action type: ERC actions
Panel: Immunity, Infection and Immunotherapy
Panel description: The immune system, related disorders and their mechanisms, biology of infectious agents and infection, biological basis of prevention and treatment of infectious diseases, innovative immunological tools and approaches, including therapies
Countries involved: Sweden
Call: ERC Advanced Grant (ERC-2015-AdG, 2015)
Start date: 2016-09-01
End date: 2021-08-31
Duration (months): 60.0
Total project cost (EUR): 2500000.0
EU funding (EUR): 2500000.0
Participants: 1
Project status: CLOSED

The overall aim of this proposal is to explain how the mucus systems of the intestine and lung act as a first innate immune defense line to protect our mucosal surfaces. Mucins are of highly glycosylated proteins that build the mucus skeleton. We previously discovered that there are two mucus layers in the colon. The inner mucus layer built around the MUC2 mucin is attached and impenetrable to bacteria, thus separating the intestinal bacteria from the epithelial cells. However, how the dynamic colon mucus layers are formed and controlled is poorly understood. We have established an international leading position in this research area and will now address fundamental and unresolved problems of high medical importance. The capacity of the inner mucus layer to separate bacteria from the epithelium is highly dependent on the luminal bacteria. Our results suggest specific bacteria signaling via smaller molecules or bacterial fragments to the host to increase protection. We will reveal how this signaling take place, important for understanding increased western world diseases including ulcerative colitis. Of special interest is our recent finding of a single sensory goblet cell protecting the colon crypts. We have developed a fluorescent human MUC2 transgenic mouse that is instrumental for the planned studies as well as in studies of gliadin uptake from the intestine and initiator for celiac disease. We will now transfer our leading edge knowledge of intestinal mucus structure and function to the respiratory tract. Normal trachea seems to be cleared by mucus strands formed the tracheal glands, but these strands are trapped cystic fibrosis. We will also reveal how a hypothetic attached mucus layer, as described by us in colon, could explain the mucus accumulation in chronic lung diseases. The proposed project will substantially increase our basic understanding of molecular mechanisms behind several mucus related diseases and pave the way for new treatments.

Keywords: mucin,mucus,bacteria,colon,large intestine,small intestine,respiratory tract,lungs,copd,mass spectrometry,proteomics
Automated framework for rapid simulations of high-detail brain models (BrainFrame)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Proof of concept grants
Action type: ERC actions
Countries involved: Netherlands
Call: ERC Proof of Concept Grant (ERC-2014-PoC, 2014)
Start date: 2015-02-01
End date: 2016-01-31
Duration (months): 12.0
Total project cost (EUR): 149937.0
EU funding (EUR): 149937.0
Participants: 1
Project status: CLOSED

Traditional methods of software modeling and simulation take prohibitive amounts of time to run for the size and complexity of biologically realistic neuron populations (e.g. >1 million). For instance, simulation of a 100 high-detail (Inferior-Olive) neuron-network for 10 minutes of brain time take 42 hours to complete on an modern PC workstation. To tackle this problem - common to the international neuroscience community -, we have turned to hardware-accelerated simulation platforms. We have employed award-winning, FPGA -based, dataflow machines provided by Maxeler Technologies, which offer high-performance computing capabilities and come with programming tools of unprecedented user friendliness. Our current Maxeler setups have offered impressive acceleration rates of more than 4 orders of magnitude (x10000). Despite the achieved speeds, however, we have found the Maxeler programming tools to be cumbersome to learn by neuromodelers with little hardware knowledge. Engineering personnel has, thus, been employed to assist them in (efficiently) translating and deploying their models. To streamline this process and bring a powerful tool directly to the hands of modelers, we propose BrainFrame, a novel system consisting of Maxeler hardware and a comprehensive software toolflow, effectively adding an abstraction level for neuromodelers to work on without specific knowledge of the underlying simulation platform. BrainFrame comprises a “one-stop shop”, integrated solution primarily targeting labs and companies active in the field of brain-modeling research and applications.

Keywords: computational neuroscience,neuroinformatics,brain modeling,simulation,hardware acceleration,fpga,dataflow computing,neural controller,brain machine interface
Construction and Demolition Waste-based “Green” Demountable Structural Components (CodeDEMO)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Individual Fellowships (IF)-Standard European Fellowships
Action type: Marie Skłodowska-Curie actions
Panel: Information Science and Engineering
Countries involved: United Kingdom
Call: Individual Fellowships (H2020-MSCA-IF-2019, 2019)
Start date: 2021-09-15
End date: 2023-09-14
Duration (months): 24.0
Total project cost (EUR): 224933.76
EU funding (EUR): 224933.76
Participants: 1
Project status: SIGNED

Generation of high volumes of construction and demolition waste (CDW) is seriously problematic from environmental, economic and societal perspectives. Furthermore, CDW is generally managed by the proposition of low-tech recycling applications which are costly, energy-inefficient and unsustainable. Within the scope of the current project, a new construction paradigm composed of pre-fabricated large-scale elements produced with 100% recycled “green” materials out of CDW that can be easily assembled/disassembled according to a cradle-to-cradle design approach is proposed. Current project will not only propose completely “green” solutions on materials’ basis but also on structural basis by developing geopolymeric (cement-free) concrete mixtures, ingredients of which will be obtained completely from CDW and large-scale structural components that will be easily demountable without creating extra waste and consuming too much energy. The action will also develop and enhance the research fellow’s transferable skills necessary for thriving his career in a flourishing area that underpins innovative technological development across a range of diverse disciplines and will make a marked impact on know-how exchange of interdisciplinary research expertise, breaking the common trend in construction industry and strengthening the ties among researchers/engineers who work on construction materials and structural aspects.

Keywords: construction and demolition waste cdw,geopolymer,waste management,environmental,sustainability,training,transferable skills,entrepreneurship,commercialization,research
Disruptive, Protected, Scalable, Smart Anti-Theft-System with AI-based Service Software for the protection of Bee Hives (BeeSafe)

Framework: Horizon 2020 — Industrial Leadership
Programme: Innovation in SMEs
Action type: SME instrument
Countries involved: Germany
Call: SME instrument (H2020-SMEInst-2018-2020-1, 2019)
Start date: 2020-05-01
End date: 2020-10-31
Duration (months): 6.0
Total project cost (EUR): 71429.0
EU funding (EUR): 50000.0
Participants: 1
Project status: CLOSED

Apiculture (Beekeeping) is a billion Euros industry. According to the European Commission, 650,000 beekeepers in the EU generate an annual value added of 1.4 billion Euros, with their 18 million beehives. More importantly apiculture contributes to a total amount of at least 24 billion Euros delivered by pollination to EU agriculture. Successful apiculture attracts beehive theft and vandalism. We are developing a disruptive, scalable, smart anti-theft system with AI-based service software to protect beehives. The innovative system is based on a GPS-tracker that reports unusual movement (=theft or vandalism) identified by our AI-software. GPS-trackers are nothing new. They are hardly never used for beekeeping though as a result of their unsatisfactory user interface (SIM cards have to be purchased with tariff independently, the provided software is often not compatible and/or user friendly). Besides the GPS-trackers are unreliable in their performance (short battery times and frequent false alarms) and do not detect vandalism, since the beehives location is not changed. With BeeSafe we are developing a different product. Intelligent, protected (patent pending) technology enables our anti-theft system to operate in zero-energy-mode until theft or vandalism occurs. Field tested AI compares unusual movement with past user patterns and mitigates false alarms. Smart services include SIM cards, access to a service platform that will manage alarms and contact the responsible authorities in the future. We create a new market by providing a plug-and-protect solution at greatly reduced production- and environmental costs due to dramatically reduced battery sizes. We aim to supply 0.5% of EU beehives with our anti-theft product within the next 5 years. Given a total of 18 million beehives in the EU we are planning to sell 90,000 products that generate a total revenue of 8.1 million Euros and create 20 new jobs over the next 5 years.
Revolutionary Ultra-Fast System for Hot-stamping of light weight structural vehicle components with Artificial Intelligence quality control monitoring (RUSH AI)

Framework: Horizon 2020 — Industrial Leadership
Action type: Innovation actions
Countries involved: Spain,Italy,Italy,Belgium
Call: Fast Track to Innovation (FTI) (H2020-EIC-FTI-2018-2020, 2018)
Start date: 2019-04-01
End date: 2022-10-31
Duration (months): 43.0
Total project cost (EUR): 3451422.5
EU funding (EUR): 2565883.25
Participants: 4
Project status: CLOSED

The automotive market is driven by many safety performance assessment programs. Less CO2 emissions and reduced production costs are the main priority for OEMs, to comply with regulations and to strengthen their advantage in an extremely competitive market. The cost-effective reduction of a vehicle’s structural components, known as Body-in-White (BIW), has been one of OEMs’ main targets. Such reduction is possible with lighter BIWs, made of Advanced High Strength Steels (>1,500MPa), using a process known as hot stamping. Components produced this way can be 20-30% lighter compared to cold stamping. Hot stamping components account for 11% of the total current value of European vehicles; the state of the art has limitations that prevent extending hot stamping to more vehicle components, e.g. components with complex geometries, very thin or locally tailored components. Our innovation, RUSH AI, is an ultra-fast system for hot stamping of light-weight components, quality-controlled by self-correcting, online AI. It reduces manufacture cycling time by over 70%, thanks to a novel hydraulic press solution and a “sweating die” made by additive manufacturing and inspired by human sweat. Its efficiency results in savings of 70-80% OPEX. We aim to upgrade RUSH AI from TRL6 to TRL9. By project’s end, 2020, the hot stamping TAM will be €11.8bn, 13% of the total BIW market (€91bn). In the first 10 years of commercialization, SAM is estimated to reach €1bn. We expect sales above €148m by 2030 and a market share of 15%. Users will be OEMs and their Tier-1 suppliers. RUSH AI can be implemented in old and new hot stamping lines. A joint venture company is considered by the 3 industrial partners to commercialize the process line, yet each keeps the separate rights of exploitation of individual results. This Consortium consists of 4 partners from 3 EU countries. All members possess the technical and market expertise to commercialize RUSH AI within 36 months.

Keywords: hot stamping,quenching,die tool,big data,artificial intelligence,monitoring system,quality control,production,materials,press hardening,advanced high strength steels,additive manufacturing
Risk Based Consenting of Offshore Renewable Energy Projects (RiCORE)

Framework: Horizon 2020 — Societal Challenges
Programme: Secure, clean and efficient energy
Theme: Low-cost, low-carbon energy supply
Action type: Coordination and support actions
Countries involved: Ireland,Spain,France,United Kingdom,Portugal
Call: Market uptake of existing and emerging renewable electricity, heating and cooling technologies (H2020-LCE-2014-3, 2014)
Start date: 2015-01-01
End date: 2016-06-30
Duration (months): 18.0
Total project cost (EUR): 1393532.5
EU funding (EUR): 1393532.5
Participants: 6
Project status: CLOSED

The consenting of offshore renewable energy is often cited as one of the main non-technical barriers to the development of this sector. A significant aspect of this is the uncertainty inherent in the potential environmental impacts of novel technology. To ensure consents are compliant with EU and national legislation, such as the Environmental Impact Assessment and Habitats Directive, costly and time consuming surveys are required even for required even for perceived lower risk technologies in sites which may not be of highest environmental sensitivity. It is therefore the aim of the RiCORE project to establish a risk-based approach to consenting where the level of survey requirement is based on the environmental sensitivity of the site, the risk profile of the technology and the scale of the proposed project. RiCORE will study the legal framework in place in the partner Member States to ensure the framework developed will be applicable for roll out across these Member States and further afield. The next stage of the RiCORE project is to consider the practices, methodologies and implementation of pre-consent surveys, post-consent and post-deployment monitoring. This will allow a feedback loop to inform the development of the risk-based framework for the environmental aspects of consent and provide best practice. The project will achieve these aims by engaging with the relevant stakeholders including the regulators, industry and EIA practitioners, through a series of expert workshops and developing their outcomes into guidance. The impact of the project will be to improve, in line with the requirements of the Renewable Energy Directive specifically Article 13 (1), consenting processes to ensure cost efficient delivery of the necessary surveys, clear and transparent reasoning for work undertaken, improving knowledge sharing and reducing the non-technical barriers to the development of the Offshore Renewable Energy sector so it can deliver the clean, secure energy

Keywords: offshore renewable energy,regulation,policy,consenting
Memory of Motion (MEMMO)

Framework: Horizon 2020 — Industrial Leadership
Programme: Information and Communication Technologies
Action type: Research and Innovation actions
Partnership: Robotics SPARC (cPPP)
Countries involved: Italy,Germany,Spain,Switzerland,France,France,United Kingdom,United Kingdom,United Kingdom
Call: Advanced robot capabilities research and take-up (H2020-ICT-2017-1, 2017)
Start date: 2018-01-01
End date: 2022-06-30
Duration (months): 54.0
Total project cost (EUR): 4157151.75
EU funding (EUR): 3964818.75
Participants: 12
Project status: SIGNED

What if we could generate complex movements for a robot with any combination of arms and legs interacting with a dynamic environment in real-time? MEMMO has the ambition to create such a motion-generation technology that will revolutionize the motion capabilities of robots and unlock a large range of industrial and service applications. Based on optimal-control theory, we develop a unified yet tractable approach to motion generation for complex robots with arms and legs. The approach relies on three innovative components. 1) a massive amount of pre-computed optimal motions are generated offline and compressed into a ``memory of motion''. 2) these trajectories are recovered during execution and adapted to new situations with real-time model predictive control. This allows generalization to dynamically changing environments. 3) available sensor modalities (vision, inertial, haptic) are exploited for feedback control which goes beyond the basic robot state with a focus on robust and adaptive behavior. To demonstrate the generality of the approach, MEMMO is organized around 3 relevant industrial applications, where MEMMO technologies have a huge innovation potential. For each application, we will demonstrate the proposed technology in relevant industrial or medical environments, following specifications designed by the end-users partners of the project. 1) A high-performance humanoid robot will perform advanced locomotion and industrial tooling tasks in a 1:1 scale demonstrator of a real aircraft assembly. 2) An advanced exoskeleton paired with a paraplegic patient will demonstrate dynamic walking on flat floor, slopes and stairs, in a rehabilitation center under medical surveillance. 3) A challenging inspection task in a real construction site will be performed with a quadruped robot. While challenging, these demonstrators are feasible, as assessed by preliminary results obtained by MEMMO partners, that are all experts or stakeholders of their domain.

Keywords: humanoid robotics,force control,visual localization and mapping,machine learning