Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
A breakthrough laser technology for all types of glass processing (ordinary, laminated, tempered, smart, low-energy and other) (GLAZER)

Framework: Horizon 2020 — Industrial Leadership
Programme: Innovation in SMEs
Action type: SME instrument
Countries involved: Finland
Call: SME instrument (H2020-SMEInst-2018-2020-1, 2018)
Start date: 2018-09-01
End date: 2019-02-28
Duration (months): 6.0
Total project cost (EUR): 71429.0
EU funding (EUR): 50000.0
Participants: 1
Project status: CLOSED

The Hypermemo Oy team has been successfully developing its laser glass-processing technology called GLAZER for several years now. GLAZER is based on an innovative CO2 Q-switched short-pulsed high average power laser. Hypermemo Oy is the first and the only company to date who has developed a laser technology specifically for industrial glass processing. GLAZER has unique parameters which allow for processing construction, functional/architectural, “smart” and automotive glass precisely, efficiently and faster than conventional methods. The main goal of GLAZER is eliminating the glass post-processing operations (grinding, polishing, washing, and drying) from the industrial processes, and bringing down the whole glass products manufacturing to one machine. GLAZER was prototyped and tested with different types of glass, and provides excellent quality, which was confirmed by glass industry leaders. This allows to fully eliminate post-processing, making glass processing 30 times faster, 3 times cheaper, and with almost zero rejects. These results make us confident in the breakthrough nature of GLAZER technology and its strong market potential. Within the scope of this project, we plan to identify and justify the market potential of GLAZER on the laminated, tempered and smart windows manufacturing equipment market. This market is going to greatly benefit from the current global trend of mass replacement of ordinary windows with low-emission, environment-friendly windows. This hot and fast-growing application area can be very lucrative for EU SMEs, and we will provide them with the manufacturing technology that can be easily implemented and can generate fast ROI and low environmental cost. Thus, GLAZER will make its positive impact on environmental sustainability and climate stabilization in the EU and worldwide.

Keywords: glass processing,co2 laser,short pulse generation,high average power,tempered glass,low e glass,smart windows
Advanced Visual and Geometric Computing for 3D Capture, Display, and Fabrication (EVOCATION)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Innovative Training Networks (ITN)-European Training Networks
Action type: Marie Skłodowska-Curie actions
Panel: Information Science and Engineering
Countries involved: Italy,Austria,Germany,Italy,Germany,Germany,Italy,Italy,Germany,Italy,Germany,Switzerland,Hungary
Call: Innovative Training Networks (H2020-MSCA-ITN-2018, 2018)
Start date: 2018-10-01
End date: 2023-05-31
Duration (months): 56.0
Total project cost (EUR): 3639335.04
EU funding (EUR): 3639335.04
Participants: 16
Project status: SIGNED

The project will create a leading European-wide doctoral Collegium for research in Advanced Visual and Geometric Computing for 3D Capture, Display, and Fabrication (EVOCATION). The Collegium will train the next generation of creative, entrepreneurial and innovative experts who will be equipped with the necessary skills and competences to face current and future major challenges in scalable and high-fidelity geometry and material acquisition, extraction of structure and semantic information, processing, visualization, 3D display and 3D fabrication in professional and consumer applications. In the future, the ESRs will lead research and development of new visual and geometric computing methods in the widest variety of applications, ranging from industrial design to humanities, from medical training to urban assessment, and from creative industries to education methodologies. The EVOCATION network of public and private entities will be naturally multidisciplinary and multi-institutional and will: (a) promote, through domain-specific challenges, the culture of open science and multidisciplinary research applied to concrete problems of the real world, in strict cooperation with end users in engineering, science and humanities; (b) advance the state-of-the-art in geometry and material acquisition, geometry processing and semantic feature extraction, interactive visualization, computational fabrication, and high-bandwidth/3D display systems; (c) bridge complementary approaches for cost-effective data digitization, visualization, fabrication, and display through the integration of different methodologies in the 3D capture, processing and fabrication pipeline; (d) demonstrate the feasibility and efficiency of scalable cost-effective end-to-end techniques to virtually and physically capture and create objects with complex shape and appearance; (e) increase awareness of the benefits of advanced visual/geometric computing technology in both professional and consumer domains.

Keywords: acquisition,geometry processing,3d printing,computational fabrication,telepresence,large high resolution displays,3d displays,visualization
Circuit elements of the cortical circuit for predictive processing (CELPRED)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Consolidator grants
Action type: ERC actions
Panel: Neuroscience and Disorders of the Nervous System
Panel description: Nervous system development, homeostasis and ageing, nervous system function and dysfunction, systems neuroscience and modelling, biological basis of cognitive processes and of behaviour, neurological and mental disorders
Countries involved: Switzerland
Call: ERC Consolidator Grant (ERC-2019-COG, 2019)
Start date: 2020-02-01
End date: 2025-01-31
Duration (months): 60.0
Total project cost (EUR): 2000000.0
EU funding (EUR): 2000000.0
Participants: 1
Project status: SIGNED

One promising theoretical framework to explain the function of cortex is predictive processing. It postulates that cortex functions by maintaining an internal model, or internal representation, of the world through a comparison of predictions based on this internal model with incoming sensory information. Implementing predictive processing in a cortical circuit would require a set of distinct functional cell types. These would include neurons that compute a difference between top-down predictions and bottom-up input, referred to as prediction error neurons, and a separate population of neurons that integrate the output of prediction error neurons to maintain an internal representation of the world. This research proposal will test the framework of predictive processing and identify different putative circuit elements and cell types that are thought to form the circuit in mouse visual cortex. We will use a combination of physiological recordings, optogenetic manipulations of neural activity, and gene expression measurements to determine the cell types that have functional responses consistent with different prediction errors, as well as those coding for the internal representation. Identifying the circuit elements underlying predictive processing in cortex may reveal a strategy to bias processing either towards top-down or bottom-up drive when the balance between the two is perturbed, as may be the case in neuropsychiatric disorders.

Keywords: predictive processing,cortex,neural computation
An autonomous Real-Time Decision Tree framework for monitoring and diagnostics on wind turbines (WINDMIL RT-DT)

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: Switzerland
Call: ERC Proof of Concept Grant (ERC-2018-PoC, 2018)
Start date: 2018-11-01
End date: 2020-07-31
Duration (months): 21.0
Total project cost (EUR): 148890.0
EU funding (EUR): 148890.0
Participants: 1
Project status: CLOSED

Operation & Maintenance (O&M) costs may account for 30 % of the total cost of energy for offshore wind power. Alarmingly, only after a few years of installation, offshore wind turbines (WT) may need emergency repairs. They also feature an extremely short lifespan hindering investments to green energy, effectively designed to reduce CO2 emissions. We have designed real-time monitoring and diagnostics platform in the context of operation and maintenance scheduling of WT components. Using this architecture, we can quantify the risk of future failure of a given component and trace back the root-cause of the failure. This is business-critical information for Energy Companies and Wind Farm Operators. The platform consists of an autonomous software-hardware solution, implementing an Object Oriented Real-Time Decision Tree learning algorithm for smart monitoring and diagnostics of structural and mechanical WT components. The innovative concept lies in running WT telemetry data through a machine learning based decision tree classification algorithm in real-time for detecting faults, errors, damage patterns, anomalies and abnormal operation. We believe our innovation creates evident value and will raise great interest as decision-support tool for WT manufacturers, Wind Farm Operators, Service Companies and Insurers. In this project, we will carry out pre-commercialisation actions to position ourselves in the market, provide unique selling proposition for future customers as well as raise interest among potential R&D collaborators and pilot customers. We will also establish technology proof of concept for the platform. For the first time, we are applying our design in difficult-to-access energy infrastructure installations and deploying it on a real-world prototype wind turbine. The project will be carried out with technical and commercialisation support from key players within the wind energy industry.

Keywords: commercialisation,decision support tool,sw hw platform,wind turbine,machine learning,artificial intelligence,decision tree classification,monitoring,root cause analysis,failure prediction,cloud computing,business critical information,energy company,wind farm
RESTORING CARDIAC MECHANICAL FUNCTION BY POLYMERIC ARTIFICIAL MUSCULAR TISSUE (REPAIR)

Framework: Horizon 2020 — Excellent Science
Programme: Future and Emerging Technologies (FET)
Theme: EIC Pathfinder
Destination: Emerging themes and communities
Action type: Research and Innovation actions
Countries involved: Italy,Italy,Italy,Hungary,Switzerland,Sweden,France,Netherlands,Netherlands
Call: FET Proactive: emerging paradigms and communities (H2020-EIC-FETPROACT-2019, 2019)
Start date: 2020-09-01
End date: 2024-08-31
Duration (months): 48.0
Total project cost (EUR): 3909707.5
EU funding (EUR): 3909707.5
Participants: 12
Project status: SIGNED

Heart Failure (HF) and Atrial Fibrillation (AF) are both associated with impairment of cardiac mechanical function. To assist ventricular contractility in HF, left ventricular assist devices (LVADs) have been developed and demonstrated able to reduce mortality in patients awaiting transplantation, but enormous disadvantages largely limit their long-term use. In parallel, promising devices to mechanically assist atrial contractile function have been successfully tested in large animals but never reached the clinical use. Our revolutionary idea to solve these clinical challenges is to exploit smart materials to support or restore the cardiac mechanical function. Among smart materials, liquid crystalline elastomers (LCEs) are able to respond to external stimuli in a reversible manner to generate movement or tension. The REPAIR consortium has recently developed a novel LCE-based artificial muscle that under external light stimulation is able to enhance cardiac muscle contraction. These results pave the way for the development of a novel generation of cardiac assist devices. We will first develop a mechanical performant and energetically efficient LCE material that, integrated with light sources (µLED array), will result in fundamental biomimetic contractile units to be structured in a suturable, remote controlled contractile tissue. The LCE-µLEDs contractile tissue will be exploited to develop a new generation of cardiac assist devices (e.g. ventriculoplasty patches, aortic rings for diastolic counterpulsation and epicardial bundles for atrial contraction assistance) and test the effects of their acute implantation in large mammals (open-chest sacrifice experiments) and human explanted hearts. Among LCE-device features: they will be self-contracting, low weight, associated with low thromboembolic risk, and most importantly, they will rely on a control unit that can modulate the exerted force providing the fist “tunable” cardiac assist device ever developed.

Keywords: smart materials,artificial muscles,cardiac contraction assistance,implantable autonomous devices and materials
Chlorine isotope fractionation in Earth's mantle and crust - Experimental investigation on their role in magmatic ore formation processes and deep volatile cycling (ExCliso)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Individual Fellowships (IF)-Global Fellowships
Action type: Marie Skłodowska-Curie actions
Panel: Environment and Geosciences
Countries involved: Germany,Australia
Call: Individual Fellowships (H2020-MSCA-IF-2020, 2020)
Start date: 2022-04-01
End date: 2025-03-31
Duration (months): 36.0
Total project cost (EUR): 270876.48
EU funding (EUR): 270876.48
Participants: 2
Project status: SIGNED

The Earth’s deep chlorine (Cl) cycle remains poorly understood. A major amount of Cl is brought into the mantle by subduction, but a significant fraction is returned as Cl-rich brines, although high concentrations of Cl in parts of the mantle are attested to by inclusions in diamonds. Cl-rich brines likely play central roles in transporting elements to form magmatic ore deposits. The proposed research will investigate the deep Cl cycle from an experimental perspective, focusing on Cl partitioning and δ37Cl stable isotope fractionation between fluids, melts and relevant mantle phases. The high pressure-temperature experiments will be performed at the Australian National University, Australia and the University of Frankfurt, Germany, with Cl analyses and isotope fractionation enabled by state-of the-art instruments (SHRIMP in Canberra and SIMS in Heidelberg, Germany). These innovative experiments will help quantify the Earth’s deep halogen cycle, while also providing constrains to test hypotheses on magmatic ore deposit formation from a perspective that is significantly different from those currently available. During his Diploma (University of Mainz), PhD, and post doctoral studies (University of Münster; Sorbonne Université Paris) the applicant used a wide range of analytical and experimental methods, while gaining a profound knowledge of halogens, isotope fractionation, and phase equilibria of mantle and crust. He thus brings a wealth of theoretical, experimental and analytical experience to the project, which will compliment the expertise that he aims to acquire in ion microprobe techniques. Both the ANU and the University of Frankfurt would benefit from the applicant’s research experience, which will be applied to a novel research direction. In return the applicant would greatly benefit from gaining ion probe analysis skills and deepening his knowledge about experiment petrology and ore geology at two of the world’s leading institutions in these fields.

Keywords: experimental petrology,high pressure,high temperature experiments,ion probe mass spectrometry,deep volatile cycling,ore formation,isotope fractionation,chlorine isotopes
AI Enabled Managemement of Patients at Risk of Diabetic Foot Ulcer (DELTA)

Framework: Horizon 2020 — Industrial Leadership
Programme: Innovation in SMEs
Action type: SME instrument
Countries involved: Ireland
Call: SME instrument (H2020-SMEInst-2018-2020-2, 2019)
Start date: 2019-08-01
End date: 2022-11-30
Duration (months): 40.0
Total project cost (EUR): 3571215.0
EU funding (EUR): 2499850.0
Participants: 1
Project status: CLOSED

Diabetic foot ulcers (DFU) lead to approximately 150,000 amputations and €23 billion in costs in Europe each year. Of the 60 million diabetics in Europe , 1.5 million currently have a diabetic foot ulcer and 15 million will develop on at some point in their life . Each ulcer takes 3-6 months to heal and costs upwards of €14,000 to treat, however, if hospitalisation or amputation is required these costs can be as high as €100,000 . Diabetic foot ulcers pose a significant burden on healthcare systems around the world. The NHS in England spends almost 1% of its entire annual budget (£1.13 billion) on the diabetic foot. Almost all of these ulcers and amputations are preventable through early identification and proper care. Bluedrop Medical are developing a remote monitoring system that uses computer vision and machine learning to predict and prevent diabetic foot ulcers (DFUs). The home-based device, taking the form of a bathroom scales, performs a daily scan of the patient’s feet. In 10 seconds, thermal and photographic data is collected and sent to the cloud. Advanced algorithms monitor daily foot scans to detect DFUs before they develop, providing actionable alerts to both patient and healthcare provider. The technique of temperature monitoring has been proven to prevent 70% of DFUs and Bluedrop Medical are building this technique into an easy and quick to use device enabled by advanced sensor technology, computer vision algorithms and machine learning. By preventing ulcers, the technology can prevent hundreds of thousands of amputations, improving lives and saving healthcare systems millions of euro each year. Bluedrop Medical is targeting the highest risk patients who currently have a 30-40% annual incidence of ulceration by preventing ulcers in this group a potential average saving of €3,000 per patient can be achieve

Keywords: diabetes,digital health,telehealth,remote patient monitoring,diabetic foot ulcers,wound care,peripheral neuropathy,connected health,amputation,ulcers
Milli-Volt Switch Technologies for Energy Efficient Computation and Sensing (Milli-Tech)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Advanced grants
Action type: ERC actions
Panel: Systems and communication engineering
Panel description: Electrical, electronic, communication, optical and systems engineering
Countries involved: Switzerland
Call: ERC Advanced Grant (ERC-2015-AdG, 2015)
Start date: 2016-06-01
End date: 2022-05-31
Duration (months): 72.0
Total project cost (EUR): 2929875.0
EU funding (EUR): 2929875.0
Participants: 1
Project status: CLOSED

The Milli-Tech proposal aims at a novel technology platform serving both computation and sensing: electronic switch architectures, called steep slope switches, exploiting new device physics and concepts in emerging 2D materials to achieve operation at voltages below 100 millivolts. Such switches will have a subthreshold slope below 10mV/decade, significantly more abrupt than MOSFET thermal limit of 60mV/decade at room temperature and in great advance to any beyond CMOS switches. Such characteristics will dramatically improve both the energy efficiency of logic circuits and the transduction sensitivity for many classes of sensors. The project will develop a technological platform called ‘millivolt technology’ focusing on low power digital and sensing/analog electronic functions exploiting steep slopes, with the goal of lowering the energy per useful function (computed and sensed bit of information) by a factor of 100x. Such ultra-low operation voltage will contribute to solving major challenges of nanoelectronics such as power issues and it will enable energy efficient super-sensitive sensors for Internet-of-Everything (IoE). Milli-Tech includes fundamental research on new solid-state steep slope device concepts: heterostructure tunnel FETs in 2D Transition-Metal-Dichalcogenides (TMD), 2D Van der Waals super-lattice energy filter switch and hybrid architectures combining two switching principles: band-to-band-tunneling and metal-insulator-transition or negative capacitance in VO2, used as additive technology boosters. Milli-Tech plans breakthroughs by precise demonstrators: (i) energy efficient computation blocks for Von-Neumann ICs at sub-100mV, (ii) Active Pixel Sensors based on 2D TMD/GeSn tunnel FETs for IR imagers, (iii) Terahertz detectors based on hybrid 2D VO2/TMD switches (iii) ultra-sensitive 2D steep slope charge detectors for biosensing. The high-risk ‘millivolt technology’ will be highly rewarding by enabling the energy efficient revolution for IoE.
REGULATION OF NEURONAL GENE EXPRESSION THROUGH CHROMOSOME ARCHITECTURE (NGECA)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Individual Fellowships (IF)-Reintegration panel
Action type: Marie Skłodowska-Curie actions
Panel: Life Sciences
Countries involved: United Kingdom
Call: Marie Skłodowska-Curie Individual Fellowships (IF-EF) (H2020-MSCA-IF-2015, 2015)
Start date: 2016-11-01
End date: 2020-06-01
Duration (months): 24.0
Total project cost (EUR): 195454.8
EU funding (EUR): 195454.8
Participants: 1
Project status: CLOSED

A remarkable feature of the brain is its ability to adapt to changing environmental conditions. Modulation of synaptic strength and neuronal circuitry underlies experience-dependent learning, and requires widespread changes in gene expression. Following neuronal depolarisation, intracellular signalling results in rapid induction of many activity-regulated genes (ARGs). There are numerous interconnected levels of gene regulation; one critical aspect relates to the three-dimensional conformation of chromosomes within the nucleus. Looping of genes to regulatory regions and to other genes is required for transcriptional activation in other cell types, but remains largely unexplored in neurons. In this proposal, I will investigate how the genome architecture changes during neuronal depolarisation, and how this influences activity-induced transcription and neuronal plasticity. I will first map the genomic interactions of ARGs in neurons before and after depolarisation. This experimental approach will allow identification of enhancer-promoter loops and multi-gene complexes in an unbiased manner. Single-cell imaging studies will be performed to quantify the frequency of interactions across individual neurons. I will use super-resolution microscopy to simultaneously analyse multiple loci with high precision, providing unprecedented detail of gene interactions in response to neuronal activity. Finally, I will use genome editing to disrupt specific chromosomal contacts and evaluate the transcriptional induction of associated genes. I will assess whether loss of genomic contacts affects dendritic growth, a process associated with neuronal plasticity and dependent on ARG induction, to understand the biological implications of chromosome looping. The aim of this project is to discover novel molecular mechanisms that govern transcription during neuronal activation, which is critical in experience-dependent learning.