Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Improved Nano-Structure for Photocatalytic Efficient Cleaning of Surfaces (INSPECS)

Framework: Horizon 2020 — Industrial Leadership
Programme: Innovation in SMEs
Action type: SME instrument
Countries involved: Norway
Call: SME instrument (H2020-SMEInst-2018-2020-1, 2018)
Start date: 2019-02-01
End date: 2019-07-31
Duration (months): 6.0
Total project cost (EUR): 71429.0
EU funding (EUR): 50000.0
Participants: 1
Project status: CLOSED

The photocatalytic self-cleaning products can be greatly beneficial by reducing the frequency of cleaning operations in exterior surfaces, reducing the maintenance and operational costs, and preserving the ecosystem, by avoiding the use of cleaning products with chemical agents. Still, available self-cleaning coatings are very limited, presenting short lifetimes and poor self-cleaning capacity. Joma developed the next generation of photocatalytic self-cleaning and anti-polluting technologies for the efficient treatment of concrete surfaces in urban areas, addressing not only the need for buildings and infrastructures’ maintenance reduction but also the major societal problem of urban air quality. Joma will deliver an improved titania-based technology, with a unique and proprietary nanostructure. The technology brings together a superior self-cleaning performance, anti-greening proven efficiency and anti-polluting potential, based on its higher photocatalytic activity, higher specificity for removal of the surrounding NO2, cost-effectiveness, high level of customization, possibility to combine with water protection and significant maintenance need reduction and consequent cost savings. The customized nanoparticle is the key component of Joma’s products representing an outstanding innovation against current stated-of-the-art in self-cleaning technologies. To bridge the remaining steps towards full scale commercialization of our nanotechnology, Joma has to widely demonstrate its economic and environmental assets over competing solutions and to consolidate our strategic partnering in the concrete manufacturing and maintenance industry value chain. Thus, Joma will leverage from the current SME instrument application as a stepping stone to prepare the company’s business strategy and to propel the commercialization of our technology, which we expect to become a new reference in the construction industry sector.

Keywords: photocatalysis,self cleaning,anti polluting,tio2 nanoparticles,concrete surfaces
Technology to turn waste from rapeseed oil production into valuable food protein. (ALSEOS)

Framework: Horizon 2020 — Industrial Leadership
Programme: Innovation in SMEs
Action type: SME instrument
Countries involved: Poland
Call: SME instrument (H2020-SMEInst-2018-2020-2, 2019)
Start date: 2019-10-01
End date: 2022-04-30
Duration (months): 31.0
Total project cost (EUR): 2864947.5
EU funding (EUR): 2005463.25
Participants: 1
Project status: CLOSED

The continuing growth of world population is pushing demand for food. Climate changes and limited resources for farming lead to increased interest in new sources of nutrients alternative to meat. Additionally, human diet undergoes a significant shift towards proteins. Therefore, the growth of plant protein market is predicted to be drastically driven up in the coming years. Oilseeds, known for their high content of nutritious proteins, are expected to take center stage as main source of plant proteins aimed for human consumption. Nowadays, the main product of rapeseed processing is oil. A by-product – rapeseed cake – is used as animal feed. There is no commercial technology capable of extracting fully functional protein from rapeseed cake. At NapiFeryn BioTech (NFB) we came up with a new technology which allows more efficient use of this by-product. Our technology is protected with three patent applications. It enables full recovery of high-quality rapeseed cake proteins, ready to be used in food industry. Our business model is based on licensing of the technology to rapeseed processors. Licensing fee will be calculated individually for each oilseed processor or integrated formulator, based on the annual scale of the processed rapeseed. We have validated the pricing strategy with rapeseed processors. The first license agreement has been signed already in 2016. In the Ph2 project we aim primarily at delivering full-scale demonstrator (TRL9) of the rapeseed protein isolation technology at the commercial level (market launch), collect and prove all the data needed to quantify the cost-effectiveness of our technology. These activities will provide the input required for the main deliverables of the project: The Basic Engineering Level design of Biorefinery. With our technology the value that could be derived from the same amount of crop will double, from EUR 450 to EUR 900 per ton, which might be the only way for rapeseed processors to stay in business.
Development of novel synthetic strategies for the synthesis of bioactive meroterpenoids (NEOTERP)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Individual Fellowships (IF)-Career Restart panel
Action type: Marie Skłodowska-Curie actions
Panel: Chemistry
Countries involved: United Kingdom
Call: Individual Fellowships (H2020-MSCA-IF-2017, 2017)
Start date: 2018-09-01
End date: 2020-08-31
Duration (months): 24.0
Total project cost (EUR): 183454.8
EU funding (EUR): 183454.8
Participants: 1
Project status: CLOSED

The project concerns the development of innovative methodologies for the synthesis of novel bioactive natural products (meroterpenoids) and their potential application for the discovery of new medicines to treat resistant cancers and resistant microbial infections. The fundamentals of its originality and innovation are: a) The synthesis of a focused library of novel bioactive meroterpenoids, planned to have an impact in the fields of anticancer and anti-infective medicinal chemistry. b) The application of a flexible and concise dual biomimetic synthetic strategy, which will significantly impact the field of natural products synthesis focused on drug discovery. c) An extensive use of novel methods for polyene cyclisation including processes based on sulfite, sulfate, phosphate and other electrophilic initiators including radical cations generated by photoredox catalysis, coupled with polyketide aromatisation, will greatly impact in the field of cyclisation cascades in general as well as meroterpenoids synthesis. Dr Fabbrizzi is an experienced experimental scientist with substantial academic and industrial experience whose research career was interrupted in October 2015 due to funding crises in Italy. The award of a H2020 fellowship, if this proposal is successful, will be critical in supporting the resumption of his independent research career. Working in the supportive environment of Imperial College under the expert guidance of professor Barrett he will considerably reinforce his knowledge of natural product synthesis and drug discovery and will resume his position in the European research community improving his profile to face the current challenges of organic and medicinal chemistry. keywords/descriptors: Synthetic organic chemistry, Natural product synthesis, Medicinal chemistry
Modelling and simulation of impact waves in brain matter (TBI-WAVES)

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: Mathematics
Countries involved: Ireland
Call: Individual Fellowships (H2020-MSCA-IF-2020, 2020)
Start date: 2021-10-01
End date: 2023-09-30
Duration (months): 24.0
Total project cost (EUR): 184590.72
EU funding (EUR): 184590.72
Participants: 1
Project status: SIGNED

During the past decade, the brain has been the subject of numerous studies aimed at understanding its function and its behaviour. An important challenge lies in the prediction and the treatment of Traumatic Brain Injury (TBI), which is a major source of death and disability worldwide. In the mechanics community, important efforts have been dedicated to the mathematical description of the response of brain matter to forces, and to the simulation of traumatic events. Nevertheless, further progress is of utmost importance for applications in biomedical engineering and medicine. In facts, despite clear evidence that dynamic solicitations such as impacts play a crucial role in head traumas, several time-dependent effects are often neglected in the modelling (inertia, fluid content, viscous effects, swelling). The main goal of the TBI-WAVES project is to improve the mechanical and computational modelling of head traumas. A first aim is to improve the mechanical modelling of brain matter by accounting for the above commonly neglected effects. A second aim is to develop dedicated head impact simulation tools. The problem will be approached from theoretical and numerical perspectives, based both on the applicant’s and the host laboratory’s expertise. The training programme is exhaustive and will allow the applicant to grow as an independent researcher. The proposed research will foster further developments in this direction, a step which is fundamental in healthcare and for the benefit of society. The applicant is a Franco-German dual citizen, trained in France in applied mathematics, theoretical mechanics, and computational mechanics. He publishes articles in international scientific journals, with topics in applied mathematics and other disciplines. He is requesting funds for two years, to work within Prof. Michel Destrade’s team at NUI Galway. A six-month secondment at the University of Trento is planned.

Keywords: traumatic brain injury,soft tissues,nonlinear waves,mechanical modelling,biomechanics
Early detection and prediction of mechanical malfunction of machinery by sound analysis (neuronSW)

Framework: Horizon 2020 — Industrial Leadership
Programme: Information and Communication Technologies
Action type: SME instrument
Countries involved: United Kingdom
Call: Open Disruptive Innovation Scheme (H2020-SMEINST-1-2016-2017, 2017)
Start date: 2018-02-01
End date: 2018-05-31
Duration (months): 4.0
Total project cost (EUR): 71429.0
EU funding (EUR): 50000.0
Participants: 1
Project status: CLOSED

This project is intended to resolve the hurdle of lack of predictive maintenance in industries. The elimination of the risk of downtime and the reduction of maintenance costs are precious for the industry, because it is currently a barrier for increasing the production and accelerating creation of better products that can provide higher profits. Therefore, the industry has strong need to adapt an easy-to-implement and easy-to-use solution. This led NEURONSW LTD. to the concept of prediction of mechanical malfunction of machinery by mechanical sound analysis. Neuron soundware (NeuronSW) is an innovative breakthrough solution combining advanced algorithms, machine learning and big data analysis to emulate the human auditory cortex enabling the early detection and prediction of mechanical malfunction of machinery. Through NeuronSW, manufacturers achieve intelligent audio diagnostics to monitor the key machinery equipment using the sound they generate. The integrated hardware and software platform automatically gathers sound of machines in real time and continuously assess the equipment health and operates similarly how experienced technicians use their ears to diagnose broken machines. It works offline and online and can be integrated into existing software or third party IoT platforms. This effectively transforms data into knowledge and actions. Sound and vibration sensors (microphones) can be quickly and cheaply installed on all types of machinery, enabling assets without digital interface or operated by legacy systems to be digitalized without expensive upgrades.

Keywords: machile learning,operational maintenance,failure prediction,sound analysis,productivity,downtime,machinery
Inertial Sensing Based on Quantum-Enhanced Levitation Systems (IQLev)

Framework: Horizon 2020 — Excellent Science
Programme: Future and Emerging Technologies (FET)
Theme: EIC Pathfinder
Destination: Novel ideas for radically new technologies
Action type: Research and Innovation actions
Countries involved: Austria,Austria,Spain,Switzerland,France
Call: FET-Open Challenging Current Thinking (H2020-FETOPEN-2018-2019-2020-01, 2019)
Start date: 2020-01-01
End date: 2023-06-30
Duration (months): 42.0
Total project cost (EUR): 2612471.25
EU funding (EUR): 2612471.25
Participants: 5
Project status: SIGNED

Information and data are becoming increasingly important both to European society and to the European economy. It will be key to the future of Europe to start having an appropriate share in the development and control of central information and data related sectors. One of these central sectors - the Internet of Things - is currently being developed world-wide, has a compound annual growth rate between 10 and 30%, and is starting to influence our daily lives. Sensors are a fundamental building block of the Internet of Things. For instance, cars would be boosted in the direction of fully self-driving vehicles, if an inertial sensing system could precisely measure their position at every instant of time. Within the framework of the Internet of Things, globally connected self-driving cars could contribute to less congestion, less pollution and an overall improvement in quality of life. It is the aim of IQLev to join in shaping such a positive European vision by developing high-end inertial sensing systems for navigation, positioning and gravimetry/seismometry. Our revolutionary approach combines quantum-enhanced sensing with noise cancellation via perfect system isolation through levitation. This combination results in inertial sensing performances beyond currently possible sensitivities. Optical, electrostatic and magnetic levitation are investigated via an effective, parallel and synergetic approach. The capabilities of our inertial sensors for linear acceleration and rotation (gyroscope) sensing in the low frequency domain (mHz) are evaluated and benchmarked according to industry standards. The first target areas of the sensors under development are scientific satellite missions and seismic measurements.

Keywords: inertial sensing,levitation systems,accelerometer,gyroscope,quantum enhanced sensing
Upgrading the Fuzzy Dark Matter Model (upgradeFDM)

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: Physics
Countries involved: United Kingdom
Call: Individual Fellowships (H2020-MSCA-IF-2019, 2019)
Start date: 2021-01-11
End date: 2023-01-10
Duration (months): 24.0
Total project cost (EUR): 224933.76
EU funding (EUR): 224933.76
Participants: 1
Project status: CLOSED

The project will illuminate implications for cosmic structure formation of the hypothesis that dark matter consists of ultra-light scalar particles, generically labeled as Axion-like particles (ALPs). Described as a continuous field, dark matter in this picture can be described as a self-gravitating superfluid on cosmological scales with distinct phenomenology from standard Cold Dark Matter (CDM) due to its coherent nature. The research will extend this new model of Dark Matter to include non-gravitational self-interactions and the effects of a non-condensed corpuscular component and examine their effects on the formation of gravitationally bound cosmological structures as well as the creation of quantum vorticity through numerical simulations. The project will, therefore, merge concepts from Cosmology and Cold Atom Physics to explore a most topical question in modern science. The experienced researcher, Dr I-Kang Liu will move from Taiwan to the UK to join the School of Mathematics, Statistics and Physics at the University of Newcastle as a member of a team of cosmologists with strong links to a team of cold atom physicists. A cold atom physicist by training, with an extended network of European and international collaborators in his research field, he will extend his knowledge to a new field, cosmological structure formation, while bringing in his considerable numerical skills to the existing groups to perform the demanding simulations required to fully understand the process of structure formation in this cosmological model.

Keywords: light scalar fields,bose einstein condensates
AI-powered pathological cancer diagnostics (AIPACA)

Framework: Horizon 2020 — Industrial Leadership
Programme: Innovation in SMEs
Action type: SME instrument
Countries involved: Germany
Call: SME instrument (H2020-EIC-SMEInst-2018-2020-3, 2020)
Start date: 2020-08-01
End date: 2022-07-31
Duration (months): 24.0
Total project cost (EUR): 1960540.0
EU funding (EUR): 1372378.0
Participants: 1
Project status: CLOSED

MindPeak is an innovative start-up that builds diagnostic support tools for cancer diagnosis. Our Artificial Intelligence (AI) software analyses biopsy slides within seconds to find tumour regions and quantify tumour markers to prepare them for visual inspection by the pathologist. In 2018, the number of diagnosed cancer cases has grown to 18 million globally. Consequently, the demand for cancer diagnosis to determine appropriate treatment has never been higher. Cancer diagnosis nowadays is a very time-consuming process and depends heavily on the knowledge and experience of specialised pathologists. Unfortunately, the number of experts can no longer meet the quickly increasing demand. As a result, patients must wait longer than necessary for their diagnosis. To overcome this challenge, MindPeak provides solutions based on deep learning to support pathologists in their diagnosis. Patients benefit from increased accuracy and a faster turn around time of diagnosis as well as reduced costs. The key USP of our AI is its reliability in the clinical context with its variations across and within labs. Funding from the EIC Accelerator will help us with a successful and fast go-to-market of our solutions that cover crucial improvements for cancer patients. With our technology, we address the market for pathology with a current size of $17.3 bn worldwide and an annual growth rate of 6.5%.

Keywords: deep learning,machine learning,cancer,pathology,biopsy,tumour,tumor,image,recognition,histology,histopathology
Improve biorefinery operations through process intensification and new end products (BioSPRINT)

Framework: Horizon 2020 — Societal Challenges
Programme: Food security, sustainable agriculture and forestry, marine and maritime and inland water research and the bioeconomy
Theme: Public-private partnerships
Destination: Bio-based Industries
Action type: Research and Innovation actions
Partnership: BBI (Art-187)
Countries involved: Germany,Germany,Germany,Spain,Spain,Italy,Germany,Germany,Cyprus,Finland,Finland,Slovenia,Netherlands,United Kingdom
Call: Improve biorefinery operations through process intensification and new end products (H2020-BBI-JTI-2019, 2019)
Start date: 2020-06-01
End date: 2024-05-31
Duration (months): 48.0
Total project cost (EUR): 5879733.65
EU funding (EUR): 4998014.0
Participants: 14
Project status: SIGNED

Building on successful projects and relevant assets from partners and an interdisciplinary approach, BioSPRINT applies process intensification in the context of biorefining operations, so as to improve the efficiency of the purification and conversion of sugars from the hemicelluloses fraction of lignocellulosic biomass and to enable their transformation into new bio-based resins for substituting fossil based polymers in a range of applications. The ultimate objective is to lead to a reduction in operation costs, feedstock and energy resources, greenhouse gas emissions and higher yields, while increasing operation safety, by concentrating on technologies which can intensify processing methods and create an integrated biorefinery concept. Of particular interest in BioSPRINT is the valorisation of hemicelluloses streams derived from hard wood and straw, from processes employed in the production of paper pulp or biofuels. Such streams are readily available from the pilot or production processes of the project’s research and industrial partners (Fraunhofer and UPM). A case study using a stream from Clariant will also be carried out. With regards to processing technologies, BioSPRINT will focus on 4 activity areas (a) Upstream purification; (b) Catalytic conversion; (c) Downstream purification and (d) Polymerisation. The project will develop and validate an intensified and integrated purification strategy leveraging innovative anti-solvent precipitation and membrane separation methods, novel intensified and integrated catalytic processes for dehydration of C5 and C6 hemicelluloses sugars into monomers, extractive-reaction methods to isolate the reaction products from the reaction medium in situ, heterogeneous catalysts and an intensified polymerisation process for furan-based derivatives. Cross-cutting activities will cover process simulation and optimisation, an integrated Lifecycle sustainability assessment, standardisation, dissemination and exploitation activities

Keywords: biorenewable resins,catalytic conversion,downstream purification,furans,hemicellulose,lignocellulosic biomass,operational safety,upstream purification,polymerisation,valorisation
Time-lapse understanding of the static and human scene and its lighting (SCENEUNDERLIGHT)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Innovative Training Networks (ITN)-European Industrial Doctorates
Action type: Marie Skłodowska-Curie actions
Panel: Information Science and Engineering
Countries involved: Italy,Italy,Germany
Call: Marie Skłodowska-Curie Innovative Training Networks (ITN-EID) (H2020-MSCA-ITN-2015, 2015)
Start date: 2015-10-01
End date: 2019-09-30
Duration (months): 48.0
Total project cost (EUR): 507277.8
EU funding (EUR): 507277.80000000005
Participants: 3
Project status: CLOSED

Europe has a strong position in the fast-growing photonics and lighting industry but has to face the fast technological changes and, most importantly, a demand for larger transfer from research to industrial innovation and application. With the introduction of Solid State Lighting (SSL) new targets of energy efficiency were set and smart lighting technology was boosted. SCENEUNDERLIGHT leverages the privileged role of Europe in photonics as well as in smart lighting technologies. We propose 1. training of early stage researchers, who would become future experts in computer vision and lighting technologies, 2. first-class research, to push the state-of-the-art of computer vision and to drive the changes of lighting technologies, and 3. new disruptive products in smart lighting to accelerate the technological transfer from academia to industry. The proposed program will make efforts on systems for energy saving, towards a more sustainable greener Europe. We plan to implement this directive by smart lighting, defining new disruptive light management system technologies. Our planned demonstrator will take long-term time-lapse top-view images of the environment, understanding it by means of computer vision algorithms and controlling lights, for optimal lighting and energy saving. This will proceed via estimating the scene illumination properties (3D structure, material of objects and light source positions) and its use, with respect to the activities of the people. Finally, all research results will converge into the creation of an “invisible light switch”: users moving within an environment (e.g. warehouse with multiple aisles) will have the feeling that all of it is lit (e.g. switching lights on in an aisle just before the person turns into it), while the system will actually manage lighting to save energy, switching off those which the user cannot see, as for an “energy saving in the invisible”.

Keywords: smart lighting