International cooperation: Round-up of 2021 retained INTER projects

In this project, we study the effect of “green” as an attribute of collateral assets on household financial constraints and housing transactions. Households’ financing needs are primarily for durable assets (houses, cars, etc.) that they pledge as collateral in exchange for bank loans. Households have the possibility to choose between green vs. brown durable assets. Our goal is to understand how the green attribute plays a role in (1) the investment decision of the buying household, and in (2) bank lending and risk-taking decisions. To do so, we have obtained access to granular data from Norway about households (including individual-level information about their employment, demographic information, sources of income, and ownership of assets for every Norwegian tax-payer), housing (including detailed transaction and physical characteristics), firms and credit (including loan level transaction information on interest rates, maturity, security and other terms). Our research project has important policy implications. Government use subsidies to stimulate investments in green assets. Banking regulation can also foster bank loans backed by green collateral. Through the effect of green on households’ financial constraints, we plan to learn more about the intended and unintended consequences of these policies on inequalities, financial stability, as well as on their environmental impact.

Mathematical and numerical modelling approaches allow us to integrate pathological processes that occur across different scales: cell, cells assembly, and tissue. The S-Keloid project aims to investigate the role of mechanical and inflammatory environmental factors on cells associated with keloid disorders. To address the multiscale aspects of these disorders, we plan to combine and integrate 3D in vitro cell models cultured under mechanical and biological stresses in order to mimic in vivo situation, and mathematical modelling of such systems. From applying experimental tests at tissue-scale and using a multiscale approach, the mechanical stress fields will be integrated into the 3D mathematical model. Parameter identification, optimization and their use across multiple scales will ensure the realism of the models and the quantitative and qualitative predictions of the keloid disorder.

The CancerProfile multidisciplinary project brings together highly complementary translational research institutes to develop a novel standard of patient stratification for predictive and personalized medicine of pancreatic adenocarcinoma cancer. The overall objective is to fully integrate cutting-edge AI-augmented anatomopathology, genomics, and tissue/liquid biopsy analysis in conjunction with functional drug response of tumors, to derive biomarkers indicative of a predictive treatment choice. We will apply an innovative Personalized Functional Profiling (PFP) proprietary technology consisting of direct large-scale analysis of the sensitivity of 3D-printed spheroids derived from patient biopsies in response to a panel of EMA and FDA approved anti-cancer chemotherapeutics. Overall, the objective is to clinically validate new prognostic markers and identify alternative treatment options for tumors resistant to standard chemotherapies.

Variable stiffness composites (VSCs) from additive manufacturing (AM) technology are fundamental for applications requiring lightness and high performances. VSCs are obtained by placing the fibre along a curvilinear path within a given topology. However, general design strategies integrating the physical responses involved at all the VSC scales and the AM process requirements are still lacking in the literature. In particular, the available design methodologies rely on many simplifying hypotheses on both the VSC stacking sequence and the fibres-path within each ply to get target macroscopic properties, which are difficult to be correctly formalised. Moreover, to get manufacturable solutions, a time-consuming post-processing phase is required to adjust plies fibres-path to satisfy the technological restrictions related to the AM process. This project aims to propose a new paradigm in the multi-scale modelling and design of VSC structures. The idea is to formulate the design problem in the most general sense, without introducing simplifying hypotheses neither on the VSC laminate stacking sequence nor on the fibres-path shape within each lamina. Therefore, the goal is to develop a general theoretical framework and a pertinent multiscale modelling strategy, which will be integrated into an optimisation methodology (and software) able to determine the best configuration of a VSC structure, in terms of topology and of anisotropy descriptors, and capable of accounting for the main manufacturing requirements and process-induced imperfections. To achieve this goal, the proposed methodology relies on:

1. The polar formalism extended to higher-order equivalent single-layer (HOESL) theories; in this context, the polar parameters (PPs) are used to describe the macroscopic anisotropic behaviour of the VSC;

2. The global-local multi-scale two-level optimisation strategy (GL-MS2LOS), based on PPs to describe both global and local design requirements (DRs);

3. The non-uniform rational basis spline (NURBS) entities to describe the topology, the PPs fields of the VSC structure and the fibres-path within each ply;

4. The solid isotropic material with penalisation (SIMP) approach based on NURBS entities (NURBS-based SIMP method) to perform the topology optimisation (TO) of the VSC;

5. The layer-wise (LW) kinematical models to predict the failure modes occurring at the VSC mesoscopic scale. In this context, the optimal design of the VSC structure is articulated in two sub-problems stated at different scales. The first-level problem (FLP) aims to determine the optimum distribution of the variables describing the anisotropy and the topology of the VSC at the macroscopic scale. At this level, the VSC is modelled as an equivalent homogeneous anisotropic continuum whose behaviour is described in terms of PPs in the framework of HOESL theories. Both the topology and the PPs fields are represented through NURBS entities and the AM process requirements are formalised as equivalent constraints on the PPs fields and on the topological variable. Local DRs of the VSC structure are introduced in the problem formulation through a suitable GL modelling strategy. The second level problem (SLP) aims to determine an optimum lay-up (in terms of fibres-path in each lamina) matching the FLP results. The generic ply fibres-path is described through a NURBS surface. The SLP formulation is coupled with a GL modelling strategy, based on LW theories to assess, for the most critical regions of the structure, local failure mechanisms, which cannot be described during the FLP.

The development of clean and renewable energy conversion and storage systems has become more important than ever due to the accelerating rate of global energy consumption and in order to meet objectives in the fight against global warming. The growing demand for renewable energies and the urgency of reducing the human ecological footprint has increased the prominence of lithium-ion batteries for mobile and stationary use, as can be inferred from the European Battery 2030+ initiative. One of the challenges for improving the performance of lithium-ion batteries, to meet increasingly demanding requirements for energy storage, is the development of suitable electrodes and electrolytes. Although Li-ion batteries have been available for several decades, there are many fundamental gaps in our understanding of the atomic and molecular scale processes that govern their behaviour, performance and failure mechanisms. A fundamental approach is therefore urgently needed to uncover the underlying principles that control the corresponding complex and interrelated processes. We can only advance this fundamental approach further with the instrumental development of techniques bringing complementary information to be intersected or combined with available characterization methods in order to draw the most complete picture possible of battery failure mechanism. The overall objective of the NanoLit project is the survey of lithium migration in both electrode/electrolyte interphases and the lithiated phases of active materials in order to develop specific analytical methods to improve interface engineering for future high-energy chemical energy storage systems.

The history of psychiatry is a history of the difference between normality and madness. However, this difference is becoming increasingly fragile. On the one hand, with the unlocking of psychiatric institutions and the social integration of inmates, madness is becoming everyday normality; on the other hand, behavioural and reaction patterns such as rush, stress or attention deficit are pathologised. Thus, established historiographies, based on the traditional dichotomy, loose purchase on this phenomenon. This is the ground assumption of the proposed research group: it takes the erosion of the difference between normal and pathological as the central development in dealing with psychic alterity. This phenomenon lacks adequate historical consideration. Our aim is to historicise the interdependent relationship between the history of psychiatry and historiography, and thus to turn it into an instrument of analysis of the perceived erosion. This goal is to be achieved by a decentralisation of the previous topography of the history of psychiatry. Phenomena cutting through established topics shall come into focus: 1. Actor-constellations involving other professional groups than psychiatrists; 2. spaces opening up other life-worlds besides conventional institutions; 3. practices and techniques of dealing with psychiatry, including media techniques, prevention strategies, processes of appropriation and artistic interventions. However, this de-centring of the history of psychiatry should by no means be limited to aspects and facets so far unexplored or underexposed. Rather, these elements are to be conflated into a history of alterity in order to explore the field of contemporary history. Along the difference between normality and madness this will help to develop an alternative to a classical disciplinary history of psychiatry. In order to include ethnological/ethnographic, historical/sociological, cultural and literary approaches, the research group consists of scholars from different disciplines. At the same time, it keeps a medical-historical focus in order to include the reference disciplines of psychiatry, psychology and social work in the research.

Contemporary global hydrological models provide conflicting estimates of water storage and flows resulting in differing estimates of current water availability or of climate change impacts on freshwater resources. The central objective of the proposed Research Unit (RU) is to improve our understanding of global freshwater resources and to improve estimates of continental water fluxes and storages. We hypothesize that a major improvement can only be achieved by combining state-of-the-art hydrological modelling and various new and geodetic and remote sensing data in an ensemble-based calibration and data assimilation (C/DA) approach that allows a flexible parameter (calibration) and state (data assimilation) adjustment tailored to the modelling purpose. Such an approach has not yet been implemented. We formulated two primary goals for the first phase: (1) develop a multi-observation ensemble-based C/DA methodology to combine observational data of model output variables with hydrological models in an optimal manner, and (2) exploit this methodology with the global hydrological model WaterGAP to provide an improved quantitative assessment of freshwater fluxes and storages including their uncertainties, for critical basins and globally. The C/DA approach (Ensemble Kalman Filter approach and a Pareto-optimal calibration approach) enables optimization of temporally constant parameters. In this second phase of the project we consider the uncertainty of calibration data and estimation of model output uncertainty. Sensitivity analyses, uncertainty information provided by C/DA as well as model validation against independent data will allow evaluating the added value of applying the C/DA approach. The RU will continue a research setup centred on C/DA that, for the first time, enables full utilization of in-situ, geodetic and remote sensing observations of model output variables for global water assessments. The RU is organized the RU research in five work blocks, centred on (1) C/DA methodology development, (2) hydrological model development, (3) development of observational data including uncertainty estimates, (4) validation, characterization of model output uncertainty and evaluation of C/DA methodology, and (5) assessment of water storages and fluxes in critical regions (five regions where in-situ data are scarce and where the regional freshwater system is of importance for a large population or from the Earth system science perspective) and globally.

Epilepsy is a common and disabling condition with a significant disease burden worldwide. Gene discovery and functional analyses of genetic defects have been major drivers to unravel disease mechanisms and to bring about first personalized treatments. However, most of the genetic alterations underlying epilepsy remain to be elucidated. Genetic epilepsies show a typical age dependency, the origin of which is largely unknown. Therefore, developmental factors are likely to play a pivotal role for epileptogenesis of genetic epilepsies. In this Research Unit (RU), we aim to investigateif and how genetic mutations induce a cascade of multidimensional epileptogenic processes, and how these interact with developmental processes which likely contribute to the age-dependent manifestation of seizure phenotypes in genetic epilepsies. To understand the interaction of epileptogenesis and brain development, we will combine

(i) gene discovery studies which will further unravel the ‘missing heritability’ in rare epileptic encephalopathies and common genetic generalized epilepsies, using state-of-the-art genetic technologies in the largest genome-wide datasets available worldwide, with

(ii) studies of cell-specific transcriptional changes (single-cell RNA sequencing) triggered by human genetic mutations in knock-in zebrafish and mouse models at different stages of development.

(iii) Bioinformatics analyses will integrate primary genetic and transcriptional data to analyze gene networks and pathways of epileptogenesis.

(iv) We will determine at different developmental time points how detected mutations change (a) biophysical and neuronal behavior in heterologous and neuronal expression systems, (b) the neurophysiological alterations induced by genetic mutations in specific neuronal populations and cellular compartments, and (c) entire neuronal networks in vivo. This combination of human genetics, adaptive cell-specific transcriptomic changes, and related physiological data will open new perspectives to understand the complex processes leading to different age-dependent epilepsy syndromes and to translate this knowledge into improved therapies. Our results will likely generate new hypotheses not only for epileptogenesis but also for brain development in general and other inherited brain disorders.

In recent years machine-learning techniques enabled significant advances in the field of medical image analysis. They promise a coherent, fast, and scalable way for diagnosis and prognosis of diseases, such as COVID-19, from radiological images. Nevertheless, the major road-blocks for a more systematic deployment of these techniques in clinical settings are bias (e.g. due to imbalanced population features) and data inhomogeneity (e.g. different data modalities). They lead to shortcomings regarding the universality and generalisation of machine learning models and thus limit the translation of scientific results into widely applicable clinical tools. In the proposed project, we will develop novel machine learning approaches to overcome these obstacles: (A) Confounder mining: a framework for the direct identification of confounders that induce bias in training machine learning models. (B) Large-scale learning from inhomogeneous data: deep learning methods to improve universality and generalisation by enabling training from massive cross-centre and cross-modality datasets. (C) Domain adaptation and transfer learning: systematisation of design patterns for neural network topologies for different sub-fields of domain adaptation and transfer learning to deal with biased data. As such, bias and data inhomogeneity will be tackled from three complementary angles that could be integrated into one stratified solution: bias identification in (A), adaptation to inhomogeneity in (B), and adaptation to bias in (C). By doing so, the negative effects of bias and inhomogeneity will be reduced substantially, enabling new potentials to bring machine-learning methods into the clinic at scale.

The project MiNaBatt targets the understanding of the chemistry and transfer processes at the interfaces between polymer- and inorganic solid electrolytes. This is essential for the functionality of solid-state batteries with hybrid electrolytes, which promise to improve the performance of electrochemical energy storage devices. Thus, this research gives fundamental insight into the lithium transport between two electrolyte types with different lithium conduction mechanisms. The lithium transfer at the interface is complicated by the fact that most material combinations react with each other to form an interphase of degradation products, which significantly influence the lithium transfer across the interface. This drastically affects cell performance and it is vital to understand the underlying mechanisms. Therefore, MiNaBatt combines the experience of experts in the fields of electrochemistry, interfaces, materials synthesis and SIMS analysis to jointly pursue a systematic investigation into the interfaces of a selection of different combinations of polymer and inorganic solid electrolytes. The sample combinations are chosen to represent different functionality and reactivity of polymer and inorganic solid electrolytes. The selected samples cover a range from very commonly used combinations, such as PEO- and garnet-type hybrid electrolytes, to less studied electrolytes, such as polyphosphacenes. Our goal is to jointly investigate the interface chemistry at multi-scale dimensions. Therefore, we analyse the morphology and chemical composition of the interface in 2D and 3D with a new instrumental setup for the HIMSIMS to enable analysis of polymer-inorganic-hybrids with highest lateral resolution. We also apply Hybrid-SIMS measurements with orbitrap analyser to obtain complementary information at high mass resolution. Along with electrochemical measurements of the interface resistance under varying temperature, pressure and current density, we intend to understand the physico-chemical principles of lithium transfer through interfaces/interphases in hybrid solid electrolytes.

Today’s best spin-based computing devices are still plagued by large power consumption because charge currents are needed to modify magnetic states. To fully exploit spintronics, we need to process, transport and store information without using charge currents. The SWIPE project will pioneer a new route using spin waves (magnons) to carry signals over long distances. We will focus on antiferromagnetic (AFM) spin waves, which are fast and impervious to perturbations, but harder to control. Our key proposal is to use lattice vibrations (phonons) to control AFM magnons: Acoustic phonons can generate and propagate signals. Optical waves can couple to electronic and magnetic excitations, and modify the magnon properties. The key advantage of spin waves is their interface with nonvolatile magnetic states, which enables ultralow power information and communication technology. A second important front is sensors and actuators, made exquisitely sensitive and efficient with magnonic devices.

There is an increasing demand of high performance coatings for paper and natural fibre-based applications in key industry sectors. The most common challenge of all coatings is to address multiple functionalities corresponding to their field of application. This often implies multilayer coating, high material use, high weight products as well as the use of solution-based approaches that involve synthetic formulations and drying/curing processes inducing swelling effects, high amounts of coating materials and recyclability to a minor degree. Beside this, the high demand of cost-saving, flexible and less-energy coating processes as well as the development of recyclable coatings based on renewable and sustainable sources to substitute fossil-based feedstock, motivate the development of an innovative composite coating approach that exceed the conventionally coating formulations and application technologies. To face these unaddressed challenges, PlasmaComp will combine fully bio-based composite coating formulations and cellulosic substrates to a dry and environmentally friendly deposition approach involving reduced amount of chemicals use and by-products generation, as well as reduced energy consumption. PlasmaComp implies the development of bio-based composite formulations using renewable feedstock from e.g. vegetable oils, as well as a sustainable reinforcement, like nanocellulose/chitin fibres or graphite particles, for enhancing specific properties for packaging usage and sport goods, e.g. water/oxygen barrier and release function for paper, hydrophobic and adhesive properties for natural fibres in composite parts, and additional functionalities like anti-microbial, anti-static properties and their combinations. The polymerization and deposition of the new composite coating will be achieved using atmospheric plasma. The composite coating formulation has to be reactively and rheologically adapted to ensure excellent interactions between matrix and reinforcement. Furthermore, concepts of the plasma processing have to be developed to achieve a homogeneous composite coating on the temperature-sensitive and porous paper substrates and hemp fibres. To demonstrate the potential of the plasma-polymerized functional bio-based composite coating for industrial application, upscaling trials and characterization of coated samples of flexible and release liner papers and hemp fibre reinforced composites will be carried out.

The project will develop a data-driven computational approach and open simulation platform for microstructure composite materials. At the heart of DeeMa project are the concepts of data-driven computational mechanics, machine learning and knowledge based engineering: i) integration of materials knowledge (ontologies), ii) automating routine design activities (interoperability) and integration of industry needs and requirement (management of KPI and ontologies), iii) integration of physical and data driven models (digital twin), iv) Design (inverse problems) and uncertainty and sensitivity analysis (Bayesian approach). The outcome of this project is expected to explore the connection between data- driven and classical computational mechanics, and thus to extend research scope in both mechanics and data sciences and demonstrate the prototype of a platform enabling integrated design of novel materials. The goal is to provide best-in-class capabilities in a business friendly open source software environment. Top-notch physical models and Data-driven model and technologies will be connected and merged. The interoperability will be ensured and pushed beyond the state-of-the-art by implementing generic interfaces as well as the low-level infrastructure needed to support the development planned within the project.

The European transport market calls for multi-functional parts having lightweight benefits based on sustainable raw materials. Insulating foams and felts have come to a limit in their thermal insulating performances developments. In addition, these parts are mainly based on polyurethanes which originate from crude oil and imply toxic isocyanates use.

To solve these issues, SAFFIA will develop enhanced thermal insulating parts with environmental and process health benefits. The proposed holistic approach will develop an in situ polymerized nanocomposites composed of biobased non-isocyanate polyurethanes and insulating low density silica-aerogel nanoparticles. Polymerizations and compatibilizations will be conducted in an extruder allowing operating conditions versatility and control over nanoparticle dispersion. The two industrial partners include an automotive producer which will ensure efficient developments from products specifications to TRL6 demonstrators.

ORACIA will address Aphasia rehabilitation. Aphasia is an acquired disorder of language that affects an individual’s comprehension, expression and communication. Aphasia is common in older adult patients in the context of vascular or neurodegenerative disorders (affecting 30~40% of stroke survivors). Effective rehabilitation therapy outcomes are dose-dependent; intensive, high-repetition, task-oriented. Home-based therapy is important in the rehabilitation process. ORACIA targets people with aphasia above 60 years old, and will engage end-users in codesign of the solution, and validation pilots that will involve 115 end-users (45 primary end-users, 45 informal caregivers, 25 care professionals). ORACIA unique solution will integrate a Clinic Backend, an Interactive Artificial Companion & Coach and Modular Device specifically design for Aphasia rehabilitation. ORACIA unique combination of Speech and Language Therapy, Cognitive Stimulation and Motor Functional Training will contribute to the digital transformation of health and care in Aphasia rehabilitation by promoting the extension of the clinic to the patient’s home. ORACIA’s commercialization leader will be NIV, already established on the market, and will sell the product in Europe (jointly with EU business partners). IPN and DT will be technology suppliers to NIV, and CRFT, RHZ and PSSJD will be expert clinical advisors. ORACIA can reach break-even at year 3, and start generating profit at year 5 and sustainable at year 6 with at least 1800 users in 60 paying organizations. ORACIA will be disseminated to the general public through websites (project, AAL, partners), social networks, brochures.

AGAPE will provide a multilayer framework of Active and Healthy Ageing (AHA) services innovation adoption (AGAPE platform) by enhancing existing local services through innovative components, specific education programmes, coaching sessions and innovation management services, all of them composed in an integrated approach for AHA.

The project will address primary, secondary and tertiary end users needs and challenges by providing smart users’ interface and carers/manager dashboards with selected KPIs to supervise AHA services and get evidence of innovation adoption figures. The Agape Multicomponent Service Plan will enable a person-centred goals with an iterative dynamic coaching and service composition through a multi-stakeholder service orchestration designed to improve inclusion, equity and universal access to the services from older adults. Such a solution will be addressed through an impact by design approach engaging in a co-creation strategy ageing people, carers and care organization managers. Agape consortium will disseminate Agape Case Studies derived by multiple pilot sites covering different EU contexts to generate an interest about the added value evidence of the Agape IT-based AHA services

The overarching goal of this project is to improve dementia prevention strategies by developing and validating a personalized medicine methodology for the detection of earliest signs of impending cognitive decline and markers enabling early and personalized multidomain interventions. While currently there are no curative treatments for dementia and Alzheimer’s disease (AD), a multidomain lifestyle intervention (FINGER randomized controlled trial (RCT)) has been shown to improve cognition and other related outcomes in older adults from the general population with elevated risk of developing dementia. Findings from FINGER and other multidomain lifestyle trials have emphasized that intervention effectiveness may be dependent on methodology that does not yet exist, i.e., accurately identifying at-risk individuals who are most likely to benefit. This project will address this methodological gap by (1) developing methods to predict future cognitive decline based on clinical data and differentiate between healthy individuals at higher risk for mild cognitive impairment and sporadic AD vs. those who remain healthy; and (2) test the methodology in ongoing RCTs for dementia prevention. Instead of a standard machine learning approach, we propose an innovative concept of personalized aging pattern rooted in data from healthy individuals. Specific deviations from the personalized aging pattern will be detected as specific risk factors for the onset of cognitive decline and subsequent dementia. The consortium will utilize multiple data resources, including a large observational healthy-aging study (Vallecas) and 3 prevention RCTs (FINGER, MIND-ADmini, MET-FINGER) based on the successful FINGER multidomain intervention model. The consortium mixes unique expertise from, e.g., machine learning, clinical neuroscience, dementia intervention/prevention, imaging, legal and ethical aspects, to achieve the critical mass. Public involvement activities will be led by Alzheimer Europe.

Endometrial cancer is the fourth most common cancer in women, and its incidence is increasing. Early detection is crucial since it is directly associated to patient’s survival. So far, no screening methods are available and diagnosis is a multistep process that includes in 80% of cases a minimally-invasive and in 20% of cases additional invasive measures. This inaccurate diagnostic process is a burden on our healthcare system, since it is performed on 7M women a year in EU, who will have an abnormal vaginal bleeding. However, only 10% will have endometrial cancer. The primary goal of this proposal is to advance the development of a non-invasive diagnostic tool of endometrial cancer using cervical fluid protein biomarkers analyzed by advanced mathematical models. In a previous study we already discovered and verified biomarkers in the cervical fluid. In this proposal, we will validate their potential in a cohort of 500 retrospective patients recruited by five hospitals across Spain and coordinated by Vall Hebron Hospital (VHIR, Spain) and with the top-edge technology on targeted proteomics, led by Prof. Gunnar Dittmar (LIH partner, Luxembourg). Molecular markers will be combined with clinical and pathological data using machine learning techniques thanks to the USC partner (Spain). The most promising biomarker proteins will be transferred to an antibody technology (ELISA and Electrochemical Immunosensors) by an expert SME company in this field: ICOSAGEN (Estonian SME partner) to develop de novo antibodies, and SolarBiotec (Turkey SME partner) to develop electrochemical biosensors and detector interfaces thereof. Through all the project, Dr. Murat Gultekin (HU partner, Turkey) and VHIR will ensure the clinical validation of the developed non-invasive tool and the valorization of the asset to meet stakeholder’s requirements. The resulting tool is a change in the paradigm on how we manage endometrial cancer and will benefit patients, doctors and the health system.

Tandem solar cells allow higher efficiencies than conventional solar cells. In this project we will investigate sulfide chalcopyrites, which are of interest for the top cells in a tandem device with a Si or CIGS bottom cell. Chalcopyrite solar cells are based on polycrystalline thin films, where grain boundaries are potential locations of high non-radiative recombination. This project will bring together University of Cambridge, who has a world class microscopy facility and the expertise to analyse defects and radiative efficiency in semiconductors, and the University of Luxembourg, who has the expertise in thin film solar cells. We will combine photoluminescence which indicates the overall electronic quality of the films with cathodoluminescence, which allows us to identify grain boundaries and other locations of low radiative efficiency, i.e. low electronic quality. With the help of markers we will then be able to study the structural and chemical properties of those areas and correlate them with the growth parameters. These experiments will allow us to correlate growth conditions. grain boundary properties, radiative efficiency and power conversion efficiency. The results of this project will allow us to refine the growth of the solar cells absorbers and improve their efficiency.

This three-year project will fund four UK and Luxembourg-based researchers with the aim of generating new knowledge among academics and policymakers about the evolution and accountability of pan-European public financial institutions. Institutions with the authority to raise funds on financial markets to provide grants, loans or guarantees were present at the outset of the European Communities and part of European responses to the economic crises of the 1970s, the reuniting of Europe in the 1990s and the euro crisis in the 2010s. They are now pivotal to the EU’s COVID-19 response, as in the Commission’s plan to borrow up to €750 billion under the Next Generation EU instrument to help with the costs of the pandemic. As pan-European public financial institutions grow in importance, they face calls for greater accountability to governments, parliaments and NGOs. And yet, there is limited political science research on either the evolution or accountability of these bodies. This project will refine and develop a novel theoretical account of pan-European public financial institutions based on new intergovernmentalism, which we will test against other contemporary theories of European integration through archival research, elite interviews, process tracing, meta-synthesis and a mixed method study of accountability practices. Our findings will generate new knowledge on: (1) the evolution and accountability of pan-European public financial institutions since 1950; (2) who is driving these developments and what this means for accountability; and (3) how accountable these bodies are in practice and how their accountability can be strengthened. Through articles, a monograph and presentations, this project will shape debates among scholars of European integration and International Political Economy. Through working papers, policy briefs and workshops, we will engage practitioners in knowledge exchange. Our findings will encourage governments, parliaments, NGOs and pan-European public financial institutions to explore new accountability practices. The project fosters collaboration on a topic of importance for the UK and Luxembourg, which are home to key pan-European public financial institutions.

Adiabatic processes are at the core of countless experiments. They find numerous applications in quantum simulations and quantum computing that range from adiabatic pulse sequences generating quantum gates in superconducting platforms to the preparation of many-body states in cold atoms, to name just a few. At the same time, adiabatic state preparation itself constitutes a computational paradigm within adiabatic quantum computing. While the adiabatic theorem enables a variety of applications, it is also a source of fundamental limitations both in required timescales and restricting to ground/eigenstate conserving operations. The project is located in the fundamental science domain but explores a novel concept as a seed for future technological implementations in adiabatic quantum simulation and computing. Its specific goal is to develop a comprehensive set of non-adiabatic building blocks that replace the adiabatic state preparation by non-adiabatic processes using shortcuts to adiabaticity (STA). This fundamentally new paradigm allows one to detach from the adiabatic limit, which currently hinders practical applications, by introducing additional unitary quantum operations to the system. In this promising approach, only early theory work and simplistic experiments exist so far. In a joint effort of leading experimental and theory groups, the project will demonstrate the first two-body STA experiment with a scalable architecture, the first STA experiment with a non-scale-invariant system, a novel theoretical framework for STA of statistical ensembles and a novel tensor network framework for STA. The impact of a novel toolbox of non-adiabatic building blocks for quantum computing and quantum simulations stems from the widespread use of adiabatic state preparation. In improving these methods and transferring them to experiments, we expect a broad impact ranging from fundamental science experiments to applications in commercial quantum devices. Regarding the latter, the general demand for scalable and technologically feasible quantum optimization tools emphasized the disruptive character of STAQS.

We propose to study the interplay between magnetism, band topology, and superconductivity with the aim of realizing robust topological states as future building blocks for quantum computation. Our project will combine experiments, materials simulations, and solid-state theory in order to study Majorana bound states based on magnetic topological insulators (MTIs) with proximity-induced superconductivity. The unique experimental platform available within this consortium as well as its theoretical know-how are ideally suited to address this challenge. On the experimental side, we will construct MTI nanostructures in-situ in different geometries, e.g., as nanowires, junctions, or interferometers. Magnetotransport measurements will allow us a detailed characterization of the devices and their topological properties in the normal-conducting state. Next, we will apply superconducting leads for studying proximity-induced superconductivity and Josephson junctions based on MTIs. This finally will make it possible to create and detect Majorana bound states based on MTIs. The techniques used by our theory and simulation cluster will allow multiscale modelling of nanostructure-based devices and will closely accompany the experimental efforts. While bulk MTIs have been investigated in detail, their nanostructures still leave open many fundamental questions. This project will therefore lead to important new insights, both in theory and experiment, about MTIs. The latter, in turn, will lead us to the ultimate goal of our project, which is to unambiguously identify Majorana bound states in MTI nanowires, and to pave the way for their future use in topological quantum computation.

REMEDIS (REgulatory and other Solutions to MitigatE online DISinformation) is an interdisciplinary project combining research(ers) in digital law, social and communication science, history, and computer science. It aims to provide innovative regulatory frameworks and legally compliant socio-technical solutions to counter online disinformation and its effects. The project intends to complement this regulatory arsenal with training modules in media education based on an understanding of the effects of media literacy skills (or incompetence) and behaviours on the spread of disinformation. REMEDIS will investigate these issues by working on specific use cases such as climate and health issues. REMEDIS intends to analyse the possible legal instruments that can regulate the phenomenon of fake news and to balance them with users’ freedom of expression. The project also aims to design automated means that question the origin and the integrity of a piece of information and reconstruct its information flow in a verifiable still privacy-preserving manner. The project will also develop a large comprehensive study of (dis)informational practices in order to anchor the proposed solutions in the practices and behaviour-determining factors of the actors (individuals, platforms, media). Lastly, REMEDIS aims to provide representations of this automated reasoning so that it can be easily understood by humans, like social media users. With regards to technical solutions, REMEDIS intends to propose operational definitions of disinformation (e.g., fabricated news, junk science, hoax, etc.), in alignment with the regulatory frameworks and in compliance with legal and ethical principles (e.g., freedom of speech, accountability, privacy, explainability of automated decisions). It will design legally attentive procedures and protocols to (1) question the origin and the integrity of a piece of news, and (2) build measures and models of its information flow. It will achieve these goals using both observable public data and, innovatively, encrypted data and metadata. Advanced cryptographic techniques are in fact available today to process encrypted data, thus enabling privacy protection beyond anonymity and also if data are lost or probed by curious “trusted” parties. Further, with the aim to provide individuals with user-friendly interfaces to spot fake news, REMEDIS will develop and assess multiple user interface designs for data visualizations using heuristics like usability and their ability to enhance people’s critical thinking. Strengthening self-awareness and a critical approach to misinformation is an educational aspect that REMEDIS intends to support also at use for content-moderation activities.

INFINITE is a Belgian-Luxembourgish project addressing a key unmet need for long-lasting flexible solid-state polymeric strain sensors for advanced applications from physiological measurements to structural health monitoring, printable electronic devices, and the internet of things (IoT), thus providing critical support to big data initiatives as well. Existing sensors are typically neither flexible nor transparent, and face challenges related to cost and the need for pre-alignment of the active phase (in the case of PVDF for example) via the application of a strong electric field (ca. 100 V/μm). In contrast, the INFINITE project will focus on the development and understanding of flexible solid-state strain sensors based on “ion gels” – innovative polyelectrolytes derived from copolymerization of complementary cationic and anionic liquid-like monomers (ILMs) with neutral mono- and/or multifunctional monomers, accompanied by ion metathesis with the concomitant in situ generation of ion conducting non-volatile ionic liquids. High-resolution 3D printing will be used to process these amorphous, transparent ion gels into complex three-dimensional frameworks with precisely controlled compositional gradients, maximizing response and providing exciting new opportunities to tune sensing performance thanks to variations in ion type, content and mobility. This breakthrough strategy will enable the creation of innovative solid-state sensing devices combining the electrical properties of high-performance ionic materials with the flexibility, toughness and film-forming ability of hierarchical polymeric networks. Following materials development, proof-of-concept flexible solid-state strain sensors will be produced via simple, scalable processing, facilitating integration into various devices for a broad range of applications.

Le succès de la chirurgie bariatrique dépend de plusieurs facteurs dont les déterminants affectifs, motivationnels et cognitifs du patient. Hélas la pratique médicale actuelle néglige ces processus psychologiques, en particulier lors du suivi postopératoire, alors qu’il sont essentiels pour la régulation des comportements. Leur considération est d’autant plus importante que la chirurgie, en raison de la grande et rapide perte pondérale, peut causer un relargage neurotoxique de polluants organiques persistants (POP) qui étaient stockés dans les tissus adipeux. Ce relargage est ainsi susceptible d’ altérer le fonctionnement du cerveau postopératoire et donc de compromettre le succès de la chirurgie bariatrique, c’est-à-dire une perte de poids stable et satisfaisante, ainsi qu’une qualité de vie (physique et psychologique) améliorée. Le but de ce projet multidisciplinaire et intersectoriel est d’apporter, selon la perspective de la neuropsychotoxicologie, un nouvel éclairage quant aux facteurs de risque touchant la santé psychologique et cognitive postopératoire, et ainsi transformer la prise en charge du patient en chirurgie bariatrique. Les objectifs sont : 1) Mesurer l’exposition aux POP à l’aide d’une méthode de détection non invasive novatrice, soit via des échantillons de cheveux; 2) Évaluer le profil affectif, motivationnel et cognitif à partir de questionnaires standardisés et des tests neuropsychologiques en ligne; 3) Déterminer si les effets des POP sur le fonctionnement cérébral sont médiés par la perturbation du système endocrinien; 4) Apporter de nouvelles connaissances sans alourdir le fardeau des patients via des approches méthodologiques d’avant-garde.

Objective: The aim of the project is to develop a virtual reality-based surgical simulator module that allows surgeons to develop and improve surgical skills in the field of arthroscopic meniscus repair. This meniscus repair module will allow the orthopedic surgeon to train on a physical knee model using adapted original instruments. Virtual reality is used to create a true-to-life image of what is seen during the surgical procedure. Furthermore, the project outcome will be scientifically validated. Research questions: What is the ideal way to acquire the skills necessary to master a knee meniscus repair surgery?What are the important and difficult steps of the surgery? How can virtual and hybrid reality assist in learning the necessary surgical skills? What difference does simulation-based education make in the context of knee meniscus repair surgery? Impact and outcome: Surgical techniques for the preservation of the injured meniscusare challenging and it takes extensive training to master these skills. To optimize patient outcome, to allow for secure repair and low complication rates and to avoid prolonged anesthesia time for patients due to poorly trained surgeons, it is crucial to provide training simulators to practice safely and without harming patients. The outcome of this project will be a training simulator for advanced meniscus repair that allows surgeons to develop the necessary surgical skills for these challenging operations. In the long-term, this will lead to shorter operation times and less intraoperative complications for the patients.

Ferroelectrics are a fascinating class of insulating materials that present the largest responses to electric fields in nature. That includes the largest polarizabilities (or dielectric permittivities), as well as the largest piezoelectric responses, namely, they can deform under the application of electric fields, or produce electrical signals, when deformed, therefore transforming mechanical energy into electrical energy and vice versa. Without piezoelectrics our hospitals would be unrecognizable (no echography scanners, nebulizers, lithotripters, ultrasound cleaners, etc), as well as our society; inkjet printers, sonars, parking aids, diesel injectors or airbags, to just mention a few, all use the piezoelectric properties of ferroelectrics. Interestingly, the material currently used in all these application (PZT) contains toxic lead, but the lack of good enough alternatives prevents the governments to install a ban on PZT. In addition, ferroelectrics possess a permanent dipole moment that can be switched with an electric field, making these materials also suitable as non-volatile memories, with lower power consumption and higher writing speeds than other existing solutions. Nevertheless, so far ferroelectric memories have only been used for niche, low memory density applications, like in Play Stations or in travel cards, because of the difficulty to achieve ferroelectric behavior at the nanoscale, which prevents to achieve sufficient miniaturization. While this was thought to be a fundamental limitation, recently, the ferroelectric community has been confronted with the discovery that HfO2-based thin films, until then a material used in transistors simply as insulating layer, can be ferroelectric at sizes of a few nanometers. This material has forced us to re-think the concept of ferroelectricity and has given rise to a multitude of recent research projects, having a huge scientific and technological impact, and putting ferroelectrics again in the spotlight as the low-power, high-speed, silicon-compatible, environmentally-friendly, non-volatile memory solution of the future. At this moment, Hafnia-based ferroelectrics are three-hurdles away from being the ideal material; 1) the polar state is relatively hard to stabilize; 2) the electric fields needed for switching the polarization are larger than those of other ferroelectrics; 3) the piezoelectric response is substantially lower than that of other ferroelectrics. In this project we aim to address these two issues, using a combined theoretical and experimental approach, by creating artificial stacking of atomic layers of HfO2 and other oxides (ZrO2, CeO2, GeO2, SiO2, etc). In particular, we will grow thin alternating layers of three different oxides on top of each other, e.g. ZrO2-HfO2-CeO2-ZrO2-HfO2-CeO2-…., producing so-called tricolor superlattices. This special arrangement creates a built-in asymmetry in the atomic structure along the growth direction (i.e. the HfO2 layer sees CeO2 above and ZrO2 below) that converts these materials into polar oxides by construction (hurdle #1), as their polarity is determined not by the chemistry of the components but rather by the differences in the chemistry of the different components. Further, including dielectrically stiff layers allows us to control the strength of the polar distortion and, thus, the switching electric field (hurdle #2). Finally, guided by theoretical calculations, optimization of the piezoelectric response by the choice of materials in the tricolor will be attained (hurdle #3).

Alzheimer’s Disease (AD) and related dementias are heterogeneous multifactorial diseases, involving a range of etiopathogenic mechanisms that lead to neuronal death and loss of cognitive function. It is assumed that the disease starts decades before diagnosis, which imposes a great challenge for treatment. Identification of prognostic biomarkers for AD is thus of utmost importance. Sleep-wake alterations are common symptoms in AD. These disturbances increase systemic inflammation, which is a known driver of AD pathogenesis. Thus, understanding the mechanisms linking cognitive impairment, sleep disturbances and inflammation could facilitate earlier diagnosis of the disease. There is increasing evidence regarding the involvement of the systemic immune system into the pathophysiology and resolution of AD. While some immunological events were associated with disease progression, recruitment of immune-regulating cells is required for reducing local central nervous system (CNS) inflammatory response, eliminating toxic elements and enhancing cell renewal and repair. This suggests that peripheral immune profiles can reflect characteristics of the disease. Indeed, it was recently demonstrated that a peripheral immune signature of a subtype of cytotoxic CD8 effector memory T (TEM) cells is associated with AD. Another type of cytotoxic lymphocytes are Natural Killer (NK) cells, which their role in AD is poorly understood. Importantly, both ImmunoBrain Checkpoint (IBC) and SCAI demonstrated the link of cytotoxic NK cells to AD, in independent studies on unrelated cohorts, using totally different approaches, either experimentally or artificial intelligence (AI), respectively. In the current program we will thoroughly characterize the role of peripheral blood cytotoxic lymphocytes as potential markers for prediction of AD and investigate the influence of sleep disturbances on these markers. Using a multidisciplinary approach for multi-omics deep immune profiling, combined with AI and Agent-Based-Modeling (ABM), this project is expected to reveal novel immune and digitally assessed physiology signatures for early prediction of the disease that can appear early in the course of the disease and are associated with rapid clinical decline.

Attention estimation and annotation are tasks aimed at revealing which parts of some content are likely to draw the users’ interest. Previous approaches have tackled these incredibly challenging tasks using a variety of behavioral signals, from dwell-time to clickthrough data, and computational models of visual correspondence to these behavioral signals. Today, these signals are leveraged by a myriad of online services to personalize social media, search engine results, recommender systems, and even in supporting critical decision making, such as financial or medical data. However, the signals that all these services are based on are rough estimations of the real underlying attention and affective preferences of the users. Indeed, users may attend to some content simply because it is salient, but not because it is really interesting, or simply because it is outrageous. In contrast, project BANANA will use brain-computer interfaces (BCIs) to infer users’ preferences and their attentional correlates towards visual content, as measured directly from the human brain. We aim for a scientific breakthrough by proposing the first-of-its-kind affective visual attention annotation via brainsourcing, i.e. crowdsourced BCI signal acquisition. First, our approach will allow accurate estimation of user preferences, attention allocation, and –critically– the affective component of attention, directly measured from the natural and implicit brain potentials evoked in response to users experiencing digital contents. Then, we will utilize the resulting data in a crowdsourcing setting to reveal how multiple users react to different stimuli and how their attention and affective responses are distributed. These collective responses will produce unified, consistent measures as a result. Our technology will be used in several downstream tasks such as segmentation of users’ attention while looking at images, identification of key events, and video summarisation. We will pilot BANANA with different user groups to test and prove its effectiveness, using objective benchmarks and evaluation strategies. The project offers an ambitious perspective that integrates BCI technology in crowd-powered applications that rely on attention estimation and introduces an affective component not previously considered in the research literature. Two parallel study protocols will be run in order to legitimate using inexpensive, easily available BCI headsets, which will be easily accessible for a wider range of users. Results will be published in open access top-tier venues and disseminated to the public audience. Software derived from this project will be publicly available in accessible formats. Annotated datasets will be anonymized and deposited in public repositories. Project BANANA will employ PhDs, PostDocs, and MSc students for training and career development. The project will seek guidance from Psychology/Psychiatry and Medical professionals in order to gain multidisciplinary insights into the studied problems, and help meet all safety and bioethical requirements.

This project is interested in material digital twins, and the development of a simplified framework for them, in the context of materials design by sustainability. A Bayesian machine learning, that serves as the digital twin, is trained with data taken from a real data and stochastic multiscale computational model. This strategy allows the use of an interpretable model (physics-based) to build a fast material digital twin (machine learning) that will be connected to the physical twin to support material design decisions. The ambition of this joint initiative between LIST- Luxembourg and Czech Technical University of Prague promises to be a game-changing effort to maximise the benefits of computational and data-driven technologies in the materials field. In summary, the proposed research aims at combining and integrating (a) physical-based and data-driven modelling and simulation, (b) material microstructure generation, reconstruction and analysis, and material informatics for sustainable composite modelling. These developed tools will be integrated in an open knowledge-based and interoperable computational platform for materials to support the design problems such as sustainable best-fitting materials, microstructural design, predicting microstructures morphology, extracting properties based on materials microstructure and material structures performance evaluations. The modelling and design methods and tools as proposed in this project will lay the foundation for materials design framework that elegantly combines data and non-data-driven modelling and simulation, advanced optimisation methods and tools and interoperability into an open framework that is expected to be much more efficient and versatile than existing platforms, with the capability of accommodating complex problems, like sustainability and circular economy.

TECHONEY proposes the development of a traceability system to guarantee the quality and safety of honey within the supply chain for more effective communication to consumers. This approach will be unfolded by the joint creation of two levels of interaction: a physical one through the characterization of honey, and a “laboratory” one through the creation of a Honey living lab (HCLL) that will be the arena to collect information from beekeepers, stakeholders, and consumers to transfer and apply the new optimized models. To reach the above-mentioned overall objective, several specific objectives (SO) are set out as intermediate goals: SO1: Map the current added-value chains and complexity level for honey products in five case studies (Spain, Algeria, Tunisia, Turkey, and Morocco, Italy). SO2: Create traceability ecosystems to guarantee the traceability of honey. SO3: Guarantee food safety in the honey supply chain and differentiate local honey in a certifiable and documentable manner. SO4: Improve the positioning of beekeepers in the market and increase competitiveness. SO5: Maximize the exploitation and transference of the findings through the creation of a website and the organizations of several seminars and events. SO6: Maximize outreach and beneficial influence of the project results and reach the target users (beekeepers, small-scale food manufacturers and local distributors, canteens and retailers, local public authorities) through an effectively established communication and dissemination plan, including innovative training capsules. To fulfil the main objective, TECHONEY is structured in four main technological pillars: i) creation of a blockchain system to ensure the traceability in the honey supply chain; ii) creation of a transformative learning community to ensure a short-resilient-shared supply chain; iii) characterization of the quality of honey to guarantee its traceability within the blockchain directly by consumers; iv) develop ICT tools for honey supply- chain participants and consumers. TECHONEY’s results will give further insights on setting up an organisational protocol for a resilient honey supply chain and with governance based on the concepts of sharing economy. The characterization of the quality and safety aspects of local honey through local certified laboratories jointly with the use of e-commerce and quality labelling schemes will increase the opportunity for beekeepers to be identified locally, and allow them to gain access to new markets (foreign markets). The implementation of e-commerce with the mobile application will enable local honey to be better traced by consumers who attach more value to local food and local beekeepers. TECHONEY contributes to increasing farm profitability and increasing flexibility and risk mitigation capabilities. A shared, short and circular supply chain will allow actors in the honey supply chain to access markets and have higher incomes, share resources and skills and save money by reducing costs (economies of scale), and increase the efficiency, sustainability and flexibility of processes to strengthen resilience and flexibility to face crises and lower risks. The learning community lab and the use of the blockchain network will secure the storing of records, will strengthen intellectual property rights, as well as bring transparency throughout the supply chain; it will reduce frauds, enhance food safety, and improve the communication between retailers and beekeepers. The traceability system offered will also allow consumers to give direct feedback to beekeepers. TECHONEYs’ commitment is not only to promote a continuation of direct sales, but also develop a common methodology and clear new optimized resilience protocol to be used by small-scale farmers and smallholders as a new business model with a more efficient added-value chain, sustainable with fair profit, accepted by final consumers, and replicable to other food products and supply chains.