Dragon09/odtu_chatbot huggingface dataset:
development of a sustainable lowcost and longterm labonachip colorimetric paperbased sensor for saxotoxin quantification in water and seafood

dr sara santiago malagn will conduct a fellowship focused on developing a labonachip loc colorimetric paperbased device for pointofneed pon detection of saxitoxin a toxin produced by cyanobacteria and certain microalgae the primary objective of this project is to mitigate cyanobacteria contamination in stagnant water and harmful algae bloom hab occurrences in seawater which can lead to the death of marine species and the poisoning of animals and humans we aim to validate an accessible ecofriendly costeffective and userfriendly technology that can be extended to detect and quantify various biotoxins relevant to public health and the environment the driving force behind this project is the current gap in the development of portable sensor systems for environmental applications in marine ecosystems freshwater and seafood quality control all without the need for specialized equipmentin addition to the research that will help control and prevent damage to the ecosystem and preserve species diversity the methodology and materials presented are sustainable and environmentally friendly aligning with the projects environmental commitment furthermore the materials used silk will facilitate the stabilization of biomacromolecules required for the detection and quantification of saxitoxin enabling the creation of longspan sensors ultimately the end goal is to validate that the colorimetric device designed as a toolless system can be operated by minimally trained volunteers end users through a userfriendly mobile application interface even if they do not possess specialized knowledge or skills these individuals play a crucial role in environmental preservation and prevention effortsthis grant would offer the applicant the opportunity to lead an international project fostering collaborations with esteemed institutions and researchers for future global initiatives
new sustainable ferich magnet using a predictive alloy and microstructure design toolbox

ndfeb magnets are central to both green mobility and the generation of electricity from renewable resources but the need to incorporate heavy rare earths like tb and dy highly critical raw materials to operate these magnets above 100 c required for electric vehicles and wind turbines makes them very costly environmentally damaging and with a very fragile value chain and despite four decades of effort no practical alternative to ndfeb has been found smfe12based compounds have superior intrinsic magnetic properties actually surpassing those of benchmark ndfeb and without the need for additional heavy rare earths the challenge with smfe12 is phase instability in the bulk form and creating a microstructure that will convert the large intrinsic anisotropy field into a usable coercivity what we know so far is that the solution lies in combining multiple alloying elements each one impacting differently in smfemxz12 for example 20 alloying elements results in around 108 combinations experiments if each element varies from 1 to 15 at in quinary compositions magtool will create a cuttingedge toolbox that combines experimental techniques with stateoftheart machinelearning algorithms eliminating the need for trialanderror this breakthrough approach drastically reduces the number of experiments from a daunting 108 down to a manageable 102 magtool will achieve that by breaking the multielement complexity starting to predict the compounds with useful phases using only three elements this knowledge will be transferred to multielement situations and combined with robust experiments to deliver superior magnetic properties in powders and meltspun ribbons magtool will also include a laserdeposition additivemanufacturing system for rapidly creating many compositions within the same bulk samples through compositional gradients the outcome of the project will be the new medium and highperformance sustainable magnet
2d heterostructure nonvolatile spin memory technology

in modern society as the use of information technology is rapidly increasing it is necessary to develop new nonvolatile faster and energyefficient electronics spintronic technologies open promising routes to achieve this however devices based on conventional materials are still too inefficient for applications in consumer electronics here we propose to develop a new energyefficient spintronic memory device platform based on emerging atomicallythin twodimensional 2d quantum materials for the next generation of memory technologies the 2d topological spinorbit materials can generate a giant currentinduced spin polarization whereas room temperature 2d magnets provide the prospective of electric control of magnetism the proposed van der waals heterostructure spintronic devices consisting of 2d topological spinorbit materials and 2d magnets will enable exceptionally efficient spinorbit torque sot functionality with low current densities and ultrafast magnetization switching speedall2d multifunctional sot will provide a strong synergy between spintronics and 2d material and take this research from trl2 to 4 we will employ novel methods to control their sot properties by the twist angle between the layers topological aspects crystal symmetries proximity interaction and strong electric field effects to achieve this challenging goal this project bringstogether pioneering and worldleading experimental and theoretical researchers and a company in the field of spintronics and 2d materials in europe the ultimate demonstration of all2d sot device units will merge the field of spintronics and twistronics allowing for physical and electrical tuning parameters to achieve enhanced control over the device functionalities these developments will enable groundbreaking 2d sot technologies for lowpower nonvolatile ultrafast and scalable data storage and processing devices and possibly new spinbased computing paradigms and architectures
explainable and robust automatic fact checking

explainyourself proposes to study explainable automatic fact checking the task of automatically predicting the veracity of textual claims using machine learning ml methods while also producing explanations about how the model arrived at the prediction automatic fact checking methods often use opaque deep neural network models whose inner workings cannot easily be explained especially for complex tasks such as automatic fact checking this hinders greater adoption as it is unclear to users when the models predictions can be trusted existing explainable ml methods partly overcome this by reducing the task of explanation generation to highlighting the right rationale while a good first step this does not fully explain how a ml model arrived at a prediction for knowledge intensive natural language understanding nlu tasks such as fact checking a ml model needs to learn complex relationships between the claim multiple evidence documents and common sense knowledge in addition to retrieving the right evidence there is currently no explainability method that aims to illuminate this highly complex process in addition existing approaches are unable to produce diverse explanations geared towards users with different information needsexplainyourself radically departs from existing work in proposing methods for explainable fact checking that more accurately reflect how fact checking models make decisions and are useful to diverse groups of end users it is expected that these innovations will apply to explanation generation for other knowledgeintensive nlu tasks such as question answering or entity linking to achieve this explainyourself builds on my pioneering work on explainable fact checking as well as my interdisciplinary expertise
deciphering subsurface carbon fixation

despite containing the vast majority of carbon on the planet the earths subsurface represents a mostly uncharacterized frontier for biogeochemistry and microbial ecology while carbon fixation at the earths surface has been drawing a lot of attention for decades subsurface carbon fixation remains poorly understood yet metabolically it engages a rich diversity of non canonical pathways contrasting to surface carbon fixation dominated by the canonical calvin cycle also its contribution to the global carbon cycle though long overlooked is now garnering increased attention in recent years several studies aiming to characterise the diversity of carbonfixing organisms within the subsurface have emerged however these studies are isolated and do not accurately describe the dependence of these metabolisms on the geoenvironmental context i propose here to provide a blueprint of subsurface carbon fixation on earth in a topdown approach i will systematically sample various subsurface environments for metagenomic and metaproteomic analysis of carbon fixing metabolisms in search of the main environmental factors determining the carbon fixation strategy used in parallel to this i will undertake a bottomup experimental approach to explore and confirm this metabolic versatility in response to changing environments through controlled culturing experiments finally i will integrate the acquired results with stable isotope analysis to build a metabolic model aiming to comprehensively predict carbon fixation fluxes in function of the geoenvironmental context the proposed holistic approach will increase our understanding of the role of subsurface microbial metabolisms in global carbon cycling
deep reinforcement learningbased battery management system for electric vehicles

battery management system bms plays a pivotal role in monitoring control and protecting the electric vehicle ev lithiumion battery packs in vehicular applications batteries are usually subjected to harsh operating cycles and varying environmental conditions leading to very complicated interactions of different aging factors and unforeseeable modeling uncertainties therefore the classical modelbased techniques cannot completely handle the foregoing factors which always leave an unwanted state estimation error in the bms this project intends to apply a multidisciplinary approach by combining the advantages of deep reinforcement learning and classical modelbased techniques to improve the bms functionality in evs specifically deepbms aims to 1develop efficient deep reinforcement learningbased algorithms which are able to capture the convoluted timevarying behavior of battery and can gradually improve themselves by learning in realtime 2 combine the beneficial features of modelbased and datadriven techniques to improve the state estimation accuracy in a wide temperature range and over the full life span of the batteries thereby increasing the reliability and extending the battery lifetime the interdisciplinary nature of deepbms is very strong involving a combination of control and state estimation theory power electronics battery storage systems and machine learning the supervisor and candidate have excellent complemental research experiences in these fields providing the necessary competencies to bring the project to successful completion the project ensures twoway transfer of knowledge including training of the candidate in cuttingedge advanced techniques in a stateoftheart laboratory which improves his future career prospects likewise deepbms is in line with the eu strategic action plan on batteries and its results have a great potential to be further developed at the fundamental and applied levels through followup research
protocells in the archaean rock record implications for the origin of life and detection of biosignatures

fossilised remains of protocells transitioning towards the first living organisms may be observable in the early rock record distorting our interpretation of biosignatures protocells cellular structures existing prior to the emergence of life would have comprised simple membranes and molecular machinery the formation of complex protocells is poorly understood the interpretation of fossilised microorganisms in the early rock record is steeped in controversy the potential for abiotic microstructures to further complicate this interpretation has long been recognised but has not been systematically tested evidence suggests that prebiotic structures are very difficult to distinguish from microfossils however little is known about the chemical and physical characteristics of these biomorphs that may enable us to tell the difference this project will use a novel microfluidic approach to expand the experimental scope for formation of protocells of increasing complexity resulting microstructures will undergo silicification and diagenesis the principal preservation pathway for early archean microfossils stateoftheart analytical techniques will be used to characterise biomorphs providing the first rigorous investigation of the feasibility of protocell preservation and how this might express in the geological record these data combined with a predictive model and biological controls will allow for identification of key observables that will act as biogenicity indicators purported microfossils will then be examined and reinterpreted using the novel biosignatures this project has the capacity to represent a paradigm shift in our fundamental understanding of the origin and evolution of life on earth and possibly elsewhere in the solar system collectively this will represent a major advance in our understanding of the geological record providing the first comprehensive methodology to distinguish between abiotic prebiotic and biological microstructures
deep learning the dark universe with gravitational waves

gravitational wave astronomy has opened an extraordinary new window to test the theory of gravity in the genuinely strong highly dynamical and relativistic regime the ligovirgo collaboration has now detected over 50 mergers of compact binary systems and this number will considerably increase in the coming years there are currently two main issues related to the possibility of testing gravity with gravitational wave observations the weakness of parametric tests of general relativity to go beyond null tests and the very long inference time required by standard samplers which can take up to months specific waveform models and new techniques to speed up statistical inference are therefore crucial to maximise the scientific return of already available and upcoming data in this project we will construct an analytical model of the gravitational waves emitted during the late inspiral and merger of compact objects in theories of gravity that are cosmologically motivated namely that have a chance to explain dark energy we will then leverage deep learning techniques to promptly produce the posterior for the corresponding parameters given the detector data to this aim we will build up on two codes developed by one of the supervisors roman and percival which pioneered the use of machine learning in gravitational wave science we will then apply this new pipeline to the real ligovirgo data and perform bayesian inference of dark energy parameters all together this project will provide a new and complete framework to test the dark universe with gravitational wave observations exploiting stateoftheart deep learning techniques
macroscopic properties of interacting bosons a unified approach to the thermodynamic challenge

interacting bosons are unique quantum systems whose low temperature phases exhibit fascinating quantum mechanics effects at a macroscopic scale in the past two decades the mathematical understanding of these systems improved tremendously however their behavior in the thermodynamic limit is still poorly understood although this is the appropriate large scale limit to prove the emergence of scaling laws and universality as well as to investigate the occurrence of phase transitions match aims at investigating the low energy properties of interacting bosons in the thermodynamic limit and at gaining a mathematical understanding of the emergence of correlated phases in the form of boseeinstein condensation and quasilong range order as well as of their instabilities due to thermal fluctuations or threebody recombination effects of efimov type our plan is to exploit scaling limits as a framework to identify and overcome one at a time the mathematical obstructions that currently prevent us to control the system at finite density in the thermodynamic limit in order to make progress on this program match will introduce novel mathematical methods inspired by renormalization group approaches and grounded in the second quantization techniques developed by the pi and collaborators valid on an increasing sequence of scalesultimately the research led by match will lay the foundation for the rigorous description of several phenomena which are at the frontiers of present theoretical and experimental research where collective excitations of quantum systems are described in terms of emergent bose gases such as in the bcs theory for superconductivity the molecular description of strongly interacting fermi gases and the spinwave theory for quantum magnetism
identification of longterm therapeutic strategy in igemediated allergy by leveraging humanized models and patient samples

ige is a key driver of allergic diseases which affect approximately onethird of the worlds population monoclonal antibodies mabs targeting ige are approved for the treatment of allergic asthma and show clinical benefit in a number of other allergic diseases yet a sizable portion of patients do not respond to the drug despite high levels of ige there is a clear need to better define which allergy features depend or not on ige and to find predictive biomarkers in order to identify patients who will benefit from antiige therapy in addition use of antiige mabs is limited by very high cost and the need to perform frequent reinfusion to maintain clinical efficiency combining unique mouse models humanized for ige and its two receptors fceri and cd23 clinical samples from patients undergoing fdaapproved antiige therapy and a novel highthroughput ige repertoire analysis method the project addresses three key questions 1 which ige features distinguish responders vs nonresponders to antiige therapy and can it be used as predictive biomarker 2 which key allergy features depend on ige and through which mechanisms 3 can we induce longterm protection against igemediated allergies with a vaccine approach this translational project will increase our understanding of the basic mechanisms underlying allergic diseases and has the potential to identify important new therapeutic strategies
commercial maturation of medicquant

medicquant is an in vitro diagnostic company where we have invented a deeptech platform technology that will revolutionize small molecule diagnostics our first product doaquant focuses on improving stroke management in the event of stroke rapid treatment is vital for survival and every minute matters it is crucial to determine the level of anticoagulant ac drugs in these patients as this determines which lifesaving treatment can be given current diagnostic solutions take more than 40 minutes thus risking patients lives doaquant can rapidly provide clinicians with the answer they need to determine the correct lifesaving management in stroke thus greatly reducing delay in treatment mortality and disability dr line debois is ceo and cofounder of medicquant and has through her academic career researched biosensors concluding in the coinvention of the technology behind medicquant she has taken the position of ceo in medicquant since incorporation with the vision to lead the company to success and make a difference in the healthcare system the overall goal with the activities performed during the women techeu program is commercial maturation of medicquant and preparing for vc fundraising this will be achieved through specific tasks and by further developing ceo lines commercial and leadership skills the specific activities to achieve this is making a market access strategy through reimbursement analysis regulatory strategy pricing strategy and willingness to buy preparation of documentation portfolio for investment and further developing commercial skills through the mentoring program that are needed for vc fundraise and manage a company in growth this program will directly help overcoming the most crucial commercial challenges that both medicquant and line as ceo are currently facing thereby enabling medicquant to become a female lead success empowering women in deeptech and fostering further diversity in the lifescience sector
cellinstructive matrices to deconstruct tumour tissues

this research addresses a clinical problem with an innovative approach pancreatic cancer is one of the deadliest cancers and only 10 of patients survive 5 years after diagnosis to find better therapies we need patientspecific models that mimic the biology of tumour tissues and target interactions between different cell types we will develop a controllable platform for modelling the human disease in the laboratory we will use our new platform to discover better ways of treating the disease
improved soilcable interaction mooring simulations

moorings of floating oil and gas og structures exhibit surprisingly large failure rates in the og industry this problem is simply handled by redundancy in the design it is unlikely that marine renewables in order to obtain a competitive levelised cost of energy lcoe can afford such redundancy in the mooring design islington has the overall objective to reduce uncertainties in estimated fatigue damage of mooring cables due to soilcable interaction in the touch down zone tdz and subsequently lower the economic cost of mooring systems for marine renewables to achieve this objective the following research actions will be carried out 1 improving the numerical modelling of the cablesoil interaction in the tdz for mooring cables by allowing for timedependent granular soil dynamics this will be done by creating an application programming interface api inside a granular particle soil solver to allow a twoway coupling to a mooring cable solver providing position and motion of the mooring cable 2 generating experimental data for mooring line trenching to be used for validation this will be done by forced oscillations of a cable in a wave flume with sandy bottom and log the development of the trenching under different forcing 3 numerical investigation of the effect of trenching on fatigue of mooring cables this will be done for standard test cases such as the iae task 30 oc3oc6 test cases the work proposed is well beyond stateofart with respect to ground modelling of mooring cables in that the bathymetry will be timedependent so trenching will be modelled together with the nonlinear cable response the work on trenching effect to mooring line fatigue and snap load generation and dissipation will be the first such investigation the resulting papers models and experimental datasets will be released openaccess in line with the open science practice