The objective of short-term traffic forecast is to predict traffic conditions (usually street flows and travel times) on the road network and its evolution in the near future (e.g. up to one hour ahead). These forecasts can be used for various traffic analysis and applications, such as traffic signals optimization, routing emergency vehicles, fleet operation optimization, journey planners, etc. based on the prediction of how traffic conditions will evolve in the most probable way. From a spectrum of tools available today, machine learning (ML) techniques are being increasingly used in various research fields, with increasing success. Their effectiveness has recently been enhanced by the possibility of accessing huge amounts of data. The main objective of the project is the validation of techniques of artificial intelligence (AI), specifically ML, for the prediction of the evolution of typical and non-typical conditions in urban traffic. The intention is to exploit big data in transportation, combining traditional data sources, such as detector counts, incidents data, and huge amount of disaggregated data, such as floating car data (FCD) providing explicit vehicle trajectories, to extract spatial-temporal network-wide correlations. Obtaining a set of typical recurring traffic states on which to base the prediction using the historical data, and prediction itself capturing spatial and temporal effects of disturbances in traffic, are some of the main challenges in this research. The outcome of the project will make a foundation for outlining the best direction for the creation of systems that will be capable of providing robust traffic forecasts.
We study and develop an on-site medical tool for diagnosing heart attacks, using additive manufacturing. The cardiovascular disease is one of the worst cause of death all over the world; for example, in the year of 2015, it was responsible for nearly 20 millions of lives lost. Amongst these victims, a little less than half died of coronary heart disease, a major cause of heart attacks. It has been investigated less that concentration of certain proteins, troponin and myoglobin, increase in the blood flow of the people suffering from the heart attack. Using these proteins as indicators, so called biomarkers, a fast, accurate, and portable blood tester will be able to save our lives. Lab-on-a tip refers to a compact and integrated analtical tool which performs required medical and /or chemical processing within a tiny volume of the sample, here a drop of blood taken from the patient. Fine fabrication of channels and reactors with submillimeter dimension for the small amount of the sample can be realized with severeal lithography techniques. They allow us to build and test microchannesl that will selectively collect and accumulate the biomarkers that are swiftly detected by optical readout.
Contact: andrey.chernev @epfl.ch
Ultimate understanding of physical phenomena of ionic transport through a solid-state nanopore can open the new pathway to a number of applications. Among them is the detailed mechanism of ion channels conductivity involved in signal transmission within the neuronal network.
Coulter counter scheme that is often used to study ionic transport through a 2D nanoporous membrane does not allow active modulation of the nanopore charge since it is provided by the variation of the solution pH. Recent progress in fabrication of the sub-nanometer pores in 2D materials revealed interesting nonlinear current-voltage characteristics (CVC) exhibiting Coulomb blockade that is defined by nonlinear CVC of MoS2 nanopores revealing the suppressed conductance area around V<0.4V. Suppression becomes stronger with growth of ion valence z but it is also strongly linked to charges hosted inside the pore. ln this proposal, I suggest to use a field effect transistor-based system containing a nanopore in conductive channel of the device to study correlation between the in-plane DC electronic transport and the ionic transport through the pore simultaneously for the sub-nanometer pores for the first time. ln my PhD I used the same concept in the study of conductive properties of the edge channels in a semiconductor nanostructures with charged biomolecules deposited. Changes in lateral and cross-sectional voltages provided information on the biomolecules that gated the edge channels. The proposed method will provide correlation between the in-plane electronic transport and ionic transport thus allowing us to achieve a steady read-out method for characterization of the ionic transport through the nanopore and will serve as a starting point for the experiments with active charge modulation.
Athanasios (Akis) Theofilatos
Advances in technology enabled the monitoring and collection of real-time traffic and weather data in freeways. However, adaptation of existing research and traffic management to introduction of autonomous vehicles (AVs) has yet to be investigated. The proposed research aims at advancing the cur-rent knowledge by applying advanced statistical models and by considering AVs through simulations. This assumes a number of hypotheses (e.g. AVs will use a dedicated lane) and an extended modification of existing behavioral models. It will also be examined how the introduction of AVs will affect safety of existing traditional road users. To enhance pro-active safety, short-term traffic forecasting will also be used as input to perform road safety evaluation of traditional road users. This approach will also be tested for different AV penetration rates. The ultimate aim is to understand crash mechanism with and without AVs and propose a proactive management system. To achieve the aims, traffic, weather and crash data from Attica Tollway in Athens will be used. The objectives are particularly useful for real-time safety evaluation of freeways not only in Greece but also of other European countries. This is a unique opportunity as it is the first time that AVs are considered in real-time traffic management. Thus, the produced results will offer new and innovative insights for better understanding real-time road safety, but also practical implications by developing a proactive traffic management system based on real-time safety. This will be a great opportunity for the candidate to make an important step towards his professional development, academic career and his improvement of his research knowledge, as it will also enable him to go to proficient important analytical techniques.
Bart van Lith
Computed tomography is an important tool in various medical, engineering and academic applications. Think for instance about breast cancer research, noninvasive inspection of specialised equipment or materials science. The current standard for image reconstruction from tomographic data is filtered backprojection (FBP). This methods works very well when noise levels are low and the object can be scanned from all angles. However, there is an increasing demand for tomography in applications where this may not be true.
Consider for example medical applications, where there is an ever increasing desire to lower the dosage of x-rays. This leads to higher noise levels and consequently a breakdown of the FBP algorithm. In some science or engineering applications, such as petrochemical extraction for instance, objects can be scanned from limited angles due to heavy-duty holding clamps.
There is therefore an increasing need for reconstruction algorithms that are able to deal with these challenges. They need to be flexible, robust and reliable. We believe that algebraic reconstruction methods fit the bill. These methods are based on the more general linear algebra formulation, and are able to incorporate prior information. Using acceleration techniques, as well as thinking about high-performance computing implementations, we aim to make algebraic reconstruction methods competitive.
A sensor which can detect bacteria efficiently in the source of contamination as well as in physiological fluids of affected patients is key for preliminary diagnosis of pathogen infestation. The conventional techniques for detection of pathogens are time-consuming and require skilled labour for performing complex assays. Biosensing devices are therefore required for continuous monitoring to check for infestation in affected patients as well as in areas important to public health, for example- public water supply systems. The aim of MOFSense is to develop a low cost sensor capable of rapidly detecting and identifying multiple species of pathogens in a drop of sample from a single test. Most of the devices fabricated so far suffer from a number of issues resulting from the necessary modification of the sensor with biomolecules to carry out the sensing activity. The MOFSense project will circumvent this important issue by using Metal Organic Frameworks (MOFs) that will be highly selective towards various species of pathogens. The envisioned biosensor will allow rapid quantitative reading of the pathogen concentration without sample manipulation.
This project falls into the fields of scientific computing and mathematical models applied to biology. The main topic is the bifurcation structure analysis of partial differential equations. Starting from theoretical results in the context of reaction diffusion systems for competitive species and predator-prey interactions, we are interested in cross-diffusion systems obtained as the limit, when suitable parameters tend to zero, of linear diffusion “microscopic” models by the exploitation of different timescales (Quasi Steady State Approximation). With this approach, we end up with models with a reduced number of equations, that naturally capture basic and specific interactions and that are able to reproduce complex phenomena such as spatial segregation of species and pattern formation. The purpose of the project are detailed numerical continuation calculations for partial differential equations arising in biology, in particular we focus on cross-diffusion systems. The aim is to understand how the global structure of equilibrium solutions of the microscopic systems behaves as the time-scale parameter becomes smaller and if it converges to the one of the corresponding limiting system. A key computational tool is the MatLab continuation package pde2path, but also the development of new numerical techniques is going to be an additional interesting aspect of this project. The achieved results are also going to improve the theoretical study of these models, clarifying the role of the cross-diffusion terms as the key ingredients in pattern formation.
Maria Cristina Momblona Rincón
Metal halide perovskites have emerged in the last decade as revolutionary semiconductors for optoelectronic devices such as photovoltaics, light-emitting devices, photodetectors or lasers, among others. The astonished evolution of the certified efficiency of perovskite solar cells (PSCs) exceeding 22% power conversion efficiency (PCE) in only one decade after its first implementation in photovoltaics, it raises perovskites above other lights absorbers. The exceptional device performance of PSCs, the low-cost of the precursors and the variety of deposition techniques from solution to vacuum indicate a promising future toward their commercialization. However, this new technology needs to address several concerns: poor material stability under operational conditions, material toxicity due to the presence of the toxic lead and the use of toxic solvents in solution-processing fabrication methods.
The aim of the project “ABSIPERO” is the suppression of toxicity in perovskite-based solar cells, using a non-toxic perovskite composition and processing. To this purpose, alternatives to the toxic lead needs to be explored, being tin and bismuth promising candidates. However, tin-based perovskites are still eco-toxic, being bismuth-based halide perovskites the most adequate alternative for non-toxic perovskite solar cells. In this project vacuum-deposited bismuth based perovskite layers will be fabricated and its characteristics will be studied and evaluated in solar cells. Due to the combination of non-toxic materials and techniques, a step forwards toward non-toxic perovskite-based solar cells will be done.
We are witnessing a massive growth of applications that generate an enormous amount of data.
Social network sites, cloud-based applications, scientific experiments, data warehouses are data- generating phenomena with rapidly growing relevance. The resulting continuous production of structured and unstructured data will have a major impact on today’s society, and has the potential to allow for better decision-making, both for individual organizations, as well as for a general community. It is more important than ever to investigate and to improve the current approaches and solutions for decision-making based on continuous data-flows. Scheduling plays an important role in these processes. A scheduling problem aims to allocate activities to resources and to sequence these activities on the resources in order to achieve a good performance on one or more desirable objectives. Resource allocation, scheduling and sequencing are decision-making processes that are carried out on a permanent basis in most production and service sectors, and not only in data processing, storage or transfer. These processes also play an important role in workforce planning, tactical production planning, operational shop floor, and so on, and because of this practical relevance, scheduling has become one of the most important areas of study within the field of operations research. More specifically, we aim to extend models and techniques for parallel machine scheduling problems. We will also customize the existing models and algorithms to relevant industrial cases that are encountered in real-life applications.
Eric Ceballos Alcantarilla
Accidental ingestion of biotoxins present in food constitutes a remarkable public health issue which can be addressed by properly monitoring their levels through the development of analytical methods for their quantification, even at trace levels. Immunochemical methods are suitable for this purpose, indeed many examples have been reported for biotoxins as relevant as ochratoxin A, anatoxin-a or aflatoxins. However, immunoassays for direct detection of those biotoxins in solution based on bioluminescent protein biosensors have not been developed yet. Such assays would have interesting advantages compared to other immunoassays, including the possibility of adapting them to very cheap paper-based devices. In this sense, LUMABS (luminescent antibody sensors) recently introduced by the team of prof. M. Merkx (TU/e) are valuable tools with promising biomedical applications, but also with a high exploitation potential in other fields such as food safety and small molecule detection.
Therefore, this project has two main objectives. The first one is to elicit new LUMABS variants capable of directly detecting small molecules in homogeneous assays, with special focus on the development of ratiometric sensor formats. Once the new sensors have been successfully obtained and their intended purpose has been demonstrated, the second aim will be adapt them for the homogeneous analysis of some of the aforementioned biotoxins, either directly in solution or making use of paper-based devices such as μ-PADs. Different approaches for sensor design will be investigated, whereas sensors will include biotoxin functionalized analogs conjugated by click chemistry with unnatural amino acids and/or covalently linked immunoglobulins by means of the recently reported LASIC technology. Moreover, in collaboration with the team of prof. B. Correia (EPFL), we will try to replace analogs with equivalent peptide motifs to simplify sensor preparation without losing molecular recognition features.
Xavier Fernandez Cassi
The production of drinking water from sewage can be an important strategy to make a sustainable use of water resources and an important tool to mitigate water scarcity. However, it poses some important challenges, as sewage is a known source of multiple pathogens such as bacteria, viruses, protozoa and helminths that are excreted through feces, urine or skin desquamation.
However, the presence of pathogens in sewage, and particularly of viruses, is a matter of concern as viral removal from sewage is not specifically addressed by conventionally treatments applied in wastewater treatment plants. On countries where wastewater is treated, viruses have been identified as the main cause of water and food-related outbreaks. Traditionally, the study of viruses present in sewage has been limited by the use of specific tests for each concrete viral species that you want to analyze.
If sewage is intended to be used as a water source to produce drinking water, a complete inventory of all possible viral hazards present in sewage that might cause disease must be elaborated. Next Generation Sequencing techniques (NGS) applied on sewage have the potential to detect all human viral pathogens in a single test. Despite its huge potential, this methodology needs some refinement to detect viruses at low concentrations and also to provide information about their infectivity, and hence the hazard that they represent for consumers. These two important limitations need to be addressed if NGS has to be implemented as a routine method to detect viruses in drinkable water.
The present project called METAVIR aims to perfect NGS techniques to overcome their potential limitations in the context of producing drinkable water from sewage. METAVIR will improve the protocols used while allowing the characterization of sewage infectious viruses and providing a complete inventory of viral species. This inventory will be used to study the possible impacts of detected viruses on human health.
Host University: DTU // AP. Dr. Philip Loldrup Fosbøl
Carbon dioxide is the largest anthropogenic contributor to the global warming, which puts three-fourths of the human population at serious risks. Modelling and simulation analysis reveals that with the current rate of carbon dioxide discharge, the observed changes can exponentially deter our environment’s stability to the point of no-return, by the end of this century. Therefore, it is imperative that essential steps must be taken to reduce the anthropogenic discharge of carbon dioxide in the atmosphere.
Many carbon capture techniques have been investigated, which include absorption, adsorption, cryogenics, membrane-based separations and many more. Of which, absorption via chemical solvents have seen extensive commercial application, because of their prevalence and operational ease. The conventional temperature swing absorption (TSA) technology removes carbon dioxide in the absorber at low temperature using a weak base, like alkanolamines. The absorbed gas is collected in another part of the plant in its pure form, using high temperature. This process results in a high energy penalty. Moreover, alkanolamines have low absorption capacity and their corrosive nature and degradative nature results in equipment loss and fugitive emissions of hazardous chemicals to environment.
Contrarily, systems using pressure-swing principle (where pressure is changed across the system to achieve separation of a gas from its stream, e.g. adsorption) are known for their superior energy savings and almost negligible environmental impact. The same concept is applied to the chemical absorption systems.
In this project, a novel pressure swing absorption (PSA) process using a catalytic biological solvent is proposed for eco-friendly and energy efficient separation of carbon dioxide from process streams. The biological solvent is expected to reduce the toxic emissions to the environment by 50% and provide an energy savings of 10%, as compared to current state-of-the-art absorption systems.
Host University: DTU // Prof. Kristian Sommer Thygesen
The project TOPPING (TOPological PlasmonIcs oN the edGe of 2D materials) aims to study topologically protected collective excitations in one-dimensional (1D) systems formed at the edge of novel two-dimensional (2D) materials, combining theoretical models and first-principles simulations (i.e. parameter-free quantum mechanical calculations).
Research performed over the past decade has uncovered the deep connection between the topology of the crystal Hamiltonian and the existence of symmetry-protected quantum states. One of the most well known examples are 2D topological insulators, whose most striking manifestation is the emergence of 1D metallic edge states that are topologically protected against disorder and impurities. Their physics, however, has been mainly analyzed at the single-particle level, using simple non-interacting models. TOPPING will advance the established understanding by exploring the physics of many-body interactions, which drive the formation of collective excitations called plasmons, at the edge of 2D materials with non-trivial topologies.
Lots of unexplored questions concerning the interplay of topology and collective plasmonic excitations are still open. For instance, what is the effect of the topological protection on edge plasmonic excitations? Are they more robust (in terms of lifetime and scattering rates) with respect to bulk 2D plasmons or ordinary non-topological materials? How do they couple to external electromagnetic fields? I aim to answer these fundamental questions combining my own knowledge of topological materials with the world-leading expertise of DTU and EPFL supervisors on first-principles simulations.
I envisage several possible applications for topological plasmonics, not least in the context of opto-electronics. Here the coupling of electromagnetic fields to collective edge excitations of topological 2D materials could pave the way to new and innovative recipes for transmitting information in a robust, protected way.
Host University: EPFL // Prof. Michel Bierlaire
A car-free city center is a valuable solution for decreasing traffic congestion and CO2 emission, and improving active mobility and quality of live. For achieving these goals, one of the biggest challenges is the relocation of parking places scattered in the inner district, which cause cruising for available parking as well as car inflows. A possible solution is a “fringe parking” system, which groups parking into a limited number of spots on the border of the district. However, this may decrease the level of accessibility moving the parking away from the final destinations. The key idea of this research project is to use “accelerated moving walkway (AMW)”, a novel transport system, in combination with a fringe parking system to design car-free city center with a high accessibility. The goal of the project is to identify the optimal configuration of a fringe parking system with AMWs. The main methodology is network design optimization and behavioral demand modeling. The research deals with a multi-layer network of vehicular traffic and pedestrians, which interact in terms of both demand and supply and are linked with each other by parking. The research is mainly dedicated to 1) developing a multi-layer network assignment model, 2) optimizing a fringe parking system with AMWs and 3) applying it to a real city case study. The research is expected to contribute to methodology developments in the traffic assignment and network design fields, as well as urban policy developments. The results could support municipalities and tow planners in designing car-free districts for future cities.
Considering that for the next decades autonomous vehicles and human drivers will be sharing the roads, one of the greatest road safety challenges globally will be the proper traffic operation of both driving behaviors. While autonomous vehicles may drive “perfectly”, their behavior does not al-ways resemble that of human drivers. The potential negative implication of this, is that human drivers might be confused by the unintuitive behavior of the automated vehicles, potentially leading to road safety problems.
The aim of the proposed research is to formulate, develop and validate a Turing test tool indicating whether an autonomous vehicle can imitate (the “positive” properties of) human driving behavior. More specifically a tool will be developed (including a mobile and desktop interface) in which users will be asked to distinguish autonomous/human driving behavior through a Turing test methodology consisting of pairs of provided information (e.g. videos or graphs). The methodological framework consists of the following steps:
– Development of a methodology for generating simulation data regarding both human driving cars and scenarios for autonomous driving through a suitable microscopic simulation tool
– Combined investigation of driving performance of autonomous vehicles and human drivers by advanced statistical analysis techniques
– Establishment of a Turing Test Tool to investigate whether an examined autonomous vehicle is behaving in a way that cannot be distinguished from human behavior
Results will have an immediate impact amongst several stakeholders including policy-makers, re-searchers, automotive and insurance industry while the proposed research will be a significant next step of the personal research and professional activities of the researcher.
Mucin glycoproteins provide lubrication and hydration to protect wet epithelia in the human body from tribological stress by reducing wear and friction. Recently, it was shown that purified mucins applied as aqueous lubricants could greatly improve the tribological properties of artificial surfaces. The putative protective potential of mucin-based multi-layer coatings has, however, not been studied yet. A key goal of this project will be to generate such mucin-based multi-layer coatings on different substrates and to study their tribological performance. For this purpose, dopamine, a molecule which serves as an important neurotransmitter in the human body but can also adsorb to a broad range of synthetic materials, will be employed to develop dopamine-mucin multi-layer films using a layer-by-layer approach. Stainless steel, Al2O3 and polydimethylsiloxane will be chosen as representatives for a metal, ceramic and polymeric substrate, respectively. The adsorption properties and multi-layer formation of the mucin-based films on different substrates will be determined using a combination of quartz-crystal-microbalance, and tribological performance both at nanoscale and macroscale will be compared using an atomic force microscopy, a rheometer-based tribometer and a mini-traction machine, repectively. The wear rates and wear mechanisms will be studied using a light profilometer as well as a scanning electron microscope, respectively. By introducing a protective multi-layer coating generated from two biomolecules, this project will be able to provide an innovative technological solution to the area of health and bioengineering by increasing the longevity of synthetic materials which are permanently brought into the human body, e.g. contact lenses, catheters and implants.
Tanveer ul Islam
Microscopic hair like structures called cilia are nearly ubiquitously present in the human body, and are important for normal functioning of organs. Synchronous motion of motile-cilia induces flow in the surrounding viscous fluid that functions as vital particle/ nutrient transporting medium. During development, for example, cilia present on the human embryonic node generate a flow that determines the left-right asymmetry of our organs. Structural alterations in cilia, mostly caused by genetic mutations, results into a number of severe diseases, called Ciliopathies, such as misplacement of organs in the body, chronic bronchitis, sinusitis, collapsing lungs, and infertility. However, there is still a lack of knowledge about the origin and consequences of ciliopathies, because of the absence of good model systems that allow studying ciliary (dys)function mechanistically. In vivo testing using animal models is problematic since control and measurement of separate parameters are severely hampered. In vitro models are entirely absent or very basic. In this project, I will develop a lab-on-chip model of cilia towards in-vitro analysis against controlled physical parameters. The model first re-quires development of a novel microfabricaton process to produce magnetic artificial cilia mimicking the biological cilium structure as closely as possible. Advanced femtosecond laser machining will be accustomed for incorporating local modifications in the structures, to represent ciliopathies. A new magnetic cilia actuation system, to re-enacting complex natural beating profiles, will be used to artificially reproduce motile cilia motions. As a proof-of-concept, I will create an artificial embryonic node and study the interaction between cilia structures and surrounding fluid at microscopic scales to understand the cilium functioning in their normal and defected forms.
The EU commission established a road-map to decarbonise the transport sector by 2050. A main focus is on urban mobility, which represents 29% of the sector emissions and is over 90% reliant on oil. Biofuels, hydrogen/fuel cells and battery/hybrid vehicles present different characteristics (infrastructure,maturity and driving range). They are foreseen to play a role in the medium and long terms to ensure energy security, address price volatility issues and reduce emissions.
However, compared to fossil fuels, biofuels and hydrogen are not yet commercially competitive because of higher capital costs and possibly low energy efficiency in the production phase. Battery/hybrid vehicle powertrains are heavier and more complex to design. There is a need for increasing the energy efficiency and cost-effectiveness of these alternative fuels and propulsion systems, developing novel processes and optimizing the current ones. This is the aim of this two-folded project.
Firstly, we will investigate how to design and optimize biofuel/hydrogen production routes and hybrid powertrains, developing process models and performing energetic and economic analyses. Secondly, we will analyse the market integration and environmental impact of these fuels and vehicles, considering uncertainties related to the driving behaviour and prices. This project will pave the way for a low-carbon transport sector: innovative and sustainable technologies will be developed in economically-acceptable conditions.
Driven by the need for more sustainable industrial processes, this project sets out to isolate a series of novel aluminium double-bonded complexes and examine their reactivity to the synthesis of value-added products. These are expected to be highly reactive compounds due to the low-valent Lewis-acidic aluminium centre, and as aluminium is the most abundant metal found within the Earth’s crust it is a prime candidate to use in catalytic processes typically dominated by non-sustainable, expensive transition metals. To fully exploit the chemistry available to aluminium a fundamental understanding of its chemical bonding properties is required and will be investigated through the targeted synthesis of heterodiatomic double-bonded complexes of Al = M, where E = p-, d-, or f- block elements.
Single cell analysis methods including microscopy, flow cytometry, and single cell transcriptomic profiling (scRNA-seq) have each contributed to our understanding of cellular identity and are increasingly used in diagnostics. However, each of these technologies currently functions as a stand-alone approach. Here, we reasoned that combining these methods into one device would yield a powerful tool to probe cellular heterogeneity and perform unbiased diagnostics. The aim of this proposal is to engineer a high-throughput, robotic approach integrating the power of microscopy, flow cytometry and molecular processing of single-cells. The core of the Machine-vision Aided Single-cell Processing (MASP) platform will be based on machine-vision systems and augmented with sophisticated imaging capacities. The platform will allow to relate cellular phenotypes to the molecular fingerprint of a cell by leveraging the strengths of high-quality imaging and scRNA-seq. We aim to obtain predictive cellular and pathologic classifiers with the goal of increasing our understanding of cell function and improving diagnostics from liquid biopsies. By combining imaging, machine-learning and microfluidics, MASP will provide the opportunity to develop an interdisciplinary approach for single-cell manipulation, morphological and molecular profiling, opening a new avenue to advance diagnostics and to tackle a plethora of unanswered biological questions, not addressable with available technologies.
Giuseppe Antonio Zampogna
The state of the art of filtering and membrane processes evidences the existence of limitations, at theoretical and practical levels, related to their efficient performance. Techniques to analyze the behavior of fluids flowing through membranes and the performances of the membrane itself, are essentially based on microscopic models with experimental justifications, or on ad hoc models derived by merging different theories. Membrane design still proceeds through trials and errors.
The purpose of this project is the design of membranes characterized by the concept of modularity, nowadays one of the fundamental needs in technological adaptation. They must also be more energy and space-saving than classical ones. A multi-scale hierarchical thin membrane is designed as a result of these needs.
In order to satisfy energy saving and downsizing, the new membrane must be thin along the direction of filtration (in contrast to the most used devices which are thick and require more operating energy). To address modularity the membrane owns heterogeneities at different scales, as represented in figure 1, where each scale corresponds to a filtration level. Since Darcy’s law, generally used to simulate flows across thick membranes, is in principle unjustified with the present design, a theoretical and numerical framework to analyze thin membranes, based on homogenization theory, is developed starting from first principles.
Resources-consuming direct numerical simulations of fluid flows and particle transportation across the studied membranes will be calculated, to validate the multi-scale model developed. As final result, an easy-to-use and computationally light reduced order model to analyze the dynamics of fluid solvent and solutes through membranes will be delivered to the scientific community, which will be able, via an inverse formulation, to identify the optimal parameters in membrane processes, in order to give some new insights into the paradigms of industrial production.
Mobility issues in Europe and all over the world are a major socio-economic problem. Even without considering physical constraints, conventional mobility solutions are not capable of meeting EU’s goals in terms of emissions and pollution. The INTERMODE (INtegrating Transportation sErvices thRough Multi-mOdal Demand Estimation) framework aims at developing a new support decision tool to help policymakers designing innovative mobility solutions. The scheme involves two phases: first, collecting deep information for a sample of the population in terms of preferences (mode of transport, departure time, activity agenda) and socio-demographic characteristics (car ownership, household composition). Starting from this sample, aggregate data (such as mobile phone network data) will be used to estimate mobility preferences for the entire population, identifying the potential demand for new services. There are two innovative elements in this project. First, while conventional approaches mostly estimate the mobility demand, we expect to also capture its evolution. Once that the sample data are available, aggregate data are used to evaluate the evolution of the demand without collecting new data at a user level, which is expensive and time-consuming. Second, the INTERMODE scheme will allow measuring the impact of new mobility solutions (such as shared mobility and integrated mobility services) in an efficient way, allowing the modeller to perform cost-benefit analysis before introducing a new service. The ultimate goal of this project is to develop a new framework to represent the mobility demand, by taking into account flexible user behaviour and the need of reducing the overall energy footprint of transport systems
Active turbulence is the chaotic multi-scale flow observed in dense suspensions of active matter. Examples include microbial suspensions, actin and microtubule networks powered by molecular motors, cellular monolayers, synthetic Janus particles, flocks of birds and schools of fish. Unlike classical turbulence, powered by an external input of energy, active turbulence is driven locally by energy injected at the level of its single components. These systems have an intrinsic tendency to self-assemble and self-organize in large spatio-temporal evolving structures. As a result, the computational study of their properties is very demanding, because one needs to address the multiscale nature of the phenomena of relevance.
This project aims at improving our understanding of active turbulence by focusing on the role and effects of confinement provided by solid walls and interfaces. This will reveal how these systems can be used as building-blocks for designing active soft materials, a new class of materials with novel qualitative features and potential. Examples range from a two-fluid emulsion encapsulating active matter used for drug delivery to biomimetic materials, such as a soft tissue made up of highly-packed active droplets capable to resist to intense deformations, or the propulsion of small motors embedded in active fluid suspensions.
We will perform our investigation by developing a highly scalable numerical code that integrates the active nematic equations and represents all the relevant physical scales of the problem by taking full advantage of High Performance Computing architectures. The development of cutting-edge computing tools and the collaboration with experimentalists will enable us to assess the validity of current theoretical models for active turbulence and improve on them.
The proposed Smart Aging research will be an integrated system to recognize ADL and forecast the wellness of an individual. The main objective is to fill the knowledge gap of two contextual observations (i.e. day and time) in the frequent behavior modeling and limitations with predefined activity sets for ambient assisted living (AAL) of service users. Behaviors’ may also change according to other contextual observations including seasonal, weather (or temperature) and social interaction. The metrics in performance evaluation of existing analysis models are completely derived from the machine learning domain, and they do not concern the explicit requirements of activity of daily living (ADLs) analysis such as timestamp, season and frequency of occurrence. Some of the latest activity learning models for behavioral analysis use a predefined set of ADLs and ignore the other activity functions performed by study subjects within the smart home monitoring environment. In the present research, I propose to perform research and redesign of the machine learning model by adding behavioral observations. AAL data sets would be applied to other most appropriate existing techniques Hidden Markov Model and Conditional Random Field to evaluate the performance of redefined wellness behavioral analysis model proposed by me. This objective presents the challenges of sensor data fusion and decision-making. Additionally, the research would be conducted to discover the possible data analytics solution to address the noisy sensor activation data. Two types of data noise would be filtered, anomaly behavioral patterns generated by visitors and anomaly sensor activation (data outlier) that will be generated by faulty sensors as well as the noises generated by the environment.
Driven by the enormous growth of the Internet, Internet of Things, and cloud services, the global data traffic has experienced an explosive growth that has led the optical networks to operate close to their capacity limit. Therefore, to support future capacity demands, a paradigm shift in the design of the next generation optical network is crucial. One viable solution to address this problem of high societal importance is by exploring spatial-division-multiplexing (SDM), by means of multicore and multimode optical fibers. However, exploring SDM is challenging due to a large number of degrees of freedom and complex signal interactions. Thus, aiming at address those challenges, this proposal will explore the latest advances in machine learning to enable SDM-based future optical net-works. Inspired by Google’s DeepMind AlphaGo algorithm, the main objective of this proposal is to perform research into intelligent optical networks that are able to actively learn and provide optimum performance in terms of energy efficiency and information throughput. Specific objectives are the development and experimental verifications of novel machine learning algorithms that are able to predict data traffic evolution and perform self-optimization to satisfy data traffic demands and energy efficiency.
By multi-model coupling, we refer to the surface coupling of a high-fidelity model to a low-fidelity model in a single simulation resulting in a spatial model adaptivity. Using the high-fidelity model for the complete simulation is computationally too expensive, while the low-fidelity model is not accurate enough. Therefore, multi-model coupling is essential for many complex applications, such as climate prediction or nuclear fusion. The fundamental challenge in multi-model coupling is, that the models are in many cases disparate, e.g. they may have different dimensionalities or different numbers of state variables. To tackle the inherent complexity, researchers in academia and industry seek strongly after a usable and scalable coupling software that is able to work with legacy and community codes. The objective of this proposal is to develop generally applicable methods for multi-model coupling and to provide them in a usable and scalable form in the coupling library preCICE.
Epithelial cancers are highly heterogeneous making this cancer sub-type incredible difficult to treat. Of significant concern are pancreatic ductal adenocarcinoma, triple negative and metastatic breast cancers which remain essentially untreatable. Transcription factor Myc is amplified in these epithelial cancers, stimulating tumourigenesis through the promotion of cell proliferation and reprogramming of cancer metabolism. The 14-3-3 adaptor protein has an essential role in homeostasis. Of particular interest is the 14-3-3σ isoform which suppresses tumourigenesis through Myc binding. This protein-protein interaction (PPI) initiates degradation of Myc via polyubiquitination. The 14-3-3σ/Myc PPI is characterised by the binding of a highly conserved phospho-peptide located on the partner protein. During complexing of 14-3-3σ with partner proteins a small ‘drug size’ pocket is formed. Small molecule (SM) binding to this pocket has been shown to improve binding affinity of the complex and has afforded therapeutic outcomes. Research efforts by Ottmann et al have resulted in multiple SM stabilisers in preclinical evaluation. Based upon the aforementioned we hypothesise that SM stabilisers of the 14-3-3σ/Myc will drive Myc degradation, preventing cancer cell reprogramming and that targeting this PPI will improve epithelial cancer patients’ outcomes. Our three objectives are to: A) design and synthesise the first SM stabilizer of the 14-3-3σ/Myc PPI; B) demonstrate 14-3-3σ/Myc stabilizers specifically target Myc and suppress metabolic reprogramming of cancer; and C) assess 14-3-3σ/Myc stabilizers as anticancer agents in human cancers. This research will result in the development of a valuable chemical probe for further oncogenic research and a novel potential therapeutic option for cancer patients.
Jin Jack Tan
The project aims to develop a numerical model that encompasses source generation, sound propagation and sound perception, with a focus on musical instrument, propagation in an indoor space and perception of relevant descriptors to musical and room acoustics. The model is to be built with a bottom-up approach, first by coupling a numerical piano model with psychoacoustic models, before the addition of a state-of-the-art room acoustic model based on discontinuous Galerkin (DG) approach. The latter step necessitates the use of high performance computing for the increased degree-of-freedom (from 2D vibration of soundboard to 3D propagation in space) and in increased scale (3m2 soundboard to a room of 300m3). A benchmarking exercise with colleagues at DTU, who employs a different room acoustic model, is envisioned, to identify strengths and weaknesses in the DG model for further improvement and refinement.
The objective of this project is to develop novel metamaterial noise barriers to combat environmental noise. The research project aims to improve the low-frequency range (20-200 Hz) performance of conventional barriers by 5 dB, achieving a surface absorption coefficient of 0.8 dB and keeping a base width of maximum 0.2 m. The obtained barrier would be of high interest for application near highways, railways and around festival areas. For the successful accomplishment of the project, metamaterials with the target properties will be developed and integrated into numerical models to optimize the barrier shape and implement it in real outdoor propagation scenarios including meteorological conditions.
Changes in metabolic activity determine the functional status of immune cells such that activated cells show a different metabolism than resting cells. While several methods exist to study cellular metabolism ex vivo, the ‘in situ immunometabolism project’ (iSIMS) aims to assess cellular metabolism in situ by taking snapshots of immune cells residing in their local microenvironment. We will combine electron microscopy and secondary ion mass spectrometry to highly zoom into tissue sections into the energy producing powerhouses termed mitochondria of individual cells, and visualize the metabolic breakdown of cellular fuels such as glucose and amino acids. We will then use immunological methods combined with pharmacological treatments to gain mechanistic insight into the early molecular events of T cell differentiation into effector and memory T cells, which differ in metabolic state and function. Our goal is to understand how metabolism determines immune cell fate and functional status to further delineate how adaptive immunity develops and can be manipulated for therapeutic purposes.
Bacterial toxins are a family of cytotoxic protein secreted by a variety of pathogenic bacterium. These bacterial toxins aid the infection of pathogens through disrupting the normal functions of host cells, and disrupting the neuromuscular transmission and critical biochemical signaling pathways. In some cases, poisoning of bacterial toxins is even lethal factors via serious tissue disruption or neural paralysis. Therefore, it is of great significance to develop vaccines against bacterial toxins, which can active specific humoral immune response to secret specific antibodies and neutralize bacterial toxins.
Although initial attempts in developing vaccines against toxin have verified their great potential against bacterium infection, existing designs still suffer intolerable low efficacy because of unacceptable levels of immunogenicity and safety issues.
In our project, we aim to develop a smart nanogel vaccine against diverse bacterial toxins with enhanced safety and clinical efficacy. We attempt to overcome the outstanding obstacles by developing a responsive, carrier-free delivery system, in which the native bacterial toxins are chemically crosslinked in a reversible way to generate nanogels (NGs) with stimuli-responsive linkers. The NGs designed for lymph node targeted delivery restrain the toxins in inactive and nontoxic status but facilitate the release of intact toxins in response to the internal or external triggers for faithful antigen presentation. We will evaluate the safety and efficacy of this new vaccine in mouse models. The new design will potentially provide a safe, efficient, and versatile vaccine platform against some mostly concerned biotoxin threats, such as Anthrax. The applicant will greatly benefit from working in such a highly interdisciplinary project and obtain unique opportunity to broaden his education and research experience by working in two universities.
Project: Vertebrates´ GC biology
Host University: TUM // Prof. Dmitrij Frishman
Co-Host University: DTU // Prof. Francesca Bertolini
Mammalian and avian genomes are extremely complex and their functions are finely coordinated. Hence, it was surprising when my team disclosed the mammalian way of genome base organization in an archaic fish lineage called gars. We discovered that gars possess GC- and AT-rich regions alternating on chromosomes and in DNA sequence, similarly as mammals do. Other fishes, amphibians and most reptiles have a homogenous AT/GC distribution in genomes. This finding sheds fully new light on vertebrate genome evolution. Hitherto, this AT/GC heterogeneity was considered an adaptation to the increased body temperature in birds and mammals to stabilize gene-rich (GC-rich) regions. However, gars are cold-blooded and their body temperature depends on the environment as in other fishes and amphibians. This topic is linked with the difference in genome size in fish and mammals. Although fish underwent an additional round of whole-genome duplication (WGD, some fish lineages even more rounds of WGD) their genomes are significantly smaller than that of mammals. Moreover, genome size reduction means a substantial enrichment in GC content in fish but not in mammals. These traits in genome architecture indicate profound differences between fish and mammals that remain ununderstood. Here, I aim to test my principal hypothesis that the anamnia to amniotes transition, accompanied among others by further increasing of genome complexity and decreasing of genomic plasticity, was coupled with a GC-aided finer tuning of regulation of gene expression. Currently, the publically available genomic resources enable to address these questions thanks to availability of sequenced genomes of ca. 200 fish species (in 2017, ca.100 fish species were available).
Sultan Gulce Iz
Project: Dendritic Cell (DC) Migration Engineering via Surface Topographies-aDCMoviE
Host University: TU/e // prof. dr. Jan de Boer
Co-Host University: EPFL // prof. dr. Mathias Luthof
Dendritic cell (DC)-based immunotherapy is a booming field in which autologous DCs are trained ex vivo to prime T cells for targeting and destroying tumor cells. Whereas the safety of DC-based immunotherapy is clinically proven, empowering its efficacy remains a great challenge as the ex vivo trained DCs struggle to migrate to lymph nodes in order to present their antigens. Virtually, the majority of the injected DCs remain clustered at the site of injection, with only less then 5% of them being able to reach the lymph node. Therefore, the aim of this project is to improve the DC cell migration potential trough an innovative in vitro training on selected surface topographies (TopoChip) prior to injection. Specifically, DCs will be cultured on the TopoChip consisting of 2176 different surface topographies, in order to select the hits that can boost DCs movement to the lymph nodes in vivo. The engineered metabolically active DCs showing an enhanced migratory phenotype are described here as ‘aDCMoviE; Dendritic Cell (DC) Migration Engineering via Surface Topographies’. High content imaging of CCR7 and CD83 expression in combination with podosome formation will be performed to assess successful migration on TopoChips. After DCs are cultured on hit surfaces, an in vitro hydrogel-based 3D biomicrofluidic platform will be used for cytokine-induced chemotaxis to determine DC functionality. This project will not only improve my scientific competitiveness and complement my current expertise but will also unite the two pioneers, i.e. Prof. Jan de Boer (TU/e) and Prof. Mathias Lutolf (EPFL). The possible outcomes of the project will have a high impact on DC immunotherapy that can be easily translated in more effective personalized therapies in line with Horizon 2020 health goals.
Project website: https://www.sultanslabbook.com
Project: Direct numerical simulation (DNS) of fluid dynamics in bubble columns: a benchmark study
Host University: TU/e // prof. dr. Niels Deen
Co-Host University: TUM // prof. dr. Nikolaus Adams
Bubble columns are extensively used as multiphase contactors and reactors in chemical and metallurgical industries due to the favorable heat and mass transfer efficiency. Bubbles with different sizes are dispersed in a liquid, and the momentum of each bubble is tightly coupled to both the liquid phase and other bubbles. The associated flow physics is not well understood, which is the main barrier in the development of more sophisticated products. The best way to explore this problem is by direct numerical simulation (DNS) of such bubbly flows, where every continuum length and time scale is fully resolved, so that the basic mechanisms can be understood. In this project, I will perform a pioneering DNS (more than one billion grid points) study of the bubble column with the aid of the high-performance computing facility of SURFsara in Amsterdam. The project aims to provide, for the first time, a benchmark dataset for bubble columns, which will be used to explore closure relations for reduced-order models. Moreover, the machine learning method will be adopted to mine the massive dataset, in order to derive closure models that correctly represent the microscopic interactions between bubbles and liquid. These models will significantly improve the accuracy for the scale-up and design of bubble columns.
Project: Online Optimization and Mechanism Design for Various Shared Vehicles: towards Integrated Mobility with Flexible Bookings
Host University: TU/e // prof. dr. Frits Spieksma
Co-Host University: TUM // prof. dr. Susanne Albers
In the modern era of urban mobility, vehicle-sharing becomes one of the most popular and efficient transportation services. Vehicle sharing system would help push more private vehicles out of the mobility system, and it will alleviate the current traffic congestion and environment issues. However, each vehicle system pursues its development independently, and little is known about the cooperation of various shared vehicles. In reality, the needs of a user and the progress of urban mobility require multiple vehicles to work collaboratively. Due to this, an integrated vehicle sharing system has the potential to address this problem. The goal of this proposal, therefore, is to develop the foundations for online optimization and mechanisms on an integrated vehicle sharing system and to apply them to relevant cases encountered in real-life applications. Specific goals include: allocate various vehicle resources to travel requests by applying optimal or near-optimal online algorithms to achieve good performance on customer satisfaction; and extend vehicle sharing models and techniques to reduce the loss of efficiency by implementing mechanisms and policies. There are three key challenges: (1) The combination of multiple types of vehicles cannot be effectively guaranteed for users who request specific travel route and travel time; (2) Schedule the requests that arrived over time without any knowledge of the future; (3) Design effective mechanisms and policies. The resulting integration of various shared vehicles will have a significant impact on urban mobility and have the potential to achieve better performance on customer satisfaction or system efficiency. These ideas could also apply to other sharing systems.
Iacopo P. Longo
Project: Carathéodory for Assisted Intelligent Driving
Host University: TUM // Prof. Christian Kuehn
Co-Host University: TU/e // Prof. Henk Nijmeijer
The main goal of this project is to develop new results and mathematical tools in non-autonomous control theory by incorporating and extending recent advances in non-autonomous dynamics concerning Carathéodory differential equations, and to prove the effectiveness of the developed theoretical toolkit in the context of urban mobility modeling and traffic management design. The problems of collective controllability and convergence of the solutions of a whole set of differential equations (ordinary or with constant delay) will be tackled under very mild assumptions on regularity, that is, using Carathéodory differential equations which allow to successfully model nonlinear phenomena which explicitly depend on time in a possibly discontinuous way. As an example of the value of the prospected achievements and of the power of the proposed framework, we aim at reviewing the effects of the new results on the non-linear time-dependent (smooth or non-smooth) generalization of two models in urban traffic management respectively from a macroscopic and a vehicle-based point of view, while emphasizing that the rate of generality of the project goes far beyond the proposed applications and involves the foundations of control theory itself. The project has the specific virtue of providing additional tools, and of contributing to obtain further insights, in non-linear non-autonomous control theory, so to allow to blend advanced mathematics into applied sciences and engineering and specifically on a problem of grand social and economic relevance as traffic management.
Lizeth Gonzalez Carabarin
Project: Epilepsy onset prediction based on power-efficient Binary Deep Neural Networks
Host University: TU/e // dr. Ruud van Sloun
Co-Host University: EPFL // dr. Alexandre Schmid
Among Machine Learning algorithms, Deep Learning (DL) is a powerful tool for classification and prediction in terms of adaptiveness, reliability and noise-tolerance. Unfortunately, current DL remains costly in terms of power consumption and resources, significantly hampering its adoption in low-power wearables. Therefore, this proposal aims to design cutting-edge deep learning algorithms suitable for highly-efficient hardware implementation. As a first demonstrator, the algorithm will be used to predict epileptic seizures based on non-invasive scalp EEG. Further, prediction based on both EEG and ECG will be investigated, since it is expected that the use of multiple biomarkers will increase accuracy. Therefore, the objectives of this proposal are as follows: a. Designing dedicated Convolutional and Recurrent Neural Networks (CNN-RNN) that are suitable for efficient hardware implementation and enable processing a plurality of biomarkers, thereby leveraging FPGA memory hierarchy and parallelism to define an architecture capable to perform DL calculations efficiently; b. Implementation of an FPGA prototype for the hybrid CNN-RNN models, exploiting new quantization techniques to drastically reduce memory usage and power consumption; c. demonstrating the prototype’s relevance for epileptic seizure prediction. The proposed FPGA prototyping facilitates ASIC implementation, targeting portable devices. The importance of this project is not uniquely enclosed in epileptic seizure prediction, but it can be transferred to monitor and predict life-threaten situations of other diseases. From the career development point of view, this fellowship will give me the opportunity to work with the best European Universities, providing me with skills to develop independently my own research.
Project: New inhibitors of reactive oxygen species production for the study and potential treatment of neurodegenerative diseases
Host University: DTU // Prof. Mads H. Clausen
Co-Host University: EPFL // Prof. László Forró
Neurodegenerative diseases (NDs) are progressive and incurable disorders affecting an increasing number of people and are becoming critical social and medical issues. Currently, available drugs for the treatment of NDs are focused on addressing symptoms but are of limited efficacy in preventing disease progression. A common feature of all of those diseases is the irreversible structural and functional damage to the nerves resulting in cell death. The increased production of reactive oxygen species (ROS), has been proven to play a significant role in the development and progression of NDs. ROS, in low doses, are important signaling molecules in several cellular processes such as differentiation, senescence, adhesion, apoptosis and cell-growth. However, when present in higher amounts, they have been reported to be toxic because of their ability to react with nucleic acids, lipids and proteins. Thus, targeting the increase production of ROS in NDs is a highly promising alternative to currently available treatment options. This project aims to identify small molecule inhibitors of ROS production for applications in NDs. In addition to the therapeutic applications, it will provide tools to gain further insights into the role of ROS in NDs.
To identify ROS inhibitors an initial cell-based screen employing ROS-sensitive fluorescent probes will be carried out on approximately 50,000 compounds, followed by several hit validation steps and counter screens. Subsequently, the target(s) of the hit compounds will be identified using mass spectrometry-based chemical proteomics. A comprehensive target validation study will be performed, including biochemical and biophysical assays to confirm binding of compounds to their targets and a cell-biological validation of the targets to confirm their role in ROS production and neurodegeneration.
Rubaiyet Iftekharul Haque
Project: Additive Manufacturing for Implantable 3D Micro-Coil Array for Cortical Activation
Host University: DTU // Prof. Anpan Han
Co-Host University: EPFL // Prof. Danick Briand
World Health Organization (WHO) documents 36 million people are blind. The social and economic burden is large, and it will grow with an aging population. Artificial vision employs electrical stimula-tion of the vision associated nervous system. In compared to electrode-based implants, recently de-veloped micro-coil implant exhibits superior performance. An external dynamic magnetic field applied by micro-coil implant induces an electrical field that can stimulate nerve tissue for cortical activation. However, current micro-coil implant for vision restoration is made of 2D lithography technique and has only half-turn. The limitation of the 2D lithography technique hinder the progress of micro-coil implant development. The objective of this proposal is to construct a 3D multi-turn micro-coil array implant that can generate much higher magnetic field density without consuming more power and can be used as a medical implant. In this regard, nanoscale additive manufacturing methods, like, a newly invented Icetronics technology at Technical University of Denmark, and 3D laser lithography at Ecole Polytechnique Federale de Lausanne will be explored. The 3D micro-coil implant will be tested in mice in collaboration with Dr. Fried at Harvard Medical School and Mass. General Hospital. Fur-thermore, focus will be given on achieving mechanically matching biocompatibility and encapsula-tion. Eventually, we believe that the multi-turn micro-coil implant can be used for artificial vision for patients that must be stimulated at the vision cortex level. In addition, the technological development during the project can be adopted and will lead to the advancement of nano- and micro-scale addi-tive manufacturing of implants and other functional devices.
Project: Smart Parcel Delivery with On-Demand Time Windows
Host University: TU/e // Prof. Tom van Woensel
Co-Host University: TUM// Prof. Stefan Minner
The continuing growth of e-commerce is responsible for an increasing demand for package delivery services. These services are complex from an operational perspective, since the potential number of delivery addresses is very large, and the opportunities for consolidation of freight are minimal. In addition, availability of customers at their home addresses during the delivery period is uncertain. As a result, in many cases deliveries cannot be performed, since customers are not available for receiving them. Inefficiencies in the process manifest themselves in the form of redelivery attempts, or queues at post-office stations for retrieval of missed parcels. These, in turn, generate negative externalities such as increased traffic of vehicles in urban areas, and waste of customer time and resources at post-office stations. In this project, we propose and develop improvements to this traditional delivery process of packages. In the new process, service providers and customers negotiate preferred delivery times. After this step, small changes in the originally planned delivery routes are made. These changes do not impact significantly the operational costs but contribute towards an increased first-time delivery rate. The idea of adapting the delivery times is a simple one but has never been researched within a parcel delivery context. In fact, current attempts to improve delivery services are centered on new technologies, which require a higher level of investment. In this project, models and methods to implement the new process are going to be developed using techniques from mathematical optimization and operations research. By the conclusion of this project, package delivery services will be equipped with new knowledge on how to implement more efficient operations.
Project: Self-assembled stimuli-responsive peptide-based nano-materials in photodynamic therapy
Host University: TU/e // Prof. Jan van Hest
Co-Host University: EPFL // Prof. Harm-Anton Klok
Self-assembled peptide nanostructures are excellent promising candidates for facilitating biomedical applications due to their advantages of structural and functional diversity, high biocompatibility and biodegradability. More specifically, peptide-based nanocapsules are highly desired in the nanomedicine field. On one hand, the nanocapsules can encapsulate drugs. On the other hand, based on various functional peptide sequences in the body, stimuli-responsive peptide nanocapsules can be designed to control and modulate therapeutic activity within the microenvironment of tumors, resulting in greatly enhanced photodynamic therapy (PDT) efficacy. However, only few responsive (poly)peptide-based nanocapsules have been reported, which is mainly caused by the lack of suitable peptide building blocks of which the self-assembly/disassembly behavior can be well controlled in tumors. We aim to create a new class of peptide-porphyrin building blocks for the fabrication of nanocapsules. The photosensitizer porphyrin is flanked by two peptide motifs which will be designed to be pH, reduction or enzyme responsive to specific tumor micro-environment conditions, such as lower pH, over-expressed glutathione and certain proteinases, improving the bioactivity of porphyrin moiety. Chemotherapeutics and metal ions can also be encapsulated. Thus, the capsules can inhibit tumor growth with high efficiency via the combined effects of PDT, metal ions release and immune therapy. This project will create an excellent opportunity for me to build upon my own expertise in controlled molecular self-assembly of peptides with the groups of Van Hest and Klok which have rich experience in polymer chemistry, bioconjugation and nanomedicine, which would contribute to the advancement of my future career.
Project: Integrating electric vehicles and home energy management systems in a co-designed INTelligent Transportation and P2P ENergy SystEm (INTENSE)
Host University: TU/e // Prof. Nikolaos Paterakis
Co-Host University: DTU// Prof. Pierre Pinson
This proposal is directed towards sustainable cities by managing the operation of distributed energy resources (DERs) including electric vehicles and residential appliances with flexible energy consumption. We envisage a network of intelligent agents that make decisions for the planning and operation of DERs, namely vehicle charging, energy consumption scheduling, and bidding in electricity markets. Our premise is to study and develop algorithmic tools for the operation and coordination of DERs, as well as their integration in the system. The objective is to minimize the social cost of the system without disturbing the preferences of end-users, by discovering tractable solution concepts for the coordinated agents’ interaction. Our proposition extracts the value of DER flexibility and incentivizes micro-investments in EVs and smart appliances, thereby facilitating large-scale RES penetration.
From a research perspective, relevant concepts include decision-making under uncertainty, learning, and game-theoretic aspects of agent interaction. Our methodology leverages state-of-the-art techniques from the area of Multi-Agent Systems and Stochastic Games (which is a generalization of Markov Decision Processes and Repeated Games). Key challenges relate to the computational complexity of such settings and the absence of generic tractable methods. We plan to tackle these problems with domain-specific approximation methods by leveraging our expertise in Smart Grids, game-theoretic mechanism design and AI.
The fellow’s aspiration is to leverage this this mobility opportunity to develop utmost expertise in smart cities and sustainable development contribute in uplifting the competitiveness of his home country’s academic sector and startup community.
Project: Integration with native tissue and enhanced regeneration using a highly adhesive hydrogel for articular cartilage: evaluation with ultrasound imaging (INTERFACE-US)
Host University: TU/e // Prof. Keita Ito
Co-Host University: EPFL // Prof. Dominique Pioletti
Osteoarthritis (OA) is a degenerative joint disease with no known cure. Untreated or improperly treated cartilage defects often progress to OA over time. Tissue Engineering (TE) approaches aim to regenerate damaged cartilage with cell-seeded implants. Although promising, TE procedures have two major drawbacks: 1) they often fail due to weak adhesion at the tissue interfaces, causing insufficient integration of the implant with the native tissue; and 2) assessment of their clinical outcome usually requires Magnetic Resonance Imaging (MRI), a costly method with long waiting times and prohibitive to many patients.
This interdisciplinary project aims to address these issues by proposing: 1) the use of a new hydrogel with outstanding adhesive properties for enhanced implant integration; and 2) the use of ultrasound to non-destructively monitor and characterize regenerated tissue at the interfaces. The new hydrogel will be seeded with chondrocytes to fill a full depth cartilage defect in osteochondral plugs that will be cultured and mechanically stimulated in an ex vivo osteochondral explant system. Tissue integration and regeneration over 6 weeks will be monitored with ultrasound speckle analysis and elastography and validated with biochemical, histological and biomechanical methods.
This project will advance the integration of TE approaches, implant design, cartilage mechanics and ultrasound imaging. When translated to clinical applications, the outcomes may eventually contribute to a decrease in the economic burden associated with cartilage defects.
Project: Guaranteed Performance and Automatic Reconfiguration Design for Time-Sensitive Networking
Host University: TUM // Prof. Sebastian Steinhorst
Co-Host University: DTU// Prof. Paul Pop
The ultimate goal of the project is to provide a set of Network Calculus (NC)-based performance analysis methods to help in the automatic reconfiguration for interconnected safety-critical in-vehicle systems using Time-Sensitive Networking (TSN). We will fill the gap of timing analysis research in the coexistence possibilities of the time-triggered shaper and various event-triggered shapers, and at addressing the challenge to develop the runtime timing analysis techniques, in order to provide the fast and accurate performance verification foundation for automatic reconfiguration of TSN networks. In addition, we will implement a prototype of automatic reconfiguration relying on the NC-based reliable verification. The project will promote the application of TSN on the in-vehicle network, and paves the way for a predictable yet flexible response to changes in system re-sources such that an optimized network behavior is achieved.
Project: ACT-ORC – Advanced Control of Organic Rankine Cycle Systems for Increased Efficiency of Heavy-Duty Transport
Host University: DTU// Prof. Fredrik Haglind
Co-Host University: TUM// Prof. Hartmut Spliethoff
The European Commission reports that heavy-duty vehicles accounted for 27 % of road transport CO2 emissions and almost 5 % of the EU total greenhouse gas emissions in 2016. Heavy-duty vehicles are based on internal combustion engines, whose efficiencies are limited to 30-46 %, whereas the remaining energy is released unused to the atmosphere as waste heat. A key technology to reduce the environmental impact and improve energy efficiency of heavy-duty vehicle powertrains is the organic Rankine cycle technology. However, the waste heat from internal combustion engines of heavy duty vehicles is characterized by large fluctuations in load, making it a difficult task to control the organic Rankine cycle unit. Therefore, it is of crucial importance to develop fast and reliable control strategies in order to enable maximum net power output and safe operation of the organic Rankine cycle power system. The present work aims to develop and demonstrate innovative advanced control strategies under realistic driving conditions, enabling a reduction in fuel consumption and CO2 emission by approximately 8 %. Identified advanced control strategies will be both simulated and demonstrated on a unique test rig at DTU. The project will have a crucial role in shaping future powertrains for efficient greener transport and sustainable mobility.
Regiane Alves de Oliveira
Project: Genetic engineering of lactic acid bacteria as efficient microbial cell factories to produce papain
Host University: DTU// Prof. Alex Toftgaard Nielsen
Co-Host University: TUM// Prof. Dirk Weuster-Botz
Papain is an enzyme of wide industrial use that is still extracted from papaya crops. In this proposal, it is intended to use state of the art molecular biology tools in the construction of an efficient biotechnological production route of papain in a non-model microorganism. This approach represents a science frontier in terms of using different areas that have so far been strongly separated. A major challenge will be to increase the throughput of genetic engineering tools to enable more rapid engineering than currently possible. A lactic acid bacteria (LAB) will be selected to be genetically modified to become a host for the production of food-grade papain from brewer’s spent grains (BSG) using solid-state fermentation. LAB have several advantages for the production of enzymes over the use of typical cell factories, such as E. coli or S. cerevisiae, including simple metabolism, small genomes with reduced redundancy, fast growth, high sugar uptake rates, the potential for uncoupling of growth and energy metabolism, scarcer high-level control systems and being generally recognized as safe and food-grade microorganisms. The proposal has an economic, sustainable, and scientific impact since it creates new possibilities for the use of LAB as food-grade production and waste valorization through the use of BSG. This serves as a suitable and low-cost substrate for the microorganism and contributes to the green economy by valorizing waste streams and reducing carbon emissions, as related to the discharge of the residue. As an outcome, it is expected that useful tools will be developed for the efficient modification of LAB so that they can be used as microbial cell factories for the production of enzymes while using agroindustrial waste as a substrate for the fermentation.
Project: Insights on the “real impact” of science
Host University: TU/e // Dr. Emilio Raiteri
Co-Host University: EPFL // Prof. Gaétan de Rassenfosse
This project seeks to advance our understanding of the economic impact of publicly-funded research. It assesses how public investment in research and development (R&D) translates into commercial products and how it impacts the long-term prospects of regional economies. Indeed, the prominence of innovation for economic growth, and the sheer size of public investments for innovation in developed countries have led economists to wonder about the actual returns of public funding for R&D. Despite the growing literature on the topic, our understanding of this issue is still limited, also due to the shortage of available data. Therefore, the project will build, at first, an original database that links public funding for R&D to patents and to final products. I will then exploit this data to identify how public funding for R&D affects the commercialization likelihood of new technologies. Secondly, I will explore how the introduction of new publicly-funded inventions in a business cluster triggers its technological core change.
The project is highly interdisciplinary, combining Economics of Innovation, Economic Geography, Complexity Economics, and Intellectual Property Policy literature. Its results will speak not only to academic researchers but also to policymakers. It will help them to define innovation policies that are the most effective in fostering economic growth and, ultimately, citizens’ well-being. Moreover, the project will provide me with the opportunity to develop new digital skills in data management and software development; strengthen my theoretical and applied knowledge in patent data; and enhance my international visibility, signal the quality of my research, and expand my network. It will considerably strengthen my long-term career prospects.
Project: Scale-span study of dynamic performance of ultra-high performance concrete containing microcracks
Host University: TU/e // Prof. Jos Brouwers
Co-Host University: DTU// Prof. Ole Jensen
Ultra-high performance concrete (UHPC), with high strength and ductility, excellent energy absorp-tion capacity and durability, is considered a promising future material for earthquake-, impact- and blast-resistant structure. However, due to low water-to-binder ratio and high cement dosage, UHPC is highly vulnerable to autogenous shrinkage microcracks that are expected to further facilitate the initiation and propagation of other microcracks under service load conditions. So, exploring the mechanism of autogenous shrinkage microcracks on the mechanical performance under dynamic loading is of significant importance for the design and evaluation of infrastructure applying UHPC. In this proposal, a cross-scale study, combing experimental study and mesoscopic observation with numerical simulation, on the mechanical performance of UHPC under dynamic loading is carried out. Firstly, the 3D distribution of initial microcracks is quantitatively characterized by computerized tomography scanning and digital image process. Then, the dynamic experiment is performed by Split-Hopkinson pressure bar and the microstructural characteristics of the failed UHPC is observed by scanning electron microscope. Finally, a 3D finite element numerical code is developed to analyze the spatio-temporal evolution of microstructure from meso- to macro-scale. The analysis of distribution of autogenous shrinkage microcracks may provide theoretical support for the design of fracturing structure made by UHPC and promote the application of UHPC in Europe. Moreover, the abundant research experiences on dynamic performance of UHPC this applicant would acquire through this project will pave a solid foundation for his career, no matter in academia or industry.
Project: Beyond the Warburg Effect in Cancer: The Metabolism of Glucose in Hypertrophying Skeletal Muscle Determined by 13C-Fluxomics
Host University: TUM // Prof. Henning Wackerhage
Co-Host University: DTU// Prof. Lars Keld
Resistance training represents an attractive and low-cost strategy to prevent skeletal muscle wasting and metabolic diseases in the ageing population. However, the knowledge of the underlying mechanism by which resistance training improves glucose uptake and insulin sensitivity in muscle is lacking. Preliminary data by the Exercise Biology group (TUM) suggest that hypertrophying muscle cells use carbon from glucose for biomass production. This phenomenon is commonly a hallmark of cancer, known as the Warburg effect. Cancer cells exhibit a high rate of glycolysis that serves to provide glycolytic intermediates as precursors for anabolic reactions to facilitate tumour growth.
Thus, in MUSCLEFLUX, we aim to investigate whether a hypertrophying muscle undergoes a Warburg-like metabolic remodelling. The objectives are to assess whether (i) muscle growth stimulation in vitro [via insulin-like growth factor (IGF)-1in skeletal muscle stem cells] and in vivo (via synergist ablation in mice) increases the flux of carbon from glucose into anabolic pathways for building biomass; and (ii) knockout of a key glycolytic enzyme of the serine pathway attenuates the Warburg effect, by using mass spectrometry-based metabolic flux analysis including 13C-labelled glucose and computational modelling.
The results may show the importance of the Warburg effect for muscle hypertrophy that could be a discovery of a paradigm shifting nature. Better understanding of the underlying mechanism that regulates skeletal muscle mass is important to develop effective therapies against muscle wasting during ageing.
Frederic Shuqing Cui
Project: Optimization of Adsorption based Thermal Battery to Im-prove Building Energy Flexibility
Host University: TU/e// Prof. Jan Hensen
Co-Host University: DTU // Prof. Menghao Qin
With the increasing share of renewable implantation in the buildings’ utility network, there is an overwhelming need to propose a novel energy storage system. Due to the heavy investment of electricity storage, thermal battery as an alternative solution is expected to respond to HVAC consumption, which accounts for more than 50% of the end user in buildings. This project aims to evaluate and optimize a previously constructed thermal battery to improve building energy flexibility. The thermal battery is based on an adsorptive machine employing novel adsorbents such as metal-organic frameworks (MOFs) and synthesized hierarchic silicates. The adsorbent reversibly adsorbs sorbate vapor and can be regenerated by waste heat from industrial processes, district heating, solar thermal, etc. Compared to commercial thermal battery based on the water tank, composites or phase change materials, systems employing novel solid adsorbents are less bulky, non-corrosive, and can use thermal sources of a lower temperature. The project will integrate the thermal battery into buildings in different climates, e.g., oceanic, northern, hot and humid, and with different types of the energy market, e.g., multiple operators (the Netherlands, Switzerland), uni-supplier (France).
Project: Single-molecule origami-plasmonic sensor for the personal continuous monitoring of biomarkers
Host University: TU/e// Prof. Menno Prins
Co-Host University: EPFL // Prof. Hatice Altug
Real-time, precise and reliable data are essential for the monitoring, treatment and coaching of pa-tients. Commercially available continuous glucose monitoring devices are key examples of biochem-ical sensors, however the underlying enzyme based electrochemical sensing principle is not suited for sensing other biomarkers such as peptides, proteins, hormones, pharmaceutical drugs, or nucleic acids. Here I propose to develop a biosensor for the personal con-tinuous monitoring of biomarkers, with focus on measuring dynamically changing markers related to the immune system. Due to the low concentrations of these immuno-biomarkers, the sensor for their continuous detection should be highly sensitive, able to monitor rare and rapid events, while being stable in biological fluids and self-contained to avoid reagent consumption or sensor component renewal. In the SensAMarker project, plasmonic nanoparticles are utilized to facilitate optical detection at single-molecule resolution.
Project: Mobiity and spatial impacts of sharing automated vehicles
Host University: TUM // Prof. Constantinos Antoniou
Co-Host University: TU/e // Prof. Soora Rasouli
Shared autonomous vehicles (SAV) hold great promise for the future transportation and will potentially change mobility in the coming years. Benefiting from both car-sharing and automation, it offers a superior option to all existing transportation modes. Both public and private transportation systems need to redefine their strategic decision making. The aim of this study is to analyze how usage of SAVs will impact individuals’ travel behavior change. Shared autonomous vehicles may profoundly change the mobility of passengers and potentially influence both public system and private transportation. Therefore, quantitative analysis methods will be used to examine how SAV system attract potential users shift from different transportation modes to SAVs. Furthermore, changing daily transportation mode may influence travel activity patterns. Our research intends to explore and expand methods and models to understand how usage of SAVs may change individuals’ daily travel activity patterns. Additionally, inter-personal interactions play roles in ones’ activity scheduling. Thus, this study will explore how usage of SAVs will impact the joint activity decision process and individuals’ travel plan.
Project: Novel halo- and hydrosilylenes for bond activation and catalysis
Host University: TUM // Prof. Shigeyoshi Inoue
Co-Host University: EPFL // Prof. Marinella Mazzanti
There has been interest in the activation of strong organic bonds using highly reactive, low-valent species of the heavy Group 14 elements (Si,Ge,Sn). Such reactions may lead to the creation of new, transition-metal-free catalysts for the functionalization of small organic molecules. Silicon, the second most abundant element in the Earth’s crust, is especially of interest due to its abundance and low-toxicity. Activation reactions using low-valent silicon species in place of transition metal complexes are of importance, as the former are more environmentally friendly and cost-effective than the latter. Although highly reduced, low-valent Group 14 species are known to facilitate the oxidative addition of small molecules, the considerable stability of the resultant oxidized products hampers further molecular transformations thus limiting the catalytic potential of such species. Accordingly, the molecular design of heavy Group 14 element-containing molecules which can undergo reversible redox processes is of importance. In this research project, I take aim at the synthesis of novel low- valent silicon species utilizing sterically bulky substituents and an intramolecular coordination. The coordination should stabilize low-oxidation state silicon which may allow for not only the activation of small-molecules, but also their subsequent release. Such a system should allow for the catalytic functionalization of strong organic bonds.
Project: Magnetic Resonance Integrated Connectivity (MR-ICon)
Host University: DTU // Prof. Tim B. Dyrby
Co-Host University: EPFL // Prof. Jean-Philippe Thiran
Magnetic Resonance Imaging (MRI) allows us to collect images of the brain. Diffusion MRI is sensitive to the displacement of water particles within the brain’s living tissue and it can be used to generate a tractogram with tractography: the 3D reconstruction of the wiring of axons within the brain. A tractogram can be used to study the connections between different brain areas: the connectivity. Connectivity is useful for a wide variety of neuroscientific studies and plays an important role in the characterization of some neurological disorders and pathological conditions. However, the processing pipeline to arrive to the calculation of the brain connectivity includes several passages and starts with the MRI acquisition. As many acquisition parameters can be specified, each influencing the way the images are collected (and the brain is viewed), different acquisitions inevitably lead to different calculations of the connectivity. The goal of this project is to study the interaction between the acquisition parameters and the connectivity, and to develop the most robust acquisition that, through modeling assumptions, guarantees a unique, integrated computation of the brain’s connectivity such that it is based on the tissue properties and minimally depends on the acquisition parameters.