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See detailAustralian Indigenous Life Writing: Analysing Discourses with Word Embedding Modelling
Kamlovskaya, Ekaterina UL

Doctoral thesis (2022)

The genre of Australian Aboriginal autobiography is a literature of significant socio-political importance, with authors sharing a history different to the one previously asserted by the European settlers ... [more ▼]

The genre of Australian Aboriginal autobiography is a literature of significant socio-political importance, with authors sharing a history different to the one previously asserted by the European settlers which ignored or misrepresented Australia's First People. While there has been a number of studies looking at the works belonging to this genre from various perspectives, Australian Indigenous life writing has never been approached from the digital humanities point of view which, given the constant development of computer technologies and growing availability of digital sources, offers humanities researchers many opportunities for exploring textual collections from various angles. With this research work I contribute to closing the above-mentioned research gap and discuss the results of the interdisciplinary research project within the scope of which I created a bibliography of published Australian Indigenous life writing works, designed and assembled a corpus and created word embedding models of this corpus which I then used to explore the discourses of identity, land, sport, and foodways, as well as gender biases present in the texts in the context of postcolonial literary studies and Australian history. Studying these discourses is crucial for gaining a better understanding of the contemporary Australian society as well as the nation's history. Word embeddings modelling has recently been used in digital humanities as an exploratory technique to complement and guide traditional close reading approaches, which is justified by their potential to identify word use patterns in a collection of texts. In this dissertation, I provide a case study of how word embedding modelling can be used to investigate humanities research questions and reflect on the issues which researchers may face while working with such models, approaching various aspects of the research project from the perspectives of digital source and tool criticism. I demonstrate how word embedding model of the analysed corpus represents discourses through relationships between word vectors that reflect the historical, political, and cultural environment of the authors and some unique experiences and perspectives related to their racial and gender identities. I show how the narrators reconstruct the analysed discourses to achieve the main goals of Australian Indigenous life writing as a genre - reclaiming identity and rewriting history. [less ▲]

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See detailMagnetic Guinier Law and Uniaxial Polarization Analysis in Small Angle Neutron Scattering
Malyeyev, Artem UL

Doctoral thesis (2022)

The present PhD thesis is devoted to the development of the use of the magnetic small-angle neutron scattering (SANS) technique for analyzing the magnetic microstructures of magnetic materials. The ... [more ▼]

The present PhD thesis is devoted to the development of the use of the magnetic small-angle neutron scattering (SANS) technique for analyzing the magnetic microstructures of magnetic materials. The emphasis is on the three aspects: (i) analytical development of the magnetic Guinier law; (ii) the application the magnetic Guinier law and of the generalized Guinier-Porod model to the analysis of experimental neutron data on various magnets such as a Nd-Fe-B nanocomposite, nanocrystalline cobalt, and Mn-Bi rare-earth-free permanent magnets; (iii) development of the theory of uniaxial neutron polarization analysis and experimental testing on a soft magnetic nanocrystalline alloy. The conventional “nonmagnetic” Guinier law represents the low-q approximation for the small-angle scattering curve from an assembly of particles. It has been derived for nonmagnetic particle-matrix-type systems and is routinely employed for the estimation of particle sizes in e.g., soft-matter physics, biology, colloidal chemistry, materials science. Here, the extension of the Guinier law is provided for magnetic SANS through the introduction of the magnetic Guinier radius, which depends on the applied magnetic field, on the magnetic interactions (exchange constant, saturation magnetization), and on the magnetic anisotropy-field radius. The latter quantity characterizes the size over which the magnetic anisotropy field is coherently aligned into the same direction. In contrast to the conventional Guinier law, the magnetic version can be applied to fully dense random-anisotropy-type ferromagnets. The range of applicability is discussed and the validity of the approach is experimentally demonstrated on a Nd-Fe-B-based ternary permanent magnet and on a nanocrystalline cobalt sample. Rare-earth-free permanent magnets in general and the Mn-Bi-based ones in particular have received a lot of attention lately due to their application potential in electronics devices and electromotors. Mn-Bi samples with three different alloy compositions were studied by means of unpolarized SANS and by very small-angle neutron scattering (VSANS). It turns out that the magnetic scattering of the Mn-Bi samples is determined by long-wavelength transversal magnetization fluctuations. The neutron data is analyzed in terms of the generalized Guinier-Porod model and the distance distribution function. The results for the so-called dimensionality parameter obtained from the Guinier-Porod model indicate that the magnetic scattering of a Mn$_{45}$Bi$_{55}$ specimen has its origin in slightly shape-anisotropic structures and the same conclusions are drawn from the distance distribution function analysis. Finally, based on Brown’s static equations of micromagnetics and the related theory of magnetic SANS, the uniaxial polarization of the scattered neutron beam of a bulk magnetic material is computed. The theoretical expressions are tested against experimental data on a soft magnetic nanocrystalline alloy, and both qualitative and quantitative correspondence is discussed. The rigorous analysis of the polarization of the scattered neutron beam establishes the framework for the emerging polarized real-space techniques such as spin-echo small-angle neutron scattering (SESANS), spin-echo modulated small-angle neutron scattering (SEMSANS), and polarized neutron dark-field contrast imaging (DFI), and opens up a new avenue for magnetic neutron data analysis on nanoscaled systems. [less ▲]

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See detailModeling and Control of Laser Wire Additive Manufacturing
Mbodj, Natago Guilé UL

Doctoral thesis (2022)

Metal Additive Manufacturing (MAM) offers many advantages such as fast product manufacturing, nearly zero material waste, prototyping of complex large parts and the automatization of the manufacturing ... [more ▼]

Metal Additive Manufacturing (MAM) offers many advantages such as fast product manufacturing, nearly zero material waste, prototyping of complex large parts and the automatization of the manufacturing process in the aerospace, automotive and other sectors. In the MAM, several parameters influence the product creation steps, making the MAM challenging. In this thesis, we modelize and control the deposition process for a type of MAM where a laser beam is used to melt a metallic wire to create the metal parts called the Laser Wire Additive Manufacturing Process (LWAM). In the dissertation, first, a novel parametric modeling approach is created. The goal of this approach is to use parametric product design features to simulate and print 3D metallic objects for the LWAM. The proposed method includes a pattern and the robot toolpath creation while considering several process requirements of LWAM, such as the deposition sequences and the robot system. This technique aims to develop adaptive robot toolpaths for a precise deposition process with nearly zero error in the product creation process. Second, a layer geometry (width and height) prediction model to improve deposition accuracy is proposed. A machine learning regression algorithm is applied to several experimental data to predict the bead geometry across layers. Furthermore, a neural network-based approach was used to study the influence of different deposition parameters, namely laser power, wire-feed rate and travel speed on bead geometry. The experimental results shows that the model has an error rate of (i.e., 2∼4%). Third, a physics-based model of the bead geometry including known process parameters and material properties was created. The model developed for the first time includes critical process parameters, the material properties and the thermal history to describe the relationship between the layer height with different process inputs (i.e., the power, the standoff distance, the temperature, the wire-feed rate and the travel speed). The numerical results show a match of the model with the experimental measurements. Finally, a Model Predictive Controller (MPC) was designed to keep the layer height trajectory constant, considering the constraints and the operating range of the parameters of the process inputs. The model simulation result shows an acceptable tracking of the reference height. [less ▲]

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See detailDeciphering the role of colorectal cancer-associated bacteria in the fibroblast-tumor cell interaction
Karta, Jessica UL

Doctoral thesis (2022)

Dysbiosis is an imbalance in the gut microbiome that is often associated with inflammation and cancer. Several microbial species, such as Fusobacterium nucleatum, have been suggested to be involved in ... [more ▼]

Dysbiosis is an imbalance in the gut microbiome that is often associated with inflammation and cancer. Several microbial species, such as Fusobacterium nucleatum, have been suggested to be involved in colorectal cancer (CRC). To date, most studies have focused on the interaction between CRC-associated bacteria and tumor cells. However, the tumor microenvironment (TME) is composed of various types of cells, among which cancer-associated fibroblasts (CAFs), one of the most vital players in the TME. The interaction between CRC-associated bacteria and CAFs and especially the impact of their cross-talk on tumor cells, remains largely unknown. In this regard, this thesis investigated the interaction between a well described and accepted CRC-associated bacteria, Fusobacterium nucleatum, and CAFs and their subsequent effects on tumor progression in CRC. Our findings show that F.nucleatum binds to CAFs and induces phenotypic changes. F.nucleatum promotes CAFs to secrete several pro-inflammatory cytokines and membrane-associated proteases. Upon exposure with F.nucleatum, CAFs also undergo metabolic rewiring with higher mitochondrial ROS and lactate secretion. Importantly, F.nucleatum-treated CAFs increase the migration ability of tumor cells in vitro through secreted cytokines, among which CXCL1. Furthermore, the co-injection of F.nucleatum-treated CAFs with tumor cells in vivo leads to a faster tumor growth as compared to the co-injection of untreated CAFs with tumor cells. Taken together, our results show that CAFs are an important player in the gut microbiome-CRC axis. Targeting the CAF-microbiome crosstalk might represent a novel therapeutic strategy for CRC. [less ▲]

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See detailNon-Orthogonal Multiple Access for Next-Generation Satellite Systems: Flexibility Exploitation and Resource Optimization
Wang, Anyue UL

Doctoral thesis (2022)

In conventional satellite communication systems, onboard resource management follows pre-design approaches with limited flexibility. On the one hand, this can simplify the satellite payload design. On the ... [more ▼]

In conventional satellite communication systems, onboard resource management follows pre-design approaches with limited flexibility. On the one hand, this can simplify the satellite payload design. On the other hand, such limited flexibility hardly fits the scenario of irregular traffic and dynamic demands in practice. As a consequence, the efficiency of resource utilization could be deteriorated, evidenced by mismatches between offered capacity and requested traffic in practical operations. To overcome this common issue, exploiting multi-dimension flexibilities and developing advanced resource management approaches are of importance for next-generation high-throughput satellites (HTS). Non-orthogonal multiple access (NOMA), as one of the promising new radio techniques for future mobile communication systems, has proved its advantages in terrestrial communication systems. Towards future satellite systems, NOMA has received considerable attention because it can enhance power-domain flexibility in resource management and achieve higher spectral efficiency than orthogonal multiple access (OMA). From ground to space, terrestrial-based NOMA schemes may not be directly applied due to distinctive features of satellite systems, e.g., channel characteristics and limited onboard capabilities, etc. To investigate the potential synergies of NOMA in satellite systems, we are motivated to enrich this line of studies in this dissertation. We aim at resolving the following questions: 1) How to optimize resource management in NOMA-enabled satellite systems and how much performance gain can NOMA bring compared to conventional schemes? 2) For complicated resource management, how to accelerate the decision-making procedure and achieve a good tradeoff between complexity reduction and performance improvement? 3) What are the mutual impacts among multiple domains of resource optimization, and how to boost the underlying synergies of NOMA and exploit flexibilities in other domains? The main contributions of the dissertation are organized in the following four chapters: First, we design an optimization framework to enable efficient resource allocation in general NOMA-enabled multi-beam satellite systems. We investigate joint optimization of power allocation, decoding orders, and terminal-timeslot assignment to improve the max-min fairness of the offered-capacity-to-requested-traffic ratio (OCTR). To solve the mixed-integer non-convex programming (MINCP) problem, we develop an optimal fast-convergence algorithmic framework and a heuristic scheme, which outperform conventional OMA in matching capacity to demand. Second, to accelerate the decision-making procedure in resource optimization, we attempt to solve optimization problems for satellite-NOMA from a machine-learning perspective and reveal the pros and cons of learning and optimization techniques. For complicated resource optimization problems in satellite-NOMA, we introduce deep neural networks (DNN) to accelerate decision making and design learning-assisted optimization schemes to jointly optimize power allocation and terminal-timeslot assignment. The proposed learning-optimization schemes achieve a good trade-off between complexity and performance. Third, from a time-domain perspective, beam hopping (BH) is promising to mitigate the capacity-demand mismatches and inter-beam interference by selectively and sequentially illuminating suited beams over timeslots. Motivated by this, we investigate the synergy and mutual influence of NOMA and BH for satellite systems to jointly exploit power- and time-domain flexibilities. We jointly optimize power allocation, beam scheduling, and terminal-timeslot assignment to minimize the capacity-demand gap. The global optimal solution may not be achieved due to the NP-hardness of the problem. We develop a bounding scheme to tightly gauge the global optimum and propose a suboptimal algorithm to enable efficient resource assignment. Numerical results demonstrate the synthetic synergy of combining NOMA and BH, and their individual performance gains compared to the benchmarks. Fourth, from the spatial domain, adaptive beam patterns can adjust the beam coverage to serve irregular traffic demand and alleviate co-channel interference, motivating us to investigate joint resource optimization for satellite systems with flexibilities in power and spatial domains. We formulate a joint optimization problem of power allocation, beam pattern selection, and terminal association, which is in the format of MINCP. To tackle the integer variables and non-convexity, we design an algorithmic framework and a low-complexity scheme based on the framework. Numerical results show the advantages of jointly optimizing NOMA and beam pattern selection compared to conventional schemes. In the end, the dissertation is concluded with the main findings and insights on future works. [less ▲]

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See detailMoral Decision-Making in Video Games
Holl, Elisabeth UL

Doctoral thesis (2022)

The present dissertation focuses on moral decision-making in single player video games. The thesis comprises four manuscripts: a theoretical book chapter (Melzer & Holl, 2021), a qualitative focus group ... [more ▼]

The present dissertation focuses on moral decision-making in single player video games. The thesis comprises four manuscripts: a theoretical book chapter (Melzer & Holl, 2021), a qualitative focus group study (Holl et al., 2020), a quantitative case study on the video game Detroit: Become Human (Holl & Melzer, 2021), and results from a large experimental laboratory study (Holl et al., 2022). With more than 2.6 billion players worldwide (Entertainment Software Association, 2018) gaming has become increasingly present in society. In addition to this growing interest, technological advances allow for more complex narratives and deeper character design. Thus, meaningful and morally-laden storylines have become increasingly popular in recent years both in popular AAA (e.g., Detroit: Become Human, The Last of Us 2) and smaller Indie titles (e.g., Papers please, Undertale). At the same time, scholars suggested that not only hedonic but also eudaimonic experiences are an essential part of (gaming) entertainment (Daneels, Bowman, et al., 2021; Oliver et al., 2015; Wirth et al., 2012). This dissertation explores in greater detail one aspect of eudaimonic gameplay, namely single player games that feature meaningful moral decision-making. Prior research on morality and gaming has relied on a variety of theoretical concepts, such as moral disengagement (Bandura, 1990; Klimmt et al., 2008) or moral foundations and intuitions (Haidt, 2001; Haidt & Joseph, 2007; Tamborini, 2013). Thus, the first task of the dissertation was to establish a previously missing model of moral processing in video games the unifies existing theories (cf. chapter 5.13; Melzer & Holl, 2021). Furthermore, the model proposes factors (e.g., moral disengagement cues, limited cognitive capacities/time pressure) promoting or hampering moral engagement while playing, thus fostering moral versus strategic processing. The model not only integrates relevant theoretical publications but was also designed using data collected in focus groups with frequent gamers (Holl et al., 2020). These qualitative results showed that moral gameplay is not a niche anymore. Furthermore, players expressed they deliberately chose between hedonic and eudaimonic gaming depending on their mood and motivation. Lastly, players mentioned several factors influencing their emotional and moral engagement while playing (e.g., identification, framing). To test parts of the proposed theoretical model, the game Detroit: Become Human, which has been praised for its emotional storytelling and meaningful choices (Pallavicini et al., 2020), was investigated in a case study (Holl & Melzer, 2021). Extensive coding of large-scale online data revealed that 73% of in-game decisions in Detroit: Become Human were morally relevant with a high prevalence for situations relating to harm/care- and authority-based morality. Overall, players preferred to choose moral options over immoral options. This tendency to act “good” was even more pronounced under time pressure and when non-human characters were involved. Furthermore, behavioral variations were found depending on what character was played. To test findings of the case study in greater detail and to also gather individual data in an experimental setup, Holl et al. (2022) conducted a laboratory study. A total of 101 participants played several chapters of Detroit: Become Human featuring up to 13 moral decisions after being randomly assigned to one of three conditions (i.e., playing a morally vs. immorally framed character vs. no framing/control). As expected, players again preferred to act morally sound. Contrary to expectations, character framing did not affect decision-making or physiological responses (i.e., heart rate variability). However, time pressure again increased the likelihood of moral decision-making. Unfortunately, anticipated effects of personality traits (i.e., trait moral disengagement, empathy) were inconclusive both regarding the outcome of decision-making and participants’ perceived guilt after playing. In summary, the work of this dissertation further underlines the relevance of eudaimonic entertainment. Studying moral decision-making in games may provide insights for moral decision-making in general. Additionally, the presented results have the potential to defuse the heated debate over violent gaming. Novel insights are gained using a mixed methods approach combining qualitative with quantitative data from a large-scale case study of worldwide user behavior and an experimental setup. [less ▲]

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See detailmmWave Cognitive Radar: Adaptive Waveform Design and Implementation
Raei, Ehsan UL

Doctoral thesis (2022)

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See detailMALDI-TOF-Enabled Subtyping and Antimicrobial Screening of the Food- and Waterborne Pathogen Campylobacter
Feucherolles, Maureen UL

Doctoral thesis (2022)

For decades, antimicrobial resistance has been considered as a global long-lasting challenge. If no action is taken, antimicrobial resistance-related diseases could give a rise up to 10 million deaths ... [more ▼]

For decades, antimicrobial resistance has been considered as a global long-lasting challenge. If no action is taken, antimicrobial resistance-related diseases could give a rise up to 10 million deaths each year by 2050 and 24 million people might end into extreme poverty. The ever-increasing spread and cross-transmission of drug-resistant foodborne pathogens such as Campylobacter spp. between reservoirs, such as human, animal and environment are of concern. Indeed, because of the over-exposition and overuse of antibiotics in food-producing animals, the latter could carry multidrug resistant Campylobacter that could be transmitted to humans via food sources or from direct animal contacts. One of the solutions to tackle antimicrobial resistances is the development of rapid diagnostics tests to swiftly detect resistances in routine laboratories. By detecting earlier AMR, adapted antibiotherapy might be administrated promptly shifting from empirical to evidence-based practices, conserving effectiveness of antimicrobials. The already implemented cost- and time-efficient MALDI-TOF MS in routine laboratories for the identification of microorganisms based on expressed protein profiles was successfully applied for bacterial typing and detection of specific AMR peak in a research context. In the line of developing rapid tests for diagnostics, MALDI-TOF MS appeared to be an ideal candidate for a powerful and promising “One fits-all” diagnostics tool. Therefore, the present study aimed to get more insights on the ability of MALDI-TOF MS-protein based signal to reflect the AMR and genetic diversity of Campylobacter spp. The groundwork of this research consisted into the phenotypic and genotypic characterization of a One-Health Campylobacter collection. Then, isolates were submitted to protein extraction for MALDI-TOF MS analysis. Firstly, mass spectra were investigated to screen AMR to different classes of antibiotics and to retrieve putative biomarkers related to already known AMR mechanisms. The second part evaluated the ability of MALDI-TOF MS to cluster mass spectra according to the genetic relatedness of isolates and congruently compare it to reference genomic-based methods. MALDI-TOF MS protein profiles combined to machine learning displayed promising results for the prediction of the susceptibility and the ciprofloxacin and tetracycline Campylobacter’s resistances. Additionally, MALDI-TOF MS C. jejuni protein clusters were highly concordant to conventional DNA-based typing methods, such as MLST and cgMLST, when a similarity cut-off of 94% was applied. A similar discriminatory power between 2-20 kDa expressed protein and cgMLST profiles was underlined as well. Finally, putative biomarkers either linked to known or unknown AMR mechanisms, or genetic structural population of Campylobacter were identified. Overall, a single spectrum based on bacterial expressed protein could be used for species identification, AMR screening and potentially as a complete pre-screening for daily surveillance, including genetic diversity and source attribution after further analysis. [less ▲]

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See detailThe Interpretation of UN Security Council Resolutions
di Gianfrancesco, Laura UL

Doctoral thesis (2022)

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See detailIs universal healthcare truly universal? Socioeconomic and migrant inequalities in healthcare
Paccoud, Ivana UL

Doctoral thesis (2022)

Through the principle of Universal Healthcare Coverage, many governments across Europe and beyond seek to ensure that all people have equal access to good quality healthcare services, without facing a ... [more ▼]

Through the principle of Universal Healthcare Coverage, many governments across Europe and beyond seek to ensure that all people have equal access to good quality healthcare services, without facing a financial burden. Despite this, studies have highlighted persistent migrant and socio-economic inequalities in the use of healthcare services, and personal health records. Therefore, understanding the complex mechanisms that produce and maintain social inequalities in the effective use of healthcare services is thus an important step towards advancing equity in healthcare. This thesis draws on Bourdieu's forms of capital (cultural, social, economic, and symbolic) to conceptualise and empirically test social inequalities related to healthcare. In doing so, it investigates the factors contributing to socioeconomic and migrant inequalities in the use, navigation and optimisation of healthcare services as well as personal health records. The three studies that make up this thesis empirically test these ideas through statistical modelling on population-based datasets as well as through the analysis of two cross-sectional surveys in Luxembourg and the Greater region. The first study draws on the fifth wave of the Survey of Health, Aging, and Retirement in Europe (SHARE). It used cluster analysis and regression models to explain how the unequal distribution of material and non-material capitals acquired in childhood shape health practices, leading to different levels of healthcare utilisation in later life. The results suggest that although related, both material and non-material capitals independently contribute to health practices associated with the use of healthcare services. The second study used data from a cross-sectional survey to investigate inequalities in the navigation and optimisation of healthcare services, taking into consideration the interplay between perceived racial discrimination and socioeconomic position. It revealed disparities between individuals born in Eastern Europe and the Global South and those born in Luxembourg which were explained by the experience of racial discrimination. It also found that the impact of discrimination on both health service navigation and optimisation was reduced after accounting for social capital. The last study used data from a cross-sectional survey developed as a part of a collaborative project (INTERREG-APPS) to examine the socioeconomic and behavioural determinants in the intention to use personal health record in the Greater region of Luxembourg (Baumann et al., 2020). This study found that people’s desire and actual access to personal health electronic records is determined by different socioeconomic factors, while educational inequalities in the intention to regularly use personal health records were explained by the role of behavioural factors. Taking together, the findings presented in this thesis thus show the value of mobilising Bourdieu’s theoretical framework to understand the mechanisms through which social inequalities in healthcare develop. In addition, it showed the importance of considering racial discrimination when examining migrant, and racial/ethnic differences in health. [less ▲]

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See detailFirst-principles investigation of ferroelectricity and related properties of HfO2
Dutta, Sangita UL

Doctoral thesis (2022)

Nonvolatile memories are in increasing demand as the world moves toward information digitization. The ferroelectric materials offer a promising alternative for this. Since the existing perovskite ... [more ▼]

Nonvolatile memories are in increasing demand as the world moves toward information digitization. The ferroelectric materials offer a promising alternative for this. Since the existing perovskite materials have various flaws, including incompatibility with complementary metal-oxide-semiconductor processes in memory applications, the discovery of new optimized FE thin films was necessary. In 2011, the disclosure of ferroelectricity in hafnia (HfO$_2$) reignited interest in ferroelectric memory devices because this material is well integrated with CMOS technology. Although the reporting of ferroelectricity in HfO$_2$ has been a decade, researchers are still enthralled by this material's properties as well as its possible applications. The ferroelectricity in HfO$_{2}$ has been attributed to the orthorhombic phase with spacegroup $Pca2_1$. This phase is believed to be the metastable phase of the system. Many experimental and theoretical research groups joined the effort to understand the root causes for the stability of this ferroelectric phase of HfO$_{2}$ by considering the role of the surface energy effects, chemical dopants, local strain, oxygen vacancies. However, the understanding was not conclusive. In this part of this work, we will present our first-principles results, predicting a situation where the ferroelectric phase becomes the thermodynamic ground state in the presence of a ordered dopant forming layers. Since the main focus was on understanding and optimizing the ferroelectricity in HfO$_{2}$, we observed that the electro-mechanical response of the system has garnered comparatively less attention. The recent discovery of the negative longitudinal piezoelectric effect in HfO$_2$ has challenged our thinking about piezoelectricity, which was molded by what we know about ferroelectric perovskites. In this work, we will discuss the atomistic underpinnings behind the negative longitudianl piezoelectric effect in HfO$_{2}$. We will also discuss the behavior of the longitudinal piezoelectric coefficient ($e_{33}$) under the application of epitaxial strain, where we find that $e_{33}$ changes sign even though the polarization does not switch. Aside from a basic understanding of piezoelectric characteristics in HfO$_2$, the application aspect is also worth considering. The piezoelectric properties of the material can be tuned to meet the needs of the applications. In this work, we will describe our findings on how the piezoelectric characteristics of the material change as a function of isovalent dopants. [less ▲]

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See detailFRACTAL DIMENSION AND POINT-WISE PROPERTIES OF TRAJECTORIES OF FRACTIONAL PROCESSES
Daw, Lara UL

Doctoral thesis (2022)

The topics of this thesis lie at the interference of probability theory with dimensional and harmonic analysis, accentuating the geometric properties of random paths of Gaussian and non-Gaussian ... [more ▼]

The topics of this thesis lie at the interference of probability theory with dimensional and harmonic analysis, accentuating the geometric properties of random paths of Gaussian and non-Gaussian stochastic processes. Such line of research has been rapidly growing in past years, paying off clear local and global properties for random paths associated to various stochastic processes such as Brownian and fractional Brownian motion. In this thesis, we start by studying the level sets associated to fractional Brownian motion using the macroscopic Hausdorff dimension. Then as a preliminary step, we establish some technical points regarding the distribution of the Rosenblatt process for the purpose of studying various geometric properties of its random paths. First, we obtain results concerning the Hausdorff (both classical and macroscopic), packing and intermediate dimensions, and the logarithmic and pixel densities of the image, level and sojourn time sets associated with sample paths of the Rosenblatt process. Second, we study the pointwise regularity of the generalized Rosenblatt and prove the existence of three kinds of local behavior: slow, ordinary and rapid points. In the last chapter, we illustrate several methods to estimate the macroscopic Hausdorff dimension, which played a key role in our results. In particular, we build the potential theoretical methods. Then, relying on this, we show that the macroscopic Hausdorff dimension of the projection of a set E ⊂ R^2 onto almost all straight lines passing through the origin in R^2 depends only on E, that is, they are almost surely independent of the choice of straight line. [less ▲]

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See detailINTERROGATING INTRA-TUMORAL HETEROGENEITY AND TREATMENT RESISTANCE IN GLIOBLASTOMA PATIENT-DERIVED XENOGRAFT MODELS USING SINGLE-CELL RNA SEQUENCING
Yabo, Yahaya Abubakar UL

Doctoral thesis (2022)

Despite available treatment options for glioblastoma (GBM), GBM has one of the poorest prognosis, resist treatment, and recur aggressively in the majority of cases. Intra-tumoral heterogeneity and ... [more ▼]

Despite available treatment options for glioblastoma (GBM), GBM has one of the poorest prognosis, resist treatment, and recur aggressively in the majority of cases. Intra-tumoral heterogeneity and phenotypic plasticity are major factors contributing to treatment resistance and underlie tumor escape in GBM. Several potential therapeutic agents showing promising therapeutic effects against GBMs at the preclinical level failed to translate into effective therapies for GBM patients. This is partly attributed to the inadequacy of preclinical models to fully recapitulate the complex biology of human GBMs. This project aimed to characterize the transcriptomic heterogeneity and understand the dynamic GBM ecosystem in patient-derived xenograft (PDOX) models at the single-cell level. To achieve this aim, I established cell purification and cryopreservation protocols that enable the generation of high-quality single-cell RNA seq data from PDOX models including longitudinal and treated PDOXs. Different computational strategies were used to interrogate the transcriptomic features as well as the interactions between GBM cells and the surrounding microenvironment. This work critically analyzed and discussed key components contributing to intra-tumoral heterogeneity and phenotypic plasticity within the GBM ecosystem and their potential contributions to treatment resistance. Here, we provide evidence that PDOX models retain histopathologic and transcriptomic features of parental human GBMs. PDOX models were further shown to recapitulate major tumor microenvironment (TME) components identified in human GBMs. Cells within the GBM ecosystem were shown to display GBM-specific transcriptomic features, indicating active TME crosstalk in PDOX models. Tumor-associated microglia/macrophages were shown to be heterogeneous and display the most prominent transcriptomic adaptations following crosstalk with GBM cells. The myeloid cells in PDOXs and human GBM displayed a microglia-derived TAMs signature. Notably, GBM-educated microglia display immunologic features of migration, phagocytosis, and antigen presentation that indicates the functional role of microglia in the GBM TME. Taking advantage of a cohort of longitudinal PDOXs and treated PDOX models, I demonstrated the utility of PDOX models in elucidating longitudinal changes in GBM. We show that temozolomide treatment leads to transcriptomic adaptation of not only the GBM tumor cells but also adjacent TME components. Overall, this work further highlights the importance and the clinical relevance of PDOX models for the testing of novel therapeutics including immunotherapies targeting certain tumor TME components in GBM. [less ▲]

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See detailNext Generation Mutation Testing: Continuous, Predictive, and ML-enabled
Ma, Wei UL

Doctoral thesis (2022)

Software has been an essential part of human life, and it substantially improves production and enriches our life. However, flaws in software can lead to tragedies, e.g. the failure of the Mariner 1 ... [more ▼]

Software has been an essential part of human life, and it substantially improves production and enriches our life. However, flaws in software can lead to tragedies, e.g. the failure of the Mariner 1 Spacecraft in 1962. At the moment, modern software systems are much different from what before. The issue gets even more severe since the complexity of software systems grows larger than before and Artificial Intelligence(AI) models are integrated into software (e.g., Tesla Deaths Report ). Testing such modern artificial software systems is challenging. Due to new requirements, software systems evolve and frequently change, and artificial intelligence(AI) models have non-determination issues. The non-determination of AI models is related to many factors, e.g., optimization algorithms, numerical problems, the labelling threshold, data of the same object but under different collecting conditions or changing the backend libraries. We have witnessed many new testing techniques emerge to guarantee the trustworthiness of modern software systems. Coverage-based Testing is one early technique to test Deep Learning(DL) systems by analyzing the neuron values statistically, e.g., Neuron Coverage(NC) . In recent years, Mutation Testing has drawn much attention. Coverage-based testing metrics can be misleading and easily be fooled by generating tests to satisfy test coverage requirements just by executing the code line. The test suite with one hundred percent coverage may detect no flaw in software. On the contrary, Mutation Testing is a robust approach to approximating the quality of a test suite. Mutation Testing is a technique based on detecting artificial defects from many crafted code perturbations (i.e., mutant) to assess and improve the quality of a test suite. The behaviour of a mutant is likely to be located on the border between correctness and non-correctness since the code perturbation is usually tiny. Through mutation testing, the border behaviour of the subject under test can be explored well, which leads to a high quality of software. It has been generalized to test software systems integrated with DL systems, e.g., image classification systems and autonomous driving systems. However, the application of Mutation Testing encounters some obstacles. One main challenge is that Mutation Testing is resource-intensive. Large resource consumption makes it unskilled in modern software development because the code frequently evolves every day. This dissertation studies how to apply Mutation Testing for modern software systems, exploring and exploiting the usages and innovations of Mutation Testing encountering AI algorithms, i.e., how to employ Mutation Testing for modern software systems under test. AI algorithms can improve Mutation Testing for modern software systems, and at the same time, Mutation Testing can test modern software integrated with DL models well. First, this dissertation adapts Mutation Testing to modern software development, Continuous Integration. Most software development teams currently employ Continuous Integration(CI) as the pipeline where the changes happen frequently. It is problematic to adopt Mutation Testing in Continuous Integration because of its expensive cost. At the same time, traditional Mutation Testing is not a good test metric for code changes as it is designed for the whole software. We adapt Mutation Testing to test these program changes by proposing commit-relevant mutants. This type of mutant affects the changed program behaviours and represents the commit-relevant test requirements. We use the benchmarks from C and Java to validate our proposal. The experiment results indicate that commit-relevant mutants can effectively enhance code change testing. Second, based on the aforementioned work, we introduce MuDelta, an AI approach that identifies commit-relevant mutants, i.e., some mutants that interact with the code change. MuDelta uses manually-designed features that require expert knowledge. MuDelta leverages a combined scheme of static code characteristics as the data feature. Our evaluation results indicate that commit-based mutation testing is suitable and promising for evolving software systems. Third, this dissertation proposes a new approach GraphCode2Vec to learn the general software code representation. Recent works utilize natural language models to embed the code into the vector representation. Code embedding is a keystone in the application of machine learning on several Software Engineering (SE) tasks. Its target is to extract universal features automatically. GraphCode2Vec considers program syntax and semantics simultaneously by combining code analysis and Graph Neural Networks(GNN). We evaluate our approach in the mutation testing task and three other tasks (method name prediction, solution classification, and overfitted patch classification). GraphCode2Vec is better or comparable to the state-of-the-art code embedding models. We also perform an ablation study and probing analysis to give insights into GraphCode2Vec. Finally, this dissertation studies Mutation Testing to select test data for deep learning systems. Since deep learning systems play an essential role in different fields, the safety of DL systems takes centre stage. Such DL systems are much different from traditional software systems, and the existed testing techniques are not supportive of guaranteeing the reliability of the deep learning systems. It is well-known that DL systems usually require extensive data for learning. It is significant to select data for training and testing DL systems. A good dataset can help DL models have a good performance. There are several metrics to guide choosing data to test DL systems. We compare a set of test selection metrics for DL systems. Our results show that uncertainty-based metrics are competent in identifying misclassified data. These metrics also improve classification accuracy faster when retraining DL systems. In summary, this dissertation shows the usage of Mutation Testing in the artificial intelligence era. The first, second and third contributions are on Mutation Testing helping modern software test in CI. The fourth contribution is a study on selecting training and testing data for DL systems. Mutation Testing is an excellent technique for testing modern software systems. At the same time, AI algorithms can alleviate the main challenges of Mutation Testing in practice by reducing the resource cost. [less ▲]

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See detailLengths and intersections of curves on surfaces
Vo, Thi Hanh UL

Doctoral thesis (2022)

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See detailMachine Learning-Based Efficient Resource Scheduling for Future Wireless Communication Networks
Yuan, Yaxiong UL

Doctoral thesis (2022)

The next-generation mobile communication system, e.g., 6G communication system, is envisioned to support unprecedented performance requirements such as exponentially increasing data requests ... [more ▼]

The next-generation mobile communication system, e.g., 6G communication system, is envisioned to support unprecedented performance requirements such as exponentially increasing data requests, heterogeneous service demands, and massive connectivity. When these challenging tasks meet the scarcity of wireless resources, efficient resource management becomes crucial. Conventionally, optimization algorithms, either optimal or suboptimal, are the main approaches for solving resource allocation problems. However, the efficiency of these iterative optimization algorithms can significantly degrade when the problems become large or difficult, e.g., non-convex or combinatorial optimization problems. Over the past few years, machine learning (ML), as an emerging approach in the toolbox, is widely investigated to accelerate the decision-making process. Since applying ML-based approaches to solve complex resource management problems is in its early-stage study, many open issues and challenges need to be solved towards the maturity and practical applications. The motivation and objective of this dissertation lie at investigating and providing answers to the following research questions: 1) How to overcome the shortcomings of extensively adopted end-to-end learning in addressing resource management problems, and which types of features are suited to be learned if supervised learning is applied? 2) What are the limitations and benefits when widely-used deep reinforcement learning (DRL) approaches are used to address constrained and combinatorial optimization problems in wireless networks, and are there tailored solutions to overcome the inherent drawbacks? 3) How to enable ML-based approaches to timely adapt to dynamic and complex wireless environments? 4) How to enlarge the performance gains when the paradigm shifts from centralized learning to distributed learning? The main contributions are organized by the following four research works. Firstly, from a supervised-learning perspective, we address common issues, e.g., unsatisfactory pre- diction performance and resultant infeasible solutions, when end-to-end learning approaches are applied to resource scheduling problems. Based on the analysis of optimal results, we design suited-to-learn features for a class of resource scheduling problems, and develop combined learning-and-optimization approaches to enable time-efficient and energy-efficient resource scheduling in multi-antenna systems. The original optimization problems are mixed-integer programming problems with high-dimensional decision vectors. The optimal solution requires exponential complexity due to the inherent difficulties of the problems. Towards an efficient and competitive solution, we apply fully-connected deep neural network (DNN) and convolutional neural network (CNN) to learn the designed features. The predicted information can effectively reduce the large search space and accelerate the optimization process. Compared to the conventional optimization and pure ML algorithms, the proposed method achieves a good trade-off between quality and complexity. Secondly, we address typical issues when DRL is adopted to deal with combinatorial and non-convex scheduling problems. The original problem is to provide energy-saving solutions via resource scheduling in energy-constrained networks. An optimal algorithm and a golden section search suboptimal approach are developed to serve as offline benchmarks. For online operations, we propose an actor-critic-based deep stochastic online scheduling (AC-DSOS) algorithm. Compared to supervised learning, DRL is suitable for dynamic environments and capable of making decisions based on the current state without an offline training phase. However, for the specific constrained scheduling problem, conventional DRL may not be able to handle two major issues of exponentially-increased action space and infeasible actions. The proposed AC-DSOS is developed to overcome these drawbacks. In simulations, AC-DSOS is able to provide feasible solutions and save around more energy compared to the conventional DRL algorithms. Compared to the offline benchmarks, AC-DSOS reduces the computational time from second-level to millisecond-level. Thirdly, the dissertation pays attention to the performance of the ML-based approaches in highly dynamic and complex environments. Most of the ML models are trained by the collected data or the observed environments. They may not be able to timely respond to the large variations of environments, such as dramatically fluctuating channel states or bursty data demands. In this work, we develop ML-based approaches in a time-varying satellite-terrestrial network and address two practical issues. The first is how to efficiently schedule resources to serve the massive number of connected users, such that more data and users can be delivered/served. The second is how to make the algorithmic solution more resilient in adapting to the time-varying wireless environments. We propose an enhanced meta-critic learning (EMCL) algorithm, combining a DRL model with a meta-learning technique, where the meta-learning can acquire meta-knowledge from different tasks and fast adapt to the new task. The results demonstrate EMCL’s effectiveness and fast-response capabilities in over-loaded systems and in adapting to dynamic environments compare to previous actor-critic and meta-learning methods. Fourthly, the dissertation focuses on reducing the energy consumption for federated learning (FL), in mobile edge computing. The power supply and computation capabilities are typically limited in edge devices, thus energy becomes a critical issue in FL. We propose a joint sparsification and resource optimization scheme (JSRO) to jointly reduce computational and transmission energy. In the first part of JSRO, we introduce sparsity and adopt sparse or binary neural networks (SNN or BNN) as the learning model to complete the local training tasks at the devices. Compared to fully-connected DNN, the computational operations can be significantly reduced, and thus requires less energy consumption and fewer transmitted data to the central node. In the second part, we develop an efficient scheduling scheme to minimize the overall transmission energy by optimizing wireless resources and learning parameters. We develop an enhanced FL algorithm in JSRO, i.e., non-smoothness and constraints - stochastic gradient descent, to handle the non-smoothness and constraints of SNN and BNN, and provide guarantees for convergence. Finally, we conclude the thesis with the main findings and insights on future research directions. [less ▲]

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See detailINVESTIGATING NEUROINFLAMMATION IN SPORADIC AND LRRK2-ASSOCIATED PARKINSON'S DISEASE
Badanjak, Katja UL

Doctoral thesis (2022)

Inflammatory responses are evolutionarily conserved reactions to pathogens, injury, or any form of a serious perturbation of a human organism. These mechanisms evolved together with us and, although ... [more ▼]

Inflammatory responses are evolutionarily conserved reactions to pathogens, injury, or any form of a serious perturbation of a human organism. These mechanisms evolved together with us and, although capable of somewhat adapting, innate responses are gravely impacted by prolonged human lifespan. Better sanitary measures, health systems, food and medicine supply have prolonged human life expectancy to ~72 years. Aging is characterized by prolonged, chronic (often low-grade) inflammation. With tissue and cellular defense mechanisms becoming dysfunctional over time, this inflammation becomes detrimental and destructive to the human body. Aging is a major risk factor for Parkinson’s disease (PD), a movement disorder characterized by the loss of dopaminergic neurons. Even though the disease is predominantly idiopathic, genetic cases are contributing to a better understanding of the underlying cellular and neuropathological mechanisms. In comparison to neuronal demise, the contribution of microglia (the immune cells of the brain) to PD is relatively understudied. Initially studied in PD patient-derived post-mortem tissue, novel in vitro technologies, such as induced pluripotent stem cells (iPSCs), are permitting the generation of specific cell types of interest in order to study disease mechanisms. We derived microglia cells from iPSCs of patients and healthy or isogenic controls to explore (shared) pathological immune responses in LRRK2-PD and idiopathic PD. Our findings suggest a significant involvement of microglia cells in the pathogenesis of PD and highlight potential therapeutic targets in alleviating overactive immune responses. [less ▲]

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See detailUnderstaining and explaining cross-border mobility : a free will / predisposition approach
Nonnenmacher, Lucas UL

Doctoral thesis (2022)

This dissertation investigates the drivers of cross-border mobility from a multidisciplinary perspective. Both qualitative and quantitative methodologies are used in order to understand and explain why ... [more ▼]

This dissertation investigates the drivers of cross-border mobility from a multidisciplinary perspective. Both qualitative and quantitative methodologies are used in order to understand and explain why workers cross borders. The major contribution of this dissertation is to highlight new determinants of cross-border mobility such as previous migration experience and health state. These drivers have been disregarded in the literature in the past. Moreover, this dissertation validates the motivations of the workers as a relevant driver of cross-border mobility and provides a state of play of the situation of the cross-border workers in Europe, with a specific focus on French cross- border workers. Firstly, this dissertation provides a review of the explanations of cross- border mobility in the existing literature. Secondly, this dissertation analyses the subjective drivers of cross-border mobility using a qualitative dataset composed of 30 interviews of French workers in Luxembourg collected between January 2018 and May 2019. Results highlight that cross-border workers motivate their decision to commute abroad with financial, professional and personal reasons. Furthermore, the motivations of the cross-border workers vary with respect to their socioeconomic profile. Based on these empirical findings, a model of cross-border labour supply was designed. Thirdly, this dissertation assesses the association between migration capital and cross-border mobility using the French part of the European Labour Force Survey called the Enquête Emploi between 2010 and 2018. Results indicate that migrants commute abroad more than non migrants and are also more likely to do so. Migrant children are more likely to commute abroad, suggesting that the capacity to deal with distance and borders can be transmitted throughout generations. The migration capital is a relevant predictor of commuting behaviour, since the higher the capital endowment, the higher the likelihood is to commute abroad. Additional findings can be mentioned. Internal migration does not increase the likelihood to commute abroad. The acquired migration experience is more useful than the inherited migration experience to be engaged in cross-border mobility. Fourthly, this dissertation examines health disparities between cross-border workers and non cross-border workers using the Enquête Emploi between 2013 and 2018. Results 6 suggest a healthy cross-border phenomenon, the existence of major health disparities among cross-border workers and the rejection of the spillover phenomenon for this specific population. Finally, this dissertation concludes that cross-border mobility is a complex phenomenon still partially explained, probably because of the lack of harmonised dataset about cross-border workers within the EU. Further research on cross- border mobility is needed to better understand this population, especially in public health, where everything remains to be done. [less ▲]

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See detailCharacterization of the surface properties of polycrystalline Cu(In,Ga)Se2 using a combination of scanning probe microscopy and X-ray photoelectron spectroscopy
Kameni Boumenou, Christian UL

Doctoral thesis (2022)

Polycrystalline Cu(In,Ga)Se2 (CIGSe) exhibit excellent properties for high power conversion efficiency (PCE) thin film solar cells. In recent years, photovoltaic cells made from CIGSe reached a PCE of 23 ... [more ▼]

Polycrystalline Cu(In,Ga)Se2 (CIGSe) exhibit excellent properties for high power conversion efficiency (PCE) thin film solar cells. In recent years, photovoltaic cells made from CIGSe reached a PCE of 23.4\%, surpassing that of multicrystalline silicon photovoltaic cells. Nevertheless, the changes in surface composition and electronic properties of the absorbers after various solution-based surface treatments are still under intensive investigation and are widely discussed in the literature. In this thesis, the front, the rear surface properties as well as the impact of post-deposition treatments (PDT) on CIGSe absorbers with different elemental compositions were analyzed by scanning tunneling microscopy and spectroscopy, Kelvin probe force microscopy, and X-ray photoelectron spectroscopy. I show that potassium cyanide (KCN) etching reduces the Cu content at the surface of Cu-rich absorbers substantially. The reduction of the Cu-content is accompanied with the formation of a large number of defects at the surface. Scanning tunneling spectroscopy measurements showed that most of these defects could be passivated with Cd ions. A semiconducting surface and no changes in the density of states were measured across the grain boundaries. In addition to the defect passivation an increase in surface band bending due to the substitution of Cu vacancies by Cd ions, which act as shallow donor defects was observed. As in the case of the front surface, the analyses carried out on the back surface of Cu-rich absorbers showed that a detrimental CuxSe secondary phase was also formed at the interface between the MoSe2 layer and CISe absorber after growth. This CuxSe secondary phase at the back contact was not present in Cu-poor absorbers. Regarding the alkali metal post-treated absorbers, I show that the occurrence of an enlarged surface bandgap, often reported on CIGSe absorbers after PDT treatment is only present after H2O rinsing. After ammonia (NH4OH) washing, which is always applied before buffer layer deposition, all the high bandgap precipitates disappeared and an increased amount of an ordered vacancy compound was observed. The thesis thereby gives a comprehensive overview of the CIGSe surfaces after various chemical and post deposition treatments. [less ▲]

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See detailWhen does finance win? A set-theoretic analysis of the conditions of European financial interest groups' lobbying success on post-crisis bank capital requirements
Commain, Sébastien Romain Jean-Louis UL

Doctoral thesis (2022)

Acknowledging the failure of the existing regulatory framework after the global financial crisis of 2008, world leaders vowed to reform financial regulation to strengthen stability and restore trust. The ... [more ▼]

Acknowledging the failure of the existing regulatory framework after the global financial crisis of 2008, world leaders vowed to reform financial regulation to strengthen stability and restore trust. The reform of bank capital requirements was a major item on this agenda: the Group of Twenty (G20) entrusted the reform to the Basel Committee on Bank Supervision (BCBS), whose so-called "Basel framework" constitutes the global standard for the prudential regulation of banking activities. While scholars have highlighted the important concessions that were made to financial interests in this reform, a series of demanding new policy tools—which were strongly opposed by financial industry representatives—were also introduced into the new Basel III framework. This dissertation explores this empirical puzzle and seeks to identify under what conditions European financial interests’ lobbying on the reform of capital requirements was successful, and whether these successes constitute cases of interest group influence. Defining influence as a situation where a proposed reform evolves during the decision-making process (policy shift) in the direction advocated by an actor (lobbying success) and where that evolution is caused by the actors’ lobbying activity vis-à-vis the proposed reform (causal path), this dissertation then considers influence as a multilevel concept, which can be considered present if and only if all three of its components—policy shift, lobbying success and a causal path—are also present. In other words, policy shift, lobbying success and causal paths are the three individually necessary and jointly sufficient conditions for influence, which this study investigates in turn in the case of post-crisis bank capital requirements. The presence or absence of a policy shift is assessed qualitatively by comparing, for twenty-nine policy issues contained in the Basel III framework, the initial BCBS reform proposals with the rules finally enacted at international and European level. The positions of financial and non-financial interest groups on each of these twenty-nine issues are then determined—through a quantitative text analysis of the position papers submitted by interest groups to BCBS and European Commission consultations on Basel III and the CRD and CRR—to determine whether the identified policy shift on a given issue constitutes a case of lobbying success for the interest group. Finally, using fuzzy-set Qualitative Comparative Analysis (fsQCA) to compare in a systematic manner cases in which success is observed and cases where it is absent, I uncover the configurations of conditions sufficient to produce successful lobbying and those sufficient to produce the absence of success, configurations which I then interpret in terms of causal mechanisms. Strong collective action is found, in several forms, to form the basis of causal mechanisms producing successful lobbying. The observed sufficient configurations of conditions however suggest that the causal mechanisms producing success also include key contextual factors that are beyong the control of financial interest groups. The absence of these enabling contextual factors is shown, conversely, to lead to the absence of success. This dissertation contributes to the existing academic literature in several ways. Empirically, first, it adds to the scholarship on bank capital requirements at the international and European level, using novel data to reassess, after the completion of the Basel III reform, the extent to which the final framework meets the initial ambitions. Methodologically, second, this dissertation employs a range of new methods and techniques to take on the challenges of measuring lobbying success and identifying multiple pathways to influence, two fundamental issues for empirical studies of interest group influence. Theoretically, third, the combinatorial approach used here to explore conditions of lobbying success permits an examination of multiple conjunctural causation patterns in interest group influence. [less ▲]

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See detailInvestigation in reusable composite flooring systems in steel and concrete based on composite behaviour by friction
Fodor, Jovan UL

Doctoral thesis (2022)

The steel-concrete composite systems proved to be very efficient structural solution in terms of material consumption and mechanical response regarding the construction of the structural floor systems ... [more ▼]

The steel-concrete composite systems proved to be very efficient structural solution in terms of material consumption and mechanical response regarding the construction of the structural floor systems whether in the case of industrial and residential buildings or especially in the case of car parks. However, their contemporary application that relies on the utilization of the welded headed studs as a mean to provide the shear connection between the steel section and the concrete chord renders the system unable to be disassembled (in the best case its steel and concrete parts are recycled). Considering the ongoing push from the linear to circular economical models and the application of 3R principle (Reduce, Reuse & Recycle) such systems are unable to furtherly improve their environmental and economic efficiency through reuse schemes. The profound task in this research is development and the verification of the new demountable shear connector solutions that could allow modularity and demountability (hence reusability) of the steel-concrete composite floor systems while retaining their inherent structural advantages. Based on the previous investigations of the demountable shear connector systems (at the first-place bolted solutions) and investigations of mechanical components that were not strictly related to the shear connectors, four demountable shear connector devices were developed. Having in mind the drawbacks of the earlier solutions, adequate detailing and structural measures were applied and the ease of assembly and disassembly was proved on the constructed prototypes. Afterwards, the mechanical properties of devised demountable connector systems were investigated thoroughly through experimental campaign (push tests) and numerical investigation. Based on the experimental and numerical results of the shear connector behaviour it is concluded that the proposed shear connector device Type B possess adequate strength and stiffness and might be considered ductile in accordance with the EN 1994-1-1 allowing for the application of existent design strategies in accordance with the same design code. The force-slip behaviour of the proposed shear connector is explained and adequate analytic model is proposed. Based on the force-slip behaviour model the applicability of the shear connector is verified on a range of composite beams that represent the demountable floor. [less ▲]

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See detailSmart cloud collocation: a unified workflow from CAD to enhanced solutions
Jacquemin, Thibault Augustin Marie UL

Doctoral thesis (2022)

Computer Aided Design (CAD) software packages are used in the industry to design mechanical systems. Then, calculations are often performed using simulation software packages to improve the quality of the ... [more ▼]

Computer Aided Design (CAD) software packages are used in the industry to design mechanical systems. Then, calculations are often performed using simulation software packages to improve the quality of the design. To speed up the development costs, companies and research centers have been trying to ease the integration of the computation phase in the design phase. The collocation methods have the potential of easing such integration thanks to their meshless nature. The geometry discretization step which is a key element of all computational method is simplified compared to mesh-based methods such as the finite element method. We propose in this thesis a unified workflow that allows the solution of engineering problems defined by partial differential equations (PDEs) directly from input CAD files. The scheme is based on point collocation methods and proposed techniques to enhance the solution. We introduce the idea of “smart clouds”. Smart clouds refer to point cloud discretizations that are aware of the exact CAD geometry, appropriate to solve a defined problem using a point collocation method and that contain information used to improve locally the solution. We introduce a unified node selection algorithm based on a generalization of the visibility criterion. The proposed algorithm leads to a significant reduction of the error for concave problems and does not have any drawback for convex problems. The point collocation methods rely on many parameters. We select in this thesis parameters for the Generalized Finite Difference (GFD) method and the Discretization-Corrected Particle Strength Exchange (DC PSE) method that we deem appropriate for most problems from the field of linear elasticity. We also show that solution improvement techniques, based on the use of Voronoi diagrams or on a stabilization of the PDE, do not lead to a reduction of the error for all of the considered benchmark problems. These methods shall therefore be used with care. We propose two types of a posteriori error indicators that both succeed in identifying the areas of the domain where the error is the greatest: a ZZ-type and a residual-type error indicator. We couple these indicators to a h-adaptive refinement scheme and show that the approach is effective. Finally, we show the performance of Algebraic Multigrid (AMG) preconditions on the solution of linear systems compared to other preconditioning/solution methods. This family of preconditioners necessitates the selection of a large number of parameters. We assess the impact of some of them on the solution time for a 3D problem from the field of linear elasticity. Despite the performance of AMG preconditions, ILU preconditioners may be preferred thanks to their ease of usage and robustness to lead to a convergence of the solution. [less ▲]

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See detailTowards a Unified and Robust Data-Driven Approach. A Digital Transformation of Production Plants in the Age of Industry 4.0
Benedick, Paul-Lou UL

Doctoral thesis (2022)

Nowadays, industrial companies are engaging their global transition toward the fourth industrial revolution (the so-called Industry 4.0). The main objective is to increase the Overall Equipment ... [more ▼]

Nowadays, industrial companies are engaging their global transition toward the fourth industrial revolution (the so-called Industry 4.0). The main objective is to increase the Overall Equipment Effectiveness (OEE), by collecting, storing and analyzing production data. Several challenges have to be tackled to propose a unified data-driven approach to rely on, from the low-layers data collection on the machine production lines using Operational Technologies (OT), to the monitoring and more importantly the analysis of the data using Information Technologies (IT). This is all the more important for companies having decades of existence – as Cebi Luxembourg S.A., our partner in a Research, Development and Innovation project subsidised by the ministry of the Economy in Luxembourg – to upgrade their on-site technologies and move towards new business models. Artificial Intelligence (AI) now knows a real interest from industrial actors and becomes a cornerstone technology for helping humans in decision-making and data-analysis tasks, thanks to the huge amount of (sensors-based) univariate time-series available in the production floor. However, such amount of data is not sufficient for AI to work properly and to make right decisions. This also requires a good data quality. Indeed, good theoretical performance and high accuracy can be obtained when trained and tested in isolation, but AI models may still provide degraded performance in real/industrial conditions. In that context, the problem is twofold: • Industrial production systems are vertically-oriented closed systems that make difficult their communication and their cooperation with each other, and intrinsically the data collection. • Industrial companies used to implement deterministic processes. Introducing AI - that can be classified as stochastic - in the industry requires a full understanding of the potential deviation of the models in order to be aware of their domain of validity. This dissertation proposes a unified strategy for digitizing an industrial system and methods for evaluating the performance and the robustness of AI models that can be used in such data-driven production plants. In the first part of the dissertation, we propose a three-steps strategy to digitize an industrial system, called TRIDENT, that enables industrial actors to implement data collection on production lines, and in fine to monitor in real-time the production plant. Such strategy has been implemented and evaluated on a pilot case-study at Cebi Luxembourg S.A. Three protocols (OPC-UA, MQTT and O-MI/O-DF) are used for investigating their impact on the real-time performance. The results show that, even if these protocols have some disparity in terms of performance, they are suitable for an industrial deployment. This strategy has now been extended and implemented by our partner - Cebi Luxembourg S.A - in its production environment. In the second part of the thesis dissertation, we aim at investigating the robustness of AI models in industrial settings. We then propose a systematic approach to evaluate the robustness under perturbations. Assuming that i) real perturbations - in particular on the data collection - cannot be recorded or generated in real industrial environment (that could lead to production stops) and ii) a model would not be implemented before evaluating its potential deviations, limits or weaknesses, our approach is based on artificial injections of perturbations into the data sets, and is evaluated on state-of-the-art classifiers (both Machine-Learning and Deep-Learning) and data sets (in particular, public sensors-based univariate time series). First, we propose a coarse-grained study, with two artificial perturbations - called swapping effect and dropping effect - in which simple random algorithms are used. This already highlights a great disparity of the models’ robustness under such perturbations that industrial actors need to be aware of. Second, we propose a fine-grained study where instead of testing randomly some parameters' values, we used Genetic Algorithms to look for the models' limits. To do so, we define our multi-objectives optimisation problem with a fitness function as: maximising the impact of the perturbations (i.e. decreasing the most the model's accuracy), while minimising the changes in the time-series (with regards to our two parameters). This can be seen as an adversarial case, where the goal is not to exploit these weaknesses in a malicious way but to be aware of. Based on such a study, methods for making more robust the model and/or for observing such behaviour on the infrastructure could be investigated and implemented if needed. The tool developed in this latter study is therefore ready for being used in a real industrial case, where data sets and perturbations can now be fitted to the scenario. [less ▲]

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See detailData Analysis for Insurance: Recommendation System Based on a Multivariate Hawkes Process
Lesage, Laurent UL

Doctoral thesis (2022)

The objective of the thesis is to build a recommendation system for insurance. By observing the behaviour and the evolution of a customer in the insurance context, customers seem to modify their insurance ... [more ▼]

The objective of the thesis is to build a recommendation system for insurance. By observing the behaviour and the evolution of a customer in the insurance context, customers seem to modify their insurance cover when a significant event happens in their life. In order to take into account the influence of life events (e.g. marriage, birth, change of job) on the insurance covering selection from customers, we model the recommendation system with a Multivariate Hawkes Process (MHP), which includes several specific features aiming to compute relevant recommendations to customers from a Luxembourgish insurance company. Several of these features are intent to propose a personalized background intensity for each customer thanks to a Machine Learning model, to use triggering functions suited for insurance data or to overcome flaws in real-world data by adding a specific penalization term in the objective function. We define a complete framework of Multivariate Hawkes Processes with a Gamma density excitation function (i.e. estimation, simulation, goodness-of-fit) and we demonstrate some mathematical properties (i.e. expectation, variance) about the transient regime of the process. Our recommendation system has been back-tested over a full year. Observations from model parameters and results from this back-test show that taking into account life events by a Multivariate Hawkes Process allows us to improve significantly the accuracy of recommendations. The thesis is presented in five chapters. Chapter 1 explains how the background intensity of the Multivariate Hawkes Process is computed thanks to a Machine Learning algorithm, so that each customer has a personalized recommendation. Chapter 1 is shown an extended version of the method presented in [1], in which the method is used to make the algorithm explainable. Chapter 2 presents a Multivariate Hawkes Processes framework in order to compute the dependency between the propensity to accept a recommendation and the occurrence of life events: definitions, notations, simulation, estimation, properties, etc. Chapter 3 presents several results of the recommendation system: estimated parameters of the model, effects of contributions, backtesting of the model’s accuracy, etc. Chapter 4 presents the implementation of our work into a R package. Chapter 5 concludes on the contributions and perspectives opened by the thesis. [less ▲]

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See detailUser Experience Design for Cybersecurity & Privacy: addressing user misperceptions of system security and privacy
Stojkovski, Borce UL

Doctoral thesis (2022)

The increasing magnitude and sophistication of malicious cyber activities by various threat actors poses major risks to our increasingly digitized and inter-connected societies. However, threats can also ... [more ▼]

The increasing magnitude and sophistication of malicious cyber activities by various threat actors poses major risks to our increasingly digitized and inter-connected societies. However, threats can also come from non-malicious users who are being assigned too complex security or privacy-related tasks, who are not motivated to comply with security policies, or who lack the capability to make good security decisions. This thesis posits that UX design methods and practices are necessary to complement security and privacy engineering practices in order to (1) identify and address user misperceptions of system security and privacy; and (2) inform the design of secure systems that are useful and appealing from end-users’ perspective. The first research objective in this thesis is to provide new empirical accounts of UX aspects in three distinct contexts that encompass security and privacy considerations, namely: cyber threat intelligence, secure and private communication, and digital health technology. The second objective is to empirically contribute to the growing research domain of mental models in security and privacy by investigating user perceptions and misperceptions in the afore-mentioned contexts. Our third objective is to explore and propose methodological approaches to incorporating users’ perceptions and misperceptions in the socio-technical security analyses of systems. Qualitative and quantitative user research methods with experts as well as end users of the applications and systems under investigation were used to achieve the first two objectives. To achieve the third objective, we also employed simulation and computational methods. Cyber Threat Intelligence: CTI sharing platforms Reporting on a number of user studies conducted over a period of two years, this thesis offers a unique contribution towards understanding the constraining and enabling factors of security information sharing within one of the leading CTI sharing platforms, called MISP. Further, we propose a conceptual workflow and toolchain that would seek to detect user (mis)perceptions of key tasks in the context of CTI sharing, such as verifying whether users have an accurate comprehension of how far information travels when shared in a CTI sharing platform, and discuss the benefits of our socio-technical approach as a potential security analysis tool, simulation tool, or educational / training support tool. Secure & Private Communication: Secure Email We propose and describe multi-layered user journeys, a conceptual framework that serves to capture the interaction of a user with a system as she performs certain goals along with the associated user beliefs and perceptions about specific security or privacy-related aspects of that system. We instantiate the framework within a use case, a recently introduced secure email system called p≡p, and demonstrate how the approach can be used to detect misperceptions of security and privacy by comparing user opinions and behavior against system values and objective technical guarantees offered by the system. We further present two sets of user studies focusing on the usability and effectiveness of p≡p’s security and privacy indicators and their traffic-light inspired metaphor to represent different privacy states and guarantees. Digital Health Technology: Contact Tracing Apps Considering human factors when exploring the adoption as well as the security and privacy aspects of COVID-19 contact tracing apps is a timely societal challenge as the effectiveness and utility of these apps highly depend on their widespread adoption by the general population. We present the findings of eight focus groups on the factors that impact people’s decisions to adopt, or not to adopt, a contact tracing app, conducted with participants living in France and Germany. We report how our participants perceived the benefits, drawbacks, and threat model of the contact tracing apps in their respective countries, and discuss the similarities and differences between and within the study groups. Finally, we consolidate the findings from these studies and discuss future challenges and directions for UX design methods and practices in cybersecurity and digital privacy. [less ▲]

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See detailMetal-oxide nanostructures for low-power gas sensors
Bhusari, Rutuja Dilip UL

Doctoral thesis (2022)

For gas sensing applications, metal oxide (MOx) nanostructures have demonstrated attractive properties due their large surface-over-volume ratio, combined with the possibility to use multiple materials ... [more ▼]

For gas sensing applications, metal oxide (MOx) nanostructures have demonstrated attractive properties due their large surface-over-volume ratio, combined with the possibility to use multiple materials and multi-functional properties. For MOx chemiresistive gas sensors, the temperature activated interaction of atmospheric oxygen with MOx surface plays a major role in the sensor kinetics as it leads to oxygen adsorption-desorption reactions, that eventually affects the gas sensing performance. Thus, MOx sensors are operated at high temperatures to achieve the desired sensitivity. This high temperature operation of MOx sensors limits their application in explosive gas detection, reduces the sensor lifetime and causes power consumption. To overcome these drawbacks of MOx sensors, researchers have proposed the use of heterostructures and light activation as alternatives. In this thesis, we aim to develop low power consuming MOx sensors using these solutions. We show the template-free bottom-up synthesis and shape control of copper hydroxide-based nanostructures grown in liquid phase which act as templates for formation of CuO nanostructures. Precise control over the pH of the solution and the reaction temperature led to intended tuning of the morphology and chemical composition of the nanostructures. We contemplate upon the rationale behind this change in shape and material as CuO nanostructures are further used in a heterostructure. We discuss synthesis and characterisation of CuO bundles and Cu2O truncated cubes, former of which lead to very interesting gas sensing properties and application. Devices made from CuO bundles network are investigated for their electrical and oxygen adsorption- desorption properties as a gas sensor. It was observed that the sensor has faster response and recovery in as deposited condition in comparison to annealed sensor. A detailed inspection of response and recovery curves enabled us to derive parameters like time constants, reaction constants and diffusion coefficients for CuO bundles, an analysis that is scarcely performed on p-type materials. Investigation of the derived parameters, role of network junctions and a hydroxylated CuO surface leads us to discuss the hypotheses for the contributing processes. CuO bundles show conduction transients upon exposure to reducing gas H2 and temperature-based inversion of response upon exposure to reducing gas CO. This has not been reported in literature for CuO exposed to H2 and/or CO. Armed with this fundamental knowledge of gas sensing, we choose ZnO, n type transducer material, and CuO, p type materials with lower band gap and higher absorption in the visible range to synthesise a heterostructure. However, sol-gel synthesis of ZnO and CuO nanostructures have different reactions parameters, like temperature, pH, etc., and do not show natural affinity to grow on the other material. These challenges are overcome by implementing a stepped synthesis procedure to fabricate a heterostructure with Cu-based nanoplatelets on ZnO Nanorods, also represented as CuO@ZnO heterostructure in this thesis. We finally demonstrate electrical and functional characterisation of CuO@ZnO heterostructure. The heterostructure responds differently to tested gasses as compared to its constituent nanostructure ZnO nanorods and a reference CuO nanostructure, CuO bundles. This is an unexpected result as heterostructures usually show response type similar to their base material but with an enhanced sensor response. We present a possible application of e-nose that can differentiate qualitatively between CO, NO2 and ethanol, using the heterostructure, ZnO nanorods and CuO bundles together. [less ▲]

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See detailCOMBINED HEATING AND VENTILATION SYSTEMS FOR LOW-ENERGY RESIDENTIAL BUILDINGS; OPTIMIZATION OF ENERGY EFFICIENCY AND USER COMFORT
Shirani, Arsalan UL

Doctoral thesis (2022)

Combined heating and ventilation systems are applied here in highly energy-efficient residential buildings to save construction cost. Combining a Heat Pump with a Heat Recovery Ventilation system to heat ... [more ▼]

Combined heating and ventilation systems are applied here in highly energy-efficient residential buildings to save construction cost. Combining a Heat Pump with a Heat Recovery Ventilation system to heat and cool the building offers faster response times, a smaller footprint and an increased cooling capacity, compared to floor heating systems. As a result, such systems are expected to have a larger market share in the future. The available research on Ventilation Based Heating systems focuses mostly on comparing Exhaust Air Heat Pumps with conventional systems in energy-efficient buildings. The majority of published research neglects the usual existence of electrical backup heaters as well as the need to develop and use an adapted and optimized control strategy for such systems. This work compares the energy efficiency of the common-standard ventilation-based heating concepts including Exhaust Air Heat Pumps with the conventional floor heating systems using single room control strategy to achieve similar user comfort. The comparison is carried out in a simulation environment in order to optimize the systems under exactly reproducible boundary conditions. Additionally, two field tests were performed to achieve a better understanding and validation of the simulation models. The measured data was used to model the dynamic behavior of the Exhaust Air Heat Pump and the air distribution system. These field tests revealed that the overall run time and heating output of the heat pump were much lower than expected. This was the motivation to investigate and optimize the heat pump and electric heater control strategy. It could be demonstrated that the applied control strategy has a significant impact on the overall performance of the system. The suggested control strategy was tested and validated in a third field measurement. Based on the gained knowledge using the system simulation tool and the conducted field tests, an improved second concept for Ventilation Based Heating systems was defined with three optimization steps. It could be demonstrated that using the suggested methodologies in the hard- and software of such a system, can significantly improve its overall efficiency. However, Ventilation Based Heating systems cannot compete with floor heating systems in terms of total system energy efficiency, due to the necessity of electrical backup heaters and due to the higher supply temperatures. [less ▲]

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See detailEvaluation von Synergieeffekten zentraler Speichersysteme in Niederspannungsnetzen durch integrative Modellbildung
Zugschwert, Christina UL

Doctoral thesis (2022)

Security of supply, affordability, and sustainability form the pillars of a new energy policy towards renewable generation and decarbonization. However, the dynamics of the power generation due to the ... [more ▼]

Security of supply, affordability, and sustainability form the pillars of a new energy policy towards renewable generation and decarbonization. However, the dynamics of the power generation due to the increasing amount of renewable energies cause temporal and local discrepancies between generation and consumption. Resulting energy transports between grid sections and different voltage levels cause additional load flows. To ensure grid stability, the grid operator provides system services and grid extension measures. With the help of energy storage systems with grid-serving control and placement strategies, the flexibility of the electricity supply can be increased. Besides, a high amount of renewable energy can be used locally while maintaining grid stability. A centralized installation approach focussing single grid sections, instead of many decentralized home storage units, offers economic and environmental advantages. Furthermore, the operation strategy can be optimized by the global view of the grid operator and thus be adapted to local conditions. This research evaluates synergy effects of central storage systems by integrative computational analysis using a rural low-voltage grid section in Luxembourg. Three linked simulation levels are used to calculate operational strategies, storage dimensioning as well as placement based on 15-minute smart meter data. The operation strategy is developed within a power system simulation and is used to control a parameterizable simulation model of a vanadium-redox-flow-battery. The operating strategy focuses on reducing the maximum power flow at the transformer and reactive power compensation to maintain voltage stability. A future photovoltaic scenario is being adopted by doubling the status quo photovoltaic generation. The simultaneous optimization of storage utilization and power reduction at the transformer provides the storage design parameters power and capacity. Storage placement is determined by the system boundary and the resulting data selection. A final sensitivity analysis evaluates an optimized storage placement while enhancing the voltage profiles. The results of this work are a differentiated active as well as reactive power related operating strategy, automated calculation algorithms to determin control parameters, optimized battery design parameters as well as the methodical approach to transfer calculation algorithms to further grid sections. [less ▲]

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See detailSEMKIS: A CONTRIBUTION TO SOFTWARE ENGINEERING METHODOLOGIES FOR NEURAL NETWORK DEVELOPMENT
Jahic, Benjamin UL

Doctoral thesis (2022)

Today, there is a high demand for neural network-based software systems supporting humans during their daily activities. Neural networks are computer programs that simulate the behaviour of simplified ... [more ▼]

Today, there is a high demand for neural network-based software systems supporting humans during their daily activities. Neural networks are computer programs that simulate the behaviour of simplified human brains. These neural networks can be deployed on various devices e.g. cars, phones, medical devices...) in many domains (e.g. automotive industry, medicine...). To meet the high demand, software engineers require methods and tools to engineer these software systems for their customers. Neural networks acquire their recognition skills e.g. recognising voice, image content...) from large datasets during a training process. Therefore, neural network engineering (NNE) shall not be only about designing and implementing neural network models, but also about dataset engineering (DSE). In the literature, there are no software engineering methodologies supporting DSE with precise dataset selection criteria for improving neural networks. Most traditional approaches focus only on improving the neural network’s architecture or follow crafted approaches based on augmenting datasets with randomly gathered data. Moreover, they do not consider a comparative evaluation of the neural network’s recognition skills and customer’s requirements for building appropriate datasets. In this thesis, we introduce a software engineering methodology (called SEMKIS) supported by a tool for engineering datasets with precise data selection criteria to improve neural networks. Our method considers mainly the improvement of neural networks through augmenting datasets with synthetic data. SEMKIS has been designed as a rigorous iterative process for guiding software engineers during their neural network-based projects. The SEMKIS process is composed of many activities covering different development phases: requirements’ specification; dataset and neural network engineering; recognition skills specification; dataset augmentation with synthetized data. We introduce the notion of key-properties, used all along the process in cooperation with a customer, to describe the recognition skills. We define a domain-specific language (called SEMKIS-DSL) for the specification of the requirements and recognition skills. The SEMKIS-DSL grammar has been designed to support a comparative evaluation of the customer’s requirements with the key-properties. We define a method for interpreting the specification and defining a dataset augmentation. Lastly, we apply the SEMKIS process to a complete case study on the recognition of a meter counter. Our experiment shows a successful application of our process in a concrete example. [less ▲]

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See detailLeveraging Graph Machine Learning for Social Network Analysis
Zhong, Zhiqiang UL

Postdoctoral thesis (2022)

As a ubiquitous complex system in quotidian life around everyone, online social networks (OSNs) provide a rich source of information about billions of users worldwide. To some extent, OSNs have mirrored ... [more ▼]

As a ubiquitous complex system in quotidian life around everyone, online social networks (OSNs) provide a rich source of information about billions of users worldwide. To some extent, OSNs have mirrored our real society: people perform a multitude of different activities in OSNs as they do in the offline world, such as establishing social relations, sharing life moments, and expressing opinions about various topics. Therefore, understanding OSNs is of immense importance. One key characteristic of human social behaviour in OSNs is their inter-relational nature, which can be represented as graphs. Due to sparsity and complex structure, analysing these graphs is quite challenging and expensive. Over the past several decades, many expert-designed approaches to graphs have been proposed with elegant theoretical properties and successfully addressed numerous practical problems. Nevertheless, most of them are either not data-driven or do not benefit from the rapidly growing scale of data. Recently, in the light of remarkable achievements of artificial intelligence, especially deep neural networks techniques, graph machine learning (GML) has emerged to provide us with novel perspectives to understanding and analysing graphs. However, the current efforts of GML are relatively immature and lack attention to specific scenarios and characteristics of OSNs. Based on the pros and cons of GML, this thesis discusses several aspects of how to build advanced approaches to better simplify and ameliorate OSN analytic tasks. Specifically: 1) Overcoming flat message-passing graph neural networks. One of the most widely pursued branches in GML research, graph neural networks (GNNs), follows a similar flat message-passing principle for representation learning. Precisely, information is iteratively passed between adjacent nodes along observed edges via non-linear transformation and aggregation functions. Its effectiveness has been widely proved; however, two limitations need to be tackled: (i) they are costly in encoding long-range information spanning the graph structure; (ii) they are failing to encode features in the high-order neighbourhood in the graphs as they only perform information aggregation across the observed edges in the original graph. To fill up the gap, we propose a novel hierarchical message-passing framework to facilitate the existing GNN mechanism. Following this idea, we design two practical implementations, i.e., HC-GNN and AdamGNN, to demonstrate the framework's superiority. 2) Extending graph machine learning to heterophilous graphs. The existing GML approaches implicitly hold a homophily assumption that nodes of the same class tend to be connected. However, previous expert studies have shown the enormous importance of addressing the heterophily scenario, where ``opposites attract'', is essential for network analysis and fairness study. We demonstrate the possibility of extending GML to heterophilous graphs by simplifying supervised node classification models on heterophilous graphs (CLP) and designing an unsupervised heterophilous graph representation learning model (Selene). 3) Online social network analysis with graph machine learning. As GML approaches have demonstrated significant effectiveness over general graph analytic tasks, we perform two practical OSN analysis projects to illustrate the possibility of employing GML in practice. Specifically, we propose a semantic image graph embedding (SiGraph) to improve OSN image recognition task with the associated hashtags semantics and a simple GNN-based neural link prediction framework (NeuLP) to boost the performance with tiny change. Keywords: Graph machine learning, Social network analysis, Graph neural networks, Hierarchical structure, Homophily/Heterophily graphs, Link prediction, Online image content understanding. [less ▲]

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See detailARGUMENT MINING AND ITS APPLICATIONS IN POLITICAL DEBATES
Haddadan, Shohreh UL

Doctoral thesis (2022)

Presidential debates are significant moments in the history of presidential campaigns. In these debates, candidates are challenged to discuss the main contemporary and historical issues in the country and ... [more ▼]

Presidential debates are significant moments in the history of presidential campaigns. In these debates, candidates are challenged to discuss the main contemporary and historical issues in the country and attempt to persuade the voters to their benefit. These debates offer a legitimate ground for argumentative analysis to investigate political discourse argument structure and strategy. The recent advances in machine learning and Natural Language Processing (NLP) algorithms with the rise of deep learning have revolutionized many natural language applications, and argument analysis from textual resources is no exception. This dissertation targets argument mining from political debates data, a platform rifled with the arguments put forward by politicians to convince a general public in voting for them and discourage them from being appealed by the other candidates. The main contributions of the thesis are: i) Creation, release and reliability assessment of a valuable resource for argumentation research. ii) Implementation of a complete argument mining pipeline applying cutting-edge technologies in NLP research. iii) Launching of a demo tool for argumentative analysis of political debates. The original dataset is composed of the transcripts of 41 presidential election debates in the U.S. from 1960 to 2016. Beside argument extraction from political debates, this research also aims at investigating the practical applications of argument structure extraction, such as fallacious argument classification and argument retrieval. In order to apply supervised machine learning and NLP methods to the data, an excessive annotation study has been conducted on the data and led to the creation of a unique dataset with argument structures composed of argument components (i.e., claim and premise) and argument relations (i.e., support and attack). This dataset includes also another annotation layer with six fallacious argument categories and 14 sub-categories annotated on the debates. The final dataset is annotated with 32,296 argument components (i.e., 16,982 claims and 15,314 premises) and 25,012 relations (i.e., 3,723 attacks and 21,289 supports), and 1628 fallacious arguments. As the methodological approach, a complete argument mining pipeline is designed and implemented, composed of the two main stages of argument component detection and argument relation prediction. Each stage takes advantage of various NLP models outperforming standard baselines in the area, with an average F-score of 0.63 for argument components classification and 0.68 for argument relation classification. Additionally, DISPUTool, an argumentative analysis online tool, is developed as proof-of-concept. DISPUTool incorporates two main functionalities. Firstly, it provides the possibility of exploring the arguments which exist in the dataset. And secondly, it allows for extracting arguments from text segments inserted by the user leveraging the embedded trained model. [less ▲]

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See detailAvoiding the Inappropriate: The European Commission and Sanctions under EU Fiscal Policy Coordination
Sacher, Martin UL

Doctoral thesis (2022)

Since the beginning of the European Economic and Monetary Union, fiscal non-compliance has been subject to the potential imposition of sanctions. However, the extent to which punitive action should be ... [more ▼]

Since the beginning of the European Economic and Monetary Union, fiscal non-compliance has been subject to the potential imposition of sanctions. However, the extent to which punitive action should be automatic – rather than political – is a point of constant discussion among European Union decision-makers. The most recent reform of the Stability and Growth Pact, in the aftermath of the European Sovereign Debt Crisis, has attempted to make sanctions more automatic and has created the possibility to trigger them at earlier stages of the surveillance procedure. With this in mind, the reform has enhanced the powers and autonomy of the European Commission in the application of the new rules. Despite the reinforcement of punitive provisions, the Commission has so far refrained from proposing the imposition of sanctions. Against this background, this thesis aims to answer the question of how we can best explain that the European Commission does not propose financial sanctions because of Member State non-compliance with the Pact’s fiscal objectives. The thesis draws upon four post-crisis cases in which sanctions for fiscal non-compliance might have been imposed – Belgium in 2013, France in 2015, Portugal and Spain in 2016, and Italy in 2018. The thesis uses theory-testing process-tracing methods and applies an adaptation of normative institutionalism that takes into account strategic actor behaviour. Based on this theoretical and methodological framework, it is argued that the normative-strategic minimum enforcement mechanism explains the Commission’s behaviour. Given that the imposition of sanctions is perceived as inappropriate in the cases at hand, Commission actors strategically refrain from applying the enforcement provisions to their full extent. [less ▲]

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See detailMACHINE LEARNING IN THE DESIGN SPACE EXPLORATION OF TSN NETWORKS
Mai, Tieu Long UL

Doctoral thesis (2022)

Real-time systems are systems that have specific timing requirements. They are critical systems that play an important role in modern societies, be it for instance control systems in factories or ... [more ▼]

Real-time systems are systems that have specific timing requirements. They are critical systems that play an important role in modern societies, be it for instance control systems in factories or automotives. In recent years, Ethernet has been increasingly adopted as layer 2 protocol in real-time systems. Indeed, the adoption of Ethernet provides many benefits, including COTS and cost-effective components, high data rates and flexible topology. The main drawback of Ethernet is that it does not offer "out-of-the-box" mechanisms to guarantee timing and reliability constraints. This is the reason why time-sensitive networking (TSN) mechanisms have been introduced to provide Quality-of-Service (QoS) on top of Ethernet and satisfy the requirements of real-time communication in critical systems. The promise of Ethernet TSN is the possibility to use a single network for different criticality levels, e.g, critical control traffic and infotainment traffic sharing the same network resources. This thesis is about the design of Ethernet TSN networks, and specifically about techniques that help quantify the extent to which a network can support current and future communication needs. The context of this work is the increasing use of design-space exploration (DSE) in the industry to master the complexity of designing (e.g. in terms of architectural and technological choices) and configuring a TSN network. One of the main steps in DSE is performing schedulability analysis to conclude about the feasibility of a network configuration, i.e., whether all traffic streams satisfy their timing constraints. This step can take weeks of computations for a large set of candidate solutions with the simplest TSN mechanisms, while more complicated TSN mechanisms will require even longer time. This thesis explores the use of Artificial Intelligence (AI) techniques to assist in the design of TSN networks by speeding up the DSE. Specifically, the thesis proposes the use of machine learning (ML) as an alternative approach to schedulability analysis. The application of ML involves two steps. In the first step, ML algorithms are trained with a large set of TSN configurations labeled as feasible or non-feasible. Due to its pattern recognition ability, ML algorithms can predict the feasibility of unseen configurations with a good accuracy. Importantly, the execution time of an ML model is only a fraction of conventional schedulability analysis and remains constant whatever the complexity of the network configurations. Several contributions make up the body of the thesis. In the first contribution, we observe that the topology and the traffic of a TSN network can be used to derive simple features that are relevant to the network feasibility. Therefore, standard and simple machine learning (ML) algorithms such as k-Nearest Neighbors are used to take these features as inputs and predict the feasibility of TSN networks. This study suggests that ML algorithms can provide a viable alternative to conventional schedulability analysis due to fast execution time and high prediction accuracy. A hybrid approach combining ML and schedulability analyses is also introduced to control the prediction uncertainty. In the next studies, we aim at further automating the feasibility prediction of TSN networks with the Graph Neural Network (GNN) model. GNN takes as inputs the raw data from the TSN configurations and encodes them as graphs. Synthetic features are generated by GNN, thus the manual feature selection step is eliminated. More importantly, the GNN model can generalize to a wide range of topologies and traffic patterns, in contrast to the standard ML algorithms tested before that can only work with a fixed topology. An ensemble of individual GNN models shows high prediction accuracies on many test cases containing realistic automotive topologies. We also explore possibilities to improve the performance of GNN with more advanced deep learning techniques. In particular, semi-supervised learning and self-supervised learning are experimented. Although these learning paradigms provide modest improvements, we consider them promising techniques due to the ability to leverage the massive amount of unlabeled training data. While this thesis focuses on the feasibility prediction of TSN configurations, AI techniques have huge potentials to automate other tasks in real-time systems. A natural follow-up work of this thesis is to apply GNN to multiple TSN mechanisms and predict which mechanism can provide the best scheduling solution for a given configuration. Although we need distinct ML models for each TSN mechanism, this research direction is promising as TSN mechanisms may share similar feasibility features and thus transfer learning techniques can be applied to facilitate the training process. Furthermore, GNN can be used as a core block in deep reinforcement learning to find the feasible priority assignment of TSN configurations. This thesis aims to make a contribution towards DSE of TSN networks with AI. [less ▲]

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See detailOptical measurement under space conditions
Bremer, Mats UL

Doctoral thesis (2022)

The growing interest in space by governmental and private institutions has increased significantly in recent years. The issue of quality control plays an extremely important role in space travel, as ... [more ▼]

The growing interest in space by governmental and private institutions has increased significantly in recent years. The issue of quality control plays an extremely important role in space travel, as possible defects can cause enormous damage. The present work deals with a possible method to improve already existing quality control procedures for space flight. With the help of a 3D scanner, different components are measured and evaluated under space conditions. In particular, the linear thermal expansions are analyzed. The work has shown that the elaborated procedure works for metallic materials. For composites or joints between different materials, positive approaches were shown, which, however, could not be validated within the scope of this work. Components made of pure carbon fiber material cannot be evaluated with the technical equipment used. [less ▲]

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See detailTHERMODYNAMICS OF CHEMICAL ENGINES: A CHEMICAL REACTION NETWORK APPROACH
Penocchio, Emanuele UL

Doctoral thesis (2022)

Chemical processes in closed systems inevitably relax to equilibrium. Energy can be employed to counteract such tendency and drive reactions against their spontaneous direction. This nonequilibrium ... [more ▼]

Chemical processes in closed systems inevitably relax to equilibrium. Energy can be employed to counteract such tendency and drive reactions against their spontaneous direction. This nonequilibrium driving is implemented in open systems, which living organisms provide the most spectacular examples of. In recent years, experiments in supramolecular chemistry, photochemistry and electrochemistry demonstrated that, by opening synthetic systems to matter and/or energy exchanges with the environment, artificial systems with life-like behaviours can be realized and used to convert energy inputs of different nature into work at both the nanoscopic and the macroscopic level. However, one tool that is still lacking is a firm grasp of the thermodynamics of these chemical engines. In this thesis, we provide it by leveraging the most recent developments of the thermodynamic description of deterministic chemical reaction networks. As main theoretical results, we extend the current theory to encompass nonideal and light-driven systems, thus providing the fundamental tools to treat electrochemical and photochemical systems in addition to the chemically driven ones. We also expand the scope of information thermodynamics to bipartite chemical reaction networks characterized by macroscopic non-normalized concentration distributions evolving in time with nonlinear dynamics. This framework potentially applies to almost every synthetic chemical engine realized until now, and to many models of biological systems too. Here, we undertake the thermodynamic analysis of some of the epitomes in the field of artificial chemical engines: a model of chemically driven self-assembly, an experimental chemically driven molecular motor, and an experimental photochemical bimolecular pump. The thesis provides a thermodynamic level of understanding of chemical engines that is general, complements previous analyses based on kinetics and stochastic thermodynamics, and has practical implications for designing and improving synthetic systems, regardless of the particular type of powering or chemical structure. [less ▲]

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See detailMicrobiome reservoirs of antimicrobial resistance
de Nies, Laura UL

Doctoral thesis (2022)

Antimicrobial resistance (AMR) presents a global threat to public health due to the inability to comprehensively treat bacterial infections. Emerging resistant bacteria residing within human, animal and ... [more ▼]

Antimicrobial resistance (AMR) presents a global threat to public health due to the inability to comprehensively treat bacterial infections. Emerging resistant bacteria residing within human, animal and environmental reservoirs may spread from one to the other, at both local and global levels. Consequently, AMR has the potential to rapidly become pandemic whereby it is no longer constrained by either geographical or human-animal borders. Therefore, to enhance our understanding on the dissemination of AMR we systematically resolved different reservoirs of antimicrobial resistance, leveraging animal, environmental and human samples, to provide a One Health perspective. To identify antimicrobial resistance genes (ARGs) and compare their identity and prevalence across different microbial reservoirs, we developed the PathoFact pipeline which also contextualizes ARG localization on mobile genetic elements (MGEs). This methodology was applied to several metagenomic datasets covering microbiomes of infants, laboratory mice, a wastewater treatment plant (WWTP) and biofilms from glacier-fed streams (GFS). Investigating the infant gut resistome we found that the abundance of ARGs against (semi)-synthetic agents were increased in infants born via caesarian section compared to those born via vaginal delivery. Additionally, we identified mobile genetic elements (MGEs) encoding ARGs such as glycopeptide, diaminopyrimidine and multidrug resistance at an early age. MGEs are often pivotal in the accumulation and dissemination of AMR within a microbial population. Therefore, we assessed the effect of selective pressure on the evolution and consecutive dissemination of AMR within the commensal gut microbiome, utilizing a mouse model. While plasmids and phages were found to contribute to the spread of AMR, we found that integrons represented the primary factors mediating AMR in the antibiotic-treated mice. In addition to the above-described studies, we investigated the environmental resistome, comprising both the urban environment, i.e., the WWTP, and a natural environment, GFS biofilms. Utilizing a multi-omics approach we investigated the WWTP resistome over a 1.5 years timeseries and found that a core group of fifteen AMR categories were always present. Additionally, we found a significant difference in AMR categories encoded on phages versus plasmids indicating that the MGEs contributed differentially to the dissemination of AMR. On the other hand, the GFS biofilms represent pristine environments with limited anthropogenic influences. Therein, we found that eukaryotes, as well as prokaryotes, may serve as AMR reservoirs owing to their potential for encoding ARGs. In addition to our identification of biosynthetic gene clusters encoding antibacterial secondary metabolites, our findings highlight the constant intra- and inter-domain competition and the underlying mechanisms influencing microbial survival in GFS epilithic biofilms. In general, we observed that the overall AMR abundances were highest in human and animal microbial reservoirs whilst environmental reservoirs demonstrated a higher diversity of ARG subtypes. Additionally, we identified human-associated, MGE-derived ARGs in all three components of the One Health triad, indicating possible transmission routes for AMR dissemination. In summary, this work provides a comprehensive assessment of the prevalence of antimicrobial resistance and its dissemination mechanisms in human, animal, and environmental mechanisms. [less ▲]

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See detailANALYSIS OF NEURODEVELOPMENTAL DEFECTS IN HUMAN MIDBRAIN ORGANOIDS FROM GBA-N370S PARKINSON'S DISEASE PATIENTS
Rosety, Isabel UL

Doctoral thesis (2022)

With increasing prevalence, Parkinson’s disease presents a major challenge for medical research and public health. Despite years of investigation, significant knowledge gaps exist and Parkinson’s disease ... [more ▼]

With increasing prevalence, Parkinson’s disease presents a major challenge for medical research and public health. Despite years of investigation, significant knowledge gaps exist and Parkinson’s disease (PD) etiology remains unclear. A recent concept in the field is that neurodevelopmental aspects might contribute to the pathogenesis of neurodegenerative diseases such as PD. Our hypothesis is that mutations in PD-linked genes have an impact on the cells’ homeostasis at the neural precursor state, giving rise to vulnerable dopaminergic (DA) neurons, thereby increasing the degree of susceptibility for neurodegeneration with aging. In order to investigate this, we used a human midbrain organoid (hMO) model generated from iPSC-derived neural precursor cells. As part of the optimization of the model, we treated the organoids with the neurotoxin 6-OHDA to develop a neurotoxin-induced PD model and set up a high-content imaging pipeline coupled with machine learning classification to predict neurotoxicity. We then used these tools to derive PD patient-derived hMOs in order to investigate our main hypothesis. First, we focused on PD patients carrying a heterozygous mutation in the GBA gene. We developed a genome-scale metabolic model that predicted significant differences in lipid metabolism between patients and controls. Then, we validated the observations by performing a comprehensive lipidomics analysis confirming a dysregulated lipidome in mutant hMOs. Moreover, GBA-PD hMOs displayed PD-relevant phenotypes, impaired DA differentiation and an increased population of neural progenitor cells (NPCs) in cell cycle arrest, confirming the presence of neurodevelopmental defects. To further investigate the neurodevelopmental component of PD, we used patient-derived cell lines carrying PINK1 mutations. PINK1-PD neural precursors presented differences in their energetic profile, imbalanced proliferation, apoptosis, mitophagy, and an impaired differentiation efficiency to DA neurons compared to controls. Correction of the PINK1 point mutation was able to improve the metabolic properties and neuronal firing rates as well as rescuing the differentiation phenotype. We performed a drug screen using repurposed drugs as well as novel compounds to evaluate their potential to rescue the observed developmental phenotype. Treatment with 2-hydroxypropyl-β- cyclodextrin increased the autophagy and mitophagy capacity of neurons which was accompanied by improved dopaminergic differentiation of patient-specific neurons in midbrain organoids and showed neuroprotective effects in an MPTP-treated mice PD model. In conlusion, PD has a neurodevelopmental component that increases susceptibility to the pathology. Thus, our findings suggest that the use of hMOs are suitable to reveal early PD pathomechanisms, as well as constituting a powerful tool for advanced therapy development. [less ▲]

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See detailA posteriori error estimation for finite element approximations of fractional Laplacian problems and applications to poro–elasticity
Bulle, Raphaël UL

Doctoral thesis (2022)

This manuscript is concerned with a posteriori error estimation for the finite element discretization of standard and fractional partial differential equations as well as an application of fractional ... [more ▼]

This manuscript is concerned with a posteriori error estimation for the finite element discretization of standard and fractional partial differential equations as well as an application of fractional calculus to the modeling of the human meniscus by poro-elasticity equations. In the introduction, we give an overview of the literature of a posteriori error estimation in finite element methods and of adaptive refine- ment methods. We emphasize the state–of–the–art of the Bank–Weiser a posteriori error estimation method and of the adaptive refinement methods convergence results. Then, we move to fractional partial differential equations. We give some of the most common discretization methods of fractional Laplacian operator based equations. We review some results of a priori error estimation for the finite element discretization of these equations and give the state–of–the–art of a posteriori error estimation. Finally, we review the literature on the use of the Caputo’s fractional derivative in applications, focusing on anomalous diffusion and poro-elasticity applications. The rest of the manuscript is organized as follow. Chapter 1 is concerned with a proof of the reliability of the Bank–Weiser estimator for three–dimensional problems, extending a result from the literature. In Chapter 2 we present a numerical study of the Bank–Weiser estimator, provide a novel implementation of the estimator in the FEniCS finite element software and apply it to a variety of elliptic equations as well as goal-oriented error estimation. In Chapter 3 we derive a novel a posteriori estimator for the L2 error induced by the finite element discretization of fractional Laplacian operator based equations. In Chapter 4 we present new theoretical results on the convergence of a rational approximation method with consequences on the approximation of fractional norms as well as a priori error estimation results for the finite element discretization of fractional equations. Finally, in Chapter 5 we provide an application of fractional calculus to the study of the human meniscus via poro-elasticity equations. [less ▲]

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See detailINCREASING THE COMPLEXITY OF MIDBRAIN ORGANOID SYSTEMS FOR DEVELOPMENTAL STUDIES AND DISEASE MODELLING
Sabaté Soler, Sonia UL

Doctoral thesis (2022)

The discovery of iPSC technology revolutionized the biomedical field, allowing the development of translatable and complex 2D and 3D cell culture systems. Organoids are 3D models containing multiple cell ... [more ▼]

The discovery of iPSC technology revolutionized the biomedical field, allowing the development of translatable and complex 2D and 3D cell culture systems. Organoids are 3D models containing multiple cell types that mimic complex microenvironments. This is highly advantageous to understand human development, physiology and disease, especially in inaccessible areas such as the brain. Human midbrain-specific organoids have been developed to study the midbrain (abundant in dopaminergic neurons). In Parkinson’s Disease (PD), dopaminergic neurons in the substantia nigra of the midbrain degenerate, causing a broad spectrum of clinical features. Midbrain organoids (MO) are rich in dopaminergic neurons, and contain spatially organized groups of neural cells and progenitors. MO generated from PD patients’ cells recapitulate dopaminergic neuron degeneration. In this thesis, we first demonstrated that dopaminergic neuron PD phenotypes and drug rescue effects were similar between MO and mice. After, we identified different neuronal clusters, progenitor cells, radial glia and mesenchymal cells in MO by scRNA-Seq. As expected, due to the neuro-ectodermal patterning of the MO’ starting cell population, we confirmed the absence of mesoderm-derived cell types, such as microglia and endothelial cells. This represents a limitation for the system in terms of cellular and molecular complexity. Microglia in the human brain perform surveillance, defence and homeostasis functions; they phagocytose metabolic waste products and cell debris. We successfully developed a novel protocol to integrate functional microglia into our MO model. SnRNA-Seq analysis and electrophysiological results suggested a reduction of stress levels and higher maturation of neurons in the presence of microglia, respectively. We then aimed to vascularise MO, which would better recapitulate the brain environment and improve oxygen and nutrient supply into the organoid core (a common 3D culture limitation). We integrated an endothelial network into MO by fusion with vascular organoids, and observed the presence of blood vessel components like pericytes and basal lamina. Furthermore, vascularized assembloids showed decreased levels of cell death and hypoxia. Finally, by co-culturing microglia with vascularized assembloids, we modelled the neurovascular unit in 3D. Altogether, this work contributes to the development of advanced 3D region-specific organoids, which better recapitulate the complexity of the human brain. These novel MO systems represent one step further into modelling neuroinflammation and blood brain barrier disruption, typical from neurodegenerative disorders such as PD, which might lead to more reliable and personalized medical approaches. [less ▲]

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See detailDevelopment of data integration tools within functional genomics
Teixeira Queiros, Pedro UL

Doctoral thesis (2022)

Due to technological advances across all scientific domains, data is generated at an extremely fast pace. This is especially true in biology, where advances in computational and sequencing technologies ... [more ▼]

Due to technological advances across all scientific domains, data is generated at an extremely fast pace. This is especially true in biology, where advances in computational and sequencing technologies led to the necessity to develop automated methods for data analysis; thus the field of bioinformatics was born. This thesis focuses on one specific field within bioinformatics - functional genomics. To be precise, in the development of techniques and software for the integration of data to generate novel insights. Indeed, as the amount of knowledge increases, so does the need to integrate it systematically. In this context, the work described herein relates to the integration of multiple resources to improve the functional annotation of proteins, which led to the development of two bioinformatic tools - Mantis and UniFunc. For the downstream integration and analysis of functional predictions, a network annotation tool was developed - UniFuncNet, which, together with the previous tools, enables the efficient functional characterisation of individual organisms or communities. [less ▲]

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See detailVerifiable, Secure and Privacy-Preserving Computation
Soroush, Najmeh UL

Doctoral thesis (2022)

In this thesis, I present the research I conducted with my co-authors on numerous areas of verifiable, secure, and privacy-preserving computation during my doctoral studies at the University of Luxembourg ... [more ▼]

In this thesis, I present the research I conducted with my co-authors on numerous areas of verifiable, secure, and privacy-preserving computation during my doctoral studies at the University of Luxembourg, where Professor Peter Ryan advised me. In the first part, I study the functional encryption scheme. In the standard setting of functional encryption, it is assumed both the Central Authority (CA) and the encryptors to run their respective algorithms faithfully. However, in the case of dishonest parties, the security of the cryptosystem may be violated. It means that dishonest parties can cause inconsistent results which may not be detected. In the first part, we improve on this situation by considering Inner-Product Encryption (IPE), a special case of functional encryption and a primitive that has attracted wide interest from practitioners and researchers in the last decade. Specifically, we construct the first efficient verifiable Inner Product Encryption (VIPE) scheme according to the inner-product functionality. As the next step, we construct a verifiable IPE that satisfies unconditional verifiability, whereas privacy relies on the standard assumption. The second part of this thesis presents my research on e-voting protocols. I revisit the coercion-resistant e-voting protocol by Juels, Catalano and Jakobsson (JCJ) and, particularly, the attempts to make it usable and practical. In JCJ the user needs to handle cryptographic credentials and fake these in case of coercion. We present a hardware-independent protocol that can be implemented using a combination of a digitally stored cryptographic length key and a PIN only known by the voter. The long credential could be stored in several places or hidden via steganography. At the ballot casting phase, the software will input the digital key and the password to form the credential submitted with the vote. Depending on the level of coercion, the coerced voter can either fake the long credential or, for stronger levels of coercion, the voter can reveal the digitally stored credential to the coercer but fake the PIN. Due to our improved tally, the coercer will not know if he got faked credentials or PINs. On the other hand, since the voter memories the PIN is a high chance of users making a PIN typo error which will invalidate the vote and remain undetected. Note that naively giving feedback on the correctness of the PIN is not possible for coercion-resistance as it would allow the coercer to check whether he got a fake PIN or not. Instead, we will define a set of allowed PIN errors (e.g., chosen by the election administrator). We will consider a ballot valid if it has a correct PIN or an allowed PIN error but invalid for other PINs. At the tally phase, we construct protocols that secretly check whether a given PIN is in the set of allowed PINs and will sort out invalid ballots. We also design another End-to-End verifiable e-voting scheme achieving coercion-resistance via deniable vote updating. We propose a new e-voting system that enables voters with an intuitive mechanism to update their possibly coerced vote in a deniable way. What is more, our e-voting system does not introduce any additional trust assumptions for end-to-end verifiability and vote privacy besides the standards. Moreover, we demonstrate that our e-voting system can be instantiated efficiently for practical elections. With these properties, our e-voting system has the potential to close the gap between theory and practice in coercion-resistant e-voting. [less ▲]

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See detailState Responsibility for Judicial Acts in Investment Arbitration
Cavdarevic, Ivan UL

Doctoral thesis (2022)

This thesis deals with the issue of state responsibility for judicial acts in investment arbitration. In general, it is undisputed that states can be held internationally liable for the conduct of their ... [more ▼]

This thesis deals with the issue of state responsibility for judicial acts in investment arbitration. In general, it is undisputed that states can be held internationally liable for the conduct of their judiciary. The protection against denial of justice was traditionally the main international standard used for determining state responsibility for judicial acts, characterized by a relatively high threshold. Only with the proliferation of primary obligations of states towards individuals, the practical relevance of this standard was somewhat decreased. Nonetheless, this type of state responsibility – arising from domestic judicial acts – remains particularly sensitive due to the special nature and position of the national judiciary. It is commonly noted that role of national courts and their decision-making process is so particular that international courts and tribunals should not treat national judicial acts in an identical manner as acts of other state organs. Against this background, this thesis examines whether and to what extent the investment tribunals’ practice on cases concerning domestic judicial acts differs from comparable practices of other international adjudicatory bodies – in particular, the International Court of Justice and the European Court of Human Rights. Through the analysis of various standards of protection – when applied to judicial acts – and the relevant case law, the thesis aims to address two different questions. Firstly, whether the standards of protection granted investment treaties substantially differ from standards commonly applied by other international adjudicatory bodies. Secondly, if the investment tribunals practice’ in cases concerning host states’ judicial acts is influenced only by these substantive standards or protection, or it is also shaped by more general features of investment law and the investor investor-state dispute settlement mechanism. Ultimately, this research – through the analysis of these particular types of investor-state disputes – tries to offer novel insights into the deficiencies of the ISDS regime that should be taken into account, especially in light of the current initiatives for the reform of this regime. [less ▲]

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See detailMECHANO-BIOLOGY OF TUMOR GROWTH WITH THE AIM OF CLINICAL APPLICATIONS, A REACTIVE MULTIPHASE POROMECHANICAL APPROACH
Urcun, Stephane UL

Doctoral thesis (2022)

We propose the modeling of glioblastoma isocitrate dehydrogenase wild-type (GBMwt) build on the following hypotheses: the brain tissue is a porous medium, the coupling of hypoxia consequences and ... [more ▼]

We propose the modeling of glioblastoma isocitrate dehydrogenase wild-type (GBMwt) build on the following hypotheses: the brain tissue is a porous medium, the coupling of hypoxia consequences and mechanical interplay between extra-cellular matrix and tumor cells is the driver of the malignant evolution of the disease. In this thesis, a poromechanical model is developed with the aim of a clinical application in oncology. A review, with a large scope, is done on mechanical applications in clinical management of cancer. The model is first validated on in vitro experimental data of encapsulated multi-cellular spheroids. Then, a clinical collaboration is initiated with the Neuro-imaging center of Toulouse, and the targeted clinical application is the modeling of non- operable GBMwt. To this end, the model is first adapted to the specificity of brain tissue mechanics. Characteristic features of the disease are modeled: necrotic core, modified extra-cellular matrix production, emerging malignant phenotype and invasion. Clinical imaging data are pre- treated to inform the model in a patient specific basis. A proposition of modeling is provided with an evaluation against clinical data. [less ▲]

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See detailPopulation structure and phenotypical traits of Campylobacter jejuni circulating in Luxembourg
Nennig, Morgane UL

Doctoral thesis (2022)

Campylobacter is the leading cause of bacterial gastroenteritis worldwide. The most prevalent species, C. jejuni, is a strict microaerobic, capnophilic, and thermotolerant pathogen. Given its growth ... [more ▼]

Campylobacter is the leading cause of bacterial gastroenteritis worldwide. The most prevalent species, C. jejuni, is a strict microaerobic, capnophilic, and thermotolerant pathogen. Given its growth requirements, the ability of C. jejuni to persist in food environments and be transmitted throughout food processing has long puzzled scientists. This study aimed to compare the different genetic profiles of C. jejuni strains, isolated in Luxembourg, at the core genome (cg) and whole genome (wg) levels to elucidate its genetic population structure. Using phenotypical assays in controlled conditions and functional genomics analyses from wgMLST data, the study also investigated the possible link between phenotypic traits and emergence or persistence of genotypes. A high concordance in strain clustering was observed between genomic lineage classifications and the epidemic and endemic signals, regardless the three cgMLST typing schemes used. The higher genome stability within genomic lineages supports the hypothesis of a clonal expansion with monomorphic patterns over time and sources. A high correlation was observed between phenotypes and host-specific or generalist clonal complexes for oxidative stress, adhesion to inert surfaces, biofilm formation, and acclimation to aerobic conditions responses. These data allowed the establishment of metaphenotypes specific to the genomic lineages. Functional genomic analysis revealed factors that may contribute to the spatiotemporal survival of recurrent strains. These results also suggest the selection of better-adapted and persistent C. jejuni strains to environmental stresses throughout the transmission to human. [less ▲]

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See detailAutomation of Controller Area Network Reverse Engineering: Approaches, Opportunities and Security Threats
Buscemi, Alessio UL

Doctoral thesis (2022)

Controller Area Network (CAN ) is the de-facto in-vehicle communication system in the automotive industry today. CAN data represents a valuable source of information regarding the vehicle, which can be ... [more ▼]

Controller Area Network (CAN ) is the de-facto in-vehicle communication system in the automotive industry today. CAN data represents a valuable source of information regarding the vehicle, which can be exploited for a multitude of purposes by aftermarket companies, from fleet management to infotainment. With the rise of Vehicular Ad Hoc Networks (VANETs) and autonomous driving, we can expect the amount of data transiting on the CAN bus to further augment in the near future. While not encrypted, the communication inside the CAN bus is typically encoded using proprietary formats of the Original Equipment Manufacturers (OEM s) in order to prevent easy access to the information exchanged on the network. However, given the unwillingness of the OEM s to disclose the formats of most of the CAN signals of commercial vehicles (cars in particular) to the general public, the most common way to obtain such information is through reverse engineering. Recently, researchers have started investigating the automation of this process to make it faster, scalable and standardised. Aside from the evident advantages that it would bring to the industry, the automation of CAN bus reverse engineering has also gained interest in the scientific community, where automotive cybersecurity is a prominent topic. While achieving convincing results, the automation of CAN reverse engineering is still invasive, often includes complex hardware configurations or requires the presence of a human operator in the vehicle. This dissertation aims to analyse the main advancements achieved in the field of CAN bus reverse engineering and shed light on open issues. In the first part of this dissertation, we explore opportunities and challenges of the automation of CAN bus reverse engineering and present three approaches that achieve different degrees of automation. The first, FastCAN, is based on the taxonomy of signals. Its goal is to provide a complete, standardised and modular pipeline for semi-automated reverse engineering and reduce the total time for data collection. The second, CSI, is a Machine Learning (ML )-based algorithm for the identification of critical signals working under limited assumptions. We use CSI as a case study to investigate whether CAN reverse engineering can be achieved with no other hardware than a dongle for the collection of raw data. The third, CANMatch, is a complete and fully automated approach based on frame matching. Through CANMatch we seek to demonstrate that the reuse of CAN frame IDs can be exploited to reverse engineer a high number of signals with minimal hardware requirements and human effort. In the second part of this dissertation, we discuss the implications that the full automation of the reverse engineering process has on the security of the bus. In this context, we investigate whether the anonymisation of the CAN frame IDs is sufficient to prevent frame-matching based reverse engineering. The results highlight that ML models can fingerprint CAN frames despite the anonymisation of their IDs. Finally, we propose a defence against frame fingerprinting based on traffic mutations, such as padding on the payload and morphing on the sending frequency. We conclude that traffic mutations are a promising study direction to prevent frame-matching based reverse engineering. [less ▲]

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See detailMachine learning force fields: towards modelling flexible molecules
Vassilev Galindo, Valentin UL

Doctoral thesis (2022)

Accurate modelling of chemical and physical interactions is crucial for obtaining thermodynamic and dynamical properties of any chemical system, enabling a myriad of possible applications. Many of these ... [more ▼]

Accurate modelling of chemical and physical interactions is crucial for obtaining thermodynamic and dynamical properties of any chemical system, enabling a myriad of possible applications. Many of these applications are computationally prohibitive when using advanced Computational Chemistry (CompChem) methods even on modern supercomputers. Because of this, machine learning (ML) force fields (FFs), combining the accuracy of state-of-the-art ab initio methods and the efficiency of classical FFs, are being increasingly used to reconstruct potential-energy surfaces (PESs) of molecules and solids. It is precisely the synergy of ML and CompChem that has revolutionized the field in the last decade, rising the applications to a qualitatively new level. Despite this great success, there are still many unsolved challenges. In this context, my thesis aims to investigate the capability of the existing MLFFs to provide simultaneously accurate and efficient models offering unprecedented insights into the (thermo)dynamics of realistic molecular systems. Using the examples of molecular interactions that are pervasive in (bio)chemical systems, we show a counterintuitive effect of strengthening of such interactions, as well as an unexpected prevalence of quantum nuclear fluctuations over thermal contributions at room temperature. We reveal that, when dealing with complex PESs, the predictions of state-of-the-art ML models (BPNN, SchNet, GAP, and sGDML) greatly depend on the descriptor used, and on the region of the PES being sampled. Given the varying performance of MLFFs, we present a descriptor optimization scheme improving simultaneously the accuracy and efficiency of ML models. Our results show that the commonly employed strategies followed to construct both local and global descriptors need to be improved because the optimal descriptors are a non-trivial combination of local and global features. Therefore, the work presented in this thesis highlights the potential of MLFFs to provide insights into chemical systems while, at the same time, discloses the current limitations preventing the construction of accurate MLFFs for more realistic systems. Also, I propose the optimization of the description of interactions within an ML model as a valuable step towards obtaining efficient and accurate MLFFs of large and flexible molecules. Overall, this thesis suggests that the full workflow for building ML models still needs significant elaboration. Despite this finding, the combination of CompChem and ML methods in atomistic modelling promises to enable us to solve multiple problems in different areas, such as medicine, materials design, pharmacology, energy production, environmental sciences, among others. [less ▲]

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See detailEssays in Financial Economics
Ignashkina, Anna UL

Doctoral thesis (2022)

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See detailControllability of complex flow networks
Mazur, Xavier UL

Doctoral thesis (2022)

When left uncontrolled, complex flow networks are susceptible to negative externalities and tend not to be used to their full potential. This work focuses more specifically on the specific instance of ... [more ▼]

When left uncontrolled, complex flow networks are susceptible to negative externalities and tend not to be used to their full potential. This work focuses more specifically on the specific instance of transportation networks that are subject to constantly increasing demand. Control strategies, based on increasingly promising technological advancements, have been developed with the aim to exploit the full potential of the existing transportation infrastructure. To improve the current state of transportation networks, control strategies rely on control technologies to impact road users on networks and redirect them, such as to improve the situation by avoiding delays, for example. However, the problem of identifying the required controller numbers, types, and locations has received little attention in the current literature. Existing research works proposed approaches to the problem but often either do not provide complete control over the considered network or lack scalability, thus are not applicable on any type and size of networks. In this dissertation, we aim at filling this gap by providing a general methodology and proposing various approaches to this problem. The first part of this work focuses specifically on studying the problem of fully controlling a transportation network and provides various approaches. Their capacity to actually impact and control transportation networks is assessed empirically, showing that the proposed approaches can fully control small networks. The second part studies the problem of scalability and provides a new method that is proved to be able to provide an efficient set of pricing controllers while being scalable. This approach is later improved by integrating flow information and demonstrated to be more reliable in the specific case where the demand is irregularly distributed over the considered network, which is a common setting in real transportation networks. Additionally, the proposed methods are applied and tested over the network of Luxembourg, demonstrating the scalability of the approaches and their capacity to improve the current state of large realistic networks subject to heavy congestion. [less ▲]

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See detailDetection of Sentiment in Luxembourgish User Comments
Gierschek, Daniela UL

Doctoral thesis (2022)

Sentiment is all around us in everyday life. It can be found in blog posts, social media comments, text messages and many other places where people express themselves. Sentiment analysis is the task of ... [more ▼]

Sentiment is all around us in everyday life. It can be found in blog posts, social media comments, text messages and many other places where people express themselves. Sentiment analysis is the task of automatically detecting those sentiments, attitudes or opinions in written text. In this research, the first sentiment analysis solution for the low-resource language, Luxembourgish, is conducted using a large corpus of user comments published on the RTL Luxembourg website www.rtl.lu. Various resources were created for this purpose to set the foundation for further sentiment research in Luxembourgish. A Luxembourgish sentiment lexicon and an annotation tool were built as external resources that can be used for collecting and enlarging training data for sentiment analysis tasks. Additionally, a corpus of mainly sentences of user comments was annotated with negative, neutral and positive labels. This corpus was furthermore automatically translated to English and German. Afterwards, diverse text representations such as word2vec, tf-idf and one-hot encoding were used on the three versions of the corpus of labeled sentences for training different machine learning models. Furthermore, one part of the experimental setup leveraged linguistic features for the classification process in order to study their impact on sentiment expressions. By following such a broad strategy, this thesis not only sets the basis for sentiment analysis with Luxembourgish texts but also intends to give recommendations for conducting sentiment detection research for other low-resource languages. It is demonstrated that creating new resources for a low-resource language is an intensive task and should be carefully planned in order to outperform working with translations where the target language is a high-resource language such as English and German. [less ▲]

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See detailANALYZING THE PRIVACY AND SECURITY OF PROOF-OF-WORK CRYPTOCURRENCIES
Cao, Tong UL

Doctoral thesis (2022)

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See detailDissecting Complex Microglia Heterogeneity in Neurodegeneration
Ameli, Corrado UL

Doctoral thesis (2022)

Detailed reference viewed: 59 (21 UL)
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See detailArchitectural Support for Hypervisor-Level Intrusion Tolerance in MPSoCs
Pinto Gouveia, Ines UL

Doctoral thesis (2022)

Increasingly, more aspects of our lives rely on the correctness and safety of computing systems, namely in the embedded and cyber-physical (CPS) domains, which directly affect the physical world. While ... [more ▼]

Increasingly, more aspects of our lives rely on the correctness and safety of computing systems, namely in the embedded and cyber-physical (CPS) domains, which directly affect the physical world. While systems have been pushed to their limits of functionality and efficiency, security threats and generic hardware quality have challenged their safety. Leveraging the enormous modular power, diversity and flexibility of these systems, often deployed in multi-processor systems-on-chip (MPSoC), requires careful orchestration of complex and heterogeneous resources, a task left to low-level software, e.g., hypervisors. In current architectures, this software forms a single point of failure (SPoF) and a worthwhile target for attacks: once compromised, adversaries can gain access to all information and full control over the platform and the environment it controls, for instance by means of privilege escalation and resource allocation. Currently, solutions to protect low-level software often rely on a simpler, underlying trusted layer which is often a SPoF itself and/or exhibits downgraded performance. Architectural hybridization allows for the introduction of trusted-trustworthy components, which combined with fault and intrusion tolerance (FIT) techniques leveraging replication, are capable of safely handling critical operations, thus eliminating SPoFs. Performing quorum-based consensus on all critical operations, in particular privilege management, ensures no compromised low-level software can single handedly manipulate privilege escalation or resource allocation to negatively affect other system resources by propagating faults or further extend an adversary’s control. However, the performance impact of traditional Byzantine fault tolerant state-machine replication (BFT-SMR) protocols is prohibitive in the context of MPSoCs due to the high costs of cryptographic operations and the quantity of messages exchanged. Furthermore, fault isolation, one of the key prerequisites in FIT, presents a complicated challenge to tackle, given the whole system resides within one chip in such platforms. There is so far no solution completely and efficiently addressing the SPoF issue in critical low-level management software. It is our aim, then, to devise such a solution that, additionally, reaps benefit of the tight-coupled nature of such manycore systems. In this thesis we present two architectures, using trusted-trustworthy mechanisms and consensus protocols, capable of protecting all software layers, specifically at low level, by performing critical operations only when a majority of correct replicas agree to their execution: iBFT and Midir. Moreover, we discuss ways in which these can be used at application level on the example of replicated applications sharing critical data structures. It then becomes possible to confine software-level faults and some hardware faults to the individual tiles of an MPSoC, converting tiles into fault containment domains, thus, enabling fault isolation and, consequently, making way to high-performance FIT at the lowest level. [less ▲]

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See detailArchitectural Support for Hypervisor-Level Intrusion Tolerance in MPSoCs
Pinto Gouveia, Inês UL

Doctoral thesis (2022)

Increasingly, more aspects of our lives rely on the correctness and safety of computing systems, namely in the embedded and cyber-physical (CPS) domains, which directly affect the physical world. While ... [more ▼]

Increasingly, more aspects of our lives rely on the correctness and safety of computing systems, namely in the embedded and cyber-physical (CPS) domains, which directly affect the physical world. While systems have been pushed to their limits of functionality and efficiency, security threats and generic hardware quality have challenged their safety. Leveraging the enormous modular power, diversity and flexibility of these systems, often deployed in multi-processor systems-on-chip (MPSoC), requires careful orchestration of complex and heterogeneous resources, a task left to low-level software, e.g., hypervisors. In current architectures, this software forms a single point of failure (SPoF) and a worthwhile target for attacks: once compromised, adversaries can gain access to all information and full control over the platform and the environment it controls, for instance by means of privilege escalation and resource allocation. Currently, solutions to protect low-level software often rely on a simpler, underlying trusted layer which is often a SPoF itself and/or exhibits downgraded performance. Architectural hybridization allows for the introduction of trusted-trustworthy components, which combined with fault and intrusion tolerance (FIT) techniques leveraging replication, are capable of safely handling critical operations, thus eliminating SPoFs. Performing quorum-based consensus on all critical operations, in particular privilege management, ensures no compromised low-level software can single handedly manipulate privilege escalation or resource allocation to negatively affect other system resources by propagating faults or further extend an adversary’s control. However, the performance impact of traditional Byzantine fault tolerant state-machine replication (BFT-SMR) protocols is prohibitive in the context of MPSoCs due to the high costs of cryptographic operations and the quantity of messages exchanged. Furthermore, fault isolation, one of the key prerequisites in FIT, presents a complicated challenge to tackle, given the whole system resides within one chip in such platforms. There is so far no solution completely and efficiently addressing the SPoF issue in critical low-level management software. It is our aim, then, to devise such a solution that, additionally, reaps benefit of the tight-coupled nature of such manycore systems. In this thesis we present two architectures, using trusted-trustworthy mechanisms and consensus protocols, capable of protecting all software layers, specifically at low level, by performing critical operations only when a majority of correct replicas agree to their execution: iBFT and Midir. Moreover, we discuss ways in which these can be used at application level on the example of replicated applications sharing critical data structures. It then becomes possible to confine software-level faults and some hardware faults to the individual tiles of an MPSoC, converting tiles into fault containment domains, thus, enabling fault isolation and, consequently, making way to high-performance FIT at the lowest level. [less ▲]

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See detailBiobanking science applied to patient specific stem cells and biomarkers for Parkinson's disease research
Mommaerts, Kathleen Michèle Ghislaine Marie UL

Doctoral thesis (2022)

Biomedical research aims to understand the pathological and pathophysiological mechanisms that cause disease. Neurodegenerative diseases, such as Parkinson’s disease (PD), are major contributors to the ... [more ▼]

Biomedical research aims to understand the pathological and pathophysiological mechanisms that cause disease. Neurodegenerative diseases, such as Parkinson’s disease (PD), are major contributors to the burden of disease across the globe. PD is an age-related, progressive neurodegenerative disease. The pathological hallmarks are a selective loss of dopaminergic neurons from the substantia nigra in conjunction with the presence of protein aggregates involving α-synuclein in the residual neurons. Cystatin C expression has been shown to become upregulated in brain injuries, neurological disorders and in animal models of neurodegenerative states, which suggests it could play a part in neurodegenerative disorders. The main function of this primarily secreted protein is the inhibition of cysteine proteases. Various tools are available to researchers to study diseases, ranging from animal models, human biospecimens and human in vitro models. Regardless of the model selected, reproducibility is crucial to ensure meaningful research. To maximise the quality of biomedical research, biobanks work to ensure the biospecimens they issue are compromised as little as possible as a consequence of the unavoidable preanalytical variables occurring during their collection, processing and storage. The scientific discipline that studies preanalytical variables and how they affect biospecimens is called biospecimen science. In this thesis, biospecimen science was applied to patient specific stem cells and cystatin C in the scope of PD research. A standardized research-grade human induced pluripotent stem cell (iPSC) workflow was established for use as an in vitro PD model, which encompasses both iPSC generation and cryostorage. Controlled-rate freezing of iPSCs using three different dimethyl sulfoxide-based cryosolutions containing ice recrystallization inhibitors was evaluated and optimized to achieve efficient iPSC cryopreservation. A double, indirect sandwich ELISA was established to quantify the concentration and the degradative state of secreted cystatin C. The ELISA was validated using well-defined and standardized cerebrospinal fluid (CSF) biospecimens, then applied as a tool to retrospectively identify CSF biospecimens that had been stored in suboptimal conditions. Secreted cystatin C was quantified and compared in blood derivatives (plasma and serum) and in the culture media of derived models (iPSCs, neuroepithelial stem cells and midbrain organoids) from three idiopathic PD patients and age-matched healthy controls. The standardized in vitro PD models, novel quality control and cryopreservation methods not only demonstrate the critical importance of preanalytical standardization but open the way to future biomedical research. [less ▲]

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See detailVun iwwerfëlltene Bussen bis bei déi beschte Witzer. Morphologische Variation im Luxemburischen – Eine variations- und perzeptionslinguistische Studie.
Entringer, Nathalie UL

Doctoral thesis (2022)

Die vorliegende Arbeit beschäftigt sich mit morphologischer Variation im Luxemburgischen und verfolgt dabei zwei Hauptziele: Erstens sollen ausgewählte, zu einem großen Teil noch unerforschte Phänomene ... [more ▼]

Die vorliegende Arbeit beschäftigt sich mit morphologischer Variation im Luxemburgischen und verfolgt dabei zwei Hauptziele: Erstens sollen ausgewählte, zu einem großen Teil noch unerforschte Phänomene auf Grundlage eines umfangreichen Sprachdaten-Korpus systematisch beschrieben und die Variation, die diese Phänomene prägt, detailliert analysiert werden. Zweitens will die Forschungsarbeit mithilfe eines maßgeschneiderten Testverfahrens anschließend eruieren, wie diese Variationsphänomene von Luxemburgisch-Sprecher.innen wahrgenommen und bewertet werden. Die Dissertation liefert somit nicht nur einen wichtigen Beitrag dazu, morphologische Variation und somit auch Wandel im Luxemburgischen besser zu verstehen, sondern auch die Etablierung der perzeptionslinguistischen Forschungsdimension innerhalb der Luxemburgistik voranzutreiben. [less ▲]

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See detailIdentité(s) transnationale(s) de l’Union européenne : Analyse juridique pour un système de protection effective des droits
Zinonos, Panagiotis UL

Doctoral thesis (2022)

La thèse forme une proposition normative sur les rapports de système entre les ordres juridiques des Etats membres et celui de l’Union. Elle analyse ces rapports à la lumière de l’objectif de protection ... [more ▼]

La thèse forme une proposition normative sur les rapports de système entre les ordres juridiques des Etats membres et celui de l’Union. Elle analyse ces rapports à la lumière de l’objectif de protection effective des droits et de(s) l’identité(s) transnationale(s) de l’Union. Des jurisprudences européennes et nationales et des éléments théoriques tracent les conditions d’une protection systématisée. La thèse s’intéresse à l’identité du système pour exclure la rivalité inhérente entre les ordres juridiques des Etats membres et celui de l’Union. La démarche aboutit grâce au déplacement du curseur des rapports entre ordres juridiques vers leur fonction pour les acteurs du système juridique de l’Union et grâce à l’analyse du fonctionnement dudit système sur la base d’échelles de concrétisation du principe transnational de loyauté. La thèse s’intéresse d’abord à la systématisation de la protection dans l’Union avant d’aborder des techniques spécifiques de protection. Tant du point de vue théorique que procédural ressort une dualité de l’identité juridique de l’Union : formelle – relative à la perpétuation du système – et matérielle – relative à ses valeurs fondamentales. [less ▲]

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See detailQuantifying some properties of curves and arcs on hyperbolic surfaces
Doan, Nhat Minh UL

Doctoral thesis (2022)

Motivated by the ergodicity of geodesic flow on the unit tangent bundle of a closed hyperbolic surface and its applications, this thesis includes three parts: Part 1. We present a type of quantitative ... [more ▼]

Motivated by the ergodicity of geodesic flow on the unit tangent bundle of a closed hyperbolic surface and its applications, this thesis includes three parts: Part 1. We present a type of quantitative density of closed geodesics and orthogeodesics on complete finite-area hyperbolic surfaces. The main results are upper bounds on the length of the shortest closed geodesic and the shortest doubly truncated orthogeodesic that are Y-dense on a given compact set on the surface. Part 2. We investigate the terms arising in Luo-Tan’s identity, namely showing that they vary monotonically in terms of lengths and that they verify certain convexity properties. Using these properties, we deduce two results. As a first application, we show how to deduce a theorem of Thurston which states, in particular for closed hyperbolic surfaces, that if a simple length spectrum "dominates" another, then in fact the two surfaces are isometric. As a second application, we show how to find upper bounds on the number of pairs of pants of bounded length that only depend on the boundary length and the topology of the surface. This is joint work with Hugo Parlier and Ser Peow Tan. Part 3. Inspired by a number theoretic application of Bridgeman’s identity, the combinatorial proof of McShane’s identity by Bowditch and its generalized version by Labourie and Tan, we describe a tree structure on the set of orthogeodesics and give a combinatorial proof of Basmajian’s identity in the case of surfaces. We also introduce the notion of orthoshapes with associated identity relations and indicate connections to length equivalent orthogeodesics and a type of Cayley-Menger determinant. As another application, dilogarithm identities following from Bridgeman’s identity are computed recursively and their terms are indexed by the Farey sequence. [less ▲]

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See detailDiet–gut microbiota interactions in immune homeostasis and food allergy
Parrish, Amy UL

Doctoral thesis (2022)

Studies during the past decade have identified correlations between abundance of certain gut microbial taxa and disease, which further stress the need to better understand the mechanistic details of the ... [more ▼]

Studies during the past decade have identified correlations between abundance of certain gut microbial taxa and disease, which further stress the need to better understand the mechanistic details of the role of the microbota in health and disease. The gut microbiome and its metabolic output is heavily influenced by environmental triggers, most notably, diet. Among various dietary components, dietary fiber is non-digestible by the host and fermentation of soluble fibers occurs in the large intestine by fiber-degrading members of the microbiome. The primary byproducts of fiber fermentation include short-chain fatty acids, which have been extensively studied in the context of immune regulation and various diseases. Nevertheless, there is an unmet need to identify additional metabolites produced by a fiber-fermenting microbiome, as this knowledge will contribute toward better unravelling the mechanistic details of the microbiome’s role in disease. Moreover, little is known about the immunological consequences of removing or limiting dietary fiber – a phenomenon that has been common in the Western world for the past decades – and its impact on both gut microbial metabolism and host immunity. In this PhD thesis, I investigated the role of dietary fiber on gut microbial metabolism and its effect on the host immunophenotype at homeostasis. I employed distinct commerical and customized rodent diets with varying sources and content of dietary fiber, in mice with both conventional and humanized gut microbiomes to better understand how fibers alter microbial metabolism and host immunity. Using broad-scale capillary electrophoresis time-of-flight mass spectrometry-based microbial metabolomics, I identified a suit of microbial metabolites whose abudance is modulated by the microbial fermentation of dietary fiber. The main finding of this first project was that fiber deprivation leads the gut microbiome to decrease production of B vitamin synthesis and alter bile acid metabolism. This was associated with an increased immune activation in the colonic lamina propria as identified by broad immunophenotyping using time-of-flight mass cytometry of host local and systemic organs. My data show that a variety of immune cell populations are differentially affected by fiber sources and content. In the second project of this thesis, I applied the findings that fiber deprivation induces a proinflammatory environment, which was type 2 skewed, to a disease model. Here, I evaluated the role of microbiome-mediated barrier dysfunction due to fiber deprivation in a food allergic mouse model. In mice colonized with both a conventional and artificially colonized synthetic human gut microbiota, I identified that fiber deprivation induces a colonic mucus barrier dysfunction, which led to the discovery of IgE-coated bacteria in the feces as a consequence, prior to allergic sensitization. Upon sensitization and subsequent allergen challenge, these mice exhibited more severe clinical symptoms compared to mice fed a standard laboratory chow. In a gnotobiotic model, I found that the removal of the mucin-specialist, Akkermansia muciniphila, was able to improve symptoms, and eliminate the diet effect observed in mice with a conventional as well as synthetic microbiota. This was associated with a stark decrease in type 2 inflammatory immune cell subsets within the colonic lamina propria. These novel findings arising from my PhD thesis will help the scientific community to enhance our understanding of the functional role of the gut microbiome in modulating the immune system. The links made in the first project connecting dietary fiber and B vitamins and its role in maintaining immune homeostasis provide an additional critical factor to be considered in personalized dietary interventions. In the second project, I identify that barrier dysfunction led to the activation of an atopic environment, without any additional immunological stimulus. This generated an IgE-mediated response towards commensal bacteria. Importantly, my experiments show a clear immunomodulatory role of a mucin-specialist bacterium, A. muciniphila, in allergic disease, which provides a functional link of the gut microbial composition and physiology to food allergy sensitization [less ▲]

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See detailA Painful Peek into the Underlying Mechanisms of Mindfulness
Vencatachellum, Shervin UL

Doctoral thesis (2022)

The burgeoning scientific interest in the clinical benefits of mindfulness has resulted in an extensive body of research linking mindfulness-based practices to improvements across a wide range of pain ... [more ▼]

The burgeoning scientific interest in the clinical benefits of mindfulness has resulted in an extensive body of research linking mindfulness-based practices to improvements across a wide range of pain-related outcomes. Yet, a clear understanding of the mechanisms via which mindfulness conveys its purported effects is still lacking. Novel insights from neuroimaging studies suggest that mindfulness may alleviate pain via unique neural mechanisms characterised by increased pain-related sensory processing and abatement of evaluative and memory-related processes. In light of these observations, recently formulated predictive processing accounts posit that the non-elaborative sustained attention to present-centred experience during mindfulness practice may lead to a weighing of incoming sensory information over prior information during the perceptual process. This interpretation hence raises the intriguing possibility that mindfulness may mitigate the well-documented biasing influence of prior expectations and information on pain perception. We tested this hypothesis across three separate pain expectancy-manipulation paradigms. Study 1 investigated whether the instructed use of a mindfulness strategy vs. an vsernative cognitive regulatory strategy (i.e., suppression) differentially modulates susceptibility to conditioned hypoalgesic and hyperalgesic effects during an implicit classical pain conditioning paradigm. The results revealed that while participants assigned to the suppression condition exhibited preserved cue-induced hypoalgesic effects, no such effects were observed for the mindfulness condition. In Study 2, we employed a recently developed pain categorization paradigm to test whether trait mindfulness level modulates the influence of prior categorical information on pain perception and pain-related decision-making. Although the paradigm successfully elicited categorization-induced perceptual biases, modulation of these effects did not differ across individuals with high and low trait mindfulness. Finally, in Study 3, we used an explicit pain-cueing paradigm in which we aimed to address some of the methodological limitations of Study 1. The analyses revealed that high trait mindfulness scorers reported smaller cue-induced hyperalgesic effects for pain unpleasantness ratings compared to low trait mindfulness scorers. There were, however, no group differences in cue-induced hypoalgesia. Results from the pain conditioning studies provide partial support for the notion that mindfulness may mitigate the influence of prior expectations and information on pain perception. These findings add to growing evidence suggesting that mindfulness may alleviate pain via neuropsychological mechanisms opposite to those typically involved in conditioning/placebo-induced hypoalgesia. The discussion section explores potential methodological and mechanistic explanations for the asymmetric pattern of results observed across the three studies and considers the potential clinical implications of those findings. [less ▲]

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See detailA TALE OF ADVERSITY: THE IMPACT OF EARLY LIFE INFECTION BEYOND THE IMMUNE SYSTEM
Merz, Myriam Pia UL

Doctoral thesis (2022)

Early life adversity (ELA) is associated with a higher risk for chronic and noncommunicable diseases in adulthood. Changes in the HPA axis and the immune system have been proposed to underlie this ... [more ▼]

Early life adversity (ELA) is associated with a higher risk for chronic and noncommunicable diseases in adulthood. Changes in the HPA axis and the immune system have been proposed to underlie this association, however current ELA research remains mainly focused on neglect, abuse and low socioeconomic status as sources of childhood adversity. Early-life adversity covers a range of physical, social and environmental stressors. Acute viral infections in early life are a major source of such adversity and have been associated with a broad spectrum of later-life effects outside the immune system or “off-target”. These include an altered hypothalamus-pituitary-adrenal axis and metabolic reactions. Here, we used a murine postnatal day 14 (PND 14) Influenza A (H1N1) infection model and applied a semi-holistic approach including phenotypic measurements, gene expression arrays and diffusion neuroimaging techniques to investigate HPA axis dysregulation, energy metabolism, brain connectivity, microbial composition and immune cell shift . We then extended our mouse model for a postnatal day 56 (PND56) viral challenge to study immune reactivation after an early life “priming” event. We could show that an early life influenza A virus infection led to an immediate shift in lung microbiota and long-term changes in gut microbiome composition. We also observed several sex-specific effects: retarded growth of males, baseline blood glucose levels being increased in females and decreased in males, and baseline corticosterone levels were reduced while total number of CD3+ cells were higher in males. At the same time, we found a microbial composition shift persistent several weeks after the early life infection. For the brain, MRI scans identified reduced connectivity in the cortex, midbrain and cerebellum which were accompanied by tissue-specific gene expression signatures. Early-life infection appeared to have independently affected each of the systems, potentially independent of HPA axis or immune perturbations. [less ▲]

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See detailLASER FUSION WELDING OF CU TO AL WITH SPIRAL TRAJECTORY AND MONITORING OF PROCESS SIGNALS
Mathivanan, Karthik UL

Doctoral thesis (2022)

Welding of Aluminium (Al) and Copper (Cu) in a dissimilar fashion is required for the manufacturing of solar thermal absorbers, battery modules and refrigeration applications. The high strength, thermal ... [more ▼]

Welding of Aluminium (Al) and Copper (Cu) in a dissimilar fashion is required for the manufacturing of solar thermal absorbers, battery modules and refrigeration applications. The high strength, thermal and electrical conductivity of Cu combined with the lightweight property of Al material enable the high performance of the product. A laser is a precise tool, which can increase the productivity and quality of the welding process. Welding Al and Cu is considered difficult because of the formation of complex intermetallic phases which reduce the strength of the joint. Laser brazing from low melting Al sheet to Cu sheet is the traditional technique to reduce the intermetallic phases. This thesis focuses on irradiation of laser beam from copper sheet to aluminium sheet in overlapped configuration. The approach is to form a large amount of intermixing to obtain (Cu) solid solution and Al-rich phase Al+Al2Cu in the interface. By this approach, it was found that a fusion zone with a large number of good phases was formed. The intermetallic compounds Al2Cu,Al3Cu4,Al4Cu9 are intermixed and small. Such a microstructure is beneficial for joint strength. The characterization was done by light optical microscopy and scanning electron microscope. EDS analysis was used to estimate the composition and identify the phases. It was found that a beneficial Cu solid solution phase is present in the joint. To qualify the joint and identify the weld status, melting characteristics during laser welding by observation of the optical emission in Ultraviolet-visible wavelength was studied. The Al melting peak at 396 nm and Cu melting peak at 578 nm was found to correlate to the welding process parameters. The signals correlate to the actual melting of Cu and Al sheets, which was investigated by the cross-sectional images and the weld images on the top of the Cu-Al weld. Therefore, the possibility for real-time analysis to identify different welding conditions is shown. [less ▲]

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See detailRefugees’ Integration Process in Luxembourg: The case of Arab refugees’ post-political transformations in the Arab region
Badawy, Haythem Kamel UL

Doctoral thesis (2022)

2011 was significant in the Arab countries; it started a people's political movement in many countries. It was known as (The Arab Spring). This Arab spring led to instability and insecurities in many ... [more ▼]

2011 was significant in the Arab countries; it started a people's political movement in many countries. It was known as (The Arab Spring). This Arab spring led to instability and insecurities in many countries, which resulted in a large flow of asylum seekers to neighbouring countries and Europe. This flow reached its peak in 2015, and many of them ended up arriving in Luxembourg. The Arab-speaking population is relatively tiny in Luxembourg compared to other neighbouring countries. Still, numbers increased from 1200 residents in 2011 to over 7000 due to this flow of asylum seekers, mainly from Syria and Iraq. While Luxembourg had a specific demographic structure and a multilingual context compared to other European countries, the refugees faced a different situation concerning their integration into Luxembourgish society. Multilingualism is one of the main challenges asylum seekers face, especially if they do not have previous experience or competencies in any European language. Language learning played a prominent role in allowing people to find job opportunities, decent housing, and be independent of the state and social support. In this dissertation, I am trying to investigate how the integration process is functioning for this specific group of people in Luxembourg, which factors play a role in their integration, and how the support measures are valid. I used a qualitative research approach with data collected through semi-structured interviews with asylum seekers who had already received their refugee status to analyse their perception and understanding of their integration process. The interviews were conducted in their native language (Arabic), which gave me access to a more straightforward free discussion with the interviewees but added a limitation of the need to translate the selected excerpts and not having the possibility to translate the whole interviews. The main results are that the Arab refugees’ integration process had several aspects. Concerning the prejudgment before arrival, the waiting time of their asylum application, then after holding the refugee status, other elements that they faced played a role as a barrier; or a challenge for their integration processes like housing, language learning, job market and family conditions. I concluded that different components are needed to fulfil their integration needs, and they can be structured in organized stages for integration. The multiculturalism of Luxembourg can play a positive role in facilitating integration but, simultaneously, can create challenges for people to adapt and progress in their integration process. The diverse components can be organized in a tower model built on the different integration components to reach the level needed for good integration. [less ▲]

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See detailPeut-on parler d'une écriture auto-socio-biographique dans l'oeuvre d'Annie Ernaux ?
Mounom Mbong, Frank UL

Doctoral thesis (2022)

Annie Ernaux renews autobiographical writing with a style that promotes social struggle for the benefit of the underprivileged classes.

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See detailIntermolecular Interactions in Static Electric Fields Studied with Quantum Mechanics and Quantum Electrodynamics
Karimpour, Mohammad Reza UL

Doctoral thesis (2022)

In the present work, the interactions between neutral molecular systems subject to external static electric fields are studied. To comprehensively explore the effects of external fields on intermolecular ... [more ▼]

In the present work, the interactions between neutral molecular systems subject to external static electric fields are studied. To comprehensively explore the effects of external fields on intermolecular interactions, the two most reliable frameworks in the subject, namely molecular quantum mechanics and quantum electrodynamics are employed while atomic and molecular responses are modeled using quantum Drude oscillators (QDO). In the first part of the work, the focus is to understand the interplay between dispersion and field-induced forces in two-body systems for both nonretarded and retarded ranges of inter-species distances. To identify the origin and the mechanism responsible for different field-induced interactions, a complementary approach based on classical electrodynamics with a zero-point radiation field, namely stochastic electrodynamics, is employed. The results show that neglecting higher-order contributions coming from field-induced hyperpolarizabilities of atoms, the dispersion interaction remains unchanged by the external uniform static field, for both regimes. However, using an external static field one can control the magnitude and characteristics of intermolecular interactions. The second part of the work is devoted to the extension of the study to many-body interacting systems. There, the total interaction energy in systems with many interacting atoms or molecules is obtained by extending the well-established theory of many-body dispersion (MBD) interactions to the presence of external static electric fields. Diagonalization of the Hamiltonian of the system in the nonretarded regime and in the framework of quantum mechanics yields the total energy of the interacting system in terms of the corresponding normal mode frequencies. Subtraction of the energy of the non-interacting QDOs-in-fields from the total energy of the interacting system results in the many-body interaction energy. The impact of the field-induced many-body contributions is investigated for a benzene dimer as well as for two carbyne chains. Varying the number of carbon atoms per chain demonstrates the significance of the field-induced many-body terms in the interplay between dispersion and field-induced interactions. Such contributions can be of great importance for controlling delamination and self-assembly of materials in static electric fields. [less ▲]

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See detailMOBILE APPLICATION FOR PHYSICAL ACTIVITY: DEVELOPMENT OF A THEORY‐INFORMED, PERSONALIZED MHEALTH INTERVENTION FOR ADOLESCENTS (MAPA)
Domin, Alex UL

Doctoral thesis (2022)

The beneficial impact of physical activity (PA) has been extensively documented, with well-evidenced improved physical and mental health across lifespan, together with increased life expectancy. Yet, a ... [more ▼]

The beneficial impact of physical activity (PA) has been extensively documented, with well-evidenced improved physical and mental health across lifespan, together with increased life expectancy. Yet, a lack of PA and increased sedentary lifestyle continue to represent a serious public health burden. Insufficient levels of physical inactivity have also been observed in adolescents, which is alarming, as PA levels may be transferred into adulthood. Given the ubiquitous use of smartphones by adolescents, these devices may offer feasible means to reach young populations and deliver an intervention aimed at increasing PA participation. The aim of the current project, therefore, concerns the development, delivery and pilot-evaluation of a smartphone application intervention for adolescents 16-18 who are insufficiently active, in order to promote PA engagement and decrease sedentary time. Overall, three independent studies were conducted. Within the first study, a scoping literature review was performed. The results present a range of evidence (both quantitative and qualitative) available on smartphone-based mHealth PA interventions as well as the development and evaluation trajectory of mHealth PA interventions and systematic theory- and evidence-based practices and methods that are implemented along this trajectory. Within the second study, a focus group discussion was conducted and features and components that are preferred by adolescents (aged 16-18) in apps promoting PA were identified. Finally, within our third study, the development and evaluation of the MAPA intervention were reported. The MAPA within-subject trial demonstrated that smartphone-based intervention produced significant reductions in sedentary time in adolescents during the first week of the trial. This trend, while remaining positive, diminished over time. Our findings indicate that there was no effect of the intervention on MET-based MVPA minutes, although the descriptive increase may give reason for further investigation. Although the results suggested no overall change in heart rate based MVPA minutes, the results from the change point analyses suggest that the personalized PA prompts significantly increased HR per minute (bpm) during the second week of the study. There were no significant increases in participants’ overall step count; however, the personalized PA prompts resulted in a marginally significant increased step counts per minute in the second week of the study. These results suggest the feasibility and promise of smartphone-based PA interventions with personalized PA suggestions for adolescents. This study also provides first information and an example for researchers and practitioners on how to guide development of future smartphone-based mHealth PA interventions in adolescents. Future investigations should focus on the replication of these findings and testing the potential of scalability of such an intervention within larger population samples. [less ▲]

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See detailOn Trustworthy AI and Localized Complex Network Analytics
Esmaeilzadeh Dilmaghani, Saharnaz UL

Doctoral thesis (2022)

We live in a world where the interaction of many different entities results in the formation of complex systems. The communication between billions of smart devices, interactions of millions of people in ... [more ▼]

We live in a world where the interaction of many different entities results in the formation of complex systems. The communication between billions of smart devices, interactions of millions of people in social networks, and the existence of our biological life, which is based on seamless interactions between hundreds of genes and proteins within our cells, all are just a few examples of the complex systems surrounding us. At the core of these complex systems, there is clear evidence of a complex network, which symbolizes the interaction between the system’s components. Analytical metrics and algorithms derived from graph theory are used in network analysis to understand the functionality of complex systems, anticipate system behavior, and control changes. Many of these global patterns in complex networks are generally influenced by decisions made by communities. Communities are a tightly connected group of nodes with sparse connections to the rest of the network. These modular structures are crucial to understanding the complex network due to being closely tied to the system’s functional and topological features. They can, for example, represent modules of proteins with similar functionality in a protein interaction network or influence dynamic network activities such as opinion and epidemic propagation. Local community detection methods have gained popularity among other strategies to dis- cover communities in a complex network. The traditional methods are based on a top-down approach acquiring global information about the entire network; however, due to the growing size and complexity of existing networks, they often result in tangled communities, hence not providing functional information of the network. The primary goal of this thesis is to provide methods and solutions for local network analysis. The following components comprise the thesis contribution: 1) Introduce a transformation approach to construct networks from relational data and describe how network structure affects community detection, 2) Provide a comprehensive evaluation of current local community detection techniques and suggest a locality exploration scheme (LES) for community detection algorithms, 3) Develop a local community detection Algorithm (LCDA) and employ it on real-world data, 4) Extend LCDA to LCDA-GO, which integrates biological functional information and detects protein communities in the cell on the PPI network. Thereby, this thesis proposes a novel community detection algorithm that addresses the shortcomings of prior algorithms by presenting a local method. The applicability of the suggested algorithm is investigated by running it on real-world PPI networks. 7 Furthermore, this thesis contributes to industrial technical reports and whitepapers on the standardization and regulation of big data and Artificial Intelligence (AI). The thesis addresses the critical issues of digital trust in big data and AI by incorporating technical standardization and cutting-edge research solutions. The key contributions include, but are not limited to: 1) A comprehensive review of the state-of-the-art in numerous scientific and standard materials regarding privacy and trustworthiness concerns, including the introduction of privacy leaks and mitigation measures in big data and AI, 2) Investigating the societal implications of artificial intelligence standards in light of the recently initiated worldwide and European standardized processes, 3) Design and implementation of a scheme that connects scientific contributions and stan- dardization efforts in the direction of AI conformity assessment. The contribution of the thesis to standards demonstrates an impact on both scientific and standardization communities by contributing to both and offering recent outcomes from each. [less ▲]

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See detailDecision-Making Processes and Certainties in Heritage Reconstructions Using the Example of Larochette Castle, Luxembourg
de Kramer, Marleen Christine UL

Doctoral thesis (2022)

To reconstruct a historical building is to make countless decisions, weighing often conflicting sources, dealing with gaps in the data and adapting to new information. This thesis investigates how ... [more ▼]

To reconstruct a historical building is to make countless decisions, weighing often conflicting sources, dealing with gaps in the data and adapting to new information. This thesis investigates how individual aspects of a reconstruction can be classified by their degree of accuracy or the certainty that they are correct, how the public understands - and can be taught to understand - these distinctions, and how this system can be applied to existing reconstructions. [less ▲]

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See detailRe/constructing Computing Experiences. From "punch girls" in the 1940s to "computer boys" in the 1980s.
van Herck, Sytze UL

Doctoral thesis (2022)

Re/constructing computing experiences from “punch girls” to “computer boys” traces the life cycle of five computing devices between the 1940s and the 1980s, each representing a key development in the ... [more ▼]

Re/constructing computing experiences from “punch girls” to “computer boys” traces the life cycle of five computing devices between the 1940s and the 1980s, each representing a key development in the history of computing. The experimental media archaeology framework of Nutzerperspektiven critically evaluates the type of user sources re/construct. The object’s life cycle traces phases of design, production, sale, installation, application and use, and decommission or re-use. The lens of intersectionality with a focus on gender facilitates (visual) discourse analysis of advertisements to expose stereotypes. User experiences differ because inequalities in computing have at times resulted in occupational segregation, and working conditions varied across case studies. Computing experiences encompass the object, the environment, and application, and a user, serving as a structure for the case studies. The first case study discusses the accounting departments of Helena Rubinstein which used Remington Rand, and later Powers-Samas, punch card machinery since 1940. Miss Summerell led the Powers room in the London branch from 1955 onward. The second case study centers around a workflow Dr. E. Blatt created for the International Business Machines (IBM) System/360 announced in 1964 used in German clinical chemistry laboratories since 1969. The Digital Equipment Company’s client applications slides form the basis of the next case study and showed several uses of the Programmable Data Processor or PDP-11 in aerospace and commercial aircraft between 1970 and 1980. The final chapter compares two educational initiatives from the 1980s. By 1981 the BBC Microcomputer kickstarted the Computer Literacy project in the United Kingdom, first targeting adults but soon entering primary and secondary schools. Apple’s Kids Can’t Wait initiative in the United States equally introduced many children to computing. Methods from user experience (UX) design and experimental media archaeology supported the re/construction or reenactment of past human-computer interaction. As a study of material culture, the historical case studies were informed by museum objects paired with additional archival sources. The research added phases to the life cycle framework and paired a reflection on the provenance of material objects with a focus on human actors. The case studies in turn demonstrated how sources limited the type of user and computing experiences historians can re/construct. [less ▲]

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See detailLarge graphene oxide flakes: self-assembly and electro-optical switching in liquid crystal phase
Shahini, Sharif UL

Doctoral thesis (2022)

Graphene oxide (GO) flakes in aqueous suspension self-organize into liquid crystal (LC) phases. They have been studied for various applications because of their unique properties, but mostly in their bi ... [more ▼]

Graphene oxide (GO) flakes in aqueous suspension self-organize into liquid crystal (LC) phases. They have been studied for various applications because of their unique properties, but mostly in their bi-phasic suspensions since high concentrations was needed for their pure liquid crystal. The stability of a suspension of GO flakes over time is essential for their application. A crucial change in GO biphasic suspension is the isotropic - liquid crystal phase separation. Here I study the isotropic-nematic phase transition for large GO flakes with an average size of ~38 µm for 570 days to examine their stability. I found that large GO flakes make pure LC phases at a very low concentration of 0.7 mg/mL (0.035 vol%), completely stable over time. I observed that the equilibrium concentration for making a LC phase increases over time, and it is not constant until it reaches a critical concentration for the pure nematic phase. As typical LCs, GO LCs exhibits birefringence property and, interestingly, low-field-induced birefringence. Thus, graphene-based lyotropic LCs are an attractive class of materials for electro-optic devices. To control the electro-optic properties of these high-performance functional materials at a macroscopic scale, it is necessary to understand their self-assembly individually. I study this here by determining the large GO flakes arrangement and spatial order in the nematic phase via synchrotron small-angle X-ray scattering and direct visualization of the flakes with fluorescence confocal laser scanning microscopy. The high fluorescence of large GO flakes makes it possible to individually study the assembly of flakes in real-time when suspended in aqueous dispersions. I successfully performed an enhanced electro-optical switching of large GO flakes in their low concentration nematic phase by using a low electric field, unlike what has been reported so far. I study the optical behavior of GO LCs using polarizing optical microscopy in static and dynamic conditions to follow the flake reassembly under the application of the electric field. [less ▲]

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See detailHigher Supergeometry and Mathematical Physics
Ibarguengoytia, Eduardo UL

Doctoral thesis (2022)

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See detailSecurity, Scalability and Privacy in Applied Cryptography
Vitto, Giuseppe UL

Doctoral thesis (2022)

In the modern digital world, cryptography finds its place in countless applications. However, as we increasingly use technology to perform potentially sensitive tasks, our actions and private data attract ... [more ▼]

In the modern digital world, cryptography finds its place in countless applications. However, as we increasingly use technology to perform potentially sensitive tasks, our actions and private data attract, more than ever, the interest of ill-intentioned actors. Due to the possible privacy implications of cryptographic flaws, new primitives’ designs need to undergo rigorous security analysis and extensive cryptanalysis to foster confidence in their adoption. At the same time, implementations of cryptographic protocols should scale on a global level and be efficiently deployable on users’ most common devices to widen the range of their applications. This dissertation will address the security, scalability and privacy of cryptosystems by presenting new designs and cryptanalytic results regarding blockchain cryptographic primitives and public-key schemes based on elliptic curves. In Part I, I will present the works I have done in regards to accumulator schemes. More precisely, in Chapter 2, I cryptanalyze Au et al. Dynamic Universal Accumulator, by showing some attacks which can completely take over the authority who manages the accumulator. In Chapter 3, I propose a design for an efficient and secure accumulator-based authentication mechanism, which is scalable, privacy-friendly, lightweight on the users’ side, and suitable to be implemented on the blockchain. In Part II, I will report some cryptanalytical results on primitives employed or considered for adoption in top blockchain-based cryptocurrencies. In particular, in Chapter 4, I describe how the zero-knowledge proof system and the commitment scheme adopted by the privacy-friendly cryptocurrency Zcash, contain multiple subliminal channels which can be exploited to embed several bytes of tagging information in users’ private transactions. In Chapter 5, instead, I report the cryptanalysis of the Legendre PRF, employed in a new consensus mechanism considered for adoption by the blockchain-based platform Ethereum, and attacks for further generalizations of this pseudo-random function, such as the Higher-Degree Legendre PRF, the Jacobi Symbol PRF, and the Power-Residue PRF. Lastly, in Part III, I present my line of research on public-key primitives based on elliptic curves. In Chapter 6, I will describe a backdooring procedure for primes so that whenever they appear as divisors of a large integer, the latter can be efficiently factored. This technique, based on elliptic curves Complex Multiplication theory, enables to eventually generate non-vulnerable certifiable semiprimes with unknown factorization in a multi-party computation setting, with no need to run a statistical semiprimality test common to other protocols. In Chapter 7, instead, I will report some attack optimizations and specific implementation design choices that allow breaking a reduced-parameters instance, proposed by Microsoft, of SIKE, a post-quantum key-encapsulation mechanism based on isogenies between supersingular elliptic curves. [less ▲]

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See detailFrom Smart to Secure Contracts: Automated Security Assessment and Improvement of Ethereum Smart Contracts
Ferreira Torres, Christof UL

Doctoral thesis (2022)

Modern blockchains, such as Ethereum, gained tremendously in popularity over the past few years. What partially enables this large increase are so-called smart contracts. These are programs that are ... [more ▼]

Modern blockchains, such as Ethereum, gained tremendously in popularity over the past few years. What partially enables this large increase are so-called smart contracts. These are programs that are deployed and executed across the blockchain. However, just like traditional programs, smart contracts are subject to programming mistakes. Although, unlike traditional programs their code is publicly available and immutable. Hence, as smart contracts become more popular and carry more value, they become a more interesting target for attackers. In the past few years, several smart contracts have been exploited, resulting in assets worth millions of dollars being stolen. In this dissertation, we explore the security of smart contracts from three different perspectives: vulnerabilities, attacks, and defenses, and demonstrate that, as so often, "smart" does not imply "secure". In the first part of the dissertation, we study the automated detection of vulnerabilities in smart contracts, without requiring prior access to source code. We start by building a symbolic execution framework for detecting integer bugs that leverages taint analysis to reduce false positives. However, as symbolic execution is notorious to produce false positives, we explore hybrid fuzzing as an alternative. As a result, we propose a hybrid fuzzer for smart contracts that combines symbolic execution with fuzz testing and leverages data dependencies across state variables to efficiently generate transaction sequences. Our approach is capable of detecting more vulnerabilities with less false positives. In the second part of the dissertation, we explore various ways to mount attacks against smart contracts. We start by proposing a framework to detect and quantify classical smart contract attacks (e.g., reentrancy, integer overflows, etc.) on past transactions by combining logic-driven and graph-driven analysis. Afterwards, we study the effectiveness of a new type of fraud known as honeypots, by scanning the entire blockchain for different types of honeypots using symbolic execution. Next, we present a methodology to measure the prevalence of so-called frontrunning attacks, which follow from the rise of decentralized finance and the sharp increase of users trading on decentralized exchanges. Our results show that attackers are making a fortune by manipulating the order of transactions. In the third and final part of the dissertation, we discuss several defense mechanisms for smart contracts. We first propose a solution that developers can use to automatically patch vulnerable smart contract bytecode using context-sensitive patches that dynamically adapt to the bytecode that is being patched. However, this does not solve the issue of already deployed smart contracts. To that end, we present a second solution that enables security experts to write attack patterns that are triggered whenever malicious control and data flows are detected. Once a transaction is detected to be malicious, all state changes are rolled back and the attack is thereby prevented. These attack patterns are written using a domain-specific language and are managed via a smart contract. The latter enables decentralization, guarantees the distribution of security updates, and warrants transparency. [less ▲]

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See detailLa construction de la politique d’immigration de l’union européenne entre 2009 et 2019 : vers un modèle commun ? Etude de cas : l’Espagne, la Grèce et l’ Italie
Tellier, Emilie Audrey Jacqueline UL

Doctoral thesis (2021)

The communautarisation of migration policies is a slow process. EU member states have often shown reluctance to share their sovereignty in this sensitive field throughout European integration. The entry ... [more ▼]

The communautarisation of migration policies is a slow process. EU member states have often shown reluctance to share their sovereignty in this sensitive field throughout European integration. The entry into force of the Lisbon treaty - supposed to enhance migration policies integration - occurred simultaneously with the euro-zone financial crisis and was followed by a so-called ‘migration crisis. Which political developments finally occurred at the supranational level? Which decision-making methods were used? Did they comply with the previous EU member states’ commitments of building a common migration policy? Greece, Spain, and Italy handled most of the migrants’ flows reception which demonstrated a lack of solidarity among EU member states. Considering this lack of solidarity, have these three states’ decisions converged towards EU norms and requests? First, we assumed that a slowdown in the integration process would have taken place following the migration crisis. Then, we affirmed that the three States would pursue national interests by developing strategies within the institutional framework of the European Union. Finally, we have stated that these strategies could lead to the emergence of national models with elements of convergence. In order to verify these hypotheses, we focused on the EU and national legislative developments, the degree of states' compliance with EU standards as well as the position of the states held during the negotiation taking place at the supranational level. We have therefore used primary sources (institutional reports, legislations, statistics, interviews contents, etc.) and secondary academic sources (results of research projects, articles, etc.) Ten years after the entry into force of the Lisbon treaty, our findings confirmed that building a common European migration policy is a difficult goal to achieve at a supranational level and hindered - in combination with other factors - the Europeanization process. Thus the intergovernmentalist theory has proven to be valid for analyzing European integration as well as the Europeanization in Spain, Greece and Italy of immigration policies between 2009 and 2019. Thus, the neo-functionalist logic which until then dominated the development of the economic and monetary policies of the European Union has been partially challenged when it comes to migration policies. As for the neo-institutional historical and rational choice approaches, they have proved relevant for the study of the construction of European immigration policy whose result consists of both existing common commitments (path dependency) and national interests. [less ▲]

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See detailLaw, Order and Postwar Purge in the Grand Duchy of Luxembourg (1944 - 1955): Transitional Justice and Redistribution through the example of Justice, Gendarmerie and Police
Wingerter, Elisabeth UL

Doctoral thesis (2021)

The present doctoral thesis examines the strategies and redistributive effects of political purge in the Grand Duchy of Luxembourg after World War II. It examines in detail the judicial and administrative ... [more ▼]

The present doctoral thesis examines the strategies and redistributive effects of political purge in the Grand Duchy of Luxembourg after World War II. It examines in detail the judicial and administrative purge of justice personnel, magistrates, police officers and gendarmerie corps members, and the development of law and order in the 20th century. This study treats regulated purge measures as phenomena of transitional justice. In Luxembourg’s case, the transition in question is one of restorative and redistributive kind: The prewar sociopolitical order is largely restored, however, in the ranks of the state administrations, redistributive practices of purge have changed the internal order. The imperfect, yet initially quite severe judicial and administrative sanctions have laid the foundations for national myths and narratives that have for many years clouded the historical analysis of both the occupation and the postwar period. This happened through the redistribution of symbolic capital during the purge proceedings by classifying persons into “patriots” and “antipatriots”. The struggle of the “antipatriots” to regain their symbolic, financial and political capital after the war and their antagonism with the heterogeneous group of “patriots” often focused on perceived or real financial and professional disadvantages. While the redistribution of financial capital was gradually reversed in the postwar and efforts for reconciliation set in almost immediately after 1945, the redistribution of symbolic capital led to a social divide that was successively forgotten through the emergence of the master narrative of collective resistance. [less ▲]

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See detailSolvability of systems of invariant differential equations on symmetric spaces G/K
Palmirotta, Guendalina UL

Doctoral thesis (2021)

We study the Fourier transform for distributional sections of vector bundles over symmetric spaces of non-compact type. We show how this can be used for questions of solvability of systems of invariant ... [more ▼]

We study the Fourier transform for distributional sections of vector bundles over symmetric spaces of non-compact type. We show how this can be used for questions of solvability of systems of invariant differential equations in analogy to Hörmander’s proof of the Ehrenpreis-Malgrange theorem. We get complete solvability for the hyperbolic plane H2 and partial results for finite products H2 × · · · × H2 and the hyperbolic 3-space H3. [less ▲]

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See detail(Trans-)Local Language Learning Spaces Of Three Newly Arrived Brazilian Preschool Children In Luxembourg: Two Multi-Sited Ethnographic Case Studies On The Supporting Structures Of Their Parents, Teachers And Educators.
Rocha Bley, Flávia UL

Doctoral thesis (2021)

Previous studies have found that students whose home language differs from the language of instruction are prone to school inadequacy and to dropping out early (Cummins, 2015; EC, 2013) This is especially ... [more ▼]

Previous studies have found that students whose home language differs from the language of instruction are prone to school inadequacy and to dropping out early (Cummins, 2015; EC, 2013) This is especially true for the lusophone population in Luxembourg. This thesis aims to capture the experiences of these migrant children in the Luxembourgish educational system to identify possible matches as well as mismatches between children’s support structures at home, school and daycare centre. Drawing on a sociocultural framework that understands that children learn languages when engaging in social practices with members of their communities (Rogoff, 1990) and that gives a prominent role to children’s active role when interacting with their environments (Van Lier, 2004), this thesis investigates the role of the adults in shaping the immediate environments of three newly arrived five-year-old Brazilian children in Luxembourg. It presents two cases studies that examine the supporting structures that parents at home, teachers at school and educators in Maison Relais pour Enfants (a non-formal education institution) provide to support language development of these children. The data from this qualitative study was collected from October 2017 to July 2018, combining participant-observation, fieldnotes, video recordings, photographs, questionnaires and interviews. The data analysis drew on approximately 170 hours of field observation, 25 hours of video material, photographs, interviews, and questionnaires. It was then analysed by employing different qualitative methods, i.e. Qualitative Content Analysis (QCA) (Vaismoradi & Snelgrove, 2019), Sociocultural Discourse Analysis (SDA) (Mercer, 2004), and Thematic Content Analysis (TCA) (Anderson, 2007; Vaisomoradi & Snelgrove, 2019). The findings show that the adults designed physical learning spaces and selected material that afforded language and literacy development. They also offered language-related activities such as phonemic awareness exercises, tracing letters, reading books for children, asking children to retell stories, proposing songs and rhymes, among many others. In addition, adults deployed scaffolding strategies when talking to children, especially questions, repetitions, and feedback. While each setting is unique, some similarities could nevertheless be found. The children encountered the following features across the different settings: literacy, play, structure, and multilingual adults with a monolingual ethos. Overall, the findings show a positive start for the three children.   [less ▲]

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See detailEmployability among university graduates in Luxembourg: the role of sociolinguistic repertoires
Domina, Oksana UL

Doctoral thesis (2021)

The primary aim of this dissertation was to investigate the role of sociolinguistic repertoires in relation to employability among young people in Luxembourg by applying Bourdieu`s theory of practice. To ... [more ▼]

The primary aim of this dissertation was to investigate the role of sociolinguistic repertoires in relation to employability among young people in Luxembourg by applying Bourdieu`s theory of practice. To date, however, little is known concerning the interplay between sociolinguistic repertoires (i.e. a bundle of linguistic resources, social skills, cultural competences) and employability. The lack of prior research on this topic has led to the development of sociolinguistic repertoires as a new concept, which was reviewed during the analysis of the empirical data collected for this project. In order to achieve the main goal, two objectives were set: 1) identify how university graduates and employers perceive employability; 2) analyse how university graduates and employers perceive language (linguistic resources) in relation to employability. The project was designed as an interview study, where qualitative interviews were conducted with both groups – employers and university students, to explore the perception of employability and the role of sociolinguistic repertoires in it. This study has shown that both, employers and students, perceive employability mainly in relation to personal qualities or resources, where even the candidate’s social engagement; leisure activities; hobbies and how well one fits into the working environment can play a decisive role when making the employment decision. These findings support Bourdieu`s theory of practice and demonstrate how its key concepts – capital, habitus and the field are applied in the context of employment. One of the most important findings of this study was that linguistic resources can serve as a transmitter of feelings not only in the sphere of private life but also in the employment context, as they can help to establish feelings of connection and rapport between an employer and an applicant; employees; clients etc.; belonginess to a certain community or group, particularly if a person has a foreign background; convenience and/or confidence when speaking a certain language. The findings further demonstrated that, except perceiving language as a transmitter of certain feelings, the interview participants associated linguistic resources with certain cultural competences, which constitute one of the main elements of sociolinguistic repertoires. Based on the findings of this study, sociolinguistic repertoires can be defined as a bundle of linguistic resources, where every resource has a certain social value, which partly depends on the awareness and ability of a speaker to use these resources appropriately in certain social contexts. This research enhances our understanding of conceptualisation of employability not only as a social construct or merely an individual`s human capital, but as a conception that depends on the interplay of individual capabilities and social context. This research also informs our empirical understanding of the concept of sociolinguistic repertoires in relation to employability in a multilingual and multicultural country such as Luxembourg. [less ▲]

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See detailCellular Heterogeneity as Emergent Behavior in Systems Biology
Martina, Silvia UL

Doctoral thesis (2021)

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See detailSELECTION OF THE LIPID ACCUMULATOR MICROTHRIX PARVICELLA IN MUNICIPAL WASTEWATER TREATMENT PLANTS FOR SUBSEQUENT BIODIESEL PRODUCTION
Uwizeye, Marie Louise UL

Doctoral thesis (2021)

The current trend of more circular use of resources have driven the new practices in wastewater treatment that propose a transition from taking wastewater as just a waste to wastewater as a source of ... [more ▼]

The current trend of more circular use of resources have driven the new practices in wastewater treatment that propose a transition from taking wastewater as just a waste to wastewater as a source of resources. Thus, lipids accumulated by the microbial biomass represent a potentially important stock of resources for biodiesel production. Within this study, a lipid accumulating bacterium Microthrix parvicella (M. parvicella), was studied using a molecular biology approach (16S rRNA gene amplicon sequencing) and GC-MS/MS to identify its occurrence and its accumulated lipids and thus, their potential use for biodiesel production. [less ▲]

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See detailPLASMA-INDUCED POLYMERIZATION OF LIQUID LAYERS FOR THE SYNTHESIS AND DEPOSITION OF INTERPENETRATING POLYMER NETWORK FILMS
Niemczyk, Edyta Monika UL

Doctoral thesis (2021)

Plasma polymerization is an attractive and powerful tool for synthesizing functional polymeric thin films with highly cross-linked structures. So far, in plasma many non-negligible side reactions promote ... [more ▼]

Plasma polymerization is an attractive and powerful tool for synthesizing functional polymeric thin films with highly cross-linked structures. So far, in plasma many non-negligible side reactions promote the synthesis of polymers with a structure different from those obtained by conventional polymerization processes. Namely, the network architecture could be a consequence of the functional group's sacrifice on behalf of the cross-link’s formation. Remarkably, conducted at atmospheric pressure plasma polymerization is evidenced to promote the conventional polymerization pathway assuring good retention of functional groups from monomers in a vapor or liquid phase. Yet, sacrificed to the benefit of the functional groups - the cross-links, lack further contribution to the mechanical and chemical stability, limiting the long-term application. In other words, the current state of the atmospheric pressure plasma polymerization requires a balance between available functional groups and cross-links. Consequently, as an effective method for an adequate reinforcement of cross-linked structure, maintaining a high concentration of the functional groups arises the combination of two polymeric networks in Interpenetrating Polymer Networks (IPN). However, the formation of IPN architecture in the plasma process has yet to be reported. In this context, this thesis aimed to examine the deposition of the thin films with the IPN architecture, utilizing an atmospheric pressure continuous sinusoidal plasma in the synthesis pathway. The primary objective was to evaluate the formation of the IPN system in-situ, in a sequential manner from telechelic oligomers. The convenience of the one-pot, in-situ, IPN formation involves foremost an immediate network's interlocking already during synthesis, thus providing forced compatibilization and reducing possible phase separation. To facilitate direct network formation, thin films were prepared from telechelic poly(ethylene glycol) (PEG) oligomers with different reactive end-groups to assure two different polymerization pathways. The latter factor was just a prerequisite to enable an IPN synthesis in a non-interfering manner, whereas a shared PEG backbone was intended as a compatibilization factor between the networks. In particular, methyl methacrylate (MA) and benzoxazine (Bz) were studied as reactive end-groups. These two distinct functional groups allowed the formation of the first network by atmospheric pressure plasma-induced polymerization of the telechelic MA-PEG, followed by the second network formation by thermal curing of the telechelic Bz-PEG thermoset. The formation of the IPN systems in a non-interfering sequential manner was corroborated by Fourier-Transform Infrared Spectroscopy (FTIR) measurements and thermal analyses. Moreover, the negligible effect of the plasma exposure on the Bz chemical structure was assessed. The evaluation of the macroscopic and microscopic properties of the IPN systems were discussed, considering telechelic PEG oligomers with two different numbers of ethylene oxide units (n=1 or 8). Nano-scratch tests evidenced an apparent effect of the IPN formation and the Bz reinforcing role with up to a threefold increase of the mechanical load resistance compared with the MA-PEG thin film. Nano-viscoelastic analysis by Atomic Force Microscopy (AFM) confirmed the formation of the IPN structure and the absence of phase separation. In addition, loss tangent and Young modulus parameters indicated the formation of a stiffer IPN when the Bz-PEG with the lower number of ethylene oxide units was used. The well-known thermal properties of Bz thermosets were reflected in the IPN systems allowing the formation of PEG thin films with enhanced thermal stability, remarkably increased up to 100 °C. A systematic study was carried out to deepen the understanding of the IPN systems prepared by this novel approach, particularly the structure-to-property relationship. Thermal studies allowed to associate the increment of the ratio between ethylene oxide units and reactive end-groups in the IPN system as a hindering effect on Bz network formation. Otherwise, the synergism between the formation of the two networks was revealed by the IPN systems prepared with the Bz-PEG with the lower number of ethylene oxide units, reaching thermal stability beyond that of their single constituents. Comparative studies conducted by nanomechanical AFM mapping between IPN thin films consisting of the same molar fraction of Bz enabled to determine the influence of the concentration of ethylene oxide units on the viscoelastic properties of the thin films. With the ultimate goal of proposing an approach for the in-situ and simultaneous formation of IPNs by a plasma process, the plasma-induced ring-opening polymerization was considered an alternative to the thermal ring-opening polymerization mechanism. For this aim, model allyl substituted cyclic carbonates were investigated. Since the driving force behind ring-opening is the release of the cycle strain, the model molecules consisted of 6-membered and N-substituted 8-membered cyclic carbonates. While the allylic 6-membered cyclic carbonate was found to mainly polymerize by a free-radical mechanism through the double C=C bond, in the more reactive allylic N-substituted cyclic carbonate, a competition of two polymerization mechanisms occurred. Interestingly, ring-opening polymerization could be prioritized over the free-radical mechanism by actuating on plasma exposition parameters such as power and time. [less ▲]

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See detailSupporting Grocery Shopping to Achieve a Healthy and Sustainable Diet – How Developing a Behavioural Theory Informs Dynamic Smartphone Applications
Blanke, Julia UL

Doctoral thesis (2021)

Health and sustainability are becoming increasingly important in current lifestyles. In this context healthy and sustainable grocery shopping is one key aspect to facilitate a balanced and environmentally ... [more ▼]

Health and sustainability are becoming increasingly important in current lifestyles. In this context healthy and sustainable grocery shopping is one key aspect to facilitate a balanced and environmentally friendly diet. Many people are interested in changing their habits to become healthier and to consider their impact on the environment through the choices they make. But many do not consider where a healthy and sustainable diet starts. In other words, people frequently have a vague idea that grocery shopping is an important aspect of a healthy and sustainable lifestyle, but they lack sufficient knowledge and action plans to act accordingly. Therefore, the observable behaviour in many cases shows what is called the intention-action/behaviour gap, the attitude-behaviour gap, or the knowing-doing gap (Ajzen, 2016; Grunert, 2011; Hoek, Pearson, James, Lawrence, & Friel, 2017; de Schutter, 2015; Bailey & Harper, 2015). To break this deadlock people, who are interested in such a lifestyle change, need the required information and support to create appropriate action plans to lead them through their grocery shopping without incurring excessive cognitive impact. The risk of such cognitive strain is that people give up easily on their good intentions and fall back into old unhealthy or environmentally impacting habits. Smartphones are ubiquitous and therefore could potentially solve many of these problems, but the design of suitable applications is mostly ad-hoc and not based on thorough modelling. On the other hand, existing behavioural models are considered to be too static and not up to the task of dynamically assessing and influencing behaviour as would be required by a smartphone-based intervention (Riley, et al., 2011; Spruijt-Metz & Nilsen, 2014). To address these problems this work proposes three major contributions: first, a novel comprehensive model of behaviour built on well-established theories used in psychology and the social science. The novelty is the consistent integration of well-proven pre-existing theories into one single comprehensive model that aims to capture the benefits and tries to overcome the limitations of each base theory. Based on this model, the second contribution of this work is the evaluation of motivation and intention to buy healthy and sustainable groceries. It has been found that health is more important than sustainability in this regard, and that health-related goals are easier to act on than sustainability related goals resulting in a bigger intention-action gap for sustainable grocery shopping. To address these issues, the third major contribution of this work is a model-derived design framework for smartphone-based interventions that provides comprehensive guidelines for developing applications to assess and support a specific behaviour, such as grocery shopping, while at the same time aiming at addressing a superordinate issue, such as health and sustainability. [less ▲]

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See detailRAN Resource Slicing Mechanisms for Multiplexing of Multiple Services in 5G Downlink Wireless Networks
Korrai, Praveenkumar UL

Doctoral thesis (2021)

The fifth-generation (5G) of wireless networks majorly supports three categories of services, namely, enhanced mobile broadband (eMBB), ultra-reliable and low latency communications (URLLC), and massive ... [more ▼]

The fifth-generation (5G) of wireless networks majorly supports three categories of services, namely, enhanced mobile broadband (eMBB), ultra-reliable and low latency communications (URLLC), and massive machine-type communications. Every service has its own set of requirements such as higher data rates, lower latency in packet delivery, high reliability, and network energy-efficiency(EE) to support applications including ultra-high definition (UHD) video streaming, virtual reality (VR), autonomous vehicles, vehicular communications, smart farming, and remote-surgery, respectively. The existing one-size-fits-all services network model is not a viable option to support these services with stringent requirements. Therefore, accommodation of these different services on the same physical network while ensuring their distinct QoS requirements is a major challenge. To address this problem, a new concept called network slicing (NS) has emerged as a promising solution for the dynamic allocation of resources to wireless services with different QoS demands. The NS can be formed in both the radio access network (RAN) and core network (CN) parts. In this thesis, we concentrate on RAN resources slicing, and more specifically on challenges that reside in the assignment of limited radio resources to manage the distinct traffic demands occurring from a wide variety of users belonging to heterogeneous services. Specifically, we address the RAN optimization method for joint allocation of time, frequency, and space resources to the eMBB, URLLC, and mMTC users according to their traffic demands (i.e., queue status). Our work in this thesis can be broadly classified into three parts based on the objective function considered in the resource optimization problem: 1) sum-rate maximization, 2) power minimization, 3) EE maximization. In the first part of this thesis, we address an adaptive modulation coding (AMC) based resource allocation problem for dynamic multiplexing of URLLC and eMBB users on the shared resources of the OFDMA-based wireless downlink (DL) network. Specifically, we formulate the resource blocks (RBs) allocation problem as a sum-rate maximization problem subject to the minimum data rate constraint, the latency-related constraint, orthogonality, and reliability constraints. Furthermore, to allocate RBs and transmit power jointly to the users, we also formulate the AMC-based optimization problem to maximize the sum good-put of eMBB users subject to URLLC and eMBB users’ QoS constraints. Importantly, in this problem formulation, we consider a probabilistic constraint to incorporate CSI imperfections and a short-packet communication model for URLLC service. In the second part of this thesis, we formulate the joint RBs and transmit power allocation problem to minimize the transmit power consumption at the BS while guaranteeing the QoS constraints of eMBB, URLLC, and mMTC users and probabilistic constraint to incorporate CSI imperfection, respectively. Importantly, we consider mixed-numerology-based RB grid models to the users according to their queue status/traffic demand for satisfying their stringent requirements. Furthermore, different slicing strategies such as slice-aware (SA) and slice-isolation (SI) resource assignment mechanisms are considered for the efficient co-existence of URLLC, mMTC, and eMBB services on the same RAN infrastructure. The resulting problems in the first and second parts of the thesis are mixed-integer non-linear programming problems (MINLPs) which are intractable to solve. To provide solutions to these problems, we first transform the problems into more tractable using the AMC approximation functions, probabilistic to non-probabilistic conversion functions, Big-M theory, and difference of convex (DC) programming. Later, these transformed problems are solved using the successive convex approximation (SCA) based iterative algorithm. Our simulation results illustrate the performance of our proposed method compared to the baseline methods. Also, the simulation results show the effectiveness of the mixed-numerology-based RB grid model over the fixed numerology grid model and the performance of SA and SI resource slicing strategies in terms of achievable data rates, packet delivery latencies, and queue status, respectively. In the third part of this thesis, different from the first two parts, for the joint assignment of beams, RBs, and transmit power to eMBB and URLLC users, we formulate an EE maximization problem by considering resources scheduling, orthogonality, power-related constraints, and QoS constraints for different services. The resulting mixed-integer non-linear fractional programming problem (MINLFP) is intractable and difficult to solve. To provide a feasible solution, we first transform the formulated problem into a tractable form using fractional programming theory, approximation functions and later utilize the Dinklebach iterative algorithm, DC programming, and SCA to solve it. Finally, we compare the performance of the proposed method against baseline schemes through simulation results. In particular, we show the performance of RAN slicing mechanisms with the mixed and fixed-numerology-based RBs grid models in terms of achievable EE, packet latencies, data rates, total sum-rate, and computational complexity. The proposed algorithm outperforms the baseline schemes in terms of achieving higher data rates for eMBB users. The results also show the trade-off between the total achievable sum rate and EE of the network. The proposed method with mixed numerology grid delivers 20% of higher URLLC packets within 1 ms of latency. Besides, it achieves the lowest computation time than that with the fixed numerology grid. [less ▲]

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See detailThe Th17 cell - IL22 axis depends on glutathione upon intestinal inflammation
Bonetti, Lynn UL

Doctoral thesis (2021)

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See detailCo-evaporation and Scanning Probe Microscopy Characterizations of Hybrid Halide Perovskite Thin Films for Solar Cells
Gallet, Thibaut UL

Doctoral thesis (2021)

Hybrid organic-inorganic perovskites (HOIPs) are the trending materials when discussing solar cells. Their power conversion efficiency went from 3.8% to 25.5% in twelve years, making them extremely ... [more ▼]

Hybrid organic-inorganic perovskites (HOIPs) are the trending materials when discussing solar cells. Their power conversion efficiency went from 3.8% to 25.5% in twelve years, making them extremely promising, especially when combined with silicon in a tandem configuration. This improvement has been achieved by finding the best candidates for charge extraction and by interface engineering, compositional engineering and surface passivation. However, the surface of the HOIPs is still not well understood, and the role of grains boundaries for example is still highly debated. Determining the intrinsic surface properties of HOIPs is therefore crucial to find the best passivation strategies or fabrication designs to limit the surface and interfacial losses, and further improve solar cell efficiencies. Currently, solution-based processes are the most used techniques for fabrication, even though their upscalability towards commercialization is highly unlikely, and the use of solvents, sometimes toxic, considerably alters the perovskite surface, which makes the interpretation of their characterization challenging and sometimes misleading. The aim of this thesis is to clarify the intrinsic surface properties of HOIPs, and mainly CH3NH3PbI3 (or MAPbI3), by using surface-sensitive techniques such as scanning tunneling microscopy and spectroscopy (STM and STS) and Kelvin probe force microscopy (KPFM). To that end, HOIP thin films are mainly fabricated by thermal co-evaporation to achieve pristine surfaces, and inert-gas transfer is used to avoid contamination before their characterization. The lateral variations of the local density of states of MAPbI3 and mixed halide HOIPs are investigated. The grain-to-grain and facet variations are linked to different density of surface states that pin the Fermi level at the surface, and different workfunctions (WF), which are both attributed for MAPbI3 to different surface terminations, and for the mixed HOIPs to an additional degradation of the perovskites. The effect of varying the methylammonium iodide (MAI) content, via the partial pressure, in co-evaporated MAPbI3 is studied and the excess of MAI proves to be detrimental, as it introduces low-dimensional perovskites and stacked perovskite sheets that considerably reduce its intrinsic stability. Therefore near-stoichiometric conditions are preferred and yield films more stable to light and heat and without photostriction. Nevertheless this intrinsic stability is still not optimal, and the continuous variations of the WF measured by KPFM upon prolonged illumination is investigated. Combined with X-ray photoelectron spectroscopy (XPS), the photo-induced degradation of MAPbI3, and evaporation of I2 are revealed as the causes of these variations. Besides, by combining KPFM and photoluminescence (PL) techniques for different thicknesses and substrates, energy band diagrams can be drawn and unveil a bending of the bands in the bulk. Lastly, the surface sensitivity of HOIPs is investigated when they are intentionally put in contact with extrinsic factors such as oxygen and solvents, and the surface properties are shown to be considerably altered. In addition, passivation strategies are used to demonstrate how surfaces can be improved. [less ▲]

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See detail5G AND BEYOND NETWORKS WITH UAV: TRAJECTORY DESIGN AND RESOURCE ALLOCATION
Tran Dinh, Hieu UL

Doctoral thesis (2021)

Over the past few years, unmanned aerial vehicle (UAV)-enabled wireless communications have attracted considerable attention from both academia and industry due to their high mobility, low cost, strong ... [more ▼]

Over the past few years, unmanned aerial vehicle (UAV)-enabled wireless communications have attracted considerable attention from both academia and industry due to their high mobility, low cost, strong light-of-sight communication links, and ease of deployment. Specifically, UAVs can be deployed to serve as aerial base stations (BSs), relays, power sources, etc., to support ground users (GUs) in various scenarios such as surveillance missions, search and rescue, crop monitoring, delivery of goods, data collection, emergency communications, secrecy communications, space-air-ground communications, etc. Despite many advantages, UAV-enabled communications are not without limitations. The limitations of UAVs have imposed technical restrictions on weight, size, and energy capability, thereby affecting the durability and performance of UAVs. The key goal of this dissertation is to propose and develop new frameworks and efficient optimization algorithms to solve novel challenging problems, facilitate the design and deployment of UAV-enabled communications. Consequently, these proposed algorithms can become one of the foundations for deploying UAVs in future wireless systems. Specifically, this dissertation investigates different UAV communication systems by addressing several important research problems through four emerging scenarios: 1) Design UAV trajectory based on traveling salesman problem with time window (TSPTW); 2) Full-duplex (FD) UAV relay-assisted emergency communications in Internet of Things (IoT) networks; 3) Backscatter- and cache-assisted UAV communications; and 4) Satellite- and cache-assisted UAV communications in 6G aerial networks. In the first scenario, we provide the coarse trajectory for the UAV based on TSPTW, which has not been investigated in UAV communications yet. Concretely, we propose two trajectory design algorithms based on TSPTW, namely heuristic algorithm and dynamic programming (DP)-based algorithm, and they are compared with exhaustive search and traveling salesman problem (TSP)-based methods. Based on the feasible path obtained from proposed algorithms, we minimize the total UAV’s energy consumption for each given path via a joint optimization of the UAV velocities in all hops. Simulation results show that the energy consumption value of DP is very close to that of the exhaustive algorithm with greatly reduced complexity. Based on this work, an efficient TSPTW-based algorithm can be used as an initialized trajectory for designing a joint problem of UAV trajectory and other communications factors (e.g., communication scheduling, transmit power allocation, time allocation), which are challenges. We then study the case of a FD UAV relaying system in IoT networks. Specifically, a UAV can be deployed as a flying base station (BS) to collect data from time-constrained IoT devices and then transfer it to a ground gateway (GW). Especially, the impact of latency constraint for the uplink (UL) and downlink (DL) transmission utilizing FD or half-duplex (HD) mode is investigated. Using the proposed system model, we aim to maximize the total number of served IoT devices subject to the maximum speed constraint of the UAV, total traveling time constant, UAV trajectory, maximum transmit power at the devices/UAV, limited cache size of the UAV, and latency constraints for both UL and DL. Next, we attempt to maximize the total throughput subject to the number of served IoT devices. The outcome of this work will motivate a new framework for UAV-aided communications in disaster or emergency communications. Next, a novel system model that considers SWIPT, backscatter and caching in UAV wireless networks is developed. Based on this model, we aim to maximize the system throughput by jointly optimizing the dynamic time splitting (DTS) ratio and the UAV’s trajectory with caching capability at the UAV. This is the first work that jointly considers wireless power transfer (WPT), caching, and BackCom in UAV communications, which provides a potential solution for a battery-free drone system that can fly for a long period in the sky to support the terrestrial communication systems. Finally, a novel system model for effective use of LEO satellite- and cache-assisted UAV communication is proposed and studied. Specifically, caching is provided by the UAV to reduce backhaul congestion, and the LEO satellite assists the UAV’s backhaul link. In this context, we aim to maximize the minimum achievable throughput per ground user (GU) by jointly optimizing cache placement, the UAV’s transmit power, bandwidth allocation, and trajectory with a limited cache capacity and operation time. The outcomes of this work can provide a new design framework for Satellite-UAV-terrestrial communications that includes two tiers, i.e., the backhaul link from satellite to UAV and the access link from UAV to ground users, which imposes new challenges and was not investigated before. [less ▲]

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See detailCopper-Carbon Nanotube Composites for Lightning Strike Protection
Duhain, Antoine Edmond UL

Doctoral thesis (2021)

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See detailThe antioxidant glutathione as a regulator of natural killer cell immunity
Frias Guerra, Luana UL

Doctoral thesis (2021)

Natural killer (NK) cells are cytotoxic lymphocytes that belong to the innate branch of the immune system. Regulation of NK cell activity relies on the expression and engagement of a wide range of ... [more ▼]

Natural killer (NK) cells are cytotoxic lymphocytes that belong to the innate branch of the immune system. Regulation of NK cell activity relies on the expression and engagement of a wide range of inhibitory and activating receptors that detect signals arising from cells in distress. Besides their cytotoxic function, NK cells are effective producers of cytokines that participate in the regulation of other immune cells, such as dendritic cells and T cells. These innate lymphocytes control microbial infections and malignant cell growth, which are pathological conditions where reactive oxygen species (ROS) play a crucial role. ROS participate in cell signaling events and constitute important secondary messengers for immune cell proliferation and growth. However, when accumulated, their presence leads to oxidative stress due to their high reactivity against biomolecules. In order to ensure coordinated levels of ROS, cells are endowed of antioxidant systems that allow for ROS detoxification. One of the most important intracellular antioxidants is glutathione (GSH). Given the subset specificity of GSH regulation in immunity, we aimed to investigate the role of this antioxidant in NK cells. Using a genetic-based approach, through a flox-Cre system, we specifically abrogated GSH production in NK cells. Mutant mice had a reduced abundance of NK cells, when compared to controls. Furthermore, in vitro stimulation of NK cells with IL-15, showed that ablation of GSH production renders NK cells unable to proliferate and these cells were less cytotoxic. NK cells lacking GSH accumulated mitochondrial ROS, resulting in reduced mitochondrial fitness. This was paralleled by a general metabolic shutdown, and reduced mTOR and STAT5 signaling. In vivo, GSH and redox regulation were demonstrated to be key for NK cell-mediated regulation of T cells, in a viral model of lymphocytic choriomeningitis virus (LCMV). Moreover, in an experimental tumor model, deletion of GSH resulted in an NK cell intrinsic impairment of tumor dissemination and increased exhaustion. Taken together, our results indicate GSH as a key checkpoint for NK cell homeostasis and function. [less ▲]

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See detailINTERFACE OPEN-CIRCUIT VOLTAGE DEFICIT IN CU(IN,GA)S2 SOLAR CELL: CHARACTERIZATION, SIMULATION AND MITIGATION
Sood, Mohit UL

Doctoral thesis (2021)

Current commercial photovoltaic technologies are close to their practical limits, and enhancing their power conversion efficiency (PCE) requires a paradigm shift to tandem approaches. Tandem solar cells ... [more ▼]

Current commercial photovoltaic technologies are close to their practical limits, and enhancing their power conversion efficiency (PCE) requires a paradigm shift to tandem approaches. Tandem solar cells can exceed the single junction practical and thermodynamic limits. The desired top cell bandgap to enhance PCE of current photovoltaic technologies is ~1.6 1.7 eV. The bandgap tunability from 1.5 eV to 2.5 eV positions Cu(In,Ga)S2 as a prime top cell candidate for next generation low-cost tandem cells. However, they are limited by a low external open circuit voltage (VOC,ex). In this thesis, we have studied the interface recombination and found it to cause a difference between VOC,ex and internal open-circuit voltage (VOC,in) in Cu(In,Ga)S2 solar cell. We have introduced a quantifiable metric that has not been used before for Cu(In,Ga)S2, to evaluate VOC disparity in terms of “interface VOC deficit” defined as (VOC,in – VOC,ex). The temperature dependent current-voltage measurement allows to investigate the activation energy (Ea) of the dominating recombination path in the device, uncovering the cause of interface VOC deficit in Cu poor and Cu-rich Cu(In,Ga)S2 devices. We find that negative conduction band offset (CBO) at the absorber/buffer interface results in interface VOC deficit in Cu poor Cu(In,Ga)S2 devices. Although the interface VOC deficit can be reduced by replacing the buffer for favorable band alignment at the absorber/buffer interface, a substantial deficiency still exists. We observe that the CBO not only at the absorber/buffer interface but also at the buffer/i-layer interface leads to an interface VOC deficit in devices. This, in general, is not an issue in Cu(In,Ga)Se2 devices. By optimizing buffer and i-layer, we mitigate and overcome buffer/i-layer losses to get Cu poor Cu(In,Ga)S2 devices with consistently low interface VOC deficit. As a result, an in-house PCE of 15.1 % is achieved together with an externally certified PCE of 14 %. This is, by far, the best Cu(In,Ga)S2 device performance except for the record PCE device. In contrast, the interface VOC deficit and the interface recombination persists in Cu-rich Cu(In,Ga)S2 devices and is not resolved by alternative buffers. To identify the possible origin of the interface VOC deficit, we characterize two sister systems CuInS2 and CuInSe2, which offer reduced complexity due to Ga exclusion. The Cu-rich devices of these systems are also known to suffer from interface recombination, and for CuInSe2, it has been linked to the “200 meV” defect. However, the underlying mechanism of how this defect leads to interface recombination remains unknown. Through results obtained from photoelectron spectroscopic measurements, we exclude the possibility of two commonly evoked causes of interface recombination: negative CBO and Fermi-level pinning. Sulfur-based post-deposition treatments on KCN etched Cu-rich CuInS2 absorbers reveal near interface defects as a possible alternative cause of interface VOC deficit. The treatment increases the VOC,ex, which originates from improved Ea and interface VOC deficit in treated devices. The capacitance transient measurements further reveal that slow metastable defects are present in the untreated sample. The treated samples show that the slow transient is suppressed, suggesting the passivation of slow metastable defects. The treatment adapted to Cu rich CuInSe2 displays a reduction in the deep defect signature in admittance spectra, which explains the observed improvement in interface VOC deficit. This indicates that the defects near the absorber/buffer interface, acting as non-radiative recombination centers, as the source of interface VOC deficit. Finally, to understand how the defect leads to interface recombination, a new model based on near interface defects is offered using the holistic analysis and evaluation of the defect characteristics. We can reproduce an interface VOC deficit with all the signatures of an interface recombination-dominated device using numerical simulations. This model provides a solution for the consideration of interface recombination by defects distributed in a thin layer within the bulk absorber, an explanation beyond classical models. The near interface defect model finally explains why Cu rich chalcopyrite solar cells are limited in their VOC,ex despite a good VOC,in, which was not discovered before. The model thus forms a new third explanation for interface recombination signature in devices and is applicable to any device with highly recombinative defects near the interface. [less ▲]

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