Judicial Activism in the World Trade Organization: A Comparative Analysis with the European Court of Justice

Doctoral thesis (2022)

FATIGUE AND BREAKDOWN STUDIES OF SOLUTION DEPOSITED OXIDE FERROELECTRIC THIN FILMS

Doctoral thesis (2022)

Ferroelectric materials are ubiquitous in several applications and offer advantages for microelectromechanical systems (MEMS) in their thin film form. However, novel applications require ferroelectric ... [more ▼]

Ferroelectric materials are ubiquitous in several applications and offer advantages for microelectromechanical systems (MEMS) in their thin film form. However, novel applications require ferroelectric films to be deposited on various substrates, which requires effective integration and know-how of the material response when selecting a substrate for film deposition. As substrate-induced stress can alter the ferroelectric properties of the films, the knowledge of how stress changes the ferroelectric response under different actuation conditions is essential. Furthermore, the stress-dependent behavior raises the question of understanding the reliability and degradation mechanisms under cyclic electric loading. Therefore, the ferroelectric thin film’s fatigue and breakdown characteristics become more relevant. Lead zirconate titanate (PZT) thin films are popular among other ferroelectric materials. However, there is a tremendous effort made in the direction of finding a lead-free alternative to PZT. Ferroelectric thin films can be deposited using different processing techniques. In this work, the chemical solution deposition route is adapted for depositing PZT thin films on transparent and non-transparent substrates. A correlation between the substrate-induced ferroelectric properties and processing conditions with different electrode configurations is established. Finite element modeling is used to understand the influence of the design parameters of the co-planar interdigitated electrodes for fabricating fully transparent PZT stacks. In-plane and out-of-plane ferroelectric properties of PZT thin films in metal-insulator-metal (MIM) and interdigitated electrode (IDE) geometries, respectively, on different substrates, are compared to establish the connection between the stress-induced effect and the actuation mode. It is shown that the out-of-plane polarization is high under in-plane compressive stress but reduced by nearly four times by in-plane tensile stress. In contrast, the in-plane polarization shows an unexpectedly weak stress dependence. The fatigue behavior of differently stressed PZT thin films with IDE structures is reported for the first time in this study. The results are compared to the fatigue behavior of the same films in MIM geometry. PZT films in MIM geometry, irrespective of the stress state, show a notable decrease in switchable polarization during fatigue cycling. In contrast, the films actuated with IDEs have much better fatigue resistance. The primary fatigue mechanism is identified as domain wall pinning by charged defects. The observed differences in fatigue behavior between MIM and IDE geometries are linked to the orientation of the electric field with respect to the columnar grain structure of the films. Hafnium oxide, an emerging and widely researched lead-free alternative to PZT for non-volatile ferroelectric memory application, is also explored in this work. The breakdown properties of chemical solution-deposited ferroelectric hafnium oxide thin films are also studied. The structure-property relationship for stabilizing the ferroelectric phase in solution-deposited hafnium oxide thin films is established. Furthermore, the effect of processing conditions on the ferroelectric switching behavior and breakdown characteristics are demonstrated and correlated with the possible mechanism. [less ▲]

CREDIT CARDS AND CASHLESS PAYMENT: BANK COMMUNICATION POLICIES IN FRANCE, GERMANY AND LUXEMBOURG (1968-2015)

Doctoral thesis (2022)

“Pecunia non olet”. Ironically, this Latin dictum strongly relates to the 20th and 21st century if one considers how banks dematerialised constantly money and changed the way a society deals with deposits ... [more ▼]

“Pecunia non olet”. Ironically, this Latin dictum strongly relates to the 20th and 21st century if one considers how banks dematerialised constantly money and changed the way a society deals with deposits. By implementing quite radical changes to the concept of money, banks became an accelerating element for social and technological innovation. Our research project within the field of computerisation and digitalisation concentrates on banking activities and services from a European perspective. Banks’ communication regarding credit cards and cashless payments is at the heart of this research. The study intertwines several case studies in selected European countries (i.e., Luxembourg, Germany, France). In particular, the study focuses on the following bank services: automated teller machines, bankcards (especially MasterCard and Eurocard) and home banking since the emergence of Minitel, Vidéotex, or Btx. The comparative and diachronic perspective of this study, starting from the 1960s onwards, aims at shedding light on a history which has often only been seen from an insider’s perspective. It should be noted that our focus is primarily the communication strategies of banks and their related advertisement campaigns for credit cards and cashless payments. This is achieved by focusing on the strategy of the banks and their economic, technical, digital, but also societal approaches. The research topic relates to contemporary history, the history of digitalisation and innovation. In this context, press, audio-visual materials, banking reports, advertising, oral history, as well as web archives serve as primary sources. Moreover, bank archives in Luxembourg, France and Germany are used to complete the study corpus. All in all, the research results help us to understand the high complex world of banking services from an unusual research angle. Therefore, the research topic changes the current scientific standard of banking history by including the perspective of various actors of the European payment market as well as their perception of banking innovations over the years (1968 – 2015) and by analysing a European transnational corpus. Furthermore, by analysing the history of the Eurocard and its relation to MasterCard in a long-term perspective, we offer a novel approach. It helps to enrich the field of banking history, which is slowly changing and introducing different research angles, thanks to pioneering research by Bernardo Bátiz-Lazo, Sabine Effosse, David Sparks Evans, Richard Schmalensee, Lana Schwartz, Sebastian Gießmann and others. In this respect, this PhD research aims to add a milestone to historical research on banking innovation and retail banking which is still in its early stages but is moving fast, driven forward in particular by the pioneers mentioned above. [less ▲]

SINGLE CELL BASED COMPUTATIONAL APPROACHES TO UNRAVEL DYSREGULATIONS IN DISEASES

Doctoral thesis (2022)

Metamaterial Design and elaborative approach for efficient selective solar absorber

Doctoral thesis (2022)

The thesis is focused on developing spectral selective coatings (SSC) composed of multilayer cermets and periodic array of resonating omega structures, turning them to behave like metamaterials, while ... [more ▼]

The thesis is focused on developing spectral selective coatings (SSC) composed of multilayer cermets and periodic array of resonating omega structures, turning them to behave like metamaterials, while showing high thermal stability up to1000°C. The developed SSC is intended to be used for the concentrated solar power (CSP) applications. With the aim of achieving highest possible absorbance in the visible region of the spectrum and highest reflectance in the infrared region of the spectrum. The thesis highlights the numerical design, the synthesis and optical characterization of the SSC of approximately 500 nm thickness. A bottom-up approach was adopted for the preparation of a stack with alternate layers, consisting of a distribution of Titanium Nitride (TiN) nanoparticles with a layer of Aluminum Nitride (AlN) on top. The TiN nanoparticles, laid on a Silicon substrate by wet chemical method, are coated with conforming layer of AlN, via Plasma-enhanced Atomic Layer Deposition (PE-ALD). The control of the morphology at the nanoscale is fundamental for tuning the optical behaviour of the material. For this reason, two composites were prepared. One starting with TiN dispersion made with dry TiN powder and deionized water, and the other with ready-made TiN dispersion. Nano-structured metamaterial based absorbers have many benefits over conventional absorbers, such as miniaturisation, adaptability and frequency tuning. Dealing with the current challenges of producing the new metamaterial based absorber with optimal nanostructure design along with its synthesis within current nano-technological limits, we were capable of turning the cermets into metamaterial. A periodic array of metallic omega structures was patterned on top of both the composites I and II, by using e-beam lithography technique. Parameters, such as the size of TiN nanoparticles, the thickness of AlN thin film and the dimensions of the omega structure were all revealed by the numerical simulations, performed using Wave-Optics module in COMSOL Multiphysics. The work showcased clearly compares the two kinds of composites, using scanning electron microscope, X-ray photoelectron spectroscopy(XPS) and electrical conductivity measurement. The improvement in the optical performance of the SSC after the inclusion of metallic omega structures in the uppermost layer of the two composites has been thoroughly investigated for light absorption boosting. In addition, the optical performance of the two prepared composites and the metamaterial is used as a means of validating the computational model. [less ▲]

APPLICATION OF NEAR FIELD TECHNOLOGY IN HEAVY COMMERCIAL VEHICLE TIRE MONITORING SYSTEM

Doctoral thesis (2022)

The thesis proposes a near-field Communication (NF) based solution, for the tire pressure monitoring system (TPMS) in heavy commercial vehicles, as an alternative to wireless far-field (FF) based ... [more ▼]

The thesis proposes a near-field Communication (NF) based solution, for the tire pressure monitoring system (TPMS) in heavy commercial vehicles, as an alternative to wireless far-field (FF) based communication used in conventional TPMS. Truck and tire manufacturers have stepped up efforts to develop TPMS solutions, as recent EU regulations will soon make TPMS mandatory in heavy commercial vehicles, but the dense metal content in this application environment attenuates the wireless communication and hinders the development of efficient and robust TPMS solutions. The thesis covers many practical aspects and includes an extensive literature review on the state-of-the-art TPMS solutions commercially available for heavy commercial vehicles. A second literature review was conducted on NF communication and its automotive applications. The researcher then conducted a finite element analysis (FEA) to simulate the application environment, represented by the tire and wheel combination, in order to evaluate the conventional TPMS signal propagation and the proposed system signal propagation. The simulations demonstrated the adverse effect of the application environment on the signal propagation of conventional TPMS and showed the merit of using NF-based communication. The proposed transmitter design was built and evaluated on an actual truck wheel and tire combination in a series of laboratory tests. The proposed transmitter unit was detected with sufficiently high signal-to-noise to establish a communication channel in the presence of limited metal objects in close proximity in laboratory conditions, which could allow for a more advance commercial vehicle TPMS. This industry driven project addresses a serious traffic safety issue and forms a proof-of-concept for the development of a complete TPMS solution. [less ▲]

WCET and Priority Assignment Analysis of Real-Time Systems using Search and Machine Learning

Doctoral thesis (2022)

Real-time systems have become indispensable for human life as they are used in numerous industries, such as vehicles, medical devices, and satellite systems. These systems are very sensitive to violations ... [more ▼]

Real-time systems have become indispensable for human life as they are used in numerous industries, such as vehicles, medical devices, and satellite systems. These systems are very sensitive to violations of their time constraints (deadlines), which can have catastrophic consequences. To verify whether the systems meet their time constraints, engineers perform schedulability analysis from early stages and throughout development. However, there are challenges in obtaining precise results from schedulability analysis due to estimating the worst-case execution times (WCETs) and assigning optimal priorities to tasks. Estimating WCET is an important activity at early design stages of real-time systems. Based on such WCET estimates, engineers make design and implementation decisions to ensure that task executions always complete before their specified deadlines. However, in practice, engineers often cannot provide a precise point of WCET estimates and they prefer to provide plausible WCET ranges. Task priority assignment is an important decision, as it determines the order of task executions and it has a substantial impact on schedulability results. It thus requires finding optimal priority assignments so that tasks not only complete their execution but also maximize the safety margins from their deadlines. Optimal priority values increase the tolerance of real-time systems to unexpected overheads in task executions so that they can still meet their deadlines. However, it is a hard problem to find optimal priority assignments because their evaluation relies on uncertain WCET values and complex engineering constraints must be accounted for. This dissertation proposes three approaches to estimate WCET and assign optimal priorities at design stages. Combining a genetic algorithm and logistic regression, we first suggest an automatic approach to infer safe WCET ranges with a probabilistic guarantee based on the worst-case scheduling scenarios. We then introduce an extended approach to account for weakly hard real-time systems with an industrial schedule simulator. We evaluate our approaches by applying them to industrial systems from different domains and several synthetic systems. The results suggest that they are possible to estimate probabilistic safe WCET ranges efficiently and accurately so the deadline constraints are likely to be satisfied with a high degree of confidence. Moreover, we propose an automated technique that aims to identify the best possible priority assignments in real-time systems. The approach deals with multiple objectives regarding safety margins and engineering constraints using a coevolutionary algorithm. Evaluation with synthetic and industrial systems shows that the approach significantly outperforms both a baseline approach and solutions defined by practitioners. All the solutions in this dissertation scale to complex industrial systems for offline analysis within an acceptable time, i.e., at most 27 hours. [less ▲]

Multi-objective Robust Machine Learning For Critical Systems With Scarce Data

Doctoral thesis (2022)

With the heavy reliance on Information Technologies in every aspect of our daily lives, Machine Learning (ML) models have become a cornerstone of these technologies’ rapid growth and pervasiveness. In ... [more ▼]

With the heavy reliance on Information Technologies in every aspect of our daily lives, Machine Learning (ML) models have become a cornerstone of these technologies’ rapid growth and pervasiveness. In particular, the most critical and fundamental technologies that handle our economic systems, transportation, health, and even privacy. However, while these systems are becoming more effective, their complexity inherently decreases our ability to understand, test, and assess the dependability and trustworthiness of these systems. This problem becomes even more challenging under a multi-objective framework: When the ML model is required to learn multiple tasks together, behave under constrained inputs or fulfill contradicting concomitant objectives. Our dissertation focuses on the context of robust ML under limited training data, i.e., use cases where it is costly to collect additional training data and/or label it. We will study this topic under the prism of three real use cases: Fraud detection, pandemic forecasting, and chest x-ray diagnosis. Each use-case covers one of the challenges of robust ML with limited data, (1) robustness to imperceptible perturbations, or (2) robustness to confounding variables. We provide a study of the challenges for each case and propose novel techniques to achieve robust learning. As the first contribution of this dissertation, we collaborate with BGL BNP Paribas. We demonstrate that their overdraft and fraud detection systems are prima facie robust to adversarial attacks because of the complexity of their feature engineering and domain constraints. However, we show that gray-box attacks that take into account domain knowledge can easily break their defense. We propose, CoEva2 adversarial fine-tuning, a new defense mechanism based on multi-objective evolutionary algorithms to augment the training data and mitigate the system’s vulnerabilities. Next, we investigate how domain knowledge can protect against adversarial attacks through multi-task learning. We show that adding domain constraints in the form of additional tasks can significantly improve the robustness of models to adversarial attacks, particularly for the robot navigation use case. We propose a new set of adaptive attacks and demonstrate that adversarial training combined with such attacks can improve robustness. While the raw data available in the BGL or Robot Navigation is vast, it is heavily cleaned, feature-engineered, and annotated by domain experts (which are expensive), and the end training data is scarce. In contrast, raw data is scarce when dealing with an outbreak, and designing robust ML systems to predict, forecast, and recommend mitigation policies is challenging. In particular, for small countries like Luxembourg. Contrary to common techniques that forecast new cases based on previous data in time series, we propose a novel surrogate-based optimization as an integrated loop. It combines a neural network prediction of the infection rate based on mobility attributes and a model-based simulation that predicts the cases and deaths. Our approach has been used by the Luxembourg government’s task force and has been recognized with a best paper award at KDD2020. Our following work focuses on the challenges that pose cofounding factors to the robustness and generalization of Chest X-ray (CXR) classification. We first investigate the robustness and generalization of multi-task models, then demonstrate that multi-task learning, leveraging the cofounding variables, can significantly improve the generalization and robustness of CXR classification models. Our results suggest that task augmentation with additional knowledge (like extraneous variables) outperforms state-of-art data augmentation techniques in improving test and robust performances. Overall, this dissertation provides insights into the importance of domain knowledge in the robustness and generalization of models. It shows that instead of building data-hungry ML models, particularly for critical systems, a better understanding of the system as a whole and its domain constraints yields improved robustness and generalization performances. This dissertation also proposes theorems, algorithms, and frameworks to effectively assess and improve the robustness of ML systems for real-world cases and applications. [less ▲]

Artificial Intelligence-enabled Automation For Ambiguity Handling And Question Answering In Natural-language Requirements

Doctoral thesis (2022)

Requirements Engineering (RE) quality control is a crucial step for a project’s success. Natural Language (NL) is by far the most commonly used means for capturing requirement specifications. Despite ... [more ▼]

Requirements Engineering (RE) quality control is a crucial step for a project’s success. Natural Language (NL) is by far the most commonly used means for capturing requirement specifications. Despite facilitating communication, NL is prone to quality defects, one of the most notable of which is ambiguity. Ambiguous requirements can lead to misunderstandings and eventually result in a system that is different from what is intended, thus wasting time, money, and effort in the process. This dissertation tackles selected quality issues in NL requirements: • Using Domain-specific Corpora for Improved Handling of Ambiguity in Requirements: Syntactic ambiguity types occurring in coordination and prepositional-phrase attachment structures are prevalent in requirements (in our document collection, as we discuss in Chapter 3, 21% and 26% of the requirements are subject to coordination and prepositional-phrase attachment ambiguity analysis, respectively). We devise an automated solution based on heuristics and patterns for improved handling of coordination and prepositional-phrase attachment ambiguity in requirements. As a prerequisite for this research, we further develop a more broadly applicable corpus generator that creates a domain-specific knowledge resource by crawling Wikipedia. • Automated Handling of Anaphoric Ambiguity in Requirements: A Multi-solution Study: Anaphoric ambiguity is another prevalent ambiguity type in requirements. Estimates from the RE literature suggest that nearly 20% of industrial requirements contain anaphora [1, 2]. We conducted a multi-solution study for anaphoric ambiguity handling. Our study investigates six alternative solutions based on three different technologies: (i) off-the-shelf natural language processing (NLP), (ii) recent NLP methods utilizing language models, and (iii) machine learning (ML). • AI-based Question Answering Assistant for Analyzing NL Requirements: Understanding NL requirements requires domain knowledge that is not necessarily shared by all the involved stakeholders. We develop an automated question-answering assistant that supports requirements engineers during requirements inspections and quality assurance. Our solution uses advanced information retrieval techniques and machine reading comprehension models to answer questions from the same requirement specifications document and/or an external domain-specific knowledge resource. All the research components in this dissertation are tool-supported. Our tools are released with open-source licenses to encourage replication and reuse. [less ▲]

Secure, privacy-preserving and practical collaborative Genome-Wide Association Studies

Doctoral thesis (2022)

Understanding the interplay between genomics and human health is a crucial step for the advancement and development of our society. Genome-Wide Association Study (GWAS) is one of the most popular methods ... [more ▼]

Understanding the interplay between genomics and human health is a crucial step for the advancement and development of our society. Genome-Wide Association Study (GWAS) is one of the most popular methods for discovering correlations between genomic variations associated with a particular phenotype (i.e., an observable trait such as a disease). Leveraging genome data from multiple institutions worldwide nowadays is essential to produce more powerful findings by operating GWAS at larger scale. However, this raises several security and privacy risks, not only in the computation of such statistics, but also in the public release of GWAS results. To that extent, several solutions in the literature have adopted cryptographic approaches to allow secure and privacy-preserving processing of genome data for federated analysis. However, conducting federated GWAS in a secure and privacy-preserving manner is not enough since the public releases of GWAS results might be vulnerable to known genomic privacy attacks, such as recovery and membership attacks. The present thesis explores possible solutions to enable end-to-end privacy-preserving federated GWAS in line with data privacy regulations such as GDPR to secure the public release of the results of Genome-Wide Association Studies (GWASes) that are dynamically updated as new genomes become available, that might overlap with their genomes and considered locations within the genome, that can support internal threats such as colluding members in the federation and that are computed in a distributed manner without shipping actual genome data. While achieving these goals, this work created several contributions described below. First, the thesis proposes DyPS, a Trusted Execution Environment (TEE)-based framework that reconciles efficient and secure genome data outsourcing with privacy-preserving data processing inside TEE enclaves to assess and create private releases of dynamic GWAS. In particular, DyPS presents the conditions for the creation of safe dynamic releases certifying that the theoretical complexity of the solution space an external probabilistic polynomial-time (p.p.t.) adversary or a group of colluders (up to all-but-one parties) would need to infer when launching recovery attacks on the observation of GWAS statistics is large enough. Besides that, DyPS executes an exhaustive verification algorithm along with a Likelihood-ratio test to measure the probability of identifying individuals in studies. Thus, also protecting individuals against membership inference attacks. Only safe genome data (i.e., genomes and SNPs) that DyPS selects are further used for the computation and release of GWAS results. At the same time, the remaining (unsafe) data is kept secluded and protected inside the enclave until it eventually can be used. Our results show that if dynamic releases are not improperly evaluated, up to 8% of genomes could be exposed to genomic privacy attacks. Moreover, the experiments show that DyPS’ TEE-based architecture can accommodate the computational resources demanded by our algorithms and present practical running times for larger-scale GWAS. Secondly, the thesis offers I-GWAS that identifies the new conditions for safe releases when considering the existence of overlapping data among multiple GWASes (e.g., same individuals participating in several studies). Indeed, it is shown that adversaries might leverage information of overlapping data to make both recovery and membership attacks feasible again (even if they are produced following the conditions for safe single-GWAS releases). Our experiments show that up to 28.6% of genetic variants of participants could be inferred during recovery attacks, and 92.3% of these variants would enable membership attacks from adversaries observing overlapping studies, which are withheld by I-GWAS. Lastly yet importantly, the thesis presents GenDPR, which encompasses extensions to our protocols so that the privacy-verification algorithms can be conducted distributively among the federation members without demanding the outsourcing of genome data across boundaries. Further, GenDPR can also cope with collusion among participants while selecting genome data that can be used to create safe releases. Additionally, GenDPRproduces the same privacy guarantees as centralized architectures, i.e., it correctly identifies and selects the same data in need of protection as with centralized approaches. In the end, the thesis presents a homogenized framework comprising DyPS, I-GWAS and GenDPR simultaneously. Thus, offering a usable approach for conducting practical GWAS. The method chosen for protection is of a statistical nature, ensuring that the theoretical complexity of attacks remains high and withholding releases of statistics that would impose membership inference risks to participants using Likelihood-ratio tests, despite adversaries gaining additional information over time, but the thesis also relates the findings to techniques that can be leveraged to protect releases (such as Differential Privacy). The proposed solutions leverage Intel SGX as Trusted Execution Environment to perform selected critical operations in a performant manner, however, the work translates equally well to other trusted execution environments and other schemes, such as Homomorphic Encryption. [less ▲]

Magnetic Guinier Law and Uniaxial Polarization Analysis in Small Angle Neutron Scattering

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 ▲]

Modeling and Control of Laser Wire Additive Manufacturing

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 ▲]

Docteur

Doctoral thesis (2022)

The last century has been characterized by the increasing presence of synthetic chemicals in human surroundings, with as consequence, the increasing exposure of individuals to a wide variety of chemical ... [more ▼]

The last century has been characterized by the increasing presence of synthetic chemicals in human surroundings, with as consequence, the increasing exposure of individuals to a wide variety of chemical substances on a regular basis. The Lancet Commission on Pollution and Health estimated that since synthetic chemicals started to be available for common use at the end of the 1940s, more than 140,000 new chemicals have been produced, including five thousand used globally in massive volume. In parallel, awareness of the adverse effects of pollutant mixtures, possibly more severe than single-chemical exposures, has drawn attention towards the need of multi-residue analytical methods to obtain the most comprehensive information on human chemical exposome. Human biomonitoring, consisting in the measurement of pollutants in biological matrices, provides information that integrates all the possible sources of exposure, and is specific to the subject the sample is collected from. For this purpose, hair appears as a particularly promising matrix to assess chemical exposure thanks to its multiple benefits. Hair enables to detect both parent chemicals and metabolites, it is suitable to investigate exposure to chemicals from different families, and allows the detection of persistent and non-persistent chemicals. Moreover, contrary to fluids such as urine and blood, which only give information on the short-term exposure and present great variability in chemical concentration, hair is representative of wider time windows that can easily cover several months. Children represent the most vulnerable part of the population, and exposure to pollutants at young ages has been associated with severe health effects during childhood, but also during the adult life. Nevertheless, most epidemiological studies investigating exposure to pollutants are still conducted on adults, and data on children remain much more limited. The present study named “Biomonitoring of children exposure to pollutants based on hair analysis” investigated the relevance of hair analysis for assessing children exposure to pollutants. In this study, 823 hair samples were collected from children and adolescents living in 9 different countries (Luxembourg, France, Spain, Uganda, Indonesia, Ecuador, Suriname, Paraguay and Uruguay), and 117 hair samples were also collected from French adults. All samples were analysed for the detection of 153 organic compounds (140 were pesticides, 4 PCBs, 7 BDEs and 2 bisphenols). Moreover, the hair samples of French adults and children were also analysed for the detection of polycyclic aromatic hydrocarbons (PAH) and their metabolites (n = 62), nicotine, cotinine and metals (n = 36). The results obtained here clearly demonstrated that children living in different geographical areas are simultaneously exposed to multiple chemicals from different chemical classes. Furthermore, the presence of persistent organic pollutants in all children, and not only in adults, suggests that exposure to these chemicals is still ongoing, although these chemicals were banned decades ago. In the sub-group of Luxembourgish children, information collected within questionnaires in parallel to hair sample collection allowed to identify some possible determinant of exposure, such as diet (organic vs conventional), residence area (urban vs countryside), and presence of pets at home. Moreover, results showed higher levels of concentration in younger children, and higher exposure of boys to non-persistent pesticides than girls, which could possibly be attributed to differences in metabolism, behaviour and gender-specific activities. Finally, the study also highlighted high level of similarity in the chemical exposome between children from the same family compared to the rest of the population. The present study strongly supports the use of hair analysis for assessing exposure to chemical pollutants, and demonstrates the relevance of multi-residue methods to investigate exposome. [less ▲]

Exploring the Institutionalisation of Science Diplomacy: A Comparison of German and Swiss Science and Innovation Centres

Doctoral thesis (2022)

This thesis explains and investigates the development and the institutionalisation of Science and Innovation Centres (SICs) as being distinct instruments of science diplomacy. SICs are a unique and ... [more ▼]

This thesis explains and investigates the development and the institutionalisation of Science and Innovation Centres (SICs) as being distinct instruments of science diplomacy. SICs are a unique and underexplored instrument in the science diplomacy toolbox and they are increasingly being adopted by highly innovative countries. This research responds to a growing interest in the field. Science diplomacy is commonly understood as a distinct governmental approach that mobilises science for wider foreign policy goals, such as improving international relations. However, science diplomacy discourse is characterised by a weak empirical basis and driven by normative perspectives. This research responds to these shortcomings and aims to lift the smokescreen of science diplomacy by providing an insight into its governance while also establishing a distinctly actor-centred perspective. In order to achieve this, two distinct SICs, Germany’s Deutsche Wissenschafts- und Innovationshäuser (DWIH) and Switzerland’s Swissnex are closely analysed in an original comparative and longitudinal study. While SICs are just one instrument in the governmental toolbox for promoting international collaboration and competition, they are distinct due to their holistic set- up and their role as a nucleus for the wider research and innovation system they represent. Moreover, SICs appear to have the potential to create a significant impact, despite their limited financial resources. This thesis takes a historical development perspective to outline how these two SICs were designed as well as their gradual development and institutionalisation. The thesis further probes why actors participate in SICs by unpacking their differing rationales, developing a distinctly actor-centred perspective on science diplomacy. This study has been designed in an inductive and exploratory way to account for the novelty of the topic; the research findings are based on the analysis of 41 interviews and a substantial collection of documents. The study finds evidence that SICs developed as a response to wider societal trends, although these trends differed for the two case studies. Moreover, the development of SICs has been characterised by aspects such as timing, contingency and critical junctures. SICs are inextricably connected to their national contexts and mirror distinct system characteristics, such as governance arrangements or degree of actor involvement. These aspects were also seen as explaining the exact shape that SICs take. Furthermore, this study finds evidence of an appropriation of SICs by key actors, in line with their organisational interests. In the case of the DWIH, this impacted and even limited its (potential) design and ways of operating. However, the analysis of SICs’ appropriation also revealed a distinct sense of collectivity, which developed among actors in the national research and innovation ecosystem due to this joint instrument. The research findings reaffirm that science diplomacy is clearly driven by national interests, while further highlighting that the notion of science diplomacy and its governance (actors, rationales and instruments) can only be fully understood by analysing the national context. [less ▲]

On the hardness of the hidden subset sum problem: algebraic and statistical attacks

Doctoral thesis (2022)

The Multi-Level System of Space Mining: Regulatory Aspects and Enforcement Options

Doctoral thesis (2022)

Few contests that space mining holds the potential to revolutionize the space sector. The utilization of space resources can reduce the costs of deep space exploration and kick-off an entirely new economy ... [more ▼]

Few contests that space mining holds the potential to revolutionize the space sector. The utilization of space resources can reduce the costs of deep space exploration and kick-off an entirely new economy in our solar system. However, whether such a revolution will happen for good or for worse depends also on the enactment of appropriate regulation. Under the right framework, space mining will be able to deliver on its promise of a new era of prosperous and sustainable space exploration. But with the wrong rules (or lack thereof), unbalanced space resource activities can destabilize the space community to a truly unprecedented scale. With companies planning mining operations on the Moon already during this decade, the regulation of space resource activities has thus become one of the most pressing and crucial topics to be addressed by the global space community. In this context, this thesis provides a first-of-its-kind, comprehensive and innovative analysis of the regulatory and enforcement options currently shaping the multi-level governance of space mining. In addition to this, the thesis also suggests a series of correctives that can improve the system and ensure the peaceful, rational, safe, and sustainable conduct of space mining. Structurally, the thesis moves from general to particular and is divided in three chapters. Chapter 1 discusses the relationship between space law and international law to contextualize the specific assessment of space mining. Chapter 2 analyses the current regulatory framework applicable to space mining, considering both the international and national levels. Finally, Chapter 3 identifies potential enforcement options, assesses them in terms of effectiveness and legitimacy, and further proposes some pragmatic correctives to reinforce the governance system. [less ▲]

Essays on Market Microstructure and Financial Markets Stability

Doctoral thesis (2022)

The present doctoral thesis consists of three main chapters. The chapters of the thesis can be considered independently. Each of the three chapters raises a research question, reviews the related ... [more ▼]

The present doctoral thesis consists of three main chapters. The chapters of the thesis can be considered independently. Each of the three chapters raises a research question, reviews the related literature, proposes a method for the analysis, and, finally, reports results and conclusions. Chapter 1 is entitled Dark Trading and Financial Markets Stability and it is based on a working paper co-authored with Prof. Dr. Jorge Goncalves and Prof. Dr. Roman Kraussl. This paper examines how the implementation of a new dark order -- Midpoint Extended Life Order (M-ELO) on Nasdaq -- impacts financial markets stability in terms of occurrences of mini-flash crashes in individual securities. We use high-frequency order book data and apply panel regression analysis to estimate the effect of dark order trading activity on market stability and liquidity provision. The results suggest a predominance of a speed bump effect of M-ELO rather than a darkness effect. We find that the introduction of M-ELO increases market stability by reducing the average number of mini-flash crashes, but its impact on market quality is mixed. Chapter 2 is entitled Dark Pools and Price Discovery in Limit Order Markets and it is a single-authored work. This paper examines how the introduction of a dark pool impacts price discovery, market quality, and aggregate welfare of traders. I use a four-period model where rational and risk-neutral agents choose the order type and the venue and obtain the equilibrium numerically. The comparative statics on the order submission probability suggests a U-shaped order migration to the dark pool. The overall effect of dark trading on market quality and aggregate welfare was found to be positive but limited in size and depended on market conditions. I find mixed results for the process of price discovery. Depending on the immediacy need of traders, price discovery may change due to the presence of the dark venue. Chapter 3 is entitled Machine Learning and Market Microstructure Predictability and it is another single-authored piece of work. This paper illustrates the application of machine learning to market microstructure research. I outline the most insightful microstructure measures, that possess the highest predictive power and are useful for the out-of-sample predictions of such features of the market as liquidity volatility and general market stability. By comparing the models' performance during the normal time versus the crisis time, I come to the conclusion that financial markets remain efficient during both periods. Additionally, I find that high-frequency traders activity is not able to forecast accurately neither of the market features. [less ▲]

Non-Orthogonal Multiple Access for Next-Generation Satellite Systems: Flexibility Exploitation and Resource Optimization

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 ▲]

Multi-scale characterisation of Parkinson's disease development in iPSCs dopaminergic differentiation

Doctoral thesis (2022)

Moral Decision-Making in Video Games

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 ▲]

Populäre Musik edieren. Editorische Probleme dargestellt an den Werken für oder mit Blasorchester von Laurent Menager (1835-1902)

Doctoral thesis (2022)

Digital Forensics AI: on Practicality, Optimality, and Interpretability of Digital Evidence Mining Techniques

Doctoral thesis (2022)

Digital forensics as a field has progressed alongside technological advancements over the years, just as digital devices have gotten more robust and sophisticated. However, criminals and attackers have ... [more ▼]

Digital forensics as a field has progressed alongside technological advancements over the years, just as digital devices have gotten more robust and sophisticated. However, criminals and attackers have devised means for exploiting the vulnerabilities or sophistication of these devices to carry out malicious activities in unprecedented ways. Their belief is that electronic crimes can be committed without identities being revealed or trails being established. Several applications of artificial intelligence (AI) have demonstrated interesting and promising solutions to seemingly intractable societal challenges. This thesis aims to advance the concept of applying AI techniques in digital forensic investigation. Our approach involves experimenting with a complex case scenario in which suspects corresponded by e-mail and deleted, suspiciously, certain communications, presumably to conceal evidence. The purpose is to demonstrate the efficacy of Artificial Neural Networks (ANN) in learning and detecting communication patterns over time, and then predicting the possibility of missing communication(s) along with potential topics of discussion. To do this, we developed a novel approach and included other existing models. The accuracy of our results is evaluated, and their performance on previously unseen data is measured. Second, we proposed conceptualizing the term “Digital Forensics AI” (DFAI) to formalize the application of AI in digital forensics. The objective is to highlight the instruments that facilitate the best evidential outcomes and presentation mechanisms that are adaptable to the probabilistic output of AI models. Finally, we enhanced our notion in support of the application of AI in digital forensics by recommending methodologies and approaches for bridging trust gaps through the development of interpretable models that facilitate the admissibility of digital evidence in legal proceedings. [less ▲]

MALDI-TOF-Enabled Subtyping and Antimicrobial Screening of the Food- and Waterborne Pathogen Campylobacter

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 ▲]

Methods and tools for analysis and management of risks and regulatory compliance in the healthcare sector: the Hospital at Home – HaH

Doctoral thesis (2022)

Changing or creating a new organization means creating a new process. Each process involves many risks that need to be identified and managed. The main risks considered here are procedural risks and legal ... [more ▼]

Changing or creating a new organization means creating a new process. Each process involves many risks that need to be identified and managed. The main risks considered here are procedural risks and legal risks. The former are related to the risks of errors that may occur during processes, while the latter are related to the compliance of processes with regulations. Therefore, managing the risks implies proposing changes to the processes that allow the desired result: an optimized process. In order to manage a company and optimize it in the best possible way, not only should the organizational aspect, risk management and legal compliance be taken into account, but it is important that they are all analyzed simultaneously with the aim of finding the right balance that satisfies them all. This is exactly the aim of this thesis, to provide methods and tools to balance these three characteristics, and to enable this type of optimization, ICT support is used. This work is not intended to be a computer science or law thesis but an interdisciplinary thesis. Most of the work done so far is vertical and in a specific domain. The particularity and aim of this thesis is not so much to carry out an in-depth analysis of a particular aspect, but rather to combine several important aspects, normally analyzed separately, which however have an impact on each other and influence each other. In order to carry out this kind of interdisciplinary analysis, the knowledge base of both areas was involved and the combination and collaboration of different experts in the various fields was necessary. Although the methodology described is generic and can be applied to all sectors, a particular use case was chosen to show its application. The case study considered is a new type of healthcare service that allows patients in acute disease to be hospitalized to their home. This provide the possibility to perform experiments using real hospital database. [less ▲]

Hybrid Artificial Intelligence to extract patterns and rules from argumentative and legal texts

Doctoral thesis (2022)

This Thesis is composed of a selection of studies realized between 2019 and 2022, whose aim is to find working methodologies of Artificial Intelligence (AI) and Machine Learning for the detection and ... [more ▼]

This Thesis is composed of a selection of studies realized between 2019 and 2022, whose aim is to find working methodologies of Artificial Intelligence (AI) and Machine Learning for the detection and classification of patterns and rules in argumentative and legal texts. We define our approach as “hybrid”, since different methods have been employed combining symbolic AI (which involves “top-dow” structured knowledge) and sub-symbolic AI (which involves “bottom-up” data-driven knowledge). The first group of these works was dedicated to the classification of argumentative patterns. Following the Waltonian model of argument (according to which arguments are composed by a set of premises and a conclusion), and the theory of Argumentation Schemes, this group of studies was focused on the detection of argumentative evidences of support and opposition. More precisely, the aim of these first works was to show that argumentative patterns of opposition and support could be classified at fine-grained levels and without resorting to highly engineered features. To show this, we firstly employed methodologies based on Tree Kernel classifiers and TFIDF. In these experiments, we explored different combinations of Tree Kernel calculation and different data structures (i.e., different tree structures). Also, some of these combinations employs a hybrid approach where the calculation of similarity among trees is influenced not only by the tree structures but also by a semantic layer (e.g. those using “smoothed” trees and “compositional” trees). After the encouraging results of this first phase, we explored the use of a new methodology which was deeply changing the NLP landscape exactly in that year, fostered and promoted by actors like Google, i.e. Transfer Learning and the use of language models. These newcomer methodologies markedly improved our previous results and provided us with stronger NLP tools. Using Transfer Learning, we were also able to perform a Sequence Labelling task for the recognition of the exact span of argumentative components (i.e. claims and premises), which is crucial to connect the sphere of natural language to the sphere of logic. The last part of this work was finally dedicated to show how to use Transfer Learning for the detection of rules and deontic modalities. In this case, we tried to explore a hybrid approach which combines structured knowledge coming from two LegalXML formats (i.e., Akoma Ntoso and LegalRuleML) with sub-symbolic knowledge coming from pre-trained (and then fine-tuned) neural architectures. [less ▲]

Essays on the Economics of Migration, Inequalities, and Culture

Doctoral thesis (2022)

The present doctoral thesis consists of three chapters of self-contained works about the economics of migration, inequalities, and culture. In the first chapter, I introduce the outline of the thesis and ... [more ▼]

The present doctoral thesis consists of three chapters of self-contained works about the economics of migration, inequalities, and culture. In the first chapter, I introduce the outline of the thesis and shortly discuss the research questions of each chapter. The second chapter explores the effects of mass migration on individual attitudes towards migrants. Using several data sources for the mass migration of Ukrainians in Poland between 2014-2016, this chapter is focused on how a massive exogenous increase in the stock of migrant residents and migrant co-workers affects the perception of migrants. Using both an IV methodology and a difference-in-difference analysis, I test two hypotheses: the labor market competition and contact theory and find some evidence favoring the second. First, difference-in-difference analysis shows that Poles become more welcoming to migrants in regions with more job opportunities for migrants. Second, I find that an increase in the size of the migrant group affects attitudes towards migrants positively inside a group of natives with similar demographic and job skills characteristics. The third chapter explores how poverty can be explained by marital status and gender using the RLMS-HSE household survey. This research shows that divorced women exhibit lower poverty levels than divorced men by employing longitudinal data from the Russian National Survey (RLMS-HSE) from 2004 to 2019. The result remains qualitatively invariant when considering a theoretical probability to divorce for married couples that take into account the age of the partners, labor force participation, and education. A higher probability to divorce impacts positively only men's poverty level. Investigating an inter-related dynamic model of poverty and labor market participation, we find that divorced women work more than divorced men, which is why divorce hits harder on husbands than on wives. In the fourth chapter of the thesis, we study the effect of past exposure to communist indoctrination during early age (9-14 years) on a set of crucial attitudes in the communist ideology aiming to create the \emph{new communist man/woman}. We focus on the indoctrination received by children during their pioneering years. School pupils automatically became pioneers when they reached 3rd or 4th grade. The purpose of the pioneer years was to educate soviet children to be loyal to the ideals of communism and the Party. We use a regression discontinuity design exploiting the discontinuity in the exposure to pioneering years due to the fall of the USSR in 1991, implying a strong association that hints to causality. We find robust evidence that has been a pioneer has long-lasting effects on interpersonal trust, life satisfaction, fertility, income, and perception of own economic rank. Overall, these results suggest that past pioneers show a higher level of optimism than non-pioneers. Finally, we look for gender differences because various forms of emulation campaigns were used to promote the desired virtues of the new communist woman. However, we find no evidence of the effect of exposure to communism on women. The indoctrination seems to have left more substantial effects on men. [less ▲]

Analysis of urban forms in Europe using radial road network simulations

Doctoral thesis (2022)

Is universal healthcare truly universal? Socioeconomic and migrant inequalities in healthcare

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 ▲]

Development of gas-generating core-shell amorphous calcium carbonate nanoparticles for contrast-enhanced ultrasound imaging

Doctoral thesis (2022)

Lengths and intersections of curves on surfaces

Doctoral thesis (2022)

Social Mobility and Inequalities in Health. Bayesian Perspectives on Mortality Risks and Methodological Implications for Statistical Inference.

Doctoral thesis (2022)

Machine Learning-Based Efficient Resource Scheduling for Future Wireless Communication Networks

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 ▲]

Characterization of the surface properties of polycrystalline Cu(In,Ga)Se2 using a combination of scanning probe microscopy and X-ray photoelectron spectroscopy

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 ▲]

Cognitive Pain Modulation in Young and Older Adults: Understanding the Role of Individual Differences in Frontal Functioning

Doctoral thesis (2022)

Cognitive pain modulation is integral to our quality of life and deeply interwoven with the success of pain treatments but is also characterized by large interindividual variations. Emerging evidence ... [more ▼]

Cognitive pain modulation is integral to our quality of life and deeply interwoven with the success of pain treatments but is also characterized by large interindividual variations. Emerging evidence suggests that one of the driving factors behind these variations are individual differences in frontal functioning. Further evidence indicates that pain-related cognitions, and possibly also emotional distress, may influence the efficacy of pain modulation. Central aim of this project was to assess the role of individual differences in frontal functioning in cognitive pain modulation, with a specific focus on older adults. With respect to this, we also wanted to assess whether individual differences in frontal functions could explain conflicting previous results on age-related changes in the efficacy of cognitive pain modulation. In addition, we wanted to address the role of negative pain-related mindsets and emotional distress on the efficacy of cognitive pain modulation. We tested these research questions across four different studies using two prime paradigms of cognitive pain modulation, namely distraction from pain and placebo analgesia. In Study I, we assessed the role of individual differences in executive functions, emotional distress, and pain-related cognitions in modulating heat pain thresholds in healthy young adults in virtual reality environments with different levels of cognitive load. We found that emotional distress and visuo-spatial short-term memory significantly predicted how participants responded to the low vs high load environment. In Study II, we investigated the role of different forms of cognitive inhibition abilities and negative pain-related cognitions in modulating the efficacy of distraction from (heat) pain by cognitive demand in healthy young adults. We found a significant influence of better cognitive inhibition and selective attention abilities on the size of the distraction effect; however, this association was moderated by the participant’s level of pain catastrophizing, i.e., high pain catastrophizers showed an especially strong association. In Study III, we tested potential age-related differences in distraction from pain in a group of young and older adults while simultaneously acquiring functional brain images. We found no age-related changes at the behavioural level, but a slightly reduced neural distraction effect in older adults. The neural distraction effect size in older adults was furthermore significantly positively related to better cognitive inhibition abilities. In Study IV, we explored potential age-related differences in placebo analgesia in a group of young and older adults (who were partly re-recruited from Study III) while recording their brain activity with an electroencephalogram. Results revealed no age-related differences in the magnitude of the behavioural or electrophysiological placebo response, but older adults showed a neural signature of the placebo effect that was distinct from young participants. Regression analyses revealed that executive functions that showed an age-related decline (as established via group comparisons) were significant predictors of the behavioural placebo response. We furthermore found that better executive functions significantly moderated the association between age group and placebo response magnitude: older adults with better executive functions showed a larger placebo response than young adults whereas worse executive functions were associated with a smaller placebo response, possibly explaining why we found no significant difference at the group level. In summary, all studies provide converging evidence that differences in cognitive functions can significantly affect the efficacy of cognitive pain modulation. Although older adults showed a significant decline in most cognitive functions that we assessed, we found no systematic reduction in the efficacy of cognitive pain modulation (except for a slight reduction in the neural distraction effect size). Closer inspection of the data revealed that older adults may have engaged compensatory mechanisms that enabled them to experience the same (or even higher) level of pain relief as younger adults. We furthermore found evidence for the notion that pain-related cognitions and emotional distress may affect how individuals respond to cognitive pain modulation although this association was less systematic than for cognitive functions. Overall, the present thesis adds to the emerging body of evidence highlighting the importance of executive functions, as indicators of frontal functioning, in cognitive pain modulation. [less ▲]

When does finance win? A set-theoretic analysis of the conditions of European financial interest groups' lobbying success on post-crisis bank capital requirements

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 ▲]

Investigation in reusable composite flooring systems in steel and concrete based on composite behaviour by friction

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 ▲]

Educational and Occupational Differentials in Cognition: Examining the Role of Gender using Population-Based Studies

Doctoral thesis (2022)

Smart cloud collocation: a unified workflow from CAD to enhanced solutions

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 ▲]

Towards a Unified and Robust Data-Driven Approach. A Digital Transformation of Production Plants in the Age of Industry 4.0

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 ▲]

User Experience Design for Cybersecurity & Privacy: addressing user misperceptions of system security and privacy

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 ▲]

THE INTRICATE LINK BETWEEN DIET, THE HOST IMMUNE SYSTEM, AND THE GUT MICROBIOTA IN THE CONTEXT OF ENTEROPATHOGENIC INFECTIONS AND MULTIPLE SCLEROSIS

Doctoral thesis (2022)

IL-6 Signaling and long non-coding RNAs in liver cancer

Doctoral thesis (2022)

EXCESSIVE MICROBIAL MUCIN FORAGING INDUCED BY DIETARY FIBER DEPRIVATION MODULATES SUSCEPTIBILITY TO INFECTIOUS AND AUTOIMMUNE DISEASES

Doctoral thesis (2022)

The gastrointestinal (GI) mucus layer is a protective and lubricating hydrogel of polymer-forming glycoproteins that covers our intestinal epithelium. This mucus layer serves as an interface between the ... [more ▼]

The gastrointestinal (GI) mucus layer is a protective and lubricating hydrogel of polymer-forming glycoproteins that covers our intestinal epithelium. This mucus layer serves as an interface between the intestinal epithelium and environment as well as a as first line of defense against the potentially harmful microorganisms. While the GI mucus layer closer to the gut epithelium is highly condensed and acts as a physical barrier for invading microorganisms, further away from the epithelium, proteolytic degradation makes it loose. This looser part of the mucus layer serves as an attachment site and a nutrient source for some commensal gut bacteria. The molecular mechanisms that drive the mucus–microbe interactions are emerging and are important to understand the functional role of the gut microbiome in health and disease. Previous work by my research group showed that a dietary fiber-deprived gut microbiota erodes the colonic mucus barrier and enhances susceptibility to a mucosal pathogen Citrobacter rodentium, a mouse model for human Escherichia coli infections. In this PhD thesis, I studied role of the gut mucus layer in the context of various other infectious and autoimmune diseases by inducing the natural erosion of the mucus layer by dietary fiber deprivation. In order to unravel the mechanistic details in the intricate interactions between diet, mucus layer and gut microbiome, I leveraged our previously established gnotobiotic mouse model hosting a synthetic human gut microbiota of fully characterized 14 commensal bacteria (14SM). I employed three different types of infectious diseases for the following reasons: 1) attaching and effacing (A/E) pathogen (C. rodentium), to better understand which commensal bacteria aid in enhancing the pathogen susceptibility when a fiber-deprived gut microbiota erodes the mucus barrier; 2) human intracellular pathogens (Listeria monocytogenes and Salmonella Tyhimurium) to investigate, whether like the A/E pathogen, erosion of the mucus layer could affect the infection dynamics; and 3) a mouse nematode parasite – Trichuris muris, which is a model for the human parasite Trichuris trichiura – to study how changes in the mucin–microbiome interactions drive the worm infection, as mucins play an important role in worm expulsion. In my thesis, I used various combinations of 14SM by dropping out individual or all mucin-degrading bacteria from the microbial community to show that, in the face of reduced dietary fiber, the commensal gut bacterium Akkermansia muciniphila is responsible for enhancing susceptibility to C. rodentium, most likely by eroding the protective gut mucus layer. For my experiments with intracellular pathogens (L. monocytogenes and S. Tyhimurium, I found that dietary fiber deprivation provided protection against the infection by both L. monocytogenes and S. Typhimurium. This protective effect against the pathogens was driven directly by diet and not by the microbial erosion of the mucus layer, since a similar protective effect was observed in both gnotobiotic and germ-free mice. Finally, for the helminth model, I showed that that fiber deprivation-led elevated microbial mucin foraging promotes clearance of the parasitic worm by shifting the host immune response from a susceptible, Th1 type to a resistant, Th2 type. In the context of autoimmune disease, I focused on inflammatory bowel disease (IBD). Although IBD results from genetic predisposition, the contribution of environmental triggers is thought to be crucial. Diet–gut microbiota interactions are considered to be an important environmental trigger, but the precise mechanisms are unknown. As a model for IBD, I employed IL-10-/- mice which are known to spontaneously develop IBD-like colitis in conventional mice. Using our 14SM gnotobiotic mouse model, I showed that in a genetically susceptible host, microbiota-mediated erosion of the mucus layer following dietary fiber deprivation is sufficient to induce lethal colitis. Furthermore, my results show that this effect was clearly dependent on interaction all three factors: microbiome, diet and genetic susceptibility. Leaving out only one of these factors eliminated the lethal phenotype. The novel findings arising from my PhD thesis will help the scientific community to enhance our understanding of the functional role of mucolytic bacteria and the GI mucus layer in shaping our health. Overall, given a reduced consumption of dietary fiber in industrialized countries compared to developing countries, my results have profound implications for potential treatment and prevention strategies by leveraging diet to engineer the gut microbiome, especially in the context of personalized medicine. [less ▲]

ARGUMENT MINING AND ITS APPLICATIONS IN POLITICAL DEBATES

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 ▲]

Dynamic Activity-Based Demand Estimation Considering Destination And Mode Choice

Doctoral thesis (2022)

Optical measurement under space conditions

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 ▲]

Effects of an e-learning platform to improve primary care physicians’ response to domestic violence

Doctoral thesis (2022)

Family Violence (FV) is a broad term that includes different types of violence and abuse that occur within the family, like domestic violence (DV) or Intimate Partner Violence (IPV), Child Abuse or ... [more ▼]

Family Violence (FV) is a broad term that includes different types of violence and abuse that occur within the family, like domestic violence (DV) or Intimate Partner Violence (IPV), Child Abuse or neglect (CA), Elder Abuse (EA) and Female Genital Mutilation (FGM), inter alia. The most prevalent is DV or IPV, declared to be one of the most serious human rights violations, even prior to the aggravated situation brought about by the COVID-19 in 2020. Margaret Chan, 2013 WHO director, called it a global public health care issue of epidemic proportions, but COVID-19 has transformed it into a pandemic as well, earning it the dubious distinction of being the shadow pandemic. Governments’ responses to stop the spread of the infection have forced many families to stay at home, triggering or aggravating cases of IPV and abuse. The prevalence of IPV around the world has been estimated at 30%, although this percentage varies depending on the region of the world or the country, ranging from 20% in the Western Pacific to 33% in the World Health Organisation (WHO) South-East Asia region. Unfortunately, the United Nations Population Fund (UNFPA) predicts that the impact of the pandemic will increase IPV by 20% during the pandemic. Despite the epidemic proportions of this healthcare problem and its enormous and horrendous consequences at all levels (social well-being, physical and mental health), not only for the individual but also their families, IPV remains largely underdiagnosed. Although victims tend to use healthcare services more, and trust health care professionals (HCP) to disclose abuse, they do not do so unless professionals specifically ask for it. Nevertheless, one of the most common barriers that prevent HCP from enquiring is that they do not feel adequately trained to tackle it. Although research on the effectiveness of IPV training for HCP suggests that they improve their knowledge, attitudes, self-perceived readiness to approach this problem, and actual response, this topic has not yet been formally included in the curricula. The World Health Organization and the National Institute for Health have published guidelines for health services and recommendations to facilitate the development and implementation of effective training, underlining the need to improve HCP education. The overall objectives of this thesis are to describe current training provisions on FV in the European Region (FAVICUE), and to investigate the effects of a digital education intervention in improving primary care physicians’ response to DV (E-DOVER). [less ▲]

THERMODYNAMICS OF CHEMICAL ENGINES: A CHEMICAL REACTION NETWORK APPROACH

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 ▲]

Older people's use of assistive technologies and alternative means to cope with health-related declines

Doctoral thesis (2022)

Health-related declines are risk factors for autonomous living and quality of life of older people. Yet, they have various action possibilities (e.g., use of technical and personal support, health ... [more ▼]

Health-related declines are risk factors for autonomous living and quality of life of older people. Yet, they have various action possibilities (e.g., use of technical and personal support, health services, environmental adaptation) which may reduce these risks. Sadly, the use rates of these options are far from perfect, so that their positive effects are not sufficiently realized. With this in mind, the main goal of this dissertation was to gain a better understanding of the factors that influence older people's use of the various action possibilities to cope with illness, functional decline, activity limitations, and participation restrictions. Five studies using different but complementary methods pursued this goal. Study I provided a systematic review of 12 empirical studies on the effectiveness and use of self-care assistive technologies (ATs). It found that self-care ATs are efficient with respect to reduced care hours and increased independence level. The use of such technologies was associated with three different kinds of factors including personal, contextual, technological device factors. Study II conducted a systematic review of 23 theoretical models of AT use from an action-theoretical perspective on lifespan development. It revealed that these models considered a limited range of internal and external context factors of AT use. None of them contained perceived discrepancies between the actual and desired developmental situation, goals to reduce these discrepancies, action alternatives to AT use, and decision-making about AT use by other persons or a joint decision-making. Study III contained a qualitative meta-synthesis of 7 qualitative studies on subjective reasons for the use or nonuse of ATs. It thus considered a branch of research that is independent from the ones covered by Study 1 and 2 and it used a different method. It found 25 subjective reasons referring to user’s beliefs and desires of which 18 were not contained in AT use models. However, they could be included in more comprehensive models to increase their predictive value. Study IV focused on the construction of an “Actional Model of older people´s coping with health related declines” for explaining the use of 8 major action possibilities (7 beyond ATs). Its development followed a recent theory construction methodology and recent principles for constructing a practically useful theory. It integrated results from the studies 1, 2 and 3 as well as from other relevant literature. Central explanatory variables are perceived discrepancies between actual and desired development, discrepancy reduction and prevention goals and internal as well as external context factors. Outcome variables are the 8 courses of action and their results. Study V examined the view of experts (professional caregivers of older people) regarding central components of the Actional Model of Older people´s Coping with Health-Related Declines developed in study 4. Theory generating expert interviews were conducted to further clarify key components of the model. The results led to their further specification, such as the contents of discrepancy reduction and prevention goals, further motivating and demotivating goals, and external context factors as barriers and facilitators of their use. The findings of the dissertation are discussed with respect to the advancement of empirical, theoretical and methodological knowledge. Implications for future research and for the improvement of practical applications in gerontological case management and developmental counselling are highlighted. [less ▲]

A posteriori error estimation for finite element approximations of fractional Laplacian problems and applications to poro–elasticity

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 ▲]