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Secret Visits to the Brussels Nunciature: Archbishop Clemente Micara, Father Donatus Wynant and the observant crisis in the Belgian Capuchin province
No full textstatus: Publishe
Local response function estimation in spherical deconvolution for comprehensive tissue representation using diffusion MRI
No full textDiffusion MRI (dMRI) plays a crucial role in studying tissue microstructure and fibre orientation. Due to the intricate nature of the dMRI signal, end users require representations that provide a straightforward interpretation. Currently, these representations rely on tissue-average estimations or simplified tissue models and are hence limited in their applicability to pathology. In this study, we propose a novel approach called LoRE-SD-a local response function estimation in spherical deconvolution. LoRE-SD minimises assumptions about tissue microstructure to improve the reconstruction of dMRI data in the presence of pathology. This is achieved by introducing a general signal representation that spans the most common multi-compartment microstructure models used in neuroimaging. Leveraging spherical deconvolution, LoRE-SD provides accurate estimations of the local fibre orientations, allowing tractography in the healthy and pathological brain. We evaluate this approach using simulations and in vivo data from a healthy volunteer and from patients with glioma. Comparing the results quantitatively with the state-of-the-art, we find that LoRE-SD accurately reconstructs fibre orientations across the brain while also significantly improving glioma reconstruction and fibre bundle estimation. Additionally, the tissue representation in LoRE-SD facilitates the generation of various image contrasts, including response function anisotropy and contrasts accentuating intra-axonal, extra-axonal, and free water spaces, which enables a more flexible approach for tractography. In conclusion, LoRE-SD introduces a framework for estimating a data-driven, local representation of tissue microstructure with minimal prior assumptions. This approach provides a new way to represent the human brain, pathology, and other organs using dMRI and opens avenues for defining novel image contrasts, which may benefit tractography.sponsorship: Vlaamse Overheid|Onderzoeksprogramma Artificiële Intelligentie (AI) Vlaanderenstatus: Publishe
Het tekort verteerd: Een onderzoek naar eiwitinname, malabsorptie en spierbehoud na een bariatrische ingreep
No full textMetabolic and bariatric surgery (MBS) induces substantial and durable weight loss, yet concerns persist about long-term muscle preservation and nutrient adequacy, particularly in older adults. Protein intake often decreases after surgery, while digestive adaptations may alter the handling of different protein sources. Selenium status may also influence muscle health after MBS, but data in older adults remain scarce. This thesis examined how older individuals (≥65 years) retain muscle mass and function after MBS, and to what extent protein intake, protein digestion, and trace element status contribute to these outcomes. Five complementary studies were undertaken. First, a cross-sectional comparison assessed the prevalence of sarcopenia, muscle mass, and muscle function in older adults with previous MBS versus non-surgical controls. Second, selenium status was evaluated in older adults with and without prior MBS. Third, a systematic review and meta-analysis synthesised evidence on muscle quantity, strength, and physical performance after MBS in studies without specific interventions to retain lean mass. Fourth, the dual-isotope tracer technique was used to compare digestion of spirulina protein in older adults with and without Roux-en-Y gastric bypass (RYGB). Finally, long-term protein requirements after RYGB were explored using the non-invasive Indicator Amino Acid Oxidation (IAAO) method. Across studies, older adults with prior MBS did not show a higher prevalence of sarcopenia than non-surgical controls. Low protein intake, higher adiposity, and age were more strongly associated with muscle impairment than surgical history. Selenium supplementation effectively increased selenium and selenoprotein P concentrations, though supersaturation occurred in some individuals. Protein digestibility appeared largely preserved after RYGB, and the IAAO protocol proved feasible in this population. Overall, adequate protein intake and appropriate supplementation support muscle preservation in older adults after MBS. These findings refine our understanding of nutrient metabolism and inform future updates of clinical nutrition guidelines for after bariatric surgery.status: Accepte
Methodologie voor stromings-structurele-akoestische voorspelling in een vroeg ontwerpstadium van leidingen
No full textTackling noise generated by flow duct systems—such as those in HVAC installations and industrial machinery—is of utmost importance for acoustic comfort and regulatory compliance. These systems act as efficient acoustic waveguides, allowing noise to propagate with minimal dissipation, while excitation of structural vibrations in the duct walls causes radiation of 'break-out' noise. Therefore, flow duct noise is inherently multiphysical, involving aerodynamic, acoustic, and structural interactions. Conventional mitigation strategies, often based on empirical guidelines and implemented late in the design process, lack predictive accuracy and lead to suboptimal solutions, certainly at low frequencies. To address this, predictive modeling during early-stage design is essential, enabling noise control to be integrated alongside primary objectives such as aerodynamic efficiency, mass reduction, compactness, and robustness.
This research aims to predict flow duct noise during early-stage design for low-speed turbulent airflow in thin-walled ducts. Two key challenges to reach this goal are inherent to early-stage design, namely the necessity of (i) a low computational cost to allow a broad 3D study in limited time and (ii) a high accuracy to make well-considered design decisions. To overcome these challenges, two objectives are pursued: (i) create a 3D simulation tool incorporating essential interactions at an acceptable computational cost, and (ii) establish a methodology for estimating realistic structural boundary conditions of flexible wall joints, which strongly influence low-frequency vibro-acoustic behavior, but are unknown during early-stage design.
For the first objective, a fully coupled mathematical model is deemed computationally prohibitive for early-stage design. Therefore, simplifying assumptions are introduced: low acoustic and structural amplitudes compared to the aerodynamics allow one-way aero-acoustic and aero-elastic interactions, whereas the confined configuration requires a two-way vibro-acoustic coupling. Linear acoustics and elasticity are assumed. The fluid domain is modeled with a hybrid Computational Aero-Acoustic approach, which splits the domain into a source region and a propagation region. The propagation region models the convective effects by computing the aerodynamic mean flow with the incompressible Navier-Stokes equations and the acoustic propagation with the Linearized Euler Equations (LEE). The LEE are solved in the time domain, enabling frequency domain analysis via impulse response. The structural domain is represented by linear elasto-dynamic plate or shell equations. A partitioned coupling strategy is implemented using the open-source library preCICE, linking an in-house LEE solver with a structural solver to capture two-way vibro-acoustic interactions. Verification on an academic case demonstrates accurate prediction of vibro-acoustic behavior under aero-acoustic convective effects compared to analytical and finite element models. The partitioned coupling of the LEE with structural dynamics for efficient simulation of two-way vibro-acoustic interactions in flow ducts constitutes the first contribution to the state of the art.
The second objective addresses the lack of reliable structural boundary conditions in early-stage design. Oversimplified assumptions such as clamped or simply-supported conditions lead to significant inaccuracies, particularly at low frequencies: in this frequency range, an accurate prediction of the vibro-acoustic interaction, visible in the spectrum as distinctive peaks due to the low modal density, is necessary for effective mitigation.} To improve the structural boundary conditions, an indirect estimation methodology is developed, leveraging acoustic two-port measurements of a prototype duct segment. The two-port method characterizes the duct's input-output acoustic behavior, abstracting from acoustic boundaries imposed by the unknown larger duct system. It is assumed that a flow does not affect the structural boundary conditions and only influences the pre-tension. By defining an objective function based on the difference between measured and simulated two-port metrics, structural boundary conditions—modeled with linear, frequency-, and mesh-independent parameters—are updated without direct measurement. This approach is tested on a rectangular duct with one flexible wall, achieving a 45% improvement in matching transmitted acoustic power ratios compared to conventional assumptions. Based on the Modal Assurance Criterion, it is confirmed that vibro-acoustic modes can be accurately reproduced with the updated numerical model, with an average eigenfrequency prediction error of 1.86%. Robustness is demonstrated through numerical studies varying material properties and through predictive validation on modified configurations, such as added cavities. This underlines the merits of the developed indirect methodology for estimating structural boundary conditions of flexible wall joints, which constitutes the second contribution to the state of the art.
In summary, this research delivers two key advancements for early-stage design of flow duct systems: (i) a computationally efficient simulation tool capturing dominant multiphysical interactions, and (ii) a robust methodology for estimating structural boundary conditions critical to low-frequency noise prediction. Together, these contributions enable effective noise control strategies to be integrated early in the design process, avoiding costly late-stage modifications and improving acoustic comfort in practical engineering applications.status: Accepte
Naar het karakteriseren van energieflexibiliteit in residentiële districten
No full textThe transition toward decarbonized and flexible energy systems requires buildings, as major energy consumers, to become active participants in demand response (DR). This dissertation investigates how building stock can be characterized, controlled, and clustered to provide energy flexibility at district scale under realistic operational constraints. Its central aim is to bridge the gap between high-fidelity building modeling and practical demand response applications by introducing scalable frameworks for emulator generation, systematic flexibility characterization, and flexibility-driven clustering. The work contributes to both the methodological foundations of control-oriented urban building energy modeling (UBEM) and to the operational integration of building flexibility in energy systems.
The thesis begins with the development of UCEGe (Urban Control-oriented Emulator Generator), a scalable framework for generating modular, interoperable, and computationally efficient urban building emulators. Implemented in Modelica/Dymola using the IDEAS library and linked through the Functional Mock-up Interface (FMI), UCEGe integrates building thermal models, heating systems, and controllers. It supports high temporal resolution and real-time co-simulation, enabling large-scale district emulation that is suitable for predictive control, flexibility analysis, and digital twin applications. UCEGe thus provides the foundational modeling infrastructure for subsequent analyses.
Building upon this foundation, the thesis examines how price signal characteristics (duration, amplitude, and shape) and building features (Heat Loss Coefficient, Thermal Capacity, Time Constant, and heating system type) determine the performance of flexibility under different DR regimes. A district of 480 residential buildings, each equipped with price-aware Model Predictive Controllers (MPCs), was subjected to seven price signals: two sets of impulse signals (2 h and 6 h, each with three amplitudes) and a 24 h step signal. A comprehensive framework of twelve Key Performance Indicators (KPIs) was introduced to quantify flexibility in terms of load shifting (SEI, SED, SER), peak shaving (PP, DP, PSE), ramping behavior (EIFF, EDFF, MRU, MRD), and timing (PPIT, DPIT). Results showed that signal characteristics decisively shape flexibility outcomes, with long-duration signals enhancing shifting potential but reducing efficiency under saturation, while shorter, moderate-amplitude signals provide efficient and predictable responses. Feature-driven analysis demonstrated that building properties strongly condition flexibility, with distinct trade-offs across time scales and control regimes.
Recognizing the limitations of quantile-based classification, the thesis introduced an unsupervised clustering framework that groups buildings directly by their flexibility response profiles rather than by predefined property categories. Using normalized flexible power (NFP), Dynamic Time Warping (DTW) distance, and Ward's linkage, the study revealed distinct flexibility archetypes emerging from flexibility-driven grouping. However, replicating the framework with different signal types showed that cluster memberships can vary significantly across different DR signals, highlighting the signal dependence of clustering outcomes.
To overcome this, the thesis developed a consensus clustering methodology that integrates multiple DR regimes to derive stable, signal-agnostic clusters. By aggregating clustering results across signals and evaluating robustness through CDF analysis, silhouette scores, and variance of flexible power (FP), the method identified building groups that consistently exhibit similar flexibility behavior regardless of the signal. These consensus clusters are interpretable in terms of building properties, and they offer a robust tool for DR program design, and enhanced forecasting models, enabling aggregators and system operators to characterize and reliably mobilize building flexibility.
The thesis concludes by outlining its contributions: (i) the creation of UCEGe as a scalable control-oriented UBEM framework; (ii) the development of a comprehensive KPI-based methodology for flexibility characterization; (iii) systematic feature-driven analysis of district flexibility under short-, mid-, and long-term DR regimes; (iv) the introduction of unsupervised clustering for emergent flexibility archetypes; and (v) the advancement of consensus clustering as a robust and transferable methodology for flexibility-oriented aggregation.
Limitations include the reliance on white-box modeling, the use of baselines for flexibility characterization, the exclusive assumption of MPC-equipped buildings, the computational intensity of high-fidelity simulations, and presuming ideal conditions of having a market mechanism for the buildings flexibility exploitation. Future work should address these by validating consensus clustering in field trials, extending the framework to heterogeneous control environments, improving data acquisition infrastructure, integrating uncertainty, and scaling to diverse building stocks and regulatory contexts.
In sum, this dissertation advances the methodological and operational understanding of urban building flexibility, offering a pipeline that links modeling, control, characterization, and clustering. By doing so, it provides scientific and practical tools to enable the integration of buildings as active, reliable, and scalable participants in future smart energy systems.status: Accepte
Van sluimerende antimicrobiële resistentie tot de globale COVID-19-pandemie: versterking van het beheer van infectieziekten in de ambulante zorg
No full textBackground: Acute infections are common in ambulatory care and most do not require antibiotic treatment. Nevertheless, antibiotics are frequently prescribed due to the complex interplay between the knowledge and attitudes of both patients and prescribers, as well as external pressures. In particular, children are often inappropriately prescribed antibiotics, partly due to the more pronounced uncertainty in distinguishing serious from nonserious infections. Antibiotic overuse leads to adverse events, increased rates of patient reattendance, and the development of antimicrobial resistance (AMR), a major public health threat. The COVID-19 pandemic, as an acute health crisis, shifted research priorities. This doctoral project aimed to study and strengthen the management of infectious diseases in ambulatory care, by advancing the long-known priority of antimicrobial stewardship and by addressing the emergent challenge of persistent symptoms after COVID-19.
Methods: In part I, we investigated antibiotic use in children presenting to ambulatory care, focusing on Belgium, high-income countries, and the determinants of inappropriate prescribing. In part II, we evaluated the impact of the pandemic on antibiotic use and investigated management strategies for patients with persistent symptoms after COVID-19. In part III, we examined the needs, feasibility, and user engagement of clinical decision support systems (CDSS) aimed at improving infectious disease management in ambulatory care.
Results: Antibiotic use in Belgian children has decreased between 2010 and 2019 (-35.5% in the number of dispensed packages). In high-income countries, antibiotic prescribing rates for children remain high, especially for acute otitis media (AOM; 85.6%). Determinants of inappropriate prescribing include diagnosis of AOM, diagnosis of respiratory infection (RTI), general practitioner (GP) as prescriber, older children, and rural area. Antibiotic use in Belgian children sharply declined during the COVID-19 pandemic and gradually returned to pre-pandemic levels afterwards. Based on evidence of very low certainty, physical training, breathing exercises, olfactory training, and multidisciplinary treatment may be effective rehabilitation therapies for patients with persistent symptoms after COVID-19. This evidence, together with stakeholder opinion, was incorporated in the first Belgian guideline on the follow-up and rehabilitation of these patients. Ambulatory care physicians expect a CDSS for infectious disease management that is pragmatic, minimally time-consuming, and provides clear and concise guidance on diagnostics and treatment. The pilot tool was quick to use (median 23 seconds), rated highly for usability (mean 78.6), and used selectively in uncertain cases. GPs and trainees valued its clarity, but broader engagement may require electronic health record integration and strategies to reach those less inclined to use such tools.
Discussion: Antibiotic prescribing may in particular be improved for GPs, in rural areas, and for RTIs and AOM. A CDSS for infectious disease management was perceived to improve knowledge and antimicrobial prescribing behaviour. Next steps should focus on broader implementation of this tool in ambulatory care, together with complementary stewardship interventions; and on evaluating its impact on prescribing volume and appropriateness, patient outcomes, costs, AMR, and long-term behaviour change. A definition of appropriate antibiotic prescribing is needed, and stewardship strategies should be embedded into pandemic response plans to maintain their effectiveness during health crises. Lastly, incorporating real-time evidence on persistent symptoms after COVID-19 into national guidelines ensures a standardised approach to follow-up and rehabilitation strategies in ambulatory care.status: Publishe
Zwaartekrachtgolfsignaturen van donkere-materieomgevingen
No full textNext-generation gravitational wave detectors will require higher accuracy waveform models to faithfully recover the parameters of the detected signals' sources. In this thesis, motivated by this consideration and by the opportunities to uncover new physics in these high-precision measurements, we investigate the impacts of a dark matter distribution on potential gravitational waves sources for this class of detectors. We focus on two kinds of candidates: a fluid-like cold dark matter and a minimally coupled scalar field sector derived from a nonlinear sigma model. This work discussed how these matter distributions could imprint signatures on a gravitational wave source.
First, we model a Schwarzschild black hole immersed in a dark matter spike, deriving a fully relativistic density profile for the fluid-like dark matter through adiabatic contraction of an initial Hernquist profile. Via black hole perturbation theory on this spacetime, we compute the system's quasi-normal modes (QNMs), and its response to a tidal perturbation via the tidal Love numbers (TLNs). We observe that the QNMs receive corrections with respect to their values in a vacuum Schwarzschild spacetime, and that the TLNs are non-vanishing. We find that the relativistic dark matter profile leads to distinct scaling laws for the quasi-normal modes frequency shifts compared to non-relativistic models, and that the shifts are suppressed for massive and diluted halos. The Love numbers, while small, provide a new channel for energy loss during the inspiral phase, which could alter a binary black hole dynamics over several orbits.
In the second part of the thesis, we introduce a scalar field resulting from nonlinear sigma models, motivated by string theory compactification arguments. We identify spherically symmetric boson stars solutions to these models, self-gravitating condensates of massive bosonic fields. The curvature of the sigma models is shown to affect significantly the properties of such solutions. The hyperbolic model yields light and diluted boson stars, while the spherical theory allows for solutions with mass and compactness of the order of those of neutron stars.
Finally, we study the dynamics of such scalar fields on black hole spacetimes, simulating their accretion onto isolated black holes and their impact on binary black hole mergers. Using numerical relativity simulations with the GRChombo code, we show that the curvature of the sigma model dictates whether the scalar field behaves as an attractive or repulsive self-interaction, altering the accretion physics and the gravitational wave emission. The hyperbolic field behaves with an attractive self-interaction, while the spherical one shows a repulsive dynamics. This emerges clearly in the accretion process when the scalar field mass is low with respect to the black hole scale. In the binary black hole case, the scalar field forms a circumbinary cloud that induces a dephasing in the GW waveform, with the direction and magnitude of the dephasing depending on the model's curvature. We observe indications that these models suppress bosenova phenomena, i.e. a rapid instability which results in the ejection of the boson cloud from the binary system.
The results of this work contribute to ongoing efforts to characterise the main contributions from an environment that will have to be included in the waveform generation pipelines for future gravitational waves observatories. We argue that this line of research will be key to unlock the maximal amount of information from future observations, allowing for precise tests of general relativity and for the detection of new fundamental physics in the gravity sector.status: Publishe
Do Lenders Price Firms’ Cybersecurity Risks?
No full textFirms are increasingly exposed to cybersecurity risk. Using syndicated loan data
covering US firms, we examine how lenders price firms’ ex-ante cybersecurity risk.
Our findings indicate that lenders charge, on average, a 4 to 13 basis points higher
loan rate when a firm exhibits greater cybersecurity risk over time. Furthermore, we
document that the pricing of cybersecurity risk differs between lender types. Commercial
banks tend to adopt a more stringent approach to pricing cybersecurity risk
compared to non-bank lenders. They also attach more financial covenants as firms
become riskier. Even within commercial banks, the pricing of cybersecurity risk is
primarily driven by lenders who show awareness of their own cybersecurity risk and
have considered an insurance policy. These findings highlight the importance of
lender awareness in pricing borrower risks, especially for risks that are not typically
assessed in standard evaluations. Lastly, purchasing cybersecurity insurance does
not mitigate higher loan spreads for borrowers.status: Accepte
PHD POSITION IN DATA ANALYTICS AND STATISTICAL MODELING
No full textIn many research fields data dimension reduction techniques are widely used. Fields such as chemometrics, signal processing, and video compression, try to deal with these issues with tools that transform high-dimensional data to lower dimensions where the meaningful properties of the data are retained. Principal Component Analysis (PCA) is a widely used tool for dimension reduction. However, it is known that PCA is not robust against outliers. Most robust PCA methods are developed to deal with rowwise outliers, which are observations that deviate from the majority. The MacroPCA method can additionally deal with outlying cells and missing values. These methods however can only be applied to two-dimensional data matrices. For multiway data, models such as parallel factor analysis (PARAFAC) have been developed to reduce their dimension. Available robust PARAFAC methods can only deal with rowwise outliers and missing values. The goal of this research is to develop and study methods that can simultaneously deal with rowwise outliers, cellwise outliers, and missing values in multiway data. Application to real-life datasets will also be considered, especially in chemometrics.status: Accepte
Magneto-elektrische Effecten op Nanometer-nanoseconde-schaal voor Spintronische Toepassingen
No full textAmong the technologies that may lead to a paradigm shift with respect to current CMOS technology, spintronics presents several advantages to achieve area and power reduction. The possibility to perform multifrequency processing and the non-volatility of the magnetic materials could provide new functionalities to circuit designers for various applications. However, a major limitation for the realization of spintronic devices is the lack of a scalable and efficient transducer between electric and magnetic domains. Current device concepts are often based on the control of the magnetization by currents, for example via generated magnetic Ørsted fields or more recent effects, such as spin-transfer torque or spin-orbit torque. However, such techniques are typically not very energy-efficient, and it would be very desirable to control the magnetization by electric fields instead. In the last years, magnetoelectricity has seen a renaissance due to technological and theoretical progress. The promise of magnetoelectricity is a large improvement in energy efficiency over current-based approaches. As an example, the energy needed to switch a nanomagnet by spin-transfer torque (~10 fJ) is several orders of magnitude larger than the energy needed to charge a magnetoelectric capacitor (~1 aJ). Thus, electric-field control is an enabling solution for many emerging applications of magnetism, which have been so far rendered uncompetitive due to large power dissipation. The most efficient magnetoelectric systems are layered compounds consisting of piezoelectric and magnetostrictive materials. So far, studies of such magnetoelectric compounds have focused on large scale systems (mm to cm size) with very few reports on micrometer size devices. Moreover, mostly only the low frequency behavior of magneto- electrics has been assessed. Microelectronic applications of magnetoelectricity will however require devices with dimensions in the nanometer range and operate at GHz frequencies, i.e. at (sub-)nanosecond timescales. The main goal of the thesis will thus be the study of nano-scale devices including magnetoelectric compounds and their behavior at GHz frequencies, especially the magnetoelectric excitation of ferromagnetic resonance or spin waves (magnons).status: Accepte