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Multi-Resolution Autonomous Linear State Space Filters for N-Dimensional Signals
No full textstatus: Accepte
Large eddy simulations of weakly turbulent diffusion flames in an oxygen-reduced co-flow using a new subgrid combustion model
No full textsponsorship: This paper is written as the extension of the earlier publication "Large eddy simulations of weakly turbulent diffusion flames in a co-flow with reduced oxygen concentration" [32] in proceedings of 4th European Symposium on Fire Safety Science held from October 9 to 11, 2024 in Barcelona, Spain. This research is funded by The Research Foundation-Flanders (FWO-Vlaanderen) via PhD fellowship 1104125N and research projects G023221N and G034725N. (Research Foundation-Flanders (FWO-Vlaanderen)|1104125N, Research Foundation-Flanders (FWO-Vlaanderen)|G023221N, Research Foundation-Flanders (FWO-Vlaanderen)|G034725N)status: Accepte
Zoonotic disease detection at the point-of-care: the best of RPA and CRISPR-Cas
No full textBiosensors are increasingly crucial in detecting biomarkers for emerging zoonotic diseases at the point-of-care (POC). This imminence was recently highlighted by the deficient response during the SARS-CoV-2 pandemic. While polymerase chain reaction (PCR) is the common nucleic acid (NA) testing method for zoonotic diseases in laboratory settings, it is impractical for the POC settings due to the equipment-related cost, lack of portability and user-friendliness. Recent advances in NA amplification introduced isothermal methods, such as recombinase polymerase amplification (RPA), which is known for its low temperature (37–42 °C), short incubation time (5–20 min) and suitability for integration in miniaturized, portable, low-cost, highly sensitive diagnostic platforms. However, RPA susceptibility to false positive results steered to its combination with CRISPR-Cas12/13, leading to the rise of SHERLOCK and DETECTR. This review first explores RPA-CRISPR-Cas bioassay development as either two- or one-step. This is followed by a discussion on the integration of canonical RPA, or its combination with CRISPR-Cas, into different diagnostic platforms towards NA amplification at the POC (e.g., mobile laboratories, centrifugal, or pump-free platforms). Finally, the advantages, limitations, and outlook for POC-based diagnostics of zoonotic diseases with RPA(-CRISPR-Cas) are discussed, highlighting the need for innovative technologies to address global health challenges. While promising, many of these approaches still require further research to achieve streamlined, single-step reactions and seamless integration into diagnostic platforms. Moreover, despite two decades of RPA(-CRISPR-Cas) development, technology readiness is limited, still missing validated platforms, integrated sample preparation, and AI-powered results analysis enabling real time epidemiological monitoring.sponsorship: Fonds Wetenschappelijk Onderzoek|1S54823N, Fonds Wetenschappelijk Onderzoek|1S56425N, Fonds Wetenschappelijk Onderzoek|S003923N, KU Leuven|IDN/21/006, Horizon Foundation|101092049, Horizon Foundation|101137242status: Publishe
Adaptive slicing for increased productivity of metal laser powder bed fusion
No full textsponsorship: Fonds Wetenschappelijk Onderzoek|1S53724Nstatus: Publishe
A Common Two-Dimensional Structure? Comparing Demand-side Political Spaces of Eight European Democracies
No full textstatus: Published onlin
Klimaatvariabiliteit en de respons van tropische ecosystemen onder invloed van ENSO: inzichten uit Ecuador met behulp van remote sensing
No full textLand degradation is defined not only as the loss or diminution of ecosystem services in an area, but as the loss of the normal functions of an ecosystem in a territory. Since degradation is a slow and progressive process, it is difficult to detect it in areas with different characteristics. That is why it is imperative to implement control and restoration activities and policies, which are only possible through the study of land degradation drivers and triggers that can lead to a degradation process, along with constant monitoring of zones with risks of degradation according to its physical characteristics: slope, amount of organic matter, salinity, annual precipitation, evapotranspiration rates, among others. Based on this situation, we have the limitation that field monitoring campaigns are highly expensive; however, we currently have remote sensing technology, which is a cost-effective measurement tool for periodic monitoring of large areas, through the analysis of satellite images, the study of vegetation indexes and the monitoring of physical characteristics of risk zones. Thus, the present study aims initially at the detection of degradation zones with the analysis of satellite images and the use of vegetation indexes in a timeline, to create a baseline of land degradation and find the potentials hot spots where the study will focus. Subsequently, the combination of physical and climatic characteristics, together with the data collected from the indices, will help to define the driver that caused the degradation process and together with this a possible trigger within the territory management. Finally, the study may help as a tool for planning and management of the territory to avoid degradation in areas with similar characteristics.status: Publishe
Bruggen tussen vervoerseconomie en verkeersmodellen: speltheoretische evenwichten in grootschalige netwerken met multi-actor prijszetting
No full textDesigning efficient pricing schemes for transportation systems has long been a central problem in both the domains of transport economics and traffic modeling. Pricing instruments such as tolls, fares, and tradable credits are among the most powerful tools available to manage demand, reduce congestion, and improve the efficiency of transport networks. Their importance has only grown as urban mobility systems continue to be more congested, and multimodal. Additionally, the number of relevant decision makers has increased: national, regional, and local governments may pursue overlapping but distinct objectives, and an increasing number of private mobility service providers who optimize their own pricing strategies have entered the market. In such settings, pricing is no longer a single-leader problem but a strategic multi-decision-maker problem, where interactions between actors can significantly alter outcomes. Addressing these challenges requires tools that are both behaviorally realistic and computationally tractable. However, despite decades of research, existing approaches struggle to address the complexity of modern urban mobility systems. Traditional transport economics models, though analytically elegant, are often restricted to highly simplified networks and cannot accommodate the behavioral and spatial heterogeneity of real systems. Conversely, full-scale traffic models capture network detail and user behavior with greater fidelity but are computationally demanding and unsuitable for direct optimization and multi-decision maker equilibration. Bridging the gap between these two branches of research remains an open challenge and is the central focus of this dissertation.
This dissertation makes progress on this challenge through three interconnected contributions. First, a mathematical programming-based transport economics model is developed that moves beyond the stylized formulations common in the literature. This model endogenizes both demand and path choice user-equilibrium, while accounting for heterogeneous user classes with different Values of Time and Willingness-To-Pay. The core of this model allows computation of optimal tolls for a single government with due consideration of reaction of the travelers. On top of this foundation, game-theoretical formulations are introduced to analyze interactions between multiple governments which optimize their own tolls. Both Nash and Stackelberg competition scenarios are modelled. Applications show that competition between governments generally reduces efficiency compared to joint optimization, while taking-up leadership can mitigate losses to an extent. Case studies also demonstrate practical methods for addressing the unintended impacts due to non-uniqueness in user equilibria.
Building on this foundation, the second contribution is the development of CHANAkYA, a comprehensive game-theoretical modelling framework of pricing and tradable credit charging in multi-modal transportation systems with public and private players. CHANAkYA extends the single-mode model to incorporate roads, public transport, and private mobility service providers, while accommodating stratified user demographics and a broad portfolio of instruments. These include link- and entry-based tolls, distance-based tolls, user class-based prices, and tradable credit schemes. Interactions between toll or credit charge optimizing governments and fare optimizing private mobility service providers are modelled. The framework also accounts for the fact that user-equilibrium flows are often non-unique by introducing player-specific perspectives: optimistic, pessimistic, or entropy-maximizing. These perspectives remove the ambiguity in the reaction of travelers to different prices. Through a series of policy-driven case studies inspired by Leuven, the framework demonstrates its capacity to generate nuanced insights. Results show, for instance, that a modest cordon toll combined with low public transport fares yields large efficiency gains over the baseline scenario; that age-differentiated tolling produces only marginal improvements while raising equity concerns; that tradable credits can replicate the efficiency of direct pricing instruments; and that privatization of public transport leads to efficiency losses. Collectively, these findings illustrate CHANAkYA's potential to support richer, more policy-relevant analyses of complex transport systems.
The third contribution addresses the computational challenge of applying such game-theoretical models to full-scale traffic models. Toll optimization in full-scale traffic models is generally studied through surrogate modelling techniques like Bayesian Optimization and Kriging models. However, traditional surrogate modeling techniques focus on approximating an input-output relationship between design variables and the objective function of a single decision maker, and therefore cannot capture the strategic interactions that arise in multi-actor settings. To overcome this limitation, a novel metamodel-based equilibration (MBE) scheme is developed. The scheme employs CHANAkYA as a fast metamodel guiding the search for equilibrium tolls in an underlying full-scale traffic model with multiple decision makers. An iterative calibration-equilibration process ensures that the metamodel parameters are regularly updated to match sensitivities observed in the full-scale traffic model, while optimization is performed at the metamodel level. This approach shifts the computational burden away from the full-scale model, enabling equilibrium pricing to be achieved with far greater efficiency. Applications confirm its effectiveness: in single decision-maker settings, MBE outperforms Bayesian Optimization benchmarks, while in multi-actor contexts it enables, for the first time, the computation of approximate Nash and Stackelberg equilibria with full-scale traffic models. These results demonstrate the feasibility of policy-relevant multi-decision-maker analysis at realistic scales.
Taken together, the three contributions form a coherent progression. The dissertation first establishes a computationally rigorous foundation for an advanced transport economics model, then generalizes this foundation into a flexible multi-modal framework which allows a portfolio of instruments and decision-makers, and finally integrates this framework with full-scale traffic models through the novel metamodel-based equilibration scheme. The resulting methods show that properly calibrated transport economics models can serve not only as stand-alone tools for illustrative case studies, but also as efficient guides for full-scale traffic models. This advances the state of the art by enabling theoretically grounded and computationally tractable analysis of multi-actor pricing problems, which until now has remained elusive.
This dissertation thus provides both methodological innovations and practical tools for the design of pricing schemes in modern transportation and urban mobility systems. By bridging the complementary strengths of transport economics and traffic modeling, it offers a pathway toward more rigorous, efficient, and policy-relevant approaches to one of the central challenges in urban mobility.status: Publishe
Alito's Way: Christian Persecution Complex from the God's Not Dead Film Series to the Supreme Court
No full textstatus: Accepte
LEIDERSCHAP IN MUSEA: Een Herdefinitie van de Rol van de Museum Directeur en de Nood aan Business Strategie en Vaardigheden.
No full textThis doctoral research focuses on contemporary museum leadership, particularly emphasising the global museum leader. Employing a multi-stage, qualitative, and participatory action research approach, this study highlights both the essential knowledge and skills of the museum director, as well as the business frameworks and strategies that underpin sustainability, innovation, and strategic change within the museum sector. Collective leadership, business strategy, agile management, and systems thinking take centre stage in the process.
The research contributes to scientific knowledge at the intersection of leadership theory, museum studies, nonprofit management, and organisational change. Its scientific significance is rooted in its original empirical contributions, theoretical integration, and methodological innovation, each addressing distinct gaps in the existing research.
Keywords: museum leadership - future museum - agile museum management - collective leadership - systems thinking - agency theory - non-profit leadership - museum governancestatus: Publishe
De klinische betekenis van HLA mismatch load bij nier- en longtransplantatie
No full textThe aims of the PhD project:
The following questions will be investigated in this PhD project:
Which mismatched HLA epitopes are immunodominant in the development of an HLA antibody response in kidney transplantation?
Why do the same mismatched HLA epitopes not always generate an antibody response?
What are the risk factors for retransplanting patients who have a prior failed transplant?
These questions are part of the following topic:
'Kidney transplantation is the treatment of choice for most patients suffering from end-stage renal disease (ESRD). HLA disparity between donor and recipient affects transplant immunity and consequently has an impact on graft outcome. One of the alloimmune mechanisms is the development of de novo donor specific antibodies (dnDSA), which represents a risk factor for humoral transplant rejection. This antibody-mediated rejection (ABMR) is a major cause of premature graft loss in kidney transplantation.
As HLA antibodies are now recognized as being specific for epitopes and donor-recipient HLA mismatch at the amino acid level can now be determined, epitope-based permissible mismatching could be a new strategy.
Although our study (Daniëls et al., Transplant Immunology, 2018) suggests that the total epitope mismatch load could be the determining factor in the risk of HLA antibody formation, it is clear that we lack data on the immunogenicity of the epitope mismatches. It could be that immunogenicity is not merely a quantitative issue, but that one or only a few epitope mismatches are sufficient to induce an antibody response. Maybe, the higher number of epitope mismatches only enhances the chance to include immunodominant epitopes.
HLA epitope mismatches can be used as a risk assessment tool at time of transplantation and for post-transplantation follow-up, to evaluate those patients at the highest risk of HLA antibody formation. This could be especially valuable in young and paediatric kidney recipients, who will probably need more than one kidney allograft in their lifetime'status: Publishe