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De impact van antropogene en klimatologische druk op vijvers en hun macrofyten- en macro-invertebratengemeenschappen
No full textPonds are increasingly exposed to the combined and interacting impacts of multiple environmental stressors. Due to their small size, shallow water depth and high surface-area-to-volume ratio, ponds are especially sensitive to global change pressures, including nutrient enrichment and climate warming. The overarching objective of this thesis was to improve understanding of how pond ecosystems respond to these stressors. Specifically, I examined (i) how pond primary producer communities respond to increasing nutrient levels and rising growing-season temperatures, (ii) whether mitigation measures such as terrestrial vegetated buffer strips can effectively reduce nutrient and sediment runoff into ponds from surrounding agricultural land, and (iii) how variation in water permanence influences macroinvertebrate communities.
In Chapter 1, I analysed a dataset comprising 220 ponds (147 permanent and 73 temporary) distributed across a broad climatic gradient spanning the United Kingdom, Denmark, Belgium, Germany, Switzerland, Spain, Turkey and Uruguay. The aim was to assess how nutrient concentrations, growing-season temperature, and their interaction influence phytoplankton biomass and the coverage of submerged, free-floating, and emergent macrophytes. Results showed that permanent ponds with low total nitrogen (TN) concentrations were typically dominated by submerged vegetation, whereas at elevated TN levels, submerged macrophytes were replaced by either phytoplankton or free-floating plants. Higher temperatures lowered the nutrient concentrations at which such changes in dominance occurred in permanent ponds. By contrast, submerged plant communities in temporary ponds displayed greater tolerance to increased nutrient concentrations.
In Chapter 2, using the same 147 permanent ponds, I explored the relationships between submerged macrophyte cover, taxonomic richness, and functional richness. I further evaluated how increasing nutrient concentrations influence submerged plant cover, species richness, functional richness, and functional redundancy, and investigated nutrient-driven changes in functional type composition. A strong positive association was observed between taxonomic and functional richness, resulting in generally low functional redundancy across ponds. Species richness was significantly lower in ponds with minimal plant cover (0-25%) compared to ponds exhibiting higher cover (>25%). However, richness levels did not differ among moderate to high cover categories, due to cases where dense plant cover occurred despite low species diversity. Submerged plant cover, species richness, functional richness and functional redundancy all declined with increasing TN. Functional groups showed contrasting nutrient responses: Utricularia spp. were most abundant at low TN levels, while Ceratophyllum/Elodea spp. and Characeae showed no apparent sensitivity to the TN gradient.
In Chapter 3, I investigated the effect of terrestrial vegetated buffer strip width on pond nutrient concentrations, total suspended solids, and phytoplankton biomass in 34 agricultural ponds in Belgium (Flanders) and Germany (Brandenburg). In Germany, buffer strip width had a strongly negative effect on total nitrogen, total phosphorus and total suspended solids concentrations, with improvements in water quality detectable even at 5 m width, although wider buffers yielded greater reductions. In Belgium, no such relationship was observed, likely due to differences in landscape characteristics, historical eutrophication and pond hydroperiod. Buffer strip width did not significantly influence phytoplankton biomass in either region, most likely because nutrient concentrations remained consistently high, even in ponds surrounded by buffer strips.
In Chapter 4, I assessed how hydroperiod affects local species richness and community structure of aquatic Coleoptera, Odonata and Gastropoda in 13 permanent and 17 temporary ponds in northeastern Germany (Brandenburg). Coleoptera species richness (both larvae and adults) was consistently higher in temporary ponds, whereas no significant richness differences were observed for Odonata or Gastropoda. In contrast, species composition differed between hydroperiod types for Gastropoda, but not for Coleoptera or Odonata. Water permanence significantly influenced species turnover in Gastropoda communities. Temporary ponds contributed disproportionately to regional diversity of Coleoptera and Gastropoda, whereas permanent ponds supported a greater proportion of regional Odonata diversity. Most recorded taxa were generalists with traits enabling survival under periodic drying. Species of national conservation concern were detected in both pond types, particularly among gastropods.status: Accepte
... Waarom zou ik (wat voor) optimist (moeten) worden?
No full textAan de vraag hoe een optimist te worden, moet de vraag voorafgaan waarom iemand dat zou willen. Als het antwoord is dat optimisme goed is voor een mens (en als duidelijk is welk type van optimisme bedoeld wordt), blijft de vraag of iemand er wel naar believen voor kan kiezen om een optimist te worden. Bestaan er adviezen die mensen kunnen volgen om optimistisch te worden – en zijn die wel nodig?status: Accepte
Scheiding van Microalgen en Cyanobacteriën met behulp van op Cellulose Nanokristallen Gebaseerde Flocculanten via Sedimentatie en Drijvende Lucht Flotatie (DAF) Scheidingstechnieken
No full textMicroalgae are a promising source of biomass, rich in lipids, proteins, and carbohydrates, with applications in nutrition, energy, and pharmaceuticals. However, they also present significant challenges. Eutrophication and climate change can trigger algal blooms that degrade water quality. When these blooms are composed of toxin-producing species, they form harmful algal blooms (HABs), which are increasing worldwide. A major hurdle in microalgae biomass production is harvesting, which can account for up to 30% of total costs. Low biomass concentration (0.5 - 5 g L-1) and small cell size (2 - 5 μm) of microalgae limit centrifugation and filtration separation. Aggregating the cells into flocs can improve sedimentation. Flocculants, such as metal salts, polyacrylamides are often used but can cause biomass contamination. Chitosan is an alternative, but its performance is limited due to coiling in high-ionic strength medium.
Cationic cellulose nanocrystals (CNCs) are promising microalgae flocculants due to their high aspect ratio favoring cell attachment and flocculation due to the larger surface area available, conformational rigidity due to crystallinity, and presence of easily modifiable hydroxyl groups. However, when used with marine microalgae, CNCs formed small flocs that can limit sedimentation. Harvesting could be improved by combining CNCs with Dissolved Air Flotation (DAF), a widely used technique for water treatment. DAF uses microbubbles to lift flocs, improving separation. DAF also offers potential for managing HABs, as bloom forming cyanobacteria resist settling in lakes. While conventional flocculants pose toxicity risks, bio-based CNCs with DAF could overcome buoyancy, enable rapid biomass removal, and reduce bloom recurrence.
DAF improved marine microalgae harvesting efficiency with pyridinium modified CNCs (PYR-CNCs), achieving up to 20 % higher separation at fivefold lower doses than sedimentation. DAF outperformed sedimentation by efficiently removing small flocs, required less flocculation time and was observed to be insensitive to variations in floc size, when used in combination with different flocculants. Overall, DAF was effective in harvesting microalgae, especially when sedimentation is limited by small flocs.
Modified CNCs flocculate microalgae but often remain in the biomass, potentially affecting quality and downstream use. To enable removal from the biomass, pH-responsive zwitterionic CNCs (zCNCs) were synthesized. Unlike PYR-CNCs, the zCNCs were cationic at low pH for flocculation and anionic at high pH for detachment. At high pH, flocs formed with zCNCs redispersed into individual cells, indicating flocculant removal from biomass. Unlike PYR-CNCs, zCNCs demonstrated a detachment capability, enabling potential biomass recovery.
PYR-CNCs were successfully used to remove cyanobacteria Microcystis aeruginosa found in freshwater blooms. The study's novelty lies in combining PYR-CNCs with DAF as a sustainable in-lake treatment. DAF removed up to 99% of Microcystis within two minutes of flocculation. For a small recreational lake with Microcystis, the simulation of accumulated residual CNCs post-DAF treatment cycles over a period of time, were observed to be non-toxic to aquatic organisms-Daphnia and C. vulgaris. Overall, these results show that PYR-CNCs offer a safe and sustainable alternative for in-lake water remediation when used with DAF.status: Publishe
Correlatiefuncties tussen eigenwaarden en singuliere waarden
No full textNon-Hermitan linear operators describe a multitude of systems like open scattering systems in physics or the signal transmission in wireless telecommunications. Such operators can be characterised either by their eigenvalues or their singular values. The long-standing problem of their relation shall be addressed for a specific class of random matrices with the goal to revealing universal correlations between these two sets of quantities. We aim for the computation of the joint level densities of the eigenvalues and singular values at finite matrix dimension. A well-known relation, called the Haagerup-Larson theorem, for infinite dimensional matrices shall be re-derived and its corrections for finite matrix dimensions shall be quantified. Furthermore, two simple deformations of the probability density of the random matrix will be investigated in this context, too. The first deformation introduces holes in the complex spectrum and makes contact with the Leuven project. The second kind of deformation squeezes the originally isotropic spectrum to an elongated shape. Both kinds of deformations are encountered in quantum field theoretical applications. The ultimate goal of this project is to find novel universal correlations between eigenvalues and singular values. The student will learn techniques from harmonic analysis on matrix spaces, bi-orthogonal functions and asymptotic analysis. The project will be complemented by the KU Leuven based project and the collaboration will ensure a successful completion of the project.status: Publishe
Datagedreven modellering van niet-lineaire trillingen: Efficiënte rekenmethoden voor systeemidentificatie gericht op regeltechniek
No full textThe identification of nonlinear systems from experimental data remains a major challenge in engineering, primarily due to two factors: the difficulty of selecting an appropriate model structure among many possible nonlinear representations, and the intrinsic non-convexity of the associated parameter estimation problem, in which conventional gradient-based optimisation methods often converge slowly or become trapped in suboptimal solutions.
The aim of this thesis is to develop a sequence of identification algorithms for modelling the nonlinear vibrations of electromechanical systems. The focus is on computational efficiency and robustness to local minima, while maintaining a balance with physical interpretability. Each proposed method progressively reduces the reliance on prior system knowledge, moving from approaches that require detailed physical insight towards more data-driven techniques that remain grounded in physical principles. All methods are built upon a nonlinear state-space framework that explicitly distinguishes between the linear dynamic behaviour and the nonlinear contributions, providing a flexible yet interpretable model structure suitable for subsequent control design.
Three complementary approaches are developed, all based on the same sequential strategy: (i) identify a baseline model, (ii) infer the nonlinear residual dynamics nonparametrically, and (iii) subsequently parametrise the nonlinearities in a static and isolated setting.This separation bypasses the otherwise computationally demanding nonlinear optimisation over dynamic trajectories. The first method introduces a sliding-window approach to infer the nonlinear residual dynamics by exploiting the known physical structure of the system. The second method generalises this framework to arbitrary systems where no such prior knowledge is available. The third method merges the previously separate inference and learning steps into a single formulation, and does so in the frequency domain, which enables inherent parallelisation across frequencies that can be efficiently exploited on modern hardware.
The proposed methods are validated on both simulated and experimental systems, yielding models that demonstrate accurate prediction while offering varying levels of physical interpretability. The corresponding identification algorithms show high computational efficiency and robustness to poor local minima. To promote openness and reproducibility, the thesis also provides an open-source Python package for frequency-domain state-space identification, along with a challenging new benchmark dataset for nonlinear system identification.status: Accepte
Public Demand-side Policies and Innovation
No full textThe dissertation will investigate the potential of governments' demand-side policies for stimulating innovation in the business sector. While technological policies such as R&D tax credits and R&D grants have received a lot of attention in the scholarly literature, demand side policies such as public procurement for innovation has not been studied intensely yet. So far, only Czarnitzki et al. (2020) have investigated the effect of this policy change on innovation in Germany. My dissertation will build on their results and will attempt to highlight benefits of the policy more broadly, analyze different variables, employ other more rigorous econometric methods and also investigates potential pitfalls of the policy, such as negative effects induced by higher procurement costs.status: Publishe
Integrated hybrid energy and time-of-use electricity tariffs for the resource-constrained project scheduling problem
No full textsponsorship: Acknowledgments This work was supported by the National Natural Science Foundation of China [http://dx.doi.org/10.13039/501100001809] under [grant numbers 71971173, 72201209 and 72571238] ; Natural Science Basic Research Plan in Shaanxi Province of China [Grant 2025JC-YBMS-800] ; China Scholarship Council [http://dx.doi.org/10.13039/501100004543] [grant number 202306290117] ; Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University under [grant number CX2024018] . (National Natural Science Foundation of China|71971173, National Natural Science Foundation of China|72201209, National Natural Science Foundation of China|72571238, Natural Science Basic Research Plan in Shaanxi Province of China|2025JC-YBMS-800, China Scholarship Council|202306290117, Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University|CX2024018)status: Accepte
TABFAIRGDT: A Fast Fair Tabular Data Generator using Autoregressive Decision Trees
No full textEnsuring fairness in machine learning remains a significant
challenge, as models often inherit biases from their training
data. Generative models have recently emerged as a promising approach to mitigate bias at the data level while preserving utility.
However, many rely on deep architectures, despite evidence that simpler models can be highly effective for tabular data. In this work, we introduce TABFAIRGDT, a novel method for generating fair synthetic tabular data using autoregressive decision trees.
To enforce fairness, we propose a soft leaf resampling technique that adjusts decision tree outputs to reduce bias while preserving predictive performance. Our approach is non-parametric, effectively capturing complex relationships between mixed feature types, without relying on assumptions about the underlying data distributions. We evaluate TABFAIRGDT on benchmark fairness
datasets and demonstrate that it outperforms state-of-the-art (SOTA) deep generative models, achieving better fairness-utility trade-off for downstream tasks, as well as higher synthetic data quality. Moreover, our method is lightweight, highly efficient, and CPU-compatible, requiring no data pre-processing. Remarkably,
TABFAIRGDT achieves a 72% average speedup over the fastest SOTA baseline across various dataset sizes, and can generate fair synthetic data for medium-sized datasets (10 features, 10K samples) in just one second on a standard CPU, making it an ideal solution for real-world fairness-sensitive applications.status: Accepte
