Refine
Year of publication
- 2021 (180) (remove)
Document Type
- Doctoral Thesis (180) (remove)
Language
- English (180) (remove)
Is part of the Bibliography
- yes (180)
Keywords
- Klimawandel (4)
- Spektroskopie (4)
- climate change (4)
- Deep Learning (3)
- spectroscopy (3)
- 3D-Visualisierung (2)
- Agrarökologie (2)
- Air pollution (2)
- Alpen (2)
- Alps (2)
Institute
- Institut für Biochemie und Biologie (28)
- Institut für Physik und Astronomie (28)
- Institut für Geowissenschaften (24)
- Institut für Chemie (17)
- Hasso-Plattner-Institut für Digital Engineering GmbH (13)
- Institut für Ernährungswissenschaft (13)
- Institut für Umweltwissenschaften und Geographie (8)
- Department Psychologie (7)
- Extern (5)
- Fachgruppe Politik- & Verwaltungswissenschaft (5)
Participation has become an orthodoxy in the field of development, an essential element of projects and programmes. This book analyses participation in development interventions as an institutionalised expectation – a rationalized myth – and examines how organisations on different levels of government process it. At least two different objectives of participation are appropriate and legitimate for international organisations in the field: the empowerment of local beneficiaries and the achievement of programme goals. Both integrate participatory forums into the organisational logic of development interventions. Local administrations react to the institutionalised expectation with means-ends decoupling, where participatory forums are implemented superficially but de facto remain marginalised in local administrative processes and activities. The book furthermore provides a thick description of the organisationality of participation in development interventions. Participatory forums are shown to be a form of partial organisation. They establish an order in the relationship between administrations and citizens through the introduction of rules and the creation of a defined membership. At the same time, this order is found to be fragile and subject to criticism and negotiation.
Over the past decades, natural hazards, many of which are aggravated by climate change and reveal an increasing trend in frequency and intensity, have caused significant human and economic losses and pose a considerable obstacle to sustainable development. Hence, dedicated action toward disaster risk reduction is needed to understand the underlying drivers and create efficient risk mitigation plans. Such action is requested by the Sendai Framework for Disaster Risk Reduction 2015-2030 (SFDRR), a global agreement launched in 2015 that establishes stating priorities for action, e.g. an improved understanding of disaster risk. Turkey is one of the SFDRR contracting countries and has been severely affected by many natural hazards, in particular earthquakes and floods. However, disproportionately little is known about flood hazards and risks in Turkey. Therefore, this thesis aims to carry out a comprehensive analysis of flood hazards for the first time in Turkey from triggering drivers to impacts. It is intended to contribute to a better understanding of flood risks, improvements of flood risk mitigation and the facilitated monitoring of progress and achievements while implementing the SFDRR.
In order to investigate the occurrence and severity of flooding in comparison to other natural hazards in Turkey and provide an overview of the temporal and spatial distribution of flood losses, the Turkey Disaster Database (TABB) was examined for the years 1960-2014. The TABB database was reviewed through comparison with the Emergency Events Database (EM-DAT), the Dartmouth Flood Observatory database, the scientific literature and news archives. In addition, data on the most severe flood events between 1960 and 2014 were retrieved. These served as a basis for analyzing triggering mechanisms (i.e. atmospheric circulation and precipitation amounts) and aggravating pathways (i.e. topographic features, catchment size, land use types and soil properties). For this, a new approach was developed and the events were classified using hierarchical cluster analyses to identify the main influencing factor per event and provide additional information about the dominant flood pathways for severe floods. The main idea of the study was to start with the event impacts based on a bottom-up approach and identify the causes that created damaging events, instead of applying a model chain with long-term series as input and searching for potentially impacting events as model outcomes. However, within the frequency analysis of the flood-triggering circulation pattern types, it was discovered that events in terms of heavy precipitation were not included in the list of most severe floods, i.e. their impacts were not recorded in national and international loss databases but were mentioned in news archives and reported by the Turkish State Meteorological Service. This finding challenges bottom-up modelling approaches and underlines the urgent need for consistent event and loss documentation. Therefore, as a next step, the aim was to enhance the flood loss documentation by calibrating, validating and applying the United Nations Office for Disaster Risk Reduction (UNDRR) loss estimation method for the recent severe flood events (2015-2020). This provided, a consistent flood loss estimation model for Turkey, allowing governments to estimate losses as quickly as possible after events, e.g. to better coordinate financial aid.
This thesis reveals that, after earthquakes, floods have the second most destructive effects in Turkey in terms of human and economic impacts, with over 800 fatalities and US$ 885.7 million in economic losses between 1960 and 2020, and that more attention should be paid on the national scale. The clustering results of the dominant flood-producing mechanisms (e.g. circulation pattern types, extreme rainfall, sudden snowmelt) present crucial information regarding the source and pathway identification, which can be used as base information for hazard identification in the preliminary risk assessment process. The implementation of the UNDRR loss estimation model shows that the model with country-specific parameters, calibrated damage ratios and sufficient event documentation (i.e. physically damaged units) can be recommended in order to provide first estimates of the magnitude of direct economic losses, even shortly after events have occurred, since it performed well when estimates were compared to documented losses.
The presented results can contribute to improving the national disaster loss database in Turkey and thus enable a better monitoring of the national progress and achievements with regard to the targets stated by the SFDRR. In addition, the outcomes can be used to better characterize and classify flood events. Information on the main underlying factors and aggravating flood pathways further supports the selection of suitable risk reduction policies.
All input variables used in this thesis were obtained from publicly available data. The results are openly accessible and can be used for further research.
As an overall conclusion, it can be stated that consistent loss data collection and better event documentation should gain more attention for a reliable monitoring of the implementation of the SFDRR. Better event documentation should be established according to a globally accepted standard for disaster classification and loss estimation in Turkey. Ultimately, this enables stakeholders to create better risk mitigation actions based on clear hazard definitions, flood event classification and consistent loss estimations.
With the downscaling of CMOS technologies, the radiation-induced Single Event Transient (SET) effects in combinational logic have become a critical reliability issue for modern integrated circuits (ICs) intended for operation under harsh radiation conditions. The SET pulses generated in combinational logic may propagate through the circuit and eventually result in soft errors. It has thus become an imperative to address the SET effects in the early phases of the radiation-hard IC design. In general, the soft error mitigation solutions should accommodate both static and dynamic measures to ensure the optimal utilization of available resources. An efficient soft-error-aware design should address synergistically three main aspects: (i) characterization and modeling of soft errors, (ii) multi-level soft error mitigation, and (iii) online soft error monitoring. Although significant results have been achieved, the effectiveness of SET characterization methods, accuracy of predictive SET models, and efficiency of SET mitigation measures are still critical issues. Therefore, this work addresses the following topics: (i) Characterization and modeling of SET effects in standard combinational cells, (ii) Static mitigation of SET effects in standard combinational cells, and (iii) Online particle detection, as a support for dynamic soft error mitigation.
Since the standard digital libraries are widely used in the design of radiation-hard ICs, the characterization of SET effects in standard cells and the availability of accurate SET models for the Soft Error Rate (SER) evaluation are the main prerequisites for efficient radiation-hard design. This work introduces an approach for the SPICE-based standard cell characterization with the reduced number of simulations, improved SET models and optimized SET sensitivity database. It has been shown that the inherent similarities in the SET response of logic cells for different input levels can be utilized to reduce the number of required simulations. Based on characterization results, the fitting models for the SET sensitivity metrics (critical charge, generated SET pulse width and propagated SET pulse width) have been developed. The proposed models are based on the principle of superposition, and they express explicitly the dependence of the SET sensitivity of individual combinational cells on design, operating and irradiation parameters. In contrast to the state-of-the-art characterization methodologies which employ extensive look-up tables (LUTs) for storing the simulation results, this work proposes the use of LUTs for storing the fitting coefficients of the SET sensitivity models derived from the characterization results. In that way the amount of characterization data in the SET sensitivity database is reduced significantly.
The initial step in enhancing the robustness of combinational logic is the application of gate-level mitigation techniques. As a result, significant improvement of the overall SER can be achieved with minimum area, delay and power overheads. For the SET mitigation in standard cells, it is essential to employ the techniques that do not require modifying the cell structure. This work introduces the use of decoupling cells for improving the robustness of standard combinational cells. By insertion of two decoupling cells at the output of a target cell, the critical charge of the cell’s output node is increased and the attenuation of short SETs is enhanced. In comparison to the most common gate-level techniques (gate upsizing and gate duplication), the proposed approach provides better SET filtering. However, as there is no single gate-level mitigation technique with optimal performance, a combination of multiple techniques is required. This work introduces a comprehensive characterization of gate-level mitigation techniques aimed to quantify their impact on the SET robustness improvement, as well as introduced area, delay and power overhead per gate. By characterizing the gate-level mitigation techniques together with the standard cells, the required effort in subsequent SER analysis of a target design can be reduced. The characterization database of the hardened standard cells can be utilized as a guideline for selection of the most appropriate mitigation solution for a given design.
As a support for dynamic soft error mitigation techniques, it is important to enable the online detection of energetic particles causing the soft errors. This allows activating the power-greedy fault-tolerant configurations based on N-modular redundancy only at the high radiation levels. To enable such a functionality, it is necessary to monitor both the particle flux and the variation of particle LET, as these two parameters contribute significantly to the system SER. In this work, a particle detection approach based on custom-sized pulse stretching inverters is proposed. Employing the pulse stretching inverters connected in parallel enables to measure the particle flux in terms of the number of detected SETs, while the particle LET variations can be estimated from the distribution of SET pulse widths. This approach requires a purely digital processing logic, in contrast to the standard detectors which require complex mixed-signal processing. Besides the possibility of LET monitoring, additional advantages of the proposed particle detector are low detection latency and power consumption, and immunity to error accumulation.
The results achieved in this thesis can serve as a basis for establishment of an overall soft-error-aware database for a given digital library, and a comprehensive multi-level radiation-hard design flow that can be implemented with the standard IC design tools. The following step will be to evaluate the achieved results with the irradiation experiments.
This thesis focuses on the study of marked Gibbs point processes, in particular presenting some results on their existence and uniqueness, with ideas and techniques drawn from different areas of statistical mechanics: the entropy method from large deviations theory, cluster expansion and the Kirkwood--Salsburg equations, the Dobrushin contraction principle and disagreement percolation.
We first present an existence result for infinite-volume marked Gibbs point processes. More precisely, we use the so-called entropy method (and large-deviation tools) to construct marked Gibbs point processes in R^d under quite general assumptions. In particular, the random marks belong to a general normed space S and are not bounded. Moreover, we allow for interaction functionals that may be unbounded and whose range is finite but random. The entropy method relies on showing that a family of finite-volume Gibbs point processes belongs to sequentially compact entropy level sets, and is therefore tight.
We then present infinite-dimensional Langevin diffusions, that we put in interaction via a Gibbsian description. In this setting, we are able to adapt the general result above to show the existence of the associated infinite-volume measure. We also study its correlation functions via cluster expansion techniques, and obtain the uniqueness of the Gibbs process for all inverse temperatures β and activities z below a certain threshold. This method relies in first showing that the correlation functions of the process satisfy a so-called Ruelle bound, and then using it to solve a fixed point problem in an appropriate Banach space. The uniqueness domain we obtain consists then of the model parameters z and β for which such a problem has exactly one solution.
Finally, we explore further the question of uniqueness of infinite-volume Gibbs point processes on R^d, in the unmarked setting. We present, in the context of repulsive interactions with a hard-core component, a novel approach to uniqueness by applying the discrete Dobrushin criterion to the continuum framework. We first fix a discretisation parameter a>0 and then study the behaviour of the uniqueness domain as a goes to 0. With this technique we are able to obtain explicit thresholds for the parameters z and β, which we then compare to existing results coming from the different methods of cluster expansion and disagreement percolation.
Throughout this thesis, we illustrate our theoretical results with various examples both from classical statistical mechanics and stochastic geometry.
The spread of shrubs in Namibian savannas raises questions about the resilience of these ecosystems to global change. This makes it necessary to understand the past dynamics of the vegetation, since there is no consensus on whether shrub encroachment is a new phenomenon, nor on its main drivers. However, a lack of long-term vegetation datasets for the region and the scarcity of suitable palaeoecological archives, makes reconstructing past vegetation and land cover of the savannas a challenge.
To help meet this challenge, this study addresses three main research questions: 1) is pollen analysis a suitable tool to reflect the vegetation change associated with shrub encroachment in savanna environments? 2) Does the current encroached landscape correspond to an alternative stable state of savanna vegetation? 3) To what extent do pollen-based quantitative vegetation reconstructions reflect changes in past land cover?
The research focuses on north-central Namibia, where despite being the region most affected by shrub invasion, particularly since the 21st century, little is known about the dynamics of this phenomenon.
Field-based vegetation data were compared with modern pollen data to assess their correspondence in terms of composition and diversity along precipitation and grazing intensity gradients. In addition, two sediment cores from Lake Otjikoto were analysed to reveal changes in vegetation composition that have occurred in the region over the past 170 years and their possible drivers. For this, a multiproxy approach (fossil pollen, sedimentary ancient DNA (sedaDNA), biomarkers, compound specific carbon (δ13C) and deuterium (δD) isotopes, bulk carbon isotopes (δ13Corg), grain size, geochemical properties) was applied at high taxonomic and temporal resolution. REVEALS modelling of the fossil pollen record from Lake Otjikoto was run to quantitatively reconstruct past vegetation cover. For this, we first made pollen productivity estimates (PPE) of the most relevant savanna taxa in the region using the extended R-value model and two pollen dispersal options (Gaussian plume model and Lagrangian stochastic model). The REVEALS-based vegetation reconstruction was then validated using remote sensing-based regional vegetation data.
The results show that modern pollen reflects the composition of the vegetation well, but diversity less well. Interestingly, precipitation and grazing explain a significant amount of the compositional change in the pollen and vegetation spectra. The multiproxy record shows that a state change from open Combretum woodland to encroached Terminalia shrubland can occur over a century, and that the transition between states spans around 80 years and is characterized by a unique vegetation composition. This transition is supported by gradual environmental changes induced by management (i.e. broad-scale logging for the mining industry, selective grazing and reduced fire activity associated with intensified farming) and related land-use change. Derived environmental changes (i.e. reduced soil moisture, reduced grass cover, changes in species composition and competitiveness, reduced fire intensity) may have affected the resilience of Combretum open woodlands, making them more susceptible to change to an encroached state by stochastic events such as consecutive years of precipitation and drought, and by high concentrations of pCO2. We assume that the resulting encroached state was further stabilized by feedback mechanisms that favour the establishment and competitiveness of woody vegetation.
The REVEALS-based quantitative estimates of plant taxa indicate the predominance of a semi-open landscape throughout the 20th century and a reduction in grass cover below 50% since the 21st century associated with the spread of encroacher woody taxa. Cover estimates show a close match with regional vegetation data, providing support for the vegetation dynamics inferred from multiproxy analyses. Reasonable PPEs were made for all woody taxa, but not for Poaceae.
In conclusion, pollen analysis is a suitable tool to reconstruct past vegetation dynamics in savannas. However, because pollen cannot identify grasses beyond family level, a multiproxy approach, particularly the use of sedaDNA, is required. I was able to separate stable encroached states from mere woodland phases, and could identify drivers and speculate about related feedbacks. In addition, the REVEALS-based quantitative vegetation reconstruction clearly reflects the magnitude of the changes in the vegetation cover that occurred during the last 130 years, despite the limitations of some PPEs.
This research provides new insights into pollen-vegetation relationships in savannas and highlights the importance of multiproxy approaches when reconstructing past vegetation dynamics in semi-arid environments. It also provides the first time series with sufficient taxonomic resolution to show changes in vegetation composition during shrub encroachment, as well as the first quantitative reconstruction of past land cover in the region. These results help to identify the different stages in savanna dynamics and can be used to calibrate predictive models of vegetation change, which are highly relevant to land management.
In Systems Medicine, in addition to high-throughput molecular data (*omics), the wealth of clinical characterization plays a major role in the overall understanding of a disease. Unique problems and challenges arise from the heterogeneity of data and require new solutions to software and analysis methods. The SMART and EurValve studies establish a Systems Medicine approach to valvular heart disease -- the primary cause of subsequent heart failure.
With the aim to ascertain a holistic understanding, different *omics as well as the clinical picture of patients with aortic stenosis (AS) and mitral regurgitation (MR) are collected. Our task within the SMART consortium was to develop an IT platform for Systems Medicine as a basis for data storage, processing, and analysis as a prerequisite for collaborative research. Based on this platform, this thesis deals on the one hand with the transfer of the used Systems Biology methods to their use in the Systems Medicine context and on the other hand with the clinical and biomolecular differences of the two heart valve diseases. To advance differential expression/abundance (DE/DA) analysis software for use in Systems Medicine, we state 21 general software requirements and features of automated DE/DA software, including a novel concept for the simple formulation of experimental designs that can represent complex hypotheses, such as comparison of multiple experimental groups, and demonstrate our handling of the wealth of clinical data in two research applications DEAME and Eatomics. In user interviews, we show that novice users are empowered to formulate and test their multiple DE hypotheses based on clinical phenotype. Furthermore, we describe insights into users' general impression and expectation of the software's performance and show their intention to continue using the software for their work in the future. Both research applications cover most of the features of existing tools or even extend them, especially with respect to complex experimental designs. Eatomics is freely available to the research community as a user-friendly R Shiny application.
Eatomics continued to help drive the collaborative analysis and interpretation of the proteomic profile of 75 human left myocardial tissue samples from the SMART and EurValve studies. Here, we investigate molecular changes within the two most common types of valvular heart disease: aortic valve stenosis (AS) and mitral valve regurgitation (MR). Through DE/DA analyses, we explore shared and disease-specific protein alterations, particularly signatures that could only be found in the sex-stratified analysis. In addition, we relate changes in the myocardial proteome to parameters from clinical imaging. We find comparable cardiac hypertrophy but differences in ventricular size, the extent of fibrosis, and cardiac function. We find that AS and MR show many shared remodeling effects, the most prominent of which is an increase in the extracellular matrix and a decrease in metabolism. Both effects are stronger in AS. In muscle and cytoskeletal adaptations, we see a greater increase in mechanotransduction in AS and an increase in cortical cytoskeleton in MR. The decrease in proteostasis proteins is mainly attributable to the signature of female patients with AS. We also find relevant therapeutic targets.
In addition to the new findings, our work confirms several concepts from animal and heart failure studies by providing the largest collection of human tissue from in vivo collected biopsies to date. Our dataset contributing a resource for isoform-specific protein expression in two of the most common valvular heart diseases. Apart from the general proteomic landscape, we demonstrate the added value of the dataset by showing proteomic and transcriptomic evidence for increased expression of the SARS-CoV-2- receptor at pressure load but not at volume load in the left ventricle and also provide the basis of a newly developed metabolic model of the heart.
Iron-sulfur clusters are essential enzyme cofactors. The most common and stable clusters are [2Fe-2S] and [4Fe-4S] that are found in nature. They are involved in crucial biological processes like respiration, gene regulation, protein translation, replication and DNA repair in prokaryotes and eukaryotes. In Escherichia coli, Fe-S clusters are essential for molybdenum cofactor (Moco) biosynthesis, which is a ubiquitous and highly conserved pathway. The first step of Moco biosynthesis is catalyzed by the MoaA protein to produce cyclic pyranopterin monophosphate (cPMP) from 5’GTP. MoaA is a [4Fe-4S] cluster containing radical S-adenosyl-L-methionine (SAM) enzyme. The focus of this study was to investigate Fe-S cluster insertion into MoaA under nitrate and TMAO respiratory conditions using E. coli as a model organism. Nitrate and TMAO respiration usually occur under anaerobic conditions, where oxygen is depleted. Under these conditions, E. coli uses nitrate and TMAO as terminal electron. Previous studies revealed that Fe-S cluster insertion is performed by Fe-S cluster carrier proteins. In E. coli, these proteins are known as A-type carrier proteins (ATC) by phylogenomic and genetic studies. So far, three of them have been characterized in detail in E. coli, namely IscA, SufA, and ErpA. This study shows that ErpA and IscA are involved in Fe-S cluster insertion into MoaA under nitrate and TMAO respiratory conditions. ErpA and IscA can partially replace each other in their role to provide [4Fe-4S] clusters for MoaA. SufA is not able to replace the functions of IscA or ErpA under nitrate respiratory conditions.
Nitrate reductase is a molybdoenzyme that coordinates Moco and Fe-S clusters. Under nitrate respiratory conditions, the expression of nitrate reductase is significantly increased in E. coli. Nitrate reductase is encoded in narGHJI genes, the expression of which is regulated by the transcriptional regulator, fumarate and nitrate reduction (FNR). The activation of FNR under conditions of nitrate respiration requires one [4Fe-4S] cluster. In this part of the study, we analyzed the insertion of Fe-S cluster into FNR for the expression of narGHJI genes in E. coli. The results indicate that ErpA is essential for the FNR-dependent expression of the narGHJI genes, a role that can be replaced partially by IscA and SufA when they are produced sufficiently under the conditions tested. This observation suggests that ErpA is indirectly regulating nitrate reductase expression via inserting Fe-S clusters into FNR.
Most molybdoenzymes are complex multi-subunit and multi-cofactor-containing enzymes that coordinate Fe-S clusters, which are functioning as electron transfer chains for catalysis. In E. coli, periplasmic aldehyde oxidoreductase (PaoAC) is a heterotrimeric molybdoenzyme that
consists of flavin, two [2Fe-2S], one [4Fe-4S] cluster and Moco. In the last part of this study, we investigated the insertion of Fe-S clusters into E. coli periplasmic aldehyde oxidoreductase (PaoAC). The results show that SufA and ErpA are involved in inserting [4Fe-4S] and [2Fe-2S] clusters into PaoABC, respectively under aerobic respiratory conditions.
Gravitational-wave (GW) astrophysics is a field in full blossom. Since the landmark detection of GWs from a binary black hole on September 14th 2015, fifty-two compact-object binaries have been reported by the LIGO-Virgo collaboration. Such events carry astrophysical and cosmological information ranging from an understanding of how black holes and neutron stars are formed, what neutron stars are composed of, how the Universe expands, and allow testing general relativity in the highly-dynamical strong-field regime. It is the goal of GW astrophysics to extract such information as accurately as possible. Yet, this is only possible if the tools and technology used to detect and analyze GWs are advanced enough. A key aspect of GW searches are waveform models, which encapsulate our best predictions for the gravitational radiation under a certain set of parameters, and that need to be cross-correlated with data to extract GW signals. Waveforms must be very accurate to avoid missing important physics in the data, which might be the key to answer the fundamental questions of GW astrophysics. The continuous improvements of the current LIGO-Virgo detectors, the development of next-generation ground-based detectors such as the Einstein Telescope or the Cosmic Explorer, as well as the development of the Laser Interferometer Space Antenna (LISA), demand accurate waveform models. While available models are enough to capture the low spins, comparable-mass binaries routinely detected in LIGO-Virgo searches, those for sources from both current and next-generation ground-based and spaceborne detectors must be accurate enough to detect binaries with large spins and asymmetry in the masses. Moreover, the thousands of sources that we expect to detect with future detectors demand accurate waveforms to mitigate biases in the estimation of signals’ parameters due to the presence of a foreground of many sources that overlap in the frequency band. This is recognized as one of the biggest challenges for the analysis of future-detectors’ data, since biases might hinder the extraction of important astrophysical and cosmological information from future detectors’ data. In the first part of this thesis, we discuss how to improve waveform models for binaries with high spins and asymmetry in the masses. In the second, we present the first generic metrics that have been proposed to predict biases in the presence of a foreground of many overlapping signals in GW data.
For the first task, we will focus on several classes of analytical techniques. Current models for LIGO and Virgo studies are based on the post-Newtonian (PN, weak-field, small velocities) approximation that is most natural for the bound orbits that are routinely detected in GW searches. However, two other approximations have risen in prominence, the post-Minkowskian (PM, weak- field only) approximation natural for unbound (scattering) orbits and the small-mass-ratio (SMR) approximation typical of binaries in which the mass of one body is much bigger than the other. These are most appropriate to binaries with high asymmetry in the masses that challenge current waveform models. Moreover, they allow one to “cover” regions of the parameter space of coalescing binaries, thereby improving the interpolation (and faithfulness) of waveform models. The analytical approximations to the relativistic two-body problem can synergically be included within the effective-one-body (EOB) formalism, in which the two-body information from each approximation can be recast into an effective problem of a mass orbiting a deformed Schwarzschild (or Kerr) black hole. The hope is that the resultant models can cover both the low-spin comparable-mass binaries that are routinely detected, and the ones that challenge current models. The first part of this thesis is dedicated to a study about how to best incorporate information from the PN, PM, SMR and EOB approaches in a synergistic way. We also discuss how accurate the resulting waveforms are, as compared against numerical-relativity (NR) simulations. We begin by comparing PM models, whether alone or recast in the EOB framework, against PN models and NR simulations. We will show that PM information has the potential to improve currently-employed models for LIGO and Virgo, especially if recast within the EOB formalism. This is very important, as the PM approximation comes with a host of new computational techniques from particle physics to exploit. Then, we show how a combination of PM and SMR approximations can be employed to access previously-unknown PN orders, deriving the third subleading PN dynamics for spin-orbit and (aligned) spin1-spin2 couplings. Such new results can then be included in the EOB models currently used in GW searches and parameter estimation studies, thereby improving them when the binaries have high spins. Finally, we build an EOB model for quasi-circular nonspinning binaries based on the SMR approximation (rather than the PN one as usually done). We show how this is done in detail without incurring in the divergences that had affected previous attempts, and compare the resultant model against NR simulations. We find that the SMR approximation is an excellent approximation for all (quasi-circular nonspinning) binaries, including both the equal-mass binaries that are routinely detected in GW searches and the ones with highly asymmetric masses. In particular, the SMR-based models compare much better than the PN models, suggesting that SMR-informed EOB models might be the key to model binaries in the future. In the second task of this thesis, we work within the linear-signal ap- proximation and describe generic metrics to predict inference biases on the parameters of a GW source of interest in the presence of confusion noise from unfitted foregrounds and from residuals of other signals that have been incorrectly fitted out. We illustrate the formalism with simple (yet realistic) LISA sources, and demonstrate its validity against Monte-Carlo simulations. The metrics we describe pave the way for more realistic studies to quantify the biases with future ground-based and spaceborne detectors.
Magmatic continental rifts often constitute the earliest stage of nascent plate boundaries. These extensional tectonic provinces are characterized by ubiquitous normal faulting and volcanic activity; the spatial pattern, the geometry, and the age of these normal faults can help to unravel the spatiotemporal relationships between extensional deformation, magmatism, and long-wavelength crustal deformation of continental rift provinces. This study focuses on the active faulting in the Kenya Rift of the Cenozoic East African Rift System (EARS) with a focus on the mid-Pleistocene to the present-day.
To examine the early stages of continental break-up in the EARS, this thesis presents a time-averaged minimum extension rate for the inner graben of the Northern Kenya Rift (NKR) for the last 0.5 m.y. Using the TanDEM-X digital elevation model, fault-scarp geometries and associated throws are determined across the volcano-tectonic axis of the inner graben of the NKR. By integrating existing geochronology of faulted units with new ⁴⁰Ar/³⁹Ar radioisotopic dates, time-averaged extension rates are calculated. This study reveals that in the inner graben of the NKR, the long-term extension rate based on mid-Pleistocene to recent brittle deformation has minimum values of 1.0 to 1.6 mm yr⁻¹, locally with values up to 2.0 mm yr⁻¹. In light of virtually inactive border faults of the NKR, we show that extension is focused in the region of the active volcano-tectonic axis in the inner graben, thus highlighting the maturing of continental rifting in the NKR.
The phenomenon of focused extension is further investigated with a structural analysis of the youngest volcanic manifestations of the Kenya Rift, their relationship with extensional structures, and their overprint by Holocene faulting. In this context I analyzed the fault characteristics at the ~36 ka old Menengai Caldera and adjacent areas in the Central Kenya Rift using detailed field mapping and a structure-from-motion-based DEM generated from UAV data. In general, the Holocene intra-rift normal faults are dip-slip faults which strike NNE and thus reflect the present-day tectonic stress field; however, inside Menengai caldera persistent magmatic activity and magmatic resurgence overprints these young structures significantly. The caldera is located at the center of an actively extending rift segment and this and the other volcanic edifices of the Kenya Rift may constitute nucleation points of faulting an magmatic extensional processes that ultimately lead into a future stage of magma-assisted rifting.
When viewed at the scale of the entire Kenya Rift the protracted normal faulting in this region compartmentalizes the larger rift depressions, and influences the sedimentology and the hydrology of the intra-rift basins at a scale of less than 100 km. In the present day, most of the fault-bounded sub-basins of the Kenya Rift are hydrologically isolated due to this combination of faulting and magmatic activity that has generated efficient hydrological barriers that maintain these basins as semi-independent geomorphic entities. This isolation, however, was overcome during wetter climatic conditions during the past when the basins were transiently connected. I therefore also investigated the hydrological connectivity of the rift basins during the African Humid Period of the early Holocene, when climate was wetter. With the help of DEM analysis, lake-highstand indicators, radiocarbon dating, and a review of the fossil record, two lake-river-cascades could be identified: one directed southward, and one directed northward. Both cascades connected presently isolated rift basins during the early Holocene via spillovers of lakes and incised river gorges. This hydrological connection fostered the dispersal of aquatic faunas along the rift, and in addition, the water divide between the two river systems represented the only terrestrial dispersal corridor across the Kenya Rift. The reconstruction explains isolated distributions of Nilotic fish species in Kenya Rift lakes and of Guineo-Congolian mammal species in forests east of the Kenya Rift. On longer timescales, repeated episodes of connectivity and isolation must have occurred. To address this problem I participated in research to analyze a sediment drill core from the Koora basin of the Southern Kenya Rift, which provides a paleo-environmental record of the last 1 Ma. Based on this record it can be concluded that at ~400 ka relatively stable environmental conditions were disrupted by tectonic, hydrological, and ecological changes, resulting in increasingly large and frequent fluctuations in water availability, grassland communities, and woody plant cover. The major environmental shifts reflected in the drill core data coincide with phases where volcano-tectonic activity affected the basin. This thesis therefore shows how protracted extensional tectonic processes and the resulting geomorphologic conditions can affect the hydrology, the paleo-environment and the biodiversity of extensional zones in Kenya and elsewhere.
In the last decades, there was a notable progress in solving the well-known Boolean satisfiability (Sat) problem, which can be witnessed by powerful Sat solvers. One of the reasons why these solvers are so fast are structural properties of instances that are utilized by the solver’s interna. This thesis deals with the well-studied structural property treewidth, which measures the closeness of an instance to being a tree. In fact, there are many problems parameterized by treewidth that are solvable in polynomial time in the instance size when parameterized by treewidth.
In this work, we study advanced treewidth-based methods and tools for problems in knowledge representation and reasoning (KR). Thereby, we provide means to establish precise runtime results (upper bounds) for canonical problems relevant to KR. Then, we present a new type of problem reduction, which we call decomposition-guided (DG) that
allows us to precisely monitor the treewidth when reducing from one problem to another problem. This new reduction type will be the basis for a long-open lower bound result for quantified Boolean formulas and allows us to design a new methodology for establishing runtime lower bounds for problems parameterized by treewidth.
Finally, despite these lower bounds, we provide an efficient implementation of algorithms that adhere to treewidth. Our approach finds suitable abstractions of instances, which are subsequently refined in a recursive fashion, and it uses Sat solvers for solving subproblems. It turns out that our resulting solver is quite competitive for two canonical counting problems related to Sat.
Precipitation forecasting has an important place in everyday life – during the day we may have tens of small talks discussing the likelihood that it will rain this evening or weekend. Should you take an umbrella for a walk? Or should you invite your friends for a barbecue? It will certainly depend on what your weather application shows.
While for years people were guided by the precipitation forecasts issued for a particular region or city several times a day, the widespread availability of weather radars allowed us to obtain forecasts at much higher spatiotemporal resolution of minutes in time and hundreds of meters in space. Hence, radar-based precipitation nowcasting, that is, very-short-range forecasting (typically up to 1–3 h), has become an essential technique, also in various professional application contexts, e.g., early warning, sewage control, or agriculture.
There are two major components comprising a system for precipitation nowcasting: radar-based precipitation estimates, and models to extrapolate that precipitation to the imminent future. While acknowledging the fundamental importance of radar-based precipitation retrieval for precipitation nowcasts, this thesis focuses only on the model development: the establishment of open and competitive benchmark models, the investigation of the potential of deep learning, and the development of procedures for nowcast errors diagnosis and isolation that can guide model development.
The present landscape of computational models for precipitation nowcasting still struggles with the availability of open software implementations that could serve as benchmarks for measuring progress. Focusing on this gap, we have developed and extensively benchmarked a stack of models based on different optical flow algorithms for the tracking step and a set of parsimonious extrapolation procedures based on image warping and advection. We demonstrate that these models provide skillful predictions comparable with or even superior to state-of-the-art operational software. We distribute the corresponding set of models as a software library, rainymotion, which is written in the Python programming language and openly available at GitHub (https://github.com/hydrogo/rainymotion). That way, the library acts as a tool for providing fast, open, and transparent solutions that could serve as a benchmark for further model development and hypothesis testing.
One of the promising directions for model development is to challenge the potential of deep learning – a subfield of machine learning that refers to artificial neural networks with deep architectures, which may consist of many computational layers. Deep learning showed promising results in many fields of computer science, such as image and speech recognition, or natural language processing, where it started to dramatically outperform reference methods.
The high benefit of using "big data" for training is among the main reasons for that. Hence, the emerging interest in deep learning in atmospheric sciences is also caused and concerted with the increasing availability of data – both observational and model-based. The large archives of weather radar data provide a solid basis for investigation of deep learning potential in precipitation nowcasting: one year of national 5-min composites for Germany comprises around 85 billion data points.
To this aim, we present RainNet, a deep convolutional neural network for radar-based precipitation nowcasting. RainNet was trained to predict continuous precipitation intensities at a lead time of 5 min, using several years of quality-controlled weather radar composites provided by the German Weather Service (DWD). That data set covers Germany with a spatial domain of 900 km x 900 km and has a resolution of 1 km in space and 5 min in time. Independent verification experiments were carried out on 11 summer precipitation events from 2016 to 2017. In these experiments, RainNet was applied recursively in order to achieve lead times of up to 1 h. In the verification experiments, trivial Eulerian persistence and a conventional model based on optical flow served as benchmarks. The latter is available in the previously developed rainymotion library.
RainNet significantly outperformed the benchmark models at all lead times up to 60 min for the routine verification metrics mean absolute error (MAE) and critical success index (CSI) at intensity thresholds of 0.125, 1, and 5 mm/h. However, rainymotion turned out to be superior in predicting the exceedance of higher intensity thresholds (here 10 and 15 mm/h). The limited ability of RainNet to predict high rainfall intensities is an undesirable property which we attribute to a high level of spatial smoothing introduced by the model. At a lead time of 5 min, an analysis of power spectral density confirmed a significant loss of spectral power at length scales of 16 km and below.
Obviously, RainNet had learned an optimal level of smoothing to produce a nowcast at 5 min lead time. In that sense, the loss of spectral power at small scales is informative, too, as it reflects the limits of predictability as a function of spatial scale. Beyond the lead time of 5 min, however, the increasing level of smoothing is a mere artifact – an analogue to numerical diffusion – that is not a property of RainNet itself but of its recursive application. In the context of early warning, the smoothing is particularly unfavorable since pronounced features of intense precipitation tend to get lost over longer lead times. Hence, we propose several options to address this issue in prospective research on model development for precipitation nowcasting, including an adjustment of the loss function for model training, model training for longer lead times, and the prediction of threshold exceedance.
The model development together with the verification experiments for both conventional and deep learning model predictions also revealed the need to better understand the source of forecast errors. Understanding the dominant sources of error in specific situations should help in guiding further model improvement. The total error of a precipitation nowcast consists of an error in the predicted location of a precipitation feature and an error in the change of precipitation intensity over lead time. So far, verification measures did not allow to isolate the location error, making it difficult to specifically improve nowcast models with regard to location prediction.
To fill this gap, we introduced a framework to directly quantify the location error. To that end, we detect and track scale-invariant precipitation features (corners) in radar images. We then consider these observed tracks as the true reference in order to evaluate the performance (or, inversely, the error) of any model that aims to predict the future location of a precipitation feature. Hence, the location error of a forecast at any lead time ahead of the forecast time corresponds to the Euclidean distance between the observed and the predicted feature location at the corresponding lead time.
Based on this framework, we carried out a benchmarking case study using one year worth of weather radar composites of the DWD. We evaluated the performance of four extrapolation models, two of which are based on the linear extrapolation of corner motion; and the remaining two are based on the Dense Inverse Search (DIS) method: motion vectors obtained from DIS are used to predict feature locations by linear and Semi-Lagrangian extrapolation.
For all competing models, the mean location error exceeds a distance of 5 km after 60 min, and 10 km after 110 min. At least 25% of all forecasts exceed an error of 5 km after 50 min, and of 10 km after 90 min. Even for the best models in our experiment, at least 5 percent of the forecasts will have a location error of more than 10 km after 45 min. When we relate such errors to application scenarios that are typically suggested for precipitation nowcasting, e.g., early warning, it becomes obvious that location errors matter: the order of magnitude of these errors is about the same as the typical extent of a convective cell. Hence, the uncertainty of precipitation nowcasts at such length scales – just as a result of locational errors – can be substantial already at lead times of less than 1 h. Being able to quantify the location error should hence guide any model development that is targeted towards its minimization. To that aim, we also consider the high potential of using deep learning architectures specific to the assimilation of sequential (track) data.
Last but not least, the thesis demonstrates the benefits of a general movement towards open science for model development in the field of precipitation nowcasting. All the presented models and frameworks are distributed as open repositories, thus enhancing transparency and reproducibility of the methodological approach. Furthermore, they are readily available to be used for further research studies, as well as for practical applications.
Simultaneously speculative and inspired by everyday experiences, this volume develops an aesthetics of metabolism that offers a new perspective on the human-environment relation, one that is processual, relational, and not dependent on conscious thought. In art installations, design prototypes, and researchcreation projects that utilize air, light, or temperature to impact subjective experience the author finds aesthetic milieus that shift our awareness to the role of different sense modalities in aesthetic experience. Metabolic and atmospheric processes allow for an aesthetics besides and beyond the usually dominant visual sense.
Media artists have been struggling for financial survival ever since media art came into being. The non-material value of the artwork, a provocative attitude towards the traditional arts world and originally anti-capitalist mindset of the movement makes it particularly difficult to provide a constructive solution. However, a cultural entrepreneurial approach can be used to build a framework in order to find a balance between culture and business while ensuring that the cultural mission remains the top priority.
Fluids in the Earth's crust can move by creating and flowing through fractures, in a process called `hydraulic fracturing’. The tip-line of such fluid-filled fractures grows at locations where stress is larger than the strength of the rock. Where the tip stress vanishes, the fracture closes and the fluid-front retreats. If stress gradients exist on the fracture's walls, induced by fluid/rock density contrasts or topographic stresses, this results in an asymmetric shape and growth of the fracture, allowing for the contained batch of fluid to propagate through the crust.
The state-of-the-art analytical and numerical methods to simulate fluid-filled fracture propagation are two-dimensional (2D). In this work I extend these to three dimensions (3D). In my analytical method, I approximate the propagating 3D fracture as a penny-shaped crack that is influenced by both an internal pressure and stress gradients. In addition, I develop a numerical method to model propagation where curved fractures can be simulated as a mesh of triangular dislocations, with the displacement of faces computed using the displacement discontinuity method. I devise a rapid technique to approximate stress intensity and use this to calculate the advance of the tip-line. My 3D models can be applied to arbitrary stresses, topographic and crack shapes, whilst retaining short computation times.
I cross-validate my analytical and numerical methods and apply them to various natural and man-made settings, to gain additional insights into the movements of hydraulic fractures such as magmatic dikes and fluid injections in rock. In particular, I calculate the `volumetric tipping point’, which once exceeded allows a fluid-filled fracture to propagate in a `self-sustaining’ manner. I discuss implications this has for hydro-fracturing in industrial operations. I also present two studies combining physical models that define fluid-filled fracture trajectories and Bayesian statistical techniques. In these studies I show that the stress history of the volcanic edifice defines the location of eruptive vents at volcanoes. Retrieval of the ratio between topographic to remote stresses allows for forecasting of probable future vent locations. Finally, I address the mechanics of 3D propagating dykes and sills in volcanic regions. I focus on Sierra Negra volcano in the Gal\'apagos islands, where in 2018, a large sill propagated with an extremely curved trajectory. Using a 3D analysis, I find that shallow horizontal intrusions are highly sensitive to topographic and buoyancy stress gradients, as well as the effects of the free surface.
3D point clouds are a universal and discrete digital representation of three-dimensional objects and environments. For geospatial applications, 3D point clouds have become a fundamental type of raw data acquired and generated using various methods and techniques. In particular, 3D point clouds serve as raw data for creating digital twins of the built environment.
This thesis concentrates on the research and development of concepts, methods, and techniques for preprocessing, semantically enriching, analyzing, and visualizing 3D point clouds for applications around transport infrastructure. It introduces a collection of preprocessing techniques that aim to harmonize raw 3D point cloud data, such as point density reduction and scan profile detection. Metrics such as, e.g., local density, verticality, and planarity are calculated for later use. One of the key contributions tackles the problem of analyzing and deriving semantic information in 3D point clouds. Three different approaches are investigated: a geometric analysis, a machine learning approach operating on synthetically generated 2D images, and a machine learning approach operating on 3D point clouds without intermediate representation.
In the first application case, 2D image classification is applied and evaluated for mobile mapping data focusing on road networks to derive road marking vector data. The second application case investigates how 3D point clouds can be merged with ground-penetrating radar data for a combined visualization and to automatically identify atypical areas in the data. For example, the approach detects pavement regions with developing potholes. The third application case explores the combination of a 3D environment based on 3D point clouds with panoramic imagery to improve visual representation and the detection of 3D objects such as traffic signs.
The presented methods were implemented and tested based on software frameworks for 3D point clouds and 3D visualization. In particular, modules for metric computation, classification procedures, and visualization techniques were integrated into a modular pipeline-based C++ research framework for geospatial data processing, extended by Python machine learning scripts. All visualization and analysis techniques scale to large real-world datasets such as road networks of entire cities or railroad networks.
The thesis shows that some use cases allow taking advantage of established image vision methods to analyze images rendered from mobile mapping data efficiently. The two presented semantic classification methods working directly on 3D point clouds are use case independent and show similar overall accuracy when compared to each other. While the geometry-based method requires less computation time, the machine learning-based method supports arbitrary semantic classes but requires training the network with ground truth data. Both methods can be used in combination to gradually build this ground truth with manual corrections via a respective annotation tool.
This thesis contributes results for IT system engineering of applications, systems, and services that require spatial digital twins of transport infrastructure such as road networks and railroad networks based on 3D point clouds as raw data. It demonstrates the feasibility of fully automated data flows that map captured 3D point clouds to semantically classified models. This provides a key component for seamlessly integrated spatial digital twins in IT solutions that require up-to-date, object-based, and semantically enriched information about the built environment.
Brown adipose tissue (BAT) is responsible for non-shivering thermogenesis, thereby allowing mammals to maintain a constant body temperature in a cold environment. Thermogenic capacity of this tissue is due to a high mitochondrial density and expression of uncoupling protein 1 (UCP1), a unique brown adipocyte marker which dissipates the mitochondrial proton gradient to produce heat instead of ATP. BAT is actively involved in whole-body metabolic homeostasis and during aging there is a loss of classical brown adipose tissue with concomitantly reduced browning capacity of white adipose tissue. Therefore, an age-dependent decrease of BAT-related energy expenditure capacity may exacerbate the development of metabolic diseases, including obesity and type 2 diabetes mellitus. Given that direct effects of age-related changes of BAT-metabolic flux have yet to be unraveled, the aim of the current thesis is to investigate potential metabolic mechanisms involved in BAT-dysfunction during aging and to identify suitable metabolic candidates as functional biomarkers of BAT-aging. To this aim, integration of transcriptomic, metabolomic and proteomic data analyses of BAT from young and aged mice was performed, and a group of candidates with age-related changes was revealed. Metabolomic analysis showed age-dependent alterations of metabolic intermediates involved in energy, nucleotide and vitamin metabolism, with major alterations regarding the purine nucleotide pool. These data suggest a potential role of nucleotide intermediates in age-related BAT defects. In addition, the screening of transcriptomic and proteomic data sets from BAT of young and aged mice allowed identification of a 60-kDa lysophospholipase, also known as L-asparaginase (Aspg), whose expression declines during BAT-aging. Involvement of Aspg in brown adipocyte thermogenic function was subsequently analyzed at the molecular level using in vitro approaches and animal models. The findings revealed sensitivity of Aspg expression to β3-adrenergic activation via different metabolic cues, including cold exposure and treatment with β3-adrenergic agonist CL. To further examine ASPG function in BAT, an over-expression model of Aspg in a brown adipocyte cell line was established and showed that these cells were metabolically more active compared to controls, revealing increased expression of the main brown-adipocyte specific marker UCP1, as well as higher lipolysis rates. An in vitro loss-of-function model of Aspg was also functionally analyzed, revealing reduced brown adipogenic characteristics and an impaired lipolysis, thus confirming physiological relevance of Aspg in brown adipocyte function. Characterization of a transgenic mouse model with whole-body inactivation of the Aspg gene (Aspg-KO) allowed investigation of the role of ASPG under in vivo conditions, indicating a mild obesogenic phenotype, hypertrophic white adipocytes, impairment of the early thermogenic response upon cold-stimulation and dysfunctional insulin sensitivity. Taken together, these data show that ASPG may represent a new functional biomarker of BAT-aging that regulates thermogenesis and therefore a potential target for the treatment of age-related metabolic disease.
This project describes the nominal, verbal and ‘truncation’ systems of Awing and explains the syntactic and semantic functions of the multifunctional l<-><-> (LE) morpheme in copular and wh-focused constructions. Awing is a Bantu Grassfields language spoken in the North West region of Cameroon. The work begins with morphological processes viz. deverbals, compounding, reduplication, borrowing and a thorough presentation of the pronominal system and takes on verbal categories viz. tense, aspect, mood, verbal extensions, negation, adverbs and triggers of a homorganic N(asal)-prefix that attaches to the verb and other verbal categories. Awing grammar also has a very unusual phenomenon whereby nouns and verbs take long and short forms. A chapter entitled truncation is dedicated to the phenomenon. It is observed that the truncation process does not apply to bare singular NPs, proper names and nouns derived via morphological processes. On the other hand, with the exception of the 1st person non-emphatic possessive determiner and the class 7 noun prefix, nouns generally take the truncated form with modifiers (i.e., articles, demonstratives and other possessives). It is concluded that nominal truncation depicts movement within the DP system (Abney 1987). Truncation of the verb occurs in three contexts: a mass/plurality conspiracy (or lattice structuring in terms of Link 1983) between the verb and its internal argument (i.e., direct object); a means to align (exhaustive) focus (in terms of Fery’s 2013), and a means to form polar questions.
The second part of the work focuses on the role of the LE morpheme in copular and wh-focused clauses. Firstly, the syntax of the Awing copular clause is presented and it is shown that copular clauses in Awing have ‘subject-focus’ vs ‘topic-focus’ partitions and that the LE morpheme indirectly relates such functions. Semantically, it is shown that LE does not express contrast or exhaustivity in copular clauses. Turning to wh-constructions, the work adheres to Hamblin’s (1973) idea that the meaning of a question is the set of its possible answers and based on Rooth’s (1985) underspecified semantic notion of alternative focus, concludes that the LE morpheme is not a Focus Marker (FM) in Awing: LE does not generate or indicate the presence of alternatives (Krifka 2007); The LE morpheme can associate with wh-elements as a focus-sensitive operator with semantic import that operates on the focus alternatives by presupposing an exhaustive answer, among other notions. With focalized categories, the project further substantiates the claim in Fominyam & Šimík (2017), namely that exhaustivity is part of the semantics of the LE morpheme and not derived via contextual implicature, via a number of diagnostics. Hence, unlike in copular clauses, the LE morpheme with wh-focused categories is analysed as a morphological exponent of a functional head Exh corresponding to Horvath's (2010) EI (Exhaustive Identification). The work ends with the syntax of verb focus and negation and modifies the idea in Fominyam & Šimík (2017), namely that the focalized verb that associates with the exhaustive (LE) particle is a lower copy of the finite verb that has been moved to Agr. It is argued that the LE-focused verb ‘cluster’ is an instantiation of adjunction. The conclusion is that verb doubling with verb focus in Awing is neither a realization of two copies of one and the same verb (Fominyam and Šimík 2017), nor a result of a copy triggered by a focus marker (Aboh and Dyakonova 2009). Rather, the focalized copy is said to be merged directly as the complement of LE forming a type of adjoining cluster.
The Arctic environments constitute rich and dynamic ecosystems, dominated by microorganisms extremely well adapted to survive and function under severe conditions. A range of physiological adaptations allow the microbiota in these habitats to withstand low temperatures, low water and nutrient availability, high levels of UV radiation, etc. In addition, other adaptations of clear competitive nature are directed at not only surviving but thriving in these environments, by disrupting the metabolism of neighboring cells and affecting intermicrobial communication. Since Arctic microbes are bioindicators which amplify climate alterations in the environment, the Arctic region presents the opportunity to study local microbiota and carry out research about interesting, potentially virulent phenotypes that could be dispersed into other habitats around the globe as a consequence of accelerating climate change. In this context, exploration of Arctic habitats as well as descriptions of the microbes inhabiting them are abundant but microbial competitive strategies commonly associated with virulence and pathogens are rarely reported. In this project, environmental samples from the Arctic region were collected and microorganisms (bacteria and fungi) were isolated. The clinical relevance of these microorganisms was assessed by observing the following virulence markers: ability to grow at a range of temperatures, expression of antimicrobial resistance and production of hemolysins. The aim of this project is to determine the frequency and relevance of these characteristics in an effort to understand microbial adaptations in habitats threatened by climate change. The isolates obtained and described here were able to grow at a range of temperatures, in some cases more than 30 °C higher than their original isolation temperature. A considerable number of them consistently expressed compounds capable of lysing sheep and bovine erythrocytes on blood agar at different incubation temperatures. Ethanolic extracts of these bacteria were able to cause rapid and complete lysis of erythrocyte suspensions and might even be hemolytic when assayed on human blood. In silico analyses showed a variety of resistance elements, some of them novel, against natural and synthetic antimicrobial compounds. In vitro experiments against a number of antimicrobial compounds showed resistance phenotypes belonging to wild-type populations and some non-wild type which clearly denote human influence in the acquisition of antimicrobial resistance. The results of this project demonstrate the presence of virulence-associated factors expressed by microorganisms of natural, non-clinical environments. This study contains some of the first reports, to the best of our knowledge, of hemolytic microbes isolated from the Arctic region. In addition, it provides additional information about the presence and expression of intrinsic and acquired antimicrobial resistance in environmental isolates, contributing to the understanding of the evolution of relevant pathogenic species and opportunistic pathogens. Finally, this study highlights some of the potential risks associated with changes in the polar regions (habitat melting and destruction, ecosystem transition and re-colonization) as important indirect consequences of global warming and altered climatic conditions around the planet.
The characterization of exoplanets applying high-resolution transmission spectroscopy ini- tiated a new era making it possible to trace atmospheric signature at high altitudes in exoplanet atmospheres and to determine atmospheric properties which enrich our under- standing of the formation and evolution of the solar system. In contrast to what is observed in our solar system, where gaseous planets orbit at wide orbits, Jupiter type exoplanets were detected in foreign stellar systems surrounding their host stars within few days, in close orbits, the so called hot- and ultra-hot Jupiters. The most well studied ones are HD209458b and HD189733b, which are the first exoplanets where absorption is detected in their atmospheres, namely from the alkali line sodium. For hot Jupiters, the resonant alkali lines are the atmospheric species with one of the strongest absorption signatures, due to their large absorption cross-section. However, al- though the alkali lines sodium and potassium were detected in low-resolution observations for various giant exoplanets, potassium was absent in different high-resolution investiga- tions in contrast to sodium. The reason for this is quite puzzling, since both alkalis have very similar physical and chemical properties (e.g. condensation and ionization proper- ties). Obtaining high-resolution transit observations of HD189733b and HD209458b, we were able to detect potassium on HD189733b (Manuscript 1), which was the first high-resolution detection of potassium on an exoplanet. The absence of potassium on HD209458b could be reasoned by depletion processes, such as condensation or photo-ionization or high-altitude clouds. In a further study (Manuscript II), we resolved the potassium line and compared this to a previously detected sodium absorption on this planet. The comparison showed, that the potassium lines are either tracing different altitudes and temperatures compared to the sodium lines, or are depleted so that the planetary Na/K- ratio is way larger than the stellar one. A comparison of the alkali lines with synthetic line profiles showed that the sodium lines were much broader than the potassium lines, probably being induced by winds. To investigate this, the effect of zonal streaming winds on the sodium lines on Jupiter-type planets is investigated in a further study (Manuscript III), showing that such winds can significantly broaden the Na- lines and that high-resolution observations can trace such winds with different properties. Furthermore, investigating the Na-line observations for different exoplanets, I showed that the Na-line broadening follows a trend with cooler planets showing stronger line broadening and so hinting on stronger winds, matching well into theoretical predictions. Each presented manuscript depends on the re- sults published within the previous manuscript, yielding a unitary study of the exoplanet HD189733b. The investigation of the potassium absorption required to account for different effects: The telluric lines removal and the effect of center-to-limb variation (see Manuscript I), the residual Rossiter-Mc-Laughlin effect (see Manuscript II) and the broadening of spectral lines on a translucent atmospheric ring by zonal jet streams (see Manuscript III). This thesis shows that high-resolution transmission spectroscopy is a powerful tool to probe sharp alkali line absorption on giant exoplanet atmospheres and to investigate on the properties and dynamics of hot Jupiter type atmospheres.
While patients are known to respond differently to drug therapies, current clinical practice often still follows a standardized dosage regimen for all patients. For drugs with a narrow range of both effective and safe concentrations, this approach may lead to a high incidence of adverse events or subtherapeutic dosing in the presence of high patient variability. Model-informedprecision dosing (MIPD) is a quantitative approach towards dose individualization based on mathematical modeling of dose-response relationships integrating therapeutic drug/biomarker monitoring (TDM) data. MIPD may considerably improve the efficacy and safety of many drug therapies. Current MIPD approaches, however, rely either on pre-calculated dosing tables or on simple point predictions of the therapy outcome. These
approaches lack a quantification of uncertainties and the ability to account for effects that are delayed. In addition, the underlying models are not improved while applied to patient data. Therefore, current approaches are not well suited for informed clinical decision-making based on a differentiated understanding of the individually predicted therapy outcome.
The objective of this thesis is to develop mathematical approaches for MIPD, which (i) provide efficient fully Bayesian forecasting of the individual therapy outcome including associated uncertainties, (ii) integrate Markov decision processes via reinforcement learning (RL) for a comprehensive decision framework for dose individualization, (iii) allow for continuous learning across patients and hospitals. Cytotoxic anticancer chemotherapy with its major dose-limiting toxicity, neutropenia, serves as a therapeutically relevant application example.
For more comprehensive therapy forecasting, we apply Bayesian data assimilation (DA) approaches, integrating patient-specific TDM data into mathematical models of chemotherapy-induced neutropenia that build on prior population analyses. The value of uncertainty quantification is demonstrated as it allows reliable computation of the patient-specific probabilities of relevant clinical quantities, e.g., the neutropenia grade. In view of novel home monitoring devices that increase the amount of TDM data available, the data processing of
sequential DA methods proves to be more efficient and facilitates handling of the variability between dosing events.
By transferring concepts from DA and RL we develop novel approaches for MIPD. While DA-guided dosing integrates individualized uncertainties into dose selection, RL-guided dosing provides a framework to consider delayed effects of dose selections. The combined
DA-RL approach takes into account both aspects simultaneously and thus represents a holistic approach towards MIPD. Additionally, we show that RL can be used to gain insights into important patient characteristics for dose selection. The novel dosing strategies substantially reduce the occurrence of both subtherapeutic and life-threatening neutropenia grades in a simulation study based on a recent clinical study (CEPAC-TDM trial) compared to currently used MIPD approaches.
If MIPD is to be implemented in routine clinical practice, a certain model bias with respect to the underlying model is inevitable, as the models are typically based on data from comparably small clinical trials that reflect only to a limited extent the diversity in real-world patient populations. We propose a sequential hierarchical Bayesian inference framework that enables continuous cross-patient learning to learn the underlying model parameters of the target patient population. It is important to note that the approach only requires summary information of the individual patient data to update the model. This separation of the individual inference from population inference enables implementation across different centers of care.
The proposed approaches substantially improve current MIPD approaches, taking into account new trends in health care and aspects of practical applicability. They enable progress towards more informed clinical decision-making, ultimately increasing patient benefits beyond the current practice.
The presented study investigated the influence of microbial and biogeochemical processes on the physical transport related properties and the fate of microplastics in freshwater reservoirs. The overarching goal was to elucidate the mechanisms leading to sedimentation and deposition of microplastics in such environments. This is of importance, as large amounts of initially buoyant microplastics are found in reservoir sediments worldwide. However, the transport processes which lead to microplastics accumulation in sediments, were up to now understudied.
The impact of biofilm formation on the density and subsequent sedimentation of microplastics was investigated in the eutrophic Bautzen reservoirs (Chapter 2). Biofilms are complex microbial communities fixed to submerged surfaces through a slimy organic film. The mineral calcite was detected in the biofilms, which led to the
sinking of the overgrown microplastic particles. The calcite was of biogenic origin, most likely precipitated by sessile cyanobacteria within the biofilms.
Biofilm formation was also studied in the mesotrophic Malter reservoir. Unlike in Bautzen reservoir, biofilm formation did not govern the sedimentation of different microplastics in Malter reservoir (Chapter 3). Instead autumnal lake mixing led to
the formation of sinking aggregates of microplastics and iron colloids. Such colloids form when anoxic, iron-rich water from the hypolimnion mixes with the oxygenated epilimnetic waters. The colloids bind organic material from the lake water, which leads to the formation of large and sinking iron-organo flocs.
Hence, iron-organo floc formation and their influence on the buoyancy or burial of microplastics into sediments of Bautzen reservoir was studied in laboratory experiments (Chapter 4). Microplastics of different shapes (fiber, fragment, sphere) and sizes were readily incorporated into sinking iron-organo flocs. By this initially buoyant polyethylene microplastics were transported on top of sediments from Bautzen reservoir. Shortly after deposition, the microplastic bearing flocs started to subside and transported the pollutants into deeper sediment layers. The microplastics were not released from the sediments within two months of laboratory incubation.
The stability of floc microplastic deposition was further investigated employing experiments with the iron reducing model organism Shewanella oneidensis (Chapter 5). It was shown, that reduction or re-mineralization of the iron minerals did not affect the integrity of the iron-organo flocs. The organic matrix was stable under iron reducing conditions. Hence, no incorporated microplastics were released from the flocs. As similar processes are likely to take place in natural sediments, this might explain the previous described low microplastic release from the sediments.
This thesis introduced different mechanisms leading to the sedimentation of initially buoyant microplastics and to their subsequent deposition in freshwater reservoirs. Novel processes such as the aggregation with iron-organo flocs were identified and the understudied issue of biofilm densification through biogenic mineral formation was further investigated. The findings might have implications for the fate of microplastics within the river-reservoir system and outline the role of freshwater reservoirs as important accumulation zone for microplastics. Microplastics deposited in the sediments of reservoirs might not be transported further by through flowing river. Hence the study might contribute to better risk assessment and transport balances of these anthropogenic contaminants.
Boon and bane
(2021)
Semi-natural habitats (SNHs) in agricultural landscapes represent important refugia for biodiversity including organisms providing ecosystem services. Their spill-over into agricultural fields may lead to the provision of regulating ecosystem services such as biological pest control ultimately affecting agricultural yield. Still, it remains largely unexplored, how different habitat types and their distributions in the surrounding landscape shape this provision of ecosystem services within arable fields. Hence, in this thesis I investigated the effect of SNHs on biodiversity-driven ecosystem services and disservices affecting wheat production with an emphasis on the role and interplay of habitat type, distance to the habitat and landscape complexity.
I established transects from the field border into the wheat field, starting either from a field-to-field border, a hedgerow, or a kettle hole, and assessed beneficial and detrimental organisms and their ecosystem functions as well as wheat yield at several in-field distances. Using this study design, I conducted three studies where I aimed to relate the impacts of SNHs at the field and at the landscape scale on ecosystem service providers to crop production.
In the first study, I observed yield losses close to SNHs for all transect types. Woody habitats, such as hedgerows, reduced yields stronger than kettle holes, most likely due to shading from the tall vegetation structure. In order to find the biotic drivers of these yield losses close to SNHs, I measured pest infestation by selected wheat pests as potential ecosystem disservices to crop production in the second study. Besides relating their damage rates to wheat yield of experimental plots, I studied the effect of SNHs on these pest rates at the field and at the landscape scale. Only weed cover could be associated to yield losses, having their strongest impact on wheat yield close to the SNH. While fungal seed infection rates did not respond to SNHs, fungal leaf infection and herbivory rates of cereal leaf beetle larvae were positively influenced by kettle holes. The latter even increased at kettle holes with increasing landscape complexity suggesting a release of natural enemies at isolated habitats within the field interior.
In the third study, I found that also ecosystem service providers benefit from the presence of kettle holes. The distance to a SNH decreased species richness of ecosystem service providers, whereby the spatial range depended on species mobility, i.e. arable weeds diminished rapidly while carabids were less affected by the distance to a SNH. Contrarily, weed seed predation increased with distance suggesting that a higher food availability at field borders might have diluted the predation on experimental seeds. Intriguingly, responses to landscape complexity were rather mixed: While weed species richness was generally elevated with increasing landscape complexity, carabids followed a hump-shaped curve with highest species numbers and activity-density in simple landscapes. The latter might give a hint that carabids profit from a minimum endowment of SNHs, while a further increase impedes their mobility. Weed seed predation was affected differently by landscape complexity depending on weed species displayed. However, in habitat-rich landscapes seed predation of the different weed species converged to similar rates, emphasising that landscape complexity can stabilize the provision of ecosystem services. Lastly, I could relate a higher weed seed predation to an increase in wheat yield even though seed predation did not diminish weed cover. The exact mechanisms of the provision of weed control to crop production remain to be investigated in future studies.
In conclusion, I found habitat-specific responses of ecosystem (dis)service providers and their functions emphasizing the need to evaluate the effect of different habitat types on the provision of ecosystem services not only at the field scale, but also at the landscape scale. My findings confirm that besides identifying species richness of ecosystem (dis)service providers the assessment of their functions is indispensable to relate the actual delivery of ecosystem (dis)services to crop production.
Noise is ubiquitous in nature and usually results in rich dynamics in stochastic systems such as oscillatory systems, which exist in such various fields as physics, biology and complex networks. The correlation and synchronization of two or many oscillators are widely studied topics in recent years.
In this thesis, we mainly investigate two problems, i.e., the stochastic bursting phenomenon in noisy excitable systems and synchronization in a three-dimensional Kuramoto model with noise. Stochastic bursting here refers to a sequence of coherent spike train, where each spike has random number of followers due to the combined effects of both time delay and noise. Synchronization, as a universal phenomenon in nonlinear dynamical systems, is well illustrated in the Kuramoto model, a prominent model in the description of collective motion.
In the first part of this thesis, an idealized point process, valid if the characteristic timescales in the problem are well separated, is used to describe statistical properties such as the power spectral density and the interspike interval distribution. We show how the main parameters of the point process, the spontaneous excitation rate, and the probability to induce a spike during the delay action can be calculated from the solutions of a stationary and a forced Fokker-Planck equation. We extend it to the delay-coupled case and derive analytically the statistics of the spikes in each neuron, the pairwise correlations between any two neurons, and the spectrum of the total output from the network.
In the second part, we investigate the three-dimensional noisy Kuramoto model, which can be used to describe the synchronization in a swarming model with helical trajectory. In the case without natural frequency, the Kuramoto model can be connected with the Vicsek model, which is widely studied in collective motion and swarming of active matter. We analyze the linear stability of the incoherent state and derive the critical coupling strength above which the incoherent state loses stability. In the limit of no natural frequency, an exact self-consistent equation of the mean field is derived and extended straightforward to any high-dimensional case.
Cellulose is the most abundant biopolymer on Earth and cell wall (CW) synthesis is one of the major carbon consumers in the plant cell. Structure and several interaction partners of plasma membrane (PM)-bound cellulose synthase (CESA) complexes, CSCs, have been studied extensively, but much less is understood about the signals that activate and translocate CESAs to the PM and how exactly cellulose synthesis is being regulated during the diel cycle. The literature describes CSC regulation possibilities through interactions with accessory proteins upon stress conditions (e.g. CC1), post-translational modifications that regulate CSC speed and their possible anchoring in the PM (e.g. with phosphorylation and S-acylation, respectively). In this thesis, 13CO2 labeling and imaging techniques were employed in the same Arabidopsis seedling growth system to elucidate how and when new carbon is incorporated into cell wall (CW) sugars and UDP-glucose, and to follow CSC behavior during the diel cycle. Additionally, an ubiquitination analysis was performed to investigate a possible mechanism to affect CSC trafficking to and/or from the PM. Carbon is being incorporated into CW glucose at a 3-fold higher rate during the light period in comparison to the night in wild-type seedlings. Furthermore, CSC density at the PM, as an indication of active cellulose synthesizing machinery, is increasing in the light and falling during the night, showing that CW biosynthesis is more active in the light. Therefore, CW synthesis might be regulated by the carbon status of the cell. This regulation is broken in the starchless pgm mutant where light and dark carbon incorporation rates into CW glucose are similar, possibly due to the high soluble sugar content in pgm during the first part of the night. Strikingly, pgm CSC abundance at the PM is constantly low during the whole diel cycle, indicating little or no cellulose synthesis, but can be restored with exogenous sucrose or a longer photoperiod. Ubiquitination was explored as a possible regulating mechanism for translocation of primary CW CSCs from the PM and several potential ubiquitination sites have been identified.. The approach in this thesis enabled to study cellulose/CW synthesis from different angles but in the same growth system, allowing direct comparison of those methodologies, which could help understand the relationship between the amount of available carbon in a plant cell and the cells capacity to synthesize cellulose/CW. Understanding which factors contribute to cellulose synthesis regulation and addressing those fundamental questions can provide essential knowledge to manage the need for increased crop production.
The energy required to drive photochemical reactions is derived from charge separation across the thylakoid membrane. As the consequence of difference in proton concentration between chloroplasts stroma and thylakoid lumen, a proton motive force (pmf) is generated. The pmf is composed out of the proton gradient (ΔpH) and membrane potential (ΔΨ), and together they drive the ATP synthesis. In nature, the amount of energy fueling photosynthesis varies due to frequent changes in the light intensity. Thylakoid ion transport can adapt the energy flow through a photosynthetic apparatus to the light availability by adjusting the pmf composition. Dissipation of ΔΨ reduces the charge recombination at the photosystem II, allowing for an increase in ΔpH component to trigger a feedback downregulation of photosynthesis. K+ Exchange Antiporter 3 (KEA3) driven K+/H+ antiport reduces the ΔpH fraction of pmf, thereby dampening a non-photochemical quenching (NPQ). As a result, it increases the photosynthesis efficiency during the transition to lower light intensity. This thesis aimed to find the answers for questions concerning KEA3 activity regulation and its role in plant development. Presented data shows that in plants lacking chloroplast ATP synthase assembly factor CGL160 with decreased ATP synthase activity, KEA3 has a pivotal role in photosynthesis regulation and plant growth during steady-state conditions. Lack of KEA3 in cgl160 mutant results in a strong growth impairment, as photosynthesis is limited due to increased pH-dependent NPQ and decreased electron flow through cytochrome b6f complex. Overexpression of KEA3 in cgl160 mutant increases charge recombination at photosystem II, promoting photosynthesis. Thus, during periods of low ATP synthase activity, plants benefit from KEA3 activity. The KEA3 undergoes dimerization via its regulatory C-terminus (RCT). The RCT responds to changes in light intensity as the plants expressing KEA3 without this domain show reduced photo-protective mechanism in light intensity transients. However, those plants fix more carbon during the photosynthesis induction phase as a trade-off for a long-term photoprotection, showing KEA3 regulatory role in plant development. The KEA3 RCT is facing thylakoid stroma, thus its regulation depends on light-induced changes in the stromal environment. KEA3 activity regulation overlaps with the stromal pH changes occurring during light fluctuations. The ATP and ADP has shown to have an affinity towards heterologously expressed KEA3 RCT. Such interaction causes conformational changes in RCT structure. The fold change of RCT-ligand interaction depends on the environmental pH value. With a combination of bioinformatics and in vitro approach, the ATP binding site at RCT was located. Introduction of binding site point mutation in planta KEA3 RCT resulted in antiporter activity deregulation during transition to low light. Together, the data presented in this thesis allowed us to assess more broadly a KEA3 role in photosynthesis adjustment and propose the models of KEA3 activity regulation throughout transition in light intensity.
Many children struggle with reading for comprehension. Reading is a complex cognitive task depending on various sub-tasks, such as word decoding and building connections across sentences. The task of connecting sentences is guided by referential expressions. References, such as anaphoric noun phrases (Minky/the cat) or pronouns (Minky/she), signal to the reader how the protagonists of adjacent sentences are connected. Readers construct a coherent mental model of the text by resolving these references. Personal pronouns (he/she) in particular need to be resolved towards an appropriate antecedent before they can be fully understood. Pronoun resolution therefore is vital for successful text comprehension. The present thesis investigated children’s resolution of personal pronouns during natural reading as a possible source of reading comprehension difficulty. Three eye tracking studies investigated whether children aged 8-9 (Grade 3-4) resolve pronouns online during reading and how the varying information around the pronoun region influences children’s eye movement behavior.
The first study investigated whether children prefer a pronoun over a noun phrase when the antecedent is highly accessible. Children read three-sentence stories that introduced a protagonist (Mia) in the first sentence and a reference to this protagonist in one of the following sentences using either a repeated name (Mia) or a pronoun (she). For proficient readers, it was repeatedly shown that there is a preference for a pronoun over the name in these contexts, i.e., when the antecedent is salient. The first study tested the repeated name penalty effect in children using eye tracking. It was hypothesized that in contrast to proficient readers, the fluency of children’s reading processing profits from an overlapping word form (i.e., the repeated noun phrase) compared to a pronoun. This is because overlapping word forms allow for direct mapping, whereas pronouns have to be resolved towards their antecedent first.
The second study investigated children’s online processing of pronominal gender in a mismatch paradigm. Children read sentences in which the pronoun either was a gender-match to the antecedent or a gender-mismatch. Reading skill and reading fluency were also tested and related to children’s ability to detect a mismatching pronoun during reading.
The third study investigated the online processing of gender information on the pronoun and whether disambiguating gender information improves the accuracy of pronoun comprehension. Offline comprehension accuracy, that is the comprehension of the pronoun, was related to children’s online eye movement behavior. This study was conducted in a semi-longitudinal paradigm: 70 children were tested in Grade 3 (age 8) and again in Grade 4 (age 9) to investigate effects of age and reading skill on pronoun processing and comprehension.
The results of this thesis clearly show that children aged 8-9, when they are in the second half of primary school, struggle with the comprehension of pronouns in reading tasks. The responses to pronoun comprehension questions revealed that children have difficulties with the comprehension of a pronoun in the absence of a disambiguating gender cue, that is when they have to apply context information. When there is a gender cue to disambiguate the pronoun, children’s accuracy improves significantly. This is true for children in Grade 3, but also in Grade 4, albeit their overall resolution accuracy slightly improves with age.
The results from the analyses of eye movements suggest that the discourse accessibility of an antecedent does play a role in children’s processing of pronouns and repeated names. The repetition of a name does not facilitate children’s reading processing like it was anticipated. Similar to adults, children showed a penalty effect for the repeated name where a pronoun is expected. However, this does not mean that children’s processing of pronouns is always adult-like. The results from eye movement analyses in the pronoun region during sentence reading revealed significant individual differences related to children’s individual reading skill and reading fluency.
The results from the mismatch study revealed that reading fluency is associated with children’s detection of incongruent pronouns. All children had longer gaze durations at mismatching than matching pronouns, but only fluent readers among the children followed this up with a regression out of the pronoun region. This was interpreted as an attempt to gain processing time and “repair” the inconsistency. Reading fluency was therefore associated with detection of the mismatch, while less fluent readers did not see any mismatch between pronoun and antecedent. The eye movement pattern of the “detectors” is more adult-like and was interpreted as reflecting successful monitoring and attempted pronoun resolution.
Children differ considerably in their reading comprehension skill. The results of this thesis show that only skilled readers among the children use gender information online for pronoun resolution. They took more time to read the pronoun when there was disambiguating gender information that was useful to resolve the pronoun, in contrast to the less skilled readers. Age was a less important factor in pronoun resolution processes and comprehension than were reading skill and reading fluency. Taken together, this suggests that the good readers direct cognitive resources towards pronoun resolution when the pronoun can be resolved, which is a successful comprehension strategy. Moreover, there was evidence that reading skill is a relevant factor in this task but not age.
The contribution of the present thesis is a depiction of the specific eye movement patterns that are related to successful and unsuccessful attempts at pronoun resolution in children. Eye movement behavior in the pronoun area is related to children’s reading skill and fluency. The results of this thesis suggest that many children do not resolve pronouns spontaneously during sentence reading, which is likely detrimental to their reading comprehension in more complex reading materials. The present thesis informs our understanding of the challenge that pronoun resolution poses for beginning readers, and gives new impulses for the study of higher-order reading processes in children’s natural reading.
Active Galactic Nuclei (AGN) are considered to be the main powering source of active galaxies, where central Super Massive Black Holes (SMBHs), with masses between 106 and 109 M⊙ gravitationally pull the surrounding material via accre- tion. AGN phenomenon expands over a very wide range of luminosities, from the most luminous high-redshift quasars (QSOs), to the local Low-Luminosity AGN (LLAGN), with significantly weaker luminosities. While "typical" luminous AGNs distinguish themselves by their characteristical blue featureless continuum, the Broad Emission Lines (BELs) with Full Widths at Half Maximum (FWHM) in order of few thousands km s1, arising from the so-called Broad Line Region (BLR), and strong radio and/or X-ray emission, detection of LLAGNs on the other hand is quite chal- lenging due to their extremely weak emission lines, and absence of the power-law continuum. In order to fully understand AGN evolution and their duty-cycles across cosmic history, we need a proper knowledge of AGN phenomenon at all luminosi- ties and redshifts, as well as perspectives from different wavelength bands.
In this thesis I present a search for AGN signatures in central spectra of 542 local (0.005 < z < 0.03) galaxies from the Calar Alto Legacy Integral Field Area (CALIFA) survey. The adopted aperture of 3′′ × 3′′ corresponds to central ∼ 100 − 500 pc for the redshift range of CALIFA. Using the standard emission-line ratio diagnostic diagrams, we initially classified all CALIFA emission-line galaxies (526) into star- forming, LINER-like, Seyfert 2 and intermediates. We further detected signatures of the broad Hα component in 89 spectra from the sample, of which more than 60% are present in the central spectra of LINER-like galaxies. These BELs are very weak, with luminosities in range 1038 − 1041 erg s−1, but with FWHMs between 1000 km s−1 and 6000 km s−1, comparable to those of luminous high-z AGN. This result implies that type 1 AGN are in fact quite frequent in the local Universe. We also identified additional 29 Seyfert 2 galaxies using the emission-line ratio diagnostic diagrams.
Using the MBH − σ∗ correlation, we estimated black hole masses of 55 type 1 AGN from CALIFA, a sample for which we had estimates of bulge stellar velocity dispersions σ∗. We compared these masses to the ones that we estimated from the virial method and found large discrepancies. We analyzed the validity of both meth- ods for black hole mass estimation of local LLAGN, and concluded that most likely virial scaling relations can no longer be applied as a valid MBH estimator in such low-luminosity regime. These black holes accrete at very low rate, having Edding- ton ratios in range 4.1 × 10−5 − 2.4 × 10−3. Detection of BELs with such low lumi- nosities and at such low Eddington rates implies that these LLAGN are still able to form the BLR, although with probably modified structure of the central engine.
In order to obtain full picture of black hole growth across cosmic time, it is es- sential that we study them in different stages of their activity. For that purpose, we estimated the broad AGN Luminosity Function (AGNLF) of our entire type 1 AGN sample using the 1/Vmax method. The shape of AGNLF indicates an apparent flattening below luminosities LHα ∼ 1039 erg s−1. Correspondingly we estimated ac- tive Black Hole Mass Function (BHMF) and Eddington Ration Distribution Function (ERDF) for a sub-sample of type 1 AGN for which we have MBH and λ estimates. The flattening is also present in both BHMF and ERDF, around log(MBH) ∼ 7.7 and log(λ) < 3, respectively. We estimated the fraction of active SMBHs in CALIFA by comparing our active BHMF to the one of the local quiescent SMBHs. The shape of
the active fraction which decreases with increasing MBH, as well as the flattening of AGNLF, BHMF and ERDF is consistent with scenario of AGN cosmic downsizing.
To complete AGN census in the CALIFA galaxy sample, it is necessary to search for them in various wavelength bands. For the purpose of completing the census we performed cross-correlations between all 542 CALIFA galaxies and multiwavelength surveys, Swift – BAT 105 month catalogue (in hard 15 - 195 keV X-ray band), and NRAO VLA Sky Survey (NVSS, in 1.4 GHz radio domain). This added 1 new AGN candidate in X-ray, and 7 in radio wavelength band to our local LLAGN count.
It is possible to detect AGN emission signatures within 10 – 20 kpc outside of the central galactic regions. This may happen when the central AGN has recently switched off and the photoionized material is spread across the galaxy within the light-travel-time, or the photoionized material is blown away from the nucleus by outflows. In order to detect these extended AGN regions we constructed spatially resolved emission-line ratio diagnostic diagrams of all emission-line galaxies from the CALIFA, and found 1 new object that was previously not identified as AGN.
Obtaining the complete AGN census in CALIFA, with five different AGN types, showed that LLAGN contribute a significant fraction of 24% of the emission-line galaxies in the CALIFA sample. This result implies that AGN are quite common in the local Universe, and although being in very low activity stage, they contribute to large fraction of all local SMBHs. Within this thesis we approached the upper limit of AGN fraction in the local Universe and gained some deeper understanding of the LLAGN phenomenon.
One third of the world's population lives in areas where earthquakes causing at least slight damage are frequently expected. Thus, the development and testing of global seismicity models is essential to improving seismic hazard estimates and earthquake-preparedness protocols for effective disaster-risk mitigation. Currently, the availability and quality of geodetic data along plate-boundary regions provides the opportunity to construct global models of plate motion and strain rate, which can be translated into global maps of forecasted seismicity. Moreover, the broad coverage of existing earthquake catalogs facilitates in present-day the calibration and testing of global seismicity models. As a result, modern global seismicity models can integrate two independent factors necessary for physics-based, long-term earthquake forecasting, namely interseismic crustal strain accumulation and sudden lithospheric stress release.
In this dissertation, I present the construction of and testing results for two global ensemble seismicity models, aimed at providing mean rates of shallow (0-70 km) earthquake activity for seismic hazard assessment. These models depend on the Subduction Megathrust Earthquake Rate Forecast (SMERF2), a stationary seismicity approach for subduction zones, based on the conservation of moment principle and the use of regional "geodesy-to-seismicity" parameters, such as corner magnitudes, seismogenic thicknesses and subduction dip angles. Specifically, this interface-earthquake model combines geodetic strain rates with instrumentally-recorded seismicity to compute long-term rates of seismic and geodetic moment. Based on this, I derive analytical solutions for seismic coupling and earthquake activity, which provide this earthquake model with the initial abilities to properly forecast interface seismicity. Then, I integrate SMERF2 interface-seismicity estimates with earthquake computations in non-subduction zones provided by the Seismic Hazard Inferred From Tectonics based on the second iteration of the Global Strain Rate Map seismicity approach to construct the global Tectonic Earthquake Activity Model (TEAM). Thus, TEAM is designed to reduce number, and potentially spatial, earthquake inconsistencies of its predecessor tectonic earthquake model during the 2015-2017 period. Also, I combine this new geodetic-based earthquake approach with a global smoothed-seismicity model to create the World Hybrid Earthquake Estimates based on Likelihood scores (WHEEL) model. This updated hybrid model serves as an alternative earthquake-rate approach to the Global Earthquake Activity Rate model for forecasting long-term rates of shallow seismicity everywhere on Earth.
Global seismicity models provide scientific hypotheses about when and where earthquakes may occur, and how big they might be. Nonetheless, the veracity of these hypotheses can only be either confirmed or rejected after prospective forecast evaluation. Therefore, I finally test the consistency and relative performance of these global seismicity models with independent observations recorded during the 2014-2019 pseudo-prospective evaluation period. As a result, hybrid earthquake models based on both geodesy and seismicity are the most informative seismicity models during the testing time frame, as they obtain higher information scores than their constituent model components. These results support the combination of interseismic strain measurements with earthquake-catalog data for improved seismicity modeling. However, further prospective evaluations are required to more accurately describe the capacities of these global ensemble seismicity models to forecast longer-term earthquake activity.
The business problem of having inefficient processes, imprecise process analyses, and simulations as well as non-transparent artificial neuronal network models can be overcome by an easy-to-use modeling concept. With the aim of developing a flexible and efficient approach to modeling, simulating, and optimizing processes, this paper proposes a flexible Concept of Neuronal Modeling (CoNM). The modeling concept, which is described by the modeling language designed and its mathematical formulation and is connected to a technical substantiation, is based on a collection of novel sub-artifacts. As these have been implemented as a computational model, the set of CoNM tools carries out novel kinds of Neuronal Process Modeling (NPM), Neuronal Process Simulations (NPS), and Neuronal Process Optimizations (NPO). The efficacy of the designed artifacts was demonstrated rigorously by means of six experiments and a simulator of real industrial production processes.
In my doctoral thesis, I examine continuous gravity measurements for monitoring of the geothermal site at Þeistareykir in North Iceland. With the help of high-precision superconducting gravity meters (iGravs), I investigate underground mass changes that are caused by operation of the geothermal power plant (i.e. by extraction of hot water and reinjection of cold water). The overall goal of this research project is to make a statement about the sustainable use of the geothermal reservoir, from which also the Icelandic energy supplier and power plant operator Landsvirkjun should benefit.
As a first step, for investigating the performance and measurement stability of the gravity meters, in summer 2017, I performed comparative measurements at the gravimetric observatory J9 in Strasbourg. From the three-month gravity time series, I examined calibration, noise and drift behaviour of the iGravs in comparison to stable long-term time series of the observatory superconducting gravity meters. After preparatory work in Iceland (setup of gravity stations, additional measuring equipment and infrastructure, discussions with Landsvirkjun and meetings with the Icelandic partner institute ISOR), gravity monitoring at Þeistareykir was started in December 2017. With the help of the iGrav records of the initial 18 months after start of measurements, I carried out the same investigations (on calibration, noise and drift behaviour) as in J9 to understand how the transport of the superconducting gravity meters to Iceland may influence instrumental parameters.
In the further course of this work, I focus on modelling and reduction of local gravity contributions at Þeistareykir. These comprise additional mass changes due to rain, snowfall and vertical surface displacements that superimpose onto the geothermal signal of the gravity measurements. For this purpose, I used data sets from additional monitoring sensors that are installed at each gravity station and adapted scripts for hydro-gravitational modelling. The third part of my thesis targets geothermal signals in the gravity measurements.
Together with my PhD colleague Nolwenn Portier from France, I carried out additional gravity measurements with a Scintrex CG5 gravity meter at 26 measuring points within the geothermal field in the summers of 2017, 2018 and 2019. These annual time-lapse gravity measurements are intended to increase the spatial coverage of gravity data from the three continuous monitoring stations to the entire geothermal field. The combination of CG5 and iGrav observations, as well as annual reference measurements with an FG5 absolute gravity meter represent the hybrid gravimetric monitoring method for Þeistareykir. Comparison of the gravimetric data to local borehole measurements (of groundwater levels, geothermal extraction and injection rates) is used to relate the observed gravity changes to the actually extracted (and reinjected) geothermal fluids. An approach to explain the observed gravity signals by means of forward modelling of the geothermal production rate is presented at the end of the third (hybrid gravimetric) study. Further modelling with the help of the processed gravity data is planned by Landsvirkjun. In addition, the experience from time-lapse and continuous gravity monitoring will be used for future gravity measurements at the Krafla geothermal field 22 km south-east of Þeistareykir.
Contributions to the theoretical analysis of the algorithms with adversarial and dependent data
(2021)
In this work I present the concentration inequalities of Bernstein's type for the norms of Banach-valued random sums under a general functional weak-dependency assumption (the so-called $\cC-$mixing). The latter is then used to prove, in the asymptotic framework, excess risk upper bounds of the regularised Hilbert valued statistical learning rules under the τ-mixing assumption on the underlying training sample. These results (of the batch statistical setting) are then supplemented with the regret analysis over the classes of Sobolev balls of the type of kernel ridge regression algorithm in the setting of online nonparametric regression with arbitrary data sequences. Here, in particular, a question of robustness of the kernel-based forecaster is investigated. Afterwards, in the framework of sequential learning, the multi-armed bandit problem under $\cC-$mixing assumption on the arm's outputs is considered and the complete regret analysis of a version of Improved UCB algorithm is given. Lastly, probabilistic inequalities of the first part are extended to the case of deviations (both of Azuma-Hoeffding's and of Burkholder's type) to the partial sums of real-valued weakly dependent random fields (under the type of projective dependence condition).
The development and optimization of carbonaceous materials is of great interest for several applications including gas sorption, electrochemical storage and conversion, or heterogeneous catalysis. In this thesis, the exploration and optimization of nitrogen containing carbonaceous materials by direct condensation of smart chosen, molecular precursors will be presented. As suggested with the concept of noble carbons, the choice of a stable, nitrogen-containing precursor will lead to an even more stable, nitrogen doped carbonaceous material with a controlled structure and electronic properties. Molecules fulfilling this requirement are for example nucleobases. The direct condensation of nucleobases leads to highly nitrogen containing carbonaceous materials without any further post or pretreatment. By using salt melt templating, pore structure adjustment is possible without the use of hazardous or toxic reagents and the template can be reused.
Using these simple tools, the synergetic effect of the pore structure and nitrogen content of the materials can be explored. Within this thesis, the influence of the condensation parameters will be correlated to the structure and performance of the materials. First, the influence of the condensation temperature to the porosity and nitrogen content of guanine will be discussed and the exploration of highly CO2 selective structural pores in C1N1 materials will be shown. Further tuning the pore structure of the materials by salt melt templating will be then explored, the potential of the prepared materials as heterogeneous catalysts and their basic catalytic strength will be correlated to their nitrogen content and pore morphology. A similar approach is used to explore the water sorption behavior of uric acid derived carbonaceous materials as potential sorbents for heat transformation applications. Changes in maximum water uptake and hydrophilicity of the prepared materials will be correlated to the nitrogen content and pore architecture. Due to the high thermal stability, porosity, and nitrogen content of ionic liquid derived nitrogen doped carbonaceous materials, a simple impregnation and calcination route can be conducted to obtain copper nano cluster decorated nitrogen-doped carbonaceous materials. The activity as catalyst for the oxygen reduction reaction of the obtained materials will be shown and structure performance relations are discussed.
In conclusion, the versatility of nitrogen doped carbonaceous materials with a nitrogen to carbon ratio of up to one will be shown. The possibility to tune the pore structure as well as the nitrogen content by using a simple procedure including salt melt templating as well as the use of molecular precursors and their effect on the performance will be discussed.
Anthropogenic climate change alters the hydrological cycle. While certain areas experience more intense precipitation events, others will experience droughts and increased evaporation, affecting water storage in long-term reservoirs, groundwater, snow, and glaciers. High elevation environments are especially vulnerable to climate change, which will impact the water supply for people living downstream. The Himalaya has been identified as a particularly vulnerable system, with nearly one billion people depending on the runoff in this system as their main water resource. As such, a more refined understanding of spatial and temporal changes in the water cycle in high altitude systems is essential to assess variations in water budgets under different climate change scenarios.
However, not only anthropogenic influences have an impact on the hydrological cycle, but changes to the hydrological cycle can occur over geological timescales, which are connected to the interplay between orogenic uplift and climate change. However, their temporal evolution and causes are often difficult to constrain. Using proxies that reflect hydrological changes with an increase in elevation, we can unravel the history of orogenic uplift in mountain ranges and its effect on the climate.
In this thesis, stable isotope ratios (expressed as δ2H and δ18O values) of meteoric waters and organic material are combined as tracers of atmospheric and hydrologic processes with remote sensing products to better understand water sources in the Himalayas. In addition, the record of modern climatological conditions based on the compound specific stable isotopes of leaf waxes (δ2Hwax) and brGDGTs (branched Glycerol dialkyl glycerol tetraethers) in modern soils in four Himalayan river catchments was assessed as proxies of the paleoclimate and (paleo-) elevation. Ultimately, hydrological variations over geological timescales were examined using δ13C and δ18O values of soil carbonates and bulk organic matter originating from sedimentological sections from the pre-Siwalik and Siwalik groups to track the response of vegetation and monsoon intensity and seasonality on a timescale of 20 Myr.
I find that Rayleigh distillation, with an ISM moisture source, mainly controls the isotopic composition of surface waters in the studied Himalayan catchments. An increase in d-excess in the spring, verified by remote sensing data products, shows the significant impact of runoff from snow-covered and glaciated areas on the surface water isotopic values in the timeseries.
In addition, I show that biomarker records such as brGDGTs and δ2Hwax have the potential to record (paleo-) elevation by yielding a significant correlation with the temperature and surface water δ2H values, respectively, as well as with elevation. Comparing the elevation inferred from both brGDGT and δ2Hwax, large differences were found in arid sections of the elevation transects due to an additional effect of evapotranspiration on δ2Hwax. A combined study of these proxies can improve paleoelevation estimates and provide recommendations based on the results found in this study.
Ultimately, I infer that the expansion of C4 vegetation between 20 and 1 Myr was not solely dependent on atmospheric pCO2, but also on regional changes in aridity and seasonality from to the stable isotopic signature of the two sedimentary sections in the Himalaya (east and west).
This thesis shows that the stable isotope chemistry of surface waters can be applied as a tool to monitor the changing Himalayan water budget under projected increasing temperatures. Minimizing the uncertainties associated with the paleo-elevation reconstructions were assessed by the combination of organic proxies (δ2Hwax and brGDGTs) in Himalayan soil. Stable isotope ratios in bulk soil and soil carbonates showed the evolution of vegetation influenced by the monsoon during the late Miocene, proving that these proxies can be used to record monsoon intensity, seasonality, and the response of vegetation. In conclusion, the use of organic proxies and stable isotope chemistry in the Himalayas has proven to successfully record changes in climate with increasing elevation. The combination of δ2Hwax and brGDGTs as a new proxy provides a more refined understanding of (paleo-)elevation and the influence of climate.
The controlled dosage of substances from a device to its environment, such as a tissue or an organ in medical applications or a reactor, room, machinery or ecosystem in technical, should ideally match the requirements of the applications, e.g. in terms of the time point at which the cargo is released. On-demand dosage systems may enable such a desired release pattern, if the device contain suitable features that can translate external signals into a release function. This study is motivated by the opportunities arising from microsystems capable of an on-demand release and the contributions that geometrical design may have in realizing such features. The goals of this work included the design, fabrication, characterization and experimental proof-of-concept of geometry-assisted triggerable dosing effect (a) with a sequential dosing release and (b) in a self-sufficient dosage system. Structure-function relationships were addressed on the molecular, morphological and, with a particular attention, the device design level, which is on the micrometer scale. Models and/or computational tools were used to screen the parameter space and provide guidance for experiments.
Botulinum neurotoxin (BoNT) is produced by the anaerobic bacterium Clostridium botulinum. It is one of the most potent toxins found in nature and can enter motor neurons (MN) to cleave proteins necessary for neurotransmission, resulting in flaccid paralysis. The toxin has applications in both traditional and esthetic medicine. Since BoNT activity varies between batches despite identical protein concentrations, the activity of each lot must be assessed. The gold standard method is the mouse lethality assay, in which mice are injected with a BoNT dilution series to determine the dose at which half of the animals suffer death from peripheral asphyxia. Ethical concerns surrounding the use of animals in toxicity testing necessitate the creation of alternative model systems to measure the potency of BoNT.
Prerequisites of a successful model are that it is human specific; it monitors the complete toxic pathway of BoNT; and it is highly sensitive, at least in the range of the mouse lethality assay. One model system was developed by our group, in which human SIMA neuroblastoma cells were genetically modified to express a reporter protein (GLuc), which is packaged into neurosecretory vesicles, and which, upon cellular depolarization, can be released – or inhibited by BoNT – simultaneously with neurotransmitters. This assay has great potential, but includes the inherent disadvantages that the GLuc sequence was randomly inserted into the genome and the tumor cells only have limited sensitivity and specificity to BoNT. This project aims to improve these deficits, whereby induced pluripotent stem cells (iPSCs) were genetically modified by the CRISPR/Cas9 method to insert the GLuc sequence into the AAVS1 genomic safe harbor locus, precluding genetic disruption through non-specific integrations. Furthermore, GLuc was modified to associate with signal peptides that direct to the lumen of both large dense core vesicles (LDCV), which transport neuropeptides, and synaptic vesicles (SV), which package neurotransmitters. Finally, the modified iPSCs were differentiated into motor neurons (MNs), the true physiological target of BoNT, and hypothetically the most sensitive and specific cells available for the MoN-Light BoNT assay.
iPSCs were transfected to incorporate one of three constructs to direct GLuc into LDCVs, one construct to direct GLuc into SVs, and one “no tag” GLuc control construct. The LDCV constructs fused GLuc with the signal peptides for proopiomelanocortin (hPOMC-GLuc), chromogranin-A (CgA-GLuc), and secretogranin II (SgII-GLuc), which are all proteins found in the LDCV lumen. The SV construct comprises a VAMP2-GLuc fusion sequence, exploiting the SV membrane-associated protein synaptobrevin (VAMP2). The no tag GLuc expresses GLuc non-specifically throughout the cell and was created to compare the localization of vesicle-directed GLuc.
The clones were characterized to ensure that the GLuc sequence was only incorporated into the AAVS1 safe harbor locus and that the signal peptides directed GLuc to the correct vesicles. The accurate insertion of GLuc was confirmed by PCR with primers flanking the AAVS1 safe harbor locus, capable of simultaneously amplifying wildtype and modified alleles. The PCR amplicons, along with an insert-specific amplicon from candidate clones were Sanger sequenced to confirm the correct genomic region and sequence of the inserted DNA. Off-target integrations were analyzed with the newly developed dc-qcnPCR method, whereby the insert DNA was quantified by qPCR against autosomal and sex-chromosome encoded genes. While the majority of clones had off-target inserts, at least one on-target clone was identified for each construct.
Finally, immunofluorescence was utilized to localize GLuc in the selected clones. In iPSCs, the vesicle-directed GLuc should travel through the Golgi apparatus along the neurosecretory pathway, while the no tag GLuc should not follow this pathway. Initial analyses excluded the CgA-GLuc and SgII-GLuc clones due to poor quality protein visualization. The colocalization of GLuc with the Golgi was analyzed by confocal microscopy and quantified. GLuc was strongly colocalized with the Golgi in the hPOMC-GLuc clone (r = 0.85±0.09), moderately in the VAMP2-GLuc clone (r = 0.65±0.01), and, as expected, only weakly in the no tag GLuc clone (r = 0.44±0.10). Confocal microscopy of differentiated MNs was used to analyze the colocalization of GLuc with proteins associated with LDCVs and SVs, SgII in the hPOMC-GLuc clone (r = 0.85±0.08) and synaptophysin in the VAMP2-GLuc clone (r = 0.65±0.07). GLuc was also expressed in the same cells as the MN-associated protein, Islet1.
A significant portion of GLuc was found in the correct cell type and compartment. However, in the MoN-Light BoNT assay, the hPOMC-GLuc clone could not be provoked to reliably release GLuc upon cellular depolarization. The depolarization protocol for hPOMC-GLuc must be further optimized to produce reliable and specific release of GLuc upon exposure to a stimulus. On the other hand, the VAMP2-GLuc clone could be provoked to release GLuc upon exposure to the muscarinic and nicotinic agonist carbachol. Furthermore, upon simultaneous exposure to the calcium chelator EGTA, the carbachol-provoked release of GLuc could be significantly repressed, indicating the detection of GLuc was likely associated with vesicular fusion at the presynaptic terminal. The application of the VAMP2-GLuc clone in the MoN-Light BoNT assay must still be verified, but the results thus far indicate that this clone could be appropriate for the application of BoNT toxicity assessment.
Mycotoxins are secondary metabolites produced by several filamentous fungal species, thus occurring ubiquitously in the environment and food. While the heterogeneous group shows differences in their bioavailability and toxicity, the low-molecular-weight xenobiotics are capable of impacting human and animal health acutely and chronically. Therefore, maximum levels for the major mycotoxins in food and feed are regulated in the current European legislation. Besides free mycotoxins, naturally occurring modified mycotoxins are gaining more attention in recent years. Modified mycotoxins constitute toxins altered by plants, microorganisms, and living organisms in different metabolic pathways or food processing steps. The toxicological relevant compounds often co-occur with their free forms in infested food and feed. Thus, the toxins may contribute to the overall toxicity of mycotoxins, wherefore their presence and toxicity should be considered in risk assessment. Until now, however, there are no regulated limits for modified mycotoxins within the European Union. In this thesis, rapid, sensitive, and robust methods for the analysis of mycotoxins and their modified forms were developed and validated using state-of-the-art high performance liquid chromatography tandem mass spectrometry (LC-MS/MS) systems. Firstly, two analytical methods for determining 38 mycotoxins in cereals and 41 mycotoxins in beer were established since agricultural products count as the primary source of mycotoxin contamination. For the analysis of cereal samples, a QuEChERS- based extraction approach was pursued, while analytes from beer samples were extracted using an acetonitrile precipitation scheme. Validation in cereals, namely wheat, corn, rice, and barley, as well as in beer, demonstrated satisfactory results. To obtain information regarding the natural occurrence of mycotoxins in food products, the developed methods were applied to the analysis of several commercial samples partly produced worldwide. The Fusarium toxins deoxynivalenol and its conjugated metabolite deoxynivalenol-3-glucoside turned out to be the most abundant toxins. None of the other modified mycotoxins were quantified in the samples. However, one cereal sample showed traces of zearalenone- 14-sulfate below the limit of quantification. Moreover, pesticides, plant growth regulators, and tropane alkaloids were investigated in this thesis. Pesticides present biologically highly effective compounds applied in the environment to protect humans from the hazardous effects of pests. While plant growth regulators show similar functions, mainly improving agricultural production, tropane alkaloids are naturally occurring secondary metabolites mainly in the species of Solanaceae that may pose unintended poisoning of humans. The third part of the present thesis aimed to analyze cereal-relevant compounds simultaneously, wherefore a multi-method for the analysis of (modified) mycotoxins, pesticides, plant growth regulators, and tropane alkaloids was established. After processing the samples, this should be done in a single extraction step with subsequent one-time measurements. Various sample preparation procedures were compared, whereby an approach based on an acidified acetonitrile/water extraction, followed by an online clean-up, was finally chosen. The simultaneous determination of more than 350 analytes required an analytical tool that offered an increased resolving power, represented as an enhanced peak capacity, and the possibility of analyzing a broad polarity range. Thus, a two-dimensional LC-MS/MS system based on two different separation mechanisms that performed orthogonal to one another was used for the analysis. Validation of the developed method revealed good performance characteristics for most analytes, while subsequent application showed that 86% of the samples were contaminated with at least one compound. In summary, this thesis provides novel insights into the analysis of food-relevant (modified) mycotoxins. Different sample preparation and LC-MS/MS approaches were introduced, resulting in the development of three new analytical methods. For the first time, such a high number of modified mycotoxins was included in multi-mycotoxin methods and a multi-method ranging both contaminants and residues. Although first steps towards the analysis of modified mycotoxins have been made, further research is needed to elucidate their (co-) occurrence and toxicological behavior in order to understand their relevance to human health in the future.
Digital inclusion
(2021)
In this thesis, we tackle two social disruptions: recent refugee waves in Germany and the COVID-19 pandemic. We focus on the use of information and communication technology (ICT) as a key means of alleviating these disruptions and promoting social inclusion. As social disruptions typically lead to frustration and fragmentation, it is essential to ensure the social inclusion of individuals and societies during such times.
In the context of the social inclusion of refugees, we focus on the Syrian refugees who arrived in Germany as of 2015, as they form a large and coherent refugee community. In particular, we address the role of ICTs in refugees’ social inclusion and investigate how different ICTs (especially smartphones and social networks) can foster refugees’ integration and social inclusion. In the context of the COVID-19 pandemic, we focus on the widespread unconventional working model of work from home (WFH). Our research here centers on the main constructs of WFH and the key differences in WFH experiences based on personal characteristics such as gender and parental status.
We reveal novel insights through four well-established research methods: literature review, mixed methods, qualitative method, and quantitative method. The results of our research have been published in the form of eight articles in major information systems venues and journals. Key results from the refugee research stream include the following: Smartphones represent a central component of refugee ICT use; refugees view ICT as a source of information and power; the social connectedness of refugees is strongly correlated with their Internet use; refugees are not relying solely on traditional methods to learn the German language or pursue further education; the ability to use smartphones anytime and anywhere gives refugees an empowering feeling of global connectedness; and ICTs empower refugees on three levels (community participation, sense of control, and self-efficacy).
Key insights from the COVID-19 WFH stream include: Gender and the presence of children under the age of 18 affect workers’ control over their time, technology usefulness, and WFH conflicts, while not affecting their WFH attitudes; and both personal and technology-related factors affect an individual’s attitude toward WFH and their productivity. Further insights are being gathered at the time of submitting this thesis.
This thesis contributes to the discussion within the information systems community regarding how to use different ICT solutions to promote the social inclusion of refugees in their new communities and foster an inclusive society. It also adds to the growing body of research on COVID-19, in particular on the sudden workplace transformation to WFH. The insights gathered in this thesis reveal theoretical implications and future opportunities for research in the field of information systems, practical implications for relevant stakeholders, and social implications related to the refugee crisis and the COVID-19 pandemic that must be addressed.
Digital surveillance fiction
(2021)
One of the key challenges in modern Facility Management (FM) is to digitally reflect the current state of the built environment, referred to as-is or as-built versus as-designed representation. While the use of Building Information Modeling (BIM) can address the issue of digital representation, the generation and maintenance of BIM data requires a considerable amount of manual work and domain expertise. Another key challenge is being able to monitor the current state of the built environment, which is used to provide feedback and enhance decision making. The need for an integrated solution for all data associated with the operational life cycle of a building is becoming more pronounced as practices from Industry 4.0 are currently being evaluated and adopted for FM use. This research presents an approach for digital representation of indoor environments in their current state within the life cycle of a given building. Such an approach requires the fusion of various sources of digital data. The key to solving such a complex issue of digital data integration, processing and representation is with the use of a Digital Twin (DT). A DT is a digital duplicate of the physical environment, states, and processes. A DT fuses as-designed and as-built digital representations of built environment with as-is data, typically in the form of floorplans, point clouds and BIMs, with additional information layers pertaining to the current and predicted states of an indoor environment or a complete building (e.g., sensor data). The design, implementation and initial testing of prototypical DT software services for indoor environments is presented and described. These DT software services are implemented within a service-oriented paradigm, and their feasibility is presented through functioning and tested key software components within prototypical Service-Oriented System (SOS) implementations. The main outcome of this research shows that key data related to the built environment can be semantically enriched and combined to enable digital representations of indoor environments, based on the concept of a DT. Furthermore, the outcomes of this research show that digital data, related to FM and Architecture, Construction, Engineering, Owner and Occupant (AECOO) activity, can be combined, analyzed and visualized in real-time using a service-oriented approach. This has great potential to benefit decision making related to Operation and Maintenance (O&M) procedures within the scope of the post-construction life cycle stages of typical office buildings.
Selfsustained oscillations are some of the most commonly observed phenomena in biological systems. They emanate from non-linear systems in a heterogeneous environment and can be described by the theory of dynamical systems. Part of this theory considers reduced models of the oscillator dynamics by means of amplitudes and a phase variable. Such variables are highly attractive for theoretical and experimental studies. Theoretically these variables correspond to an integrable linearization of the generally non-linear system. Experimentally, there exist well established approaches to extract phases from oscillator signals. Notably, one can define phase models also for networks of oscillators. One highly active field examines effects of non-local coupling among oscillators, which is thought to play a key role in networks with strong coupling. The dissertation introduces and expands the knowledge about high-order phase coupling in networks of oscillators. Mathematical calculations consider the Stuart-Landau oscillator. A novel phase estimation scheme for direct observations of an oscillator dynamics is introduced based on numerics. A numerical study of high-order phase coupling applies a Fourier fit for the Stuart-Landau and for the van-der-Pol oscillator. The numerical approach is finally tested on observation-based phase estimates of the Morris-Lecar neuron. A popular approach for the construction of phases from signals is based on phase demodulation by means of the Hilbert transform. Generally, observations of oscillations contain a small and generic variation of their amplitude. The work presents a way to quantify how much the variations of signal amplitude spoil a phase demodulation procedure. For the ideal case of phase modulated signals, amplitude modulations vanish. However, the Hilbert transform produces artificial variations of the reconstructed amplitude even in this case. The work proposes a novel procedure called Iterative Hilbert Transform Embedding to obtain an optimal demodulation of signals. The text presents numerous examples and tests of application for the method, covering multicomponent signals, observables of highly stable limit cycle oscillations and noisy phase dynamics. The numerical results are supported by a spectral theory of convergence for weak phase modulations.
Eukaryotic cells can be regarded as complex microreactors capable of performing various biochemical reactions in parallel which are necessary to sustain life. An essential prerequisite for these complex metabolic reactions to occur is the evolution of lipid membrane-bound organelles enabling compartmental- ization of reactions and biomolecules. This allows for a spatiotemporal control over the metabolic reactions within the cellular system. Intracellular organi- zation arising due to compartmentalization is a key feature of all living cells and has inspired synthetic biologists to engineer such systems with bottom-up approaches.
Artificial cells provide an ideal platform to isolate and study specific re- actions without the interference from the complex network of biomolecules present in biological cells. To mimic the hierarchical architecture of eukaryotic cells, multi-compartment assemblies with nested liposomal structures also re- ferred to as multi-vesicular vesicles (MVVs) have been widely adopted. Most of the previously reported multi-compartment systems adopt bulk method- ologies which suffer from low yield and poor control over size. Microfluidic strategies help circumvent these issues and facilitate a high-throughput and robust technique to assemble MVVs of uniform size distribution.
In this thesis, firstly, the bulk methodologies are explored to build MVVs and implement a synthetic signalling cascade. Next, a polydimethylsiloxane (PDMS)-based microfluidic platform is introduced to build MVVs and the significance of PEGylated lipids for the successful encapsulation of inner com- partments to generate stable multi-compartment systems is highlighted.
Next, a novel two-inlet channel PDMS-based microfluidic device to create MVVs encompassing a three-step enzymatic reaction cascade is presented. A directed reaction pathway comprising of the enzymes α-glucosidase (α-Glc), glucose oxidase (GOx), and horseradish peroxidase (HRP) spanning across three compartments via reconstitution of size-selective membrane proteins is described. Furthermore, owing to the monodispersity of our MVVs due to microfluidic strategies, this platform is employed to study the effect of com- partmentalization on reaction kinetics.
Further integration of cell-free expression module into the MVVs would allow for gene-mediated signal transduction within artificial eukaryotic cells. Therefore, the chemically inducible cell-free expression of a membrane protein alpha-hemolysin and its further reconstitution into liposomes is carried out.
In conclusion, the present thesis aims to build artificial eukaryotic cells to achieve size-selective chemical communication that also show potential for applications as micro reactors and as vehicles for drug delivery.
Discriminative Models for Biometric Identification using Micro- and Macro-Movements of the Eyes
(2021)
Human visual perception is an active process. Eye movements either alternate between fixations and saccades or follow a smooth pursuit movement in case of moving targets. Besides these macroscopic gaze patterns, the eyes perform involuntary micro-movements during fixations which are commonly categorized into micro-saccades, drift and tremor. Eye movements are frequently studied in cognitive psychology, because they reflect a complex interplay of perception, attention and oculomotor control.
A common insight of psychological research is that macro-movements are highly individual. Inspired by this finding, there has been a considerable amount of prior research on oculomotoric biometric identification. However, the accuracy of known approaches is too low and the time needed for identification is too long for any practical application. This thesis explores discriminative models for the task of biometric identification.
Discriminative models optimize a quality measure of the predictions and are usually superior to generative approaches in discriminative tasks. However, using discriminative models requires to select a suitable form of data representation for sequential eye gaze data; i.e., by engineering features or constructing a sequence kernel and the performance of the classification model strongly depends on the data representation. We study two fundamentally different ways of representing eye gaze within a discriminative framework. In the first part of this thesis, we explore the integration of data and psychological background knowledge in the form of generative models to construct representations. To this end, we first develop generative statistical models of gaze behavior during reading and scene viewing that account for viewer-specific distributional properties of gaze patterns. In a second step, we develop a discriminative identification model by deriving Fisher kernel functions from these and several baseline models. We find that an SVM with Fisher kernel is able to reliably identify users based on their eye gaze during reading and scene viewing. However, since the generative models are constrained to use low-frequency macro-movements, they discard a significant amount of information contained in the raw eye tracking signal at a high cost: identification requires about one minute of input recording, which makes it inapplicable for real world biometric systems. In the second part of this thesis, we study a purely data-driven modeling approach. Here, we aim at automatically discovering the individual pattern hidden in the raw eye tracking signal. To this end, we develop a deep convolutional neural network DeepEyedentification that processes yaw and pitch gaze velocities and learns a representation end-to-end. Compared to prior work, this model increases the identification accuracy by one order of magnitude and the time to identification decreases to only seconds. The DeepEyedentificationLive model further improves upon the identification performance by processing binocular input and it also detects presentation-attacks.
We find that by learning a representation, the performance of oculomotoric identification and presentation-attack detection can be driven close to practical relevance for biometric applications. Eye tracking devices with high sampling frequency and precision are expensive and the applicability of eye movement as a biometric feature heavily depends on cost of recording devices.
In the last part of this thesis, we therefore study the requirements on data quality by evaluating the performance of the DeepEyedentificationLive network under reduced spatial and temporal resolution. We find that the method still attains a high identification accuracy at a temporal resolution of only 250 Hz and a precision of 0.03 degrees. Reducing both does not have an additive deteriorating effect.
Generative adversarial networks (GANs) have been broadly applied to a wide range of application domains since their proposal. In this thesis, we propose several methods that aim to tackle different existing problems in GANs. Particularly, even though GANs are generally able to generate high-quality samples, the diversity of the generated set is often sub-optimal. Moreover, the common increase of the number of models in the original GANs framework, as well as their architectural sizes, introduces additional costs. Additionally, even though challenging, the proper evaluation of a generated set is an important direction to ultimately improve the generation process in GANs. We start by introducing two diversification methods that extend the original GANs framework to multiple adversaries to stimulate sample diversity in a generated set. Then, we introduce a new post-training compression method based on Monte Carlo methods and importance sampling to quantize and prune the weights and activations of pre-trained neural networks without any additional training. The previous method may be used to reduce the memory and computational costs introduced by increasing the number of models in the original GANs framework. Moreover, we use a similar procedure to quantize and prune gradients during training, which also reduces the communication costs between different workers in a distributed training setting. We introduce several topology-based evaluation methods to assess data generation in different settings, namely image generation and language generation. Our methods retrieve both single-valued and double-valued metrics, which, given a real set, may be used to broadly assess a generated set or separately evaluate sample quality and sample diversity, respectively. Moreover, two of our metrics use locality-sensitive hashing to accurately assess the generated sets of highly compressed GANs. The analysis of the compression effects in GANs paves the way for their efficient employment in real-world applications. Given their general applicability, the methods proposed in this thesis may be extended beyond the context of GANs. Hence, they may be generally applied to enhance existing neural networks and, in particular, generative frameworks.
There is a general consensus that diverse ecological communities are better equipped to adapt to changes in their environment, but our understanding of the mechanisms by which they do so remains incomplete. Accurately predicting how the global biodiversity crisis affects the functioning of ecosystems, and the services they provide, requires extensive knowledge about these mechanisms.
Mathematical models of food webs have been successful in uncovering many aspects of the link between diversity and ecosystem functioning in small food web modules, containing at most two adaptive trophic levels. Meaningful extrapolation of this understanding to the functioning of natural food webs remains difficult, due to the presence of complex interactions that are not always accurately captured by bitrophic descriptions of food webs. In this dissertation, we expand this approach to tritrophic food web models by including the third trophic level. Using a functional trait approach, coexistence of all species is ensured using fitness-balancing trade-offs. For example, the defense-growth trade-off implies that species may be defended against predation, but this defense comes at the cost of a lower maximal growth rate. In these food webs, the functional diversity on a given trophic level can be varied by modifying the trait differences between the species on that level.
In the first project, we find that functional diversity promotes high biomass on the top level, which, in turn, leads to a reduction in the temporal variability due to compensatory dynamical patterns governed by the top level. Next, these results are generalized by investigating the average behavior of tritrophic food webs, for wide intervals of all parameters describing species interactions in the food web. We find that the diversity on the top level is most important for determining the biomass and temporal variability of all other trophic levels, and show how biomass is only transferred efficiently to the top level when diversity is high everywhere in the food web. In the third project, we compare the response of a simple food chain against a nutrient pulse perturbation, to that of a food web with diversity on every trophic level. By joint consideration of the resistance, resilience, and elasticity, we uncover that the response is efficiently buffered when biomass on the top level is high, which is facilitated by functional diversity on every trophic level in the food web. Finally, in the fourth project, we show that even in a simple consumer-resource model without any diversity, top-down control on the intermediate level frequently causes the phase difference between the intermediate and basal level to deviate from the quarter-cycle lag rule. By adding a top predator, we show that these deviations become even more likely, and anti-phase cycles are often observed.
The combined results of these projects show how the properties of the top trophic level, including its functional diversity, have a decisive influence on the functioning of tritrophic food webs from a mechanistic perspective. Because top species are often among the most vulnerable to extinction, our results emphasize the importance of their conservation in ecosystem management and restoration strategies.
Kenya and Uganda are amongst the countries that, for different historical, political, and economic reasons, have embarked on law reform processes as regards to citizenship. In 2009, Uganda made provisions in its laws to allow citizens to have dual citizenship while Kenya’s 2010 constitution similarly introduced it, and at the same time, a general prohibition on dual citizenship was lifted, that is, a ban on state officers, including the President and Deputy President, being dual nationals (Manby, 2018).
Against this background, I analysed the reasons for which these countries that previously held stringent laws and policies against dual citizenship, made a shift in a close time proximity. Given their geo-political roles, location, regional, continental, and international obligations, I conducted a comparative study on the processes, actors, impact, and effect. A specific period of 2000 to 2010 was researched, that is, from when the debates for law reforms emerged, to the processes being implemented, the actors, and the implications.
According to Rubenstein (2000, p. 520), citizenship is observed in terms of “political institutions” that are free to act according to the will of, in the interests of, or with authority over, their citizenry. Institutions are emergent national or international, higher-order factors above the individual spectrum, having the interests and political involvement of their actors without requiring recurring collective mobilisation or imposing intervention to realise these regularities. Transnational institutions are organisations with authority beyond single governments. Given their International obligations, I analysed the role of the UN, AU, and EAC in influencing the citizenship debates and reforms in Kenya and Uganda. Further, non-state actors, such as civil society, were considered.
Veblen, (1899) describes institutions as a set of settled habits of thought common to the generality of men. Institutions function only because the rules involved are rooted in shared habits of thought and behaviour although there is some ambiguity in the definition of the term “habit”. Whereas abstracts and definitions depend on different analytical procedures, institutions restrain some forms of action and facilitate others. Transnational institutions both restrict and aid behaviour. The famous “invisible hand” is nothing else but transnational institutions. Transnational theories, as applied to politics, posit two distinct forms that are of influence over policy and political action (Veblen, 1899). This influence and durability of institutions is “a function of the degree to which they are instilled in political actors at the individual or organisational level, and the extent to which they thereby “tie up” material resources and networks. Against this background, transitional networks with connection to Kenya and Uganda were considered alongside the diaspora from these two countries and their role in the debate and reforms on Dual citizenship.
Sterian (2013, p. 310) notes that Nation states may be vulnerable to institutional influence and this vulnerability can pose a threat to a nation’s autonomy, political legitimacy, and to the democratic public law. Transnational institutions sometimes “collide with the sovereignty of the state when they create new structures for regulating cross-border relationships”. However, Griffin (2003) disagrees that transnational institutional behaviour is premised on the principles of neutrality, impartiality, and independence. Transnational institutions have become the main target of the lobby groups and civil society, consequently leading to excessive politicisation. Kenya and Uganda are member states not only of the broader African union but also of the E.A.C which has adopted elements of socio-economic uniformity. Therefore, in the comparative analysis, I examine the role of the East African Community and its partners in the dual citizenship debate on the two countries.
I argue in the analysis that it is not only important to be a citizen within Kenya or Uganda but also important to discover how the issue of dual citizenship is legally interpreted within the borders of each individual nation-state. In light of this discussion, I agree with Mamdani’s definition of the nation-state as a unique form of power introduced in Africa by colonial powers between 1880 and 1940 whose outcomes can be viewed as “debris of a modernist postcolonial project, an attempt to create a centralised modern state as the bearer of Westphalia sovereignty against the background of indirect rule” (Mamdani, 1996, p. xxii). I argue that this project has impacted the citizenship debate through the adopted legal framework of post colonialism, built partly on a class system, ethnic definitions, and political affiliation. I, however, insist that the nation-state should still be a vital custodian of the citizenship debate, not in any way denying the individual the rights to identity and belonging. The question then that arises is which type of nation-state? Mamdani (1996, p. 298) asserts that the core agenda that African states faced at independence was threefold: deracialising civil society; detribalising the native authority; and developing the economy in the context of unequal international relations. Post-independence governments grappled with overcoming the citizen and subject dichotomy through either preserving the customary in the name of “defending tradition against alien encroachment or abolishing it in the name of overcoming backwardness and embracing triumphant modernism”. Kenya and Uganda are among countries that have reformed their citizenship laws attesting to Mamdani’s latter assertion.
Mamdani’s (1996) assertions on how African states continue to deal with the issue of citizenship through either the defence of tradition against subjects or abolishing it in the name of overcoming backwardness and acceptance of triumphant modernism are based on the colonial legal theory and the citizen-subject dichotomy within Africa communities. To further create a wider perspective on legal theory, I argue that those assertions above, point to the historical divergence between the republican model of citizenship, which places emphasis on political agency as envisioned in Rousseau´s social contract, as opposed to the liberal model of citizenship, which stresses the legal status and protection (Pocock, 1995).
I, therefore, compare the contexts of both Kenya and Uganda, the actors, the implications of transnationalism and post-nationalism, on the citizens, the nation-state and the region. I conclude by highlighting the shortcomings in the law reforms that allowed for dual citizenship, further demonstrating an urgent need to address issues, such as child statelessness, gender nationality laws, and the rights of dual citizens. Ethnicity, a weak nation state, and inconsistent citizenship legal reforms are closely linked to the historical factors of both countries. I further indicate the economic and political incentives that influenced the reform.
Keywords: Citizenship, dual citizenship, nation state, republicanism, liberalism, transnationalism, post-nationalism
Compound values are not universally supported in virtual machine (VM)-based programming systems and languages. However, providing data structures with value characteristics can be beneficial. On one hand, programming systems and languages can adequately represent physical quantities with compound values and avoid inconsistencies, for example, in representation of large numbers. On the other hand, just-in-time (JIT) compilers, which are often found in VMs, can rely on the fact that compound values are immutable, which is an important property in optimizing programs. Considering this, compound values have an optimization potential that can be put to use by implementing them in VMs in a way that is efficient in memory usage and execution time. Yet, optimized compound values in VMs face certain challenges: to maintain consistency, it should not be observable by the program whether compound values are represented in an optimized way by a VM; an optimization should take into account, that the usage of compound values can exhibit certain patterns at run-time; and that necessary value-incompatible properties due to implementation restrictions should be reduced.
We propose a technique to detect and compress common patterns of compound value usage at run-time to improve memory usage and execution speed. Our approach identifies patterns of frequent compound value references and introduces abbreviated forms for them. Thus, it is possible to store multiple inter-referenced compound values in an inlined memory representation, reducing the overhead of metadata and object references. We extend our approach by a notion of limited mutability, using cells that act as barriers for our approach and provide a location for shared, mutable access with the possibility of type specialization. We devise an extension to our approach that allows us to express automatic unboxing of boxed primitive data types in terms of our initial technique. We show that our approach is versatile enough to express another optimization technique that relies on values, such as Booleans, that are unique throughout a programming system. Furthermore, we demonstrate how to re-use learned usage patterns and optimizations across program runs, thus reducing the performance impact of pattern recognition.
We show in a best-case prototype that the implementation of our approach is feasible and can also be applied to general purpose programming systems, namely implementations of the Racket language and Squeak/Smalltalk. In several micro-benchmarks, we found that our approach can effectively reduce memory consumption and improve execution speed.
To achieve a sustainable energy economy, it is necessary to turn back on the combustion of fossil fuels as a means of energy production and switch to renewable sources. However, their temporal availability does not match societal consumption needs, meaning that renewably generated energy must be stored in its main generation times and allocated during peak consumption periods. Electrochemical energy storage (EES) in general is well suited due to its infrastructural independence and scalability. The lithium ion battery (LIB) takes a special place, among EES systems due to its energy density and efficiency, but the scarcity and uneven geological occurrence of minerals and ores vital for many cell components, and hence the high and fluctuating costs will decelerate its further distribution.
The sodium ion battery (SIB) is a promising successor to LIB technology, as the fundamental setup and cell chemistry is similar in the two systems. Yet, the most widespread negative electrode material in LIBs, graphite, cannot be used in SIBs, as it cannot store sufficient amounts of sodium at reasonable potentials. Hence, another carbon allotrope, non-graphitizing or hard carbon (HC) is used in SIBs. This material consists of turbostratically disordered, curved graphene layers, forming regions of graphitic stacking and zones of deviating layers, so-called internal or closed pores.
The structural features of HC have a substantial impact of the charge-potential curve exhibited by the carbon when it is used as the negative electrode in an SIB. At defects and edges an adsorption-like mechanism of sodium storage is prevalent, causing a sloping voltage curve, ill-suited for the practical application in SIBs, whereas a constant voltage plateau of relatively high capacities is found immediately after the sloping region, which recent research attributed to the deposition of quasimetallic sodium into the closed pores of HC.
Literature on the general mechanism of sodium storage in HCs and especially the role of the closed pore is abundant, but the influence of the pore geometry and chemical nature of the HC on the low-potential sodium deposition is yet in an early stage. Therefore, the scope of this thesis is to investigate these relationships using suitable synthetic and characterization methods. Materials of precisely known morphology, porosity, and chemical structure are prepared in clear distinction to commonly obtained ones and their impact on the sodium storage characteristics is observed. Electrochemical impedance spectroscopy in combination with distribution of relaxation times analysis is further established as a technique to study the sodium storage process, in addition to classical direct current techniques, and an equivalent circuit model is proposed to qualitatively describe the HC sodiation mechanism, based on the recorded data. The obtained knowledge is used to develop a method for the preparation of closed porous and non-porous materials from open porous ones, proving not only the necessity of closed pores for efficient sodium storage, but also providing a method for effective pore closure and hence the increase of the sodium storage capacity and efficiency of carbon materials.
The insights obtained and methods developed within this work hence not only contribute to the better understanding of the sodium storage mechanism in carbon materials of SIBs, but can also serve as guidance for the design of efficient electrode materials.