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Institute
- Institut für Biochemie und Biologie (731)
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Additive manufacturing (AM) processes enable the production of metal structures with exceptional design freedom, of which laser powder bed fusion (PBF-LB) is one of the most common. In this process, a laser melts a bed of loose feedstock powder particles layer-by-layer to build a structure with the desired geometry. During fabrication, the repeated melting and rapid, directional solidification create large temperature gradients that generate large thermal stress. This thermal stress can itself lead to cracking or delamination during fabrication. More often, large residual stresses remain in the final part as a footprint of the thermal stress. This residual stress can cause premature distortion or even failure of the part in service. Hence, knowledge of the residual stress field is critical for both process optimization and structural integrity.
Diffraction-based techniques allow the non-destructive characterization of the residual stress fields. However, such methods require a good knowledge of the material of interest, as certain assumptions must be made to accurately determine residual stress. First, the measured lattice plane spacings must be converted to lattice strains with the knowledge of a strain-free material state. Second, the measured lattice strains must be related to the macroscopic stress using Hooke's law, which requires knowledge of the stiffness of the material. Since most crystal structures exhibit anisotropic material behavior, the elastic behavior is specific to each lattice plane of the single crystal. Thus, the use of individual lattice planes in monochromatic diffraction residual stress analysis requires knowledge of the lattice plane-specific elastic properties. In addition, knowledge of the microstructure of the material is required for a reliable assessment of residual stress.
This work presents a toolbox for reliable diffraction-based residual stress analysis. This is presented for a nickel-based superalloy produced by PBF-LB. First, this work reviews the existing literature in the field of residual stress analysis of laser-based AM using diffraction-based techniques. Second, the elastic and plastic anisotropy of the nickel-based superalloy Inconel 718 produced by PBF-LB is studied using in situ energy dispersive synchrotron X-ray and neutron diffraction techniques. These experiments are complemented by ex situ material characterization techniques. These methods establish the relationship between the microstructure and texture of the material and its elastic and plastic anisotropy. Finally, surface, sub-surface, and bulk residual stress are determined using a texture-based approach. Uncertainties of different methods for obtaining stress-free reference values are discussed.
The tensile behavior in the as-built condition is shown to be controlled by texture and cellular sub-grain structure, while in the heat-treated condition the precipitation of strengthening phases and grain morphology dictate the behavior. In fact, the results of this thesis show that the diffraction elastic constants depend on the underlying microstructure, including texture and grain morphology. For columnar microstructures in both as-built and heat-treated conditions, the diffraction elastic constants are best described by the Reuss iso-stress model. Furthermore, the low accumulation of intergranular strains during deformation demonstrates the robustness of using the 311 reflection for the diffraction-based residual stress analysis with columnar textured microstructures. The differences between texture-based and quasi-isotropic approaches for the residual stress analysis are shown to be insignificant in the observed case. However, the analysis of the sub-surface residual stress distributions show, that different scanning strategies result in a change in the orientation of the residual stress tensor. Furthermore, the location of the critical sub-surface tensile residual stress is related to the surface roughness and the microstructure. Finally, recommendations are given for the diffraction-based determination and evaluation of residual stress in textured additively manufactured alloys.
The European Water Framework Directive (WFD) has identified river morphological alteration and diffuse pollution as the two main pressures affecting water bodies in Europe at the catchment scale. Consequently, river restoration has become a priority to achieve the WFD's objective of good ecological status. However, little is known about the effects of stream morphological changes, such as re-meandering, on in-stream nitrate retention at the river network scale. Therefore, catchment nitrate modeling is necessary to guide the implementation of spatially targeted and cost-effective mitigation measures. Meanwhile, Germany, like many other regions in central Europe, has experienced consecutive summer droughts from 2015-2018, resulting in significant changes in river nitrate concentrations in various catchments. However, the mechanistic exploration of catchment nitrate responses to changing weather conditions is still lacking.
Firstly, a fully distributed, process-based catchment Nitrate model (mHM-Nitrate) was used, which was properly calibrated and comprehensively evaluated at numerous spatially distributed nitrate sampling locations. Three calibration schemes were designed, taking into account land use, stream order, and mean nitrate concentrations, and they varied in spatial coverage but used data from the same period (2011–2019). The model performance for discharge was similar among the three schemes, with Nash-Sutcliffe Efficiency (NSE) scores ranging from 0.88 to 0.92. However, for nitrate concentrations, scheme 2 outperformed schemes 1 and 3 when compared to observed data from eight gauging stations. This was likely because scheme 2 incorporated a diverse range of data, including low discharge values and nitrate concentrations, and thus provided a better representation of within-catchment heterogenous. Therefore, the study suggests that strategically selecting gauging stations that reflect the full range of within-catchment heterogeneity is more important for calibration than simply increasing the number of stations.
Secondly, the mHM-Nitrate model was used to reveal the causal relations between sequential droughts and nitrate concentration in the Bode catchment (3200 km2) in central Germany, where stream nitrate concentrations exhibited contrasting trends from upstream to downstream reaches. The model was evaluated using data from six gauging stations, reflecting different levels of runoff components and their associated nitrate-mixing from upstream to downstream. Results indicated that the mHM-Nitrate model reproduced dynamics of daily discharge and nitrate concentration well, with Nash-Sutcliffe Efficiency ≥ 0.73 for discharge and Kling-Gupta Efficiency ≥ 0.50 for nitrate concentration at most stations. Particularly, the spatially contrasting trends of nitrate concentration were successfully captured by the model. The decrease of nitrate concentration in the lowland area in drought years (2015-2018) was presumably due to (1) limited terrestrial export loading (ca. 40% lower than that of normal years 2004-2014), and (2) increased in-stream retention efficiency (20% higher in summer within the whole river network). From a mechanistic modelling perspective, this study provided insights into spatially heterogeneous flow and nitrate dynamics and effects of sequential droughts, which shed light on water-quality responses to future climate change, as droughts are projected to be more frequent.
Thirdly, this study investigated the effects of stream restoration via re-meandering on in-stream nitrate retention at network-scale in the well-monitored Bode catchment. The mHM-Nitrate model showed good performance in reproducing daily discharge and nitrate concentrations, with median Kling-Gupta values of 0.78 and 0.74, respectively. The mean and standard deviation of gross nitrate retention efficiency, which accounted for both denitrification and assimilatory uptake, were 5.1 ± 0.61% and 74.7 ± 23.2% in winter and summer, respectively, within the stream network. The study found that in the summer, denitrification rates were about two times higher in lowland sub-catchments dominated by agricultural lands than in mountainous sub-catchments dominated by forested areas, with median ± SD of 204 ± 22.6 and 102 ± 22.1 mg N m-2 d-1, respectively. Similarly, assimilatory uptake rates were approximately five times higher in streams surrounded by lowland agricultural areas than in those in higher-elevation, forested areas, with median ± SD of 200 ± 27.1 and 39.1 ± 8.7 mg N m-2 d-1, respectively. Therefore, restoration strategies targeting lowland agricultural areas may have greater potential for increasing nitrate retention. The study also found that restoring stream sinuosity could increase net nitrate retention efficiency by up to 25.4 ± 5.3%, with greater effects seen in small streams. These results suggest that restoration efforts should consider augmenting stream sinuosity to increase nitrate retention and decrease nitrate concentrations at the catchment scale.
Climate change fundamentally transforms glaciated high-alpine regions, with well-known cryospheric and hydrological implications, such as accelerating glacier retreat, transiently increased runoff, longer snow-free periods and more frequent and intense summer rainstorms. These changes affect the availability and transport of sediments in high alpine areas by altering the interaction and intensity of different erosion processes and catchment properties.
Gaining insight into the future alterations in suspended sediment transport by high alpine streams is crucial, given its wide-ranging implications, e.g. for flood damage potential, flood hazard in downstream river reaches, hydropower production, riverine ecology and water quality. However, the current understanding of how climate change will impact suspended sediment dynamics in these high alpine regions is limited. For one, this is due to the scarcity of measurement time series that are long enough to e.g. infer trends. On the other hand, it is difficult – if not impossible – to develop process-based models, due to the complexity and multitude of processes involved in high alpine sediment dynamics. Therefore, knowledge has so far been confined to conceptual models (which do not facilitate deriving concrete timings or magnitudes for individual catchments) or qualitative estimates (‘higher export in warmer years’) that may not be able to capture decreases in sediment export. Recently, machine-learning approaches have gained in popularity for modeling sediment dynamics, since their black box nature tailors them to the problem at hand, i.e. relatively well-understood input and output data, linked by very complex processes.
Therefore, the overarching aim of this thesis is to estimate sediment export from the high alpine Ötztal valley in Tyrol, Austria, over decadal timescales in the past and future – i.e. timescales relevant to anthropogenic climate change. This is achieved by informing, extending, evaluating and applying a quantile regression forest (QRF) approach, i.e. a nonparametric, multivariate machine-learning technique based on random forest.
The first study included in this thesis aimed to understand present sediment dynamics, i.e. in the period with available measurements (up to 15 years). To inform the modeling setup for the two subsequent studies, this study identified the most important predictors, areas within the catchments and time periods. To that end, water and sediment yields from three nested gauges in the upper Ötztal, Vent, Sölden and Tumpen (98 to almost 800 km² catchment area, 930 to 3772 m a.s.l.) were analyzed for their distribution in space, their seasonality and spatial differences therein, and the relative importance of short-term events. The findings suggest that the areas situated above 2500 m a.s.l., containing glacier tongues and recently deglaciated areas, play a pivotal role in sediment generation across all sub-catchments. In contrast, precipitation events were relatively unimportant (on average, 21 % of annual sediment yield was associated to precipitation events). Thus, the second and third study focused on the Vent catchment and its sub-catchment above gauge Vernagt (11.4 and 98 km², 1891 to 3772 m a.s.l.), due to their higher share of areas above 2500 m. Additionally, they included discharge, precipitation and air temperature (as well as their antecedent conditions) as predictors.
The second study aimed to estimate sediment export since the 1960s/70s at gauges Vent and Vernagt. This was facilitated by the availability of long records of the predictors, discharge, precipitation and air temperature, and shorter records (four and 15 years) of turbidity-derived sediment concentrations at the two gauges. The third study aimed to estimate future sediment export until 2100, by applying the QRF models developed in the second study to pre-existing precipitation and temperature projections (EURO-CORDEX) and discharge projections (physically-based hydroclimatological and snow model AMUNDSEN) for the three representative concentration pathways RCP2.6, RCP4.5 and RCP8.5.
The combined results of the second and third study show overall increasing sediment export in the past and decreasing export in the future. This suggests that peak sediment is underway or has already passed – unless precipitation changes unfold differently than represented in the projections or changes in the catchment erodibility prevail and override these trends. Despite the overall future decrease, very high sediment export is possible in response to precipitation events. This two-fold development has important implications for managing sediment, flood hazard and riverine ecology.
This thesis shows that QRF can be a very useful tool to model sediment export in high-alpine areas. Several validations in the second study showed good performance of QRF and its superiority to traditional sediment rating curves – especially in periods that contained high sediment export events, which points to its ability to deal with threshold effects. A technical limitation of QRF is the inability to extrapolate beyond the range of values represented in the training data. We assessed the number and severity of such out-of-observation-range (OOOR) days in both studies, which showed that there were few OOOR days in the second study and that uncertainties associated with OOOR days were small before 2070 in the third study. As the pre-processed data and model code have been made publically available, future studies can easily test further approaches or apply QRF to further catchments.
Large parts of the Earth’s interior are inaccessible to direct observation, yet global geodynamic processes are governed by the physical material properties under extreme pressure and temperature conditions. It is therefore essential to investigate the deep Earth’s physical properties through in-situ laboratory experiments. With this goal in mind, the optical properties of mantle minerals at high pressure offer a unique way to determine a variety of physical properties, in a straight-forward, reproducible, and time-effective manner, thus providing valuable insights into the physical processes of the deep Earth. This thesis focusses on the system Mg-Fe-O, specifically on the optical properties of periclase (MgO) and its iron-bearing variant ferropericlase ((Mg,Fe)O), forming a major planetary building block. The primary objective is to establish links between physical material properties and optical properties. In particular the spin transition in ferropericlase, the second-most abundant phase of the lower mantle, is known to change the physical material properties. Although the spin transition region likely extends down to the core-mantle boundary, the ef-fects of the mixed-spin state, where both high- and low-spin state are present, remains poorly constrained.
In the studies presented herein, we show how optical properties are linked to physical properties such as electrical conductivity, radiative thermal conductivity and viscosity. We also show how the optical properties reveal changes in the chemical bonding. Furthermore, we unveil how the chemical bonding, the optical and other physical properties are affected by the iron spin transition. We find opposing trends in the pres-sure dependence of the refractive index of MgO and (Mg,Fe)O. From 1 atm to ~140 GPa, the refractive index of MgO decreases by ~2.4% from 1.737 to 1.696 (±0.017). In contrast, the refractive index of (Mg0.87Fe0.13)O (Fp13) and (Mg0.76Fe0.24)O (Fp24) ferropericlase increases with pressure, likely because Fe Fe interactions between adjacent iron sites hinder a strong decrease of polarizability, as it is observed with increasing density in the case of pure MgO. An analysis of the index dispersion in MgO (decreasing by ~23% from 1 atm to ~103 GPa) reflects a widening of the band gap from ~7.4 eV at 1 atm to ~8.5 (±0.6) eV at ~103 GPa. The index dispersion (between 550 and 870 nm) of Fp13 reveals a decrease by a factor of ~3 over the spin transition range (~44–100 GPa). We show that the electrical band gap of ferropericlase significantly widens up to ~4.7 eV in the mixed spin region, equivalent to an increase by a factor of ~1.7. We propose that this is due to a lower electron mobility between adjacent Fe2+ sites of opposite spin, explaining the previously observed low electrical conductivity in the mixed spin region. From the study of absorbance spectra in Fp13, we show an increasing covalency of the Fe-O bond with pressure for high-spin ferropericlase, whereas in the low-spin state a trend to a more ionic nature of the Fe-O bond is observed, indicating a bond weakening effect of the spin transition. We found that the spin transition is ultimately caused by both an increase of the ligand field-splitting energy and a decreasing spin-pairing energy of high-spin Fe2+.
The reliance on fossil fuels has resulted in an abnormal increase in the concentration of greenhouse gases, contributing to the global climate crisis. In response, a rapid transition to renewable energy sources has begun, particularly lithium-ion batteries, playing a crucial role in the green energy transformation. However, concerns regarding the availability and geopolitical implications of lithium have prompted the exploration of alternative rechargeable battery systems, such as sodium-ion batteries. Sodium is significantly abundant and more homogeneously distributed in the crust and seawater, making it easier and less expensive to extract than lithium. However, because of the mysterious nature of its components, sodium-ion batteries are not yet sufficiently advanced to take the place of lithium-ion batteries. Specifically, sodium exhibits a more metallic character and a larger ionic radius, resulting in a different ion storage mechanism utilized in lithium-ion batteries. Innovations in synthetic methods, post-treatments, and interface engineering clearly demonstrate the significance of developing high-performance carbonaceous anode materials for sodium-ion batteries. The objective of this dissertation is to present a systematic approach for fabricating efficient, high-performance, and sustainable carbonaceous anode materials for sodium-ion batteries. This will involve a comprehensive investigation of different chemical environments and post-modification techniques as well.
This dissertation focuses on three main objectives. Firstly, it explores the significance of post-synthetic methods in designing interfaces. A conformal carbon nitride coating is deposited through chemical vapor deposition on a carbon electrode as an artificial solid-electrolyte interface layer, resulting in improved electrochemical performance. The interaction between the carbon nitride artificial interface and the carbon electrode enhances initial Coulombic efficiency, rate performance, and total capacity. Secondly, a novel process for preparing sulfur-rich carbon as a high-performing anode material for sodium-ion batteries is presented. The method involves using an oligo-3,4-ethylenedioxythiophene precursor for high sulfur content hard carbon anode to investigate the sulfur heteroatom effect on the electrochemical sodium storage mechanism. By optimizing the condensation temperature, a significant transformation in the materials’ nanostructure is achieved, leading to improved electrochemical performance. The use of in-operando small-angle X-ray scattering provides valuable insights into the interaction between micropores and sodium ions during the electrochemical processes. Lastly, the development of high-capacity hard carbon, derived from 5-hydroxymethyl furfural, is examined. This carbon material exhibits exceptional performance at both low and high current densities. Extensive electrochemical and physicochemical characterizations shed light on the sodium storage mechanism concerning the chemical environment, establishing the material’s stability and potential applications in sodium-ion batteries.
The evaluation of process-oriented cognitive theories through time-ordered observations is crucial for the advancement of cognitive science. The findings presented herein integrate insights from research on eye-movement control and sentence comprehension during reading, addressing challenges in modeling time-ordered data, statistical inference, and interindividual variability. Using kernel density estimation and a pseudo-marginal likelihood for fixation durations and locations, a likelihood implementation of the SWIFT model of eye-movement control during reading (Engbert et al., Psychological Review, 112, 2005, pp. 777–813) is proposed. Within the broader framework of data assimilation, Bayesian parameter inference with adaptive Markov Chain Monte Carlo techniques is facilitated for reliable model fitting. Across the different studies, this framework has shown to enable reliable parameter recovery from simulated data and prediction of experimental summary statistics. Despite its complexity, SWIFT can be fitted within a principled Bayesian workflow, capturing interindividual differences and modeling experimental effects on reading across different geometrical alterations of text. Based on these advancements, the integrated dynamical model SEAM is proposed, which combines eye-movement control, a traditionally psychological research area, and post-lexical language processing in the form of cue-based memory retrieval (Lewis & Vasishth, Cognitive Science, 29, 2005, pp. 375–419), typically the purview of psycholinguistics. This proof-of-concept integration marks a significant step forward in natural language comprehension during reading and suggests that the presented methodology can be useful to develop complex cognitive dynamical models that integrate processes at levels of perception, higher cognition, and (oculo-)motor control. These findings collectively advance process-oriented cognitive modeling and highlight the importance of Bayesian inference, individual differences, and interdisciplinary integration for a holistic understanding of reading processes. Implications for theory and methodology, including proposals for model comparison and hierarchical parameter inference, are briefly discussed.
The origin and structure of magnetic fields in the Galaxy are largely unknown. What is known is that they are essential for several astrophysical processes, in particular the propagation of cosmic rays. Our ability to describe the propagation of cosmic rays through the Galaxy is severely limited by the lack of observational data needed to probe the structure of the Galactic magnetic field on many different length scales. This is particularly true for modelling the propagation of cosmic rays into the Galactic halo, where our knowledge of the magnetic field is particularly poor.
In the last decade, observations of the Galactic halo in different frequency regimes have revealed the existence of out-of-plane bubble emission in the Galactic halo. In gamma rays these bubbles have been termed Fermi bubbles with a radial extent of ≈ 3 kpc and an azimuthal height of ≈ 6 kpc. The radio counterparts of the Fermi bubbles were seen by both the S-PASS telescopes and the Planck satellite, and showed a clear spatial overlap. The X-ray counterparts of the Fermi bubbles were named eROSITA bubbles after the eROSITA satellite, with a radial width of ≈ 7 kpc and an azimuthal height of ≈ 14 kpc. Taken together, these observations suggest the presence of large extended Galactic Halo Bubbles (GHB) and have stimulated interest in exploring the less explored Galactic halo.
In this thesis, a new toy model (GHB model) for the magnetic field and non-thermal electron distribution in the Galactic halo has been proposed. The new toy model has been used to produce polarised synchrotron emission sky maps. Chi-square analysis was used to compare the synthetic skymaps with the Planck 30 GHz polarised skymaps. The obtained constraints on the strength and azimuthal height were found to be in agreement with the S-PASS radio observations.
The upper, lower and best-fit values obtained from the above chi-squared analysis were used to generate three separate toy models. These three models were used to propagate ultra-high energy cosmic rays. This study was carried out for two potential sources, Centaurus A and NGC 253, to produce magnification maps and arrival direction skymaps. The simulated arrival direction skymaps were found to be consistent with the hotspots of Centaurus A and NGC 253 as seen in the observed arrival direction skymaps provided by the Pierre Auger Observatory (PAO).
The turbulent magnetic field component of the GHB model was also used to investigate the extragalactic dipole suppression seen by PAO. UHECRs with an extragalactic dipole were forward-tracked through the turbulent GHB model at different field strengths. The suppression in the dipole due to the varying diffusion coefficient from the simulations was noted. The results could also be compared with an analytical analogy of electrostatics. The simulations of the extragalactic dipole suppression were in agreement with similar studies carried out for galactic cosmic rays.
Organic solar cells (OSCs) represent a new generation of solar cells with a range of captivating attributes including low-cost, light-weight, aesthetically pleasing appearance, and flexibility. Different from traditional silicon solar cells, the photon-electron conversion in OSCs is usually accomplished in an active layer formed by blending two kinds of organic molecules (donor and acceptor) with different energy levels together.
The first part of this thesis focuses on a better understanding of the role of the energetic offset and each recombination channel on the performance of these low-offset OSCs. By combining advanced experimental techniques with optical and electrical simulation, the energetic offsets between CT and excitons, several important insights were achieved: 1. The short circuit current density and fill-factor of low-offset systems are largely determined by field-dependent charge generation in such low-offset OSCs. Interestingly, it is strongly evident that such field-dependent charge generation originates from a field-dependent exciton dissociation yield. 2. The reduced energetic offset was found to be accompanied by strongly enhanced bimolecular recombination coefficient, which cannot be explained solely by exciton repopulation from CT states. This implies the existence of another dark decay channel apart from CT.
The second focus of the thesis was on the technical perspective. In this thesis, the influence of optical artifacts in differential absorption spectroscopy upon the change of sample configuration and active layer thickness was studied. It is exemplified and discussed thoroughly and systematically in terms of optical simulations and experiments, how optical artifacts originated from non-uniform carrier profile and interference can manipulate not only the measured spectra, but also the decay dynamics in various measurement conditions. In the end of this study, a generalized methodology based on an inverse optical transfer matrix formalism was provided to correct the spectra and decay dynamics manipulated by optical artifacts.
Overall, this thesis paves the way for a deeper understanding of the keys toward higher PCEs in low-offset OSC devices, from the perspectives of both device physics and characterization techniques.
It is a well-attested finding in head-initial languages that individuals with aphasia (IWA) have greater difficulties in comprehending object-extracted relative clauses (ORCs) as compared to subject-extracted relative clauses (SRCs). Adopting the linguistically based approach of Relativized Minimality (RM; Rizzi, 1990, 2004), the subject-object asymmetry is attributed to the occurrence of a Minimality effect in ORCs due to reduced processing capacities in IWA (Garraffa & Grillo, 2008; Grillo, 2008, 2009). For ORCs, it is claimed that the embedded subject intervenes in the syntactic dependency between the moved object and its trace, resulting in greater processing demands. In contrast, no such intervener is present in SRCs. Based on the theoretical framework of RM and findings from language acquisition (Belletti et al., 2012; Friedmann et al., 2009), it is assumed that Minimality effects are alleviated when the moved object and the intervening subject differ in terms of relevant syntactic features. For German, the language under investigation, the RM approach predicts that number (i.e., singular vs. plural) and the lexical restriction [+NP] feature (i.e., lexically restricted determiner phrases vs. lexically unrestricted pronouns) are considered relevant in the computation of Minimality. Greater degrees of featural distinctiveness are predicted to result in more facilitated processing of ORCs, because IWA can more easily distinguish between the moved object and the intervener.
This cumulative dissertation aims to provide empirical evidence on the validity of the RM approach in accounting for comprehension patterns during relative clause (RC) processing in German-speaking IWA. For that purpose, I conducted two studies including visual-world eye-tracking experiments embedded within an auditory referent-identification task to study the offline and online processing of German RCs. More specifically, target sentences were created to evaluate (a) whether IWA demonstrate a subject-object asymmetry, (b) whether dissimilarity in the number and/or the [+NP] features facilitates ORC processing, and (c) whether sentence processing in IWA benefits from greater degrees of featural distinctiveness. Furthermore, by comparing RCs disambiguated through case marking (at the relative pronoun or the following noun phrase) and number marking (inflection of the sentence-final verb), it was possible to consider the role of the relative position of the disambiguation point. The RM approach predicts that dissimilarity in case should not affect the occurrence of Minimality effects. However, the case cue to sentence interpretation appears earlier within RCs than the number cue, which may result in lower processing costs in case-disambiguated RCs compared to number-disambiguated RCs.
In study I, target sentences varied with respect to word order (SRC vs. ORC) and dissimilarity in the [+NP] feature (lexically restricted determiner phrase vs. pronouns as embedded element). Moreover, by comparing the impact of these manipulations in case- and number-disambiguated RCs, the effect of dissimilarity in the number feature was explored. IWA demonstrated a subject-object asymmetry, indicating the occurrence of a Minimality effect in ORCs. However, dissimilarity neither in the number feature nor in the [+NP] feature alone facilitated ORC processing. Instead, only ORCs involving distinct specifications of both the number and the [+NP] features were well comprehended by IWA. In study II, only temporarily ambiguous ORCs disambiguated through case or number marking were investigated, while controlling for varying points of disambiguation. There was a slight processing advantage of case marking as cue to sentence interpretation as compared to number marking.
Taken together, these findings suggest that the RM approach can only partially capture empirical data from German IWA. In processing complex syntactic structures, IWA are susceptible to the occurrence of the intervening subject in ORCs. The new findings reported in the thesis show that structural dissimilarity can modulate sentence comprehension in aphasia. Interestingly, IWA can override Minimality effects in ORCs and derive correct sentence meaning if the featural specifications of the constituents are maximally different, because they can more easily distinguish the moved object and the intervening subject given their reduced processing capacities. This dissertation presents new scientific knowledge that highlights how the syntactic theory of RM helps to uncover selective effects of morpho-syntactic features on sentence comprehension in aphasia, emphasizing the close link between assumptions from theoretical syntax and empirical research.
The biosecurity individual
(2024)
Discoveries in biomedicine and biotechnology, especially in diagnostics, have made prevention and (self)surveillance increasingly important in the context of health practices. Frederike Offizier offers a cultural critique of the intersection between health, security and identity, and explores how the focus on risk and security changes our understanding of health and transforms our relationship to our bodies. Analyzing a wide variety of texts, from life writing to fiction, she offers a critical intervention on how this shift in the medical gaze produces new paradigms of difference and new biomedically facilitated identities: biosecurity individuals.
To manage tabular data files and leverage their content in a given downstream task, practitioners often design and execute complex transformation pipelines to prepare them. The complexity of such pipelines stems from different factors, including the nature of the preparation tasks, often exploratory or ad-hoc to specific datasets; the large repertory of tools, algorithms, and frameworks that practitioners need to master; and the volume, variety, and velocity of the files to be prepared. Metadata plays a fundamental role in reducing this complexity: characterizing a file assists end users in the design of data preprocessing pipelines, and furthermore paves the way for suggestion, automation, and optimization of data preparation tasks.
Previous research in the areas of data profiling, data integration, and data cleaning, has focused on extracting and characterizing metadata regarding the content of tabular data files, i.e., about the records and attributes of tables. Content metadata are useful for the latter stages of a preprocessing pipeline, e.g., error correction, duplicate detection, or value normalization, but they require a properly formed tabular input. Therefore, these metadata are not relevant for the early stages of a preparation pipeline, i.e., to correctly parse tables out of files. In this dissertation, we turn our focus to what we call the structure of a tabular data file, i.e., the set of characters within a file that do not represent data values but are required to parse and understand the content of the file. We provide three different approaches to represent file structure, an explicit representation based on context-free grammars; an implicit representation based on file-wise similarity; and a learned representation based on machine learning.
In our first contribution, we use the grammar-based representation to characterize a set of over 3000 real-world csv files and identify multiple structural issues that let files deviate from the csv standard, e.g., by having inconsistent delimiters or containing multiple tables. We leverage our learnings about real-world files and propose Pollock, a benchmark to test how well systems parse csv files that have a non-standard structure, without any previous preparation. We report on our experiments on using Pollock to evaluate the performance of 16 real-world data management systems.
Following, we characterize the structure of files implicitly, by defining a measure of structural similarity for file pairs. We design a novel algorithm to compute this measure, which is based on a graph representation of the files' content. We leverage this algorithm and propose Mondrian, a graphical system to assist users in identifying layout templates in a dataset, classes of files that have the same structure, and therefore can be prepared by applying the same preparation pipeline.
Finally, we introduce MaGRiTTE, a novel architecture that uses self-supervised learning to automatically learn structural representations of files in the form of vectorial embeddings at three different levels: cell level, row level, and file level. We experiment with the application of structural embeddings for several tasks, namely dialect detection, row classification, and data preparation efforts estimation.
Our experimental results show that structural metadata, either identified explicitly on parsing grammars, derived implicitly as file-wise similarity, or learned with the help of machine learning architectures, is fundamental to automate several tasks, to scale up preparation to large quantities of files, and to provide repeatable preparation pipelines.
The landscape of software self-adaptation is shaped in accordance with the need to cost-effectively achieve and maintain (software) quality at runtime and in the face of dynamic operation conditions. Optimization-based solutions perform an exhaustive search in the adaptation space, thus they may provide quality guarantees. However, these solutions render the attainment of optimal adaptation plans time-intensive, thereby hindering scalability. Conversely, deterministic rule-based solutions yield only sub-optimal adaptation decisions, as they are typically bound by design-time assumptions, yet they offer efficient processing and implementation, readability, expressivity of individual rules supporting early verification. Addressing the quality-cost trade-of requires solutions that simultaneously exhibit the scalability and cost-efficiency of rulebased policy formalism and the optimality of optimization-based policy formalism as explicit artifacts for adaptation. Utility functions, i.e., high-level specifications that capture system objectives, support the explicit treatment of quality-cost trade-off. Nevertheless, non-linearities, complex dynamic architectures, black-box models, and runtime uncertainty that makes the prior knowledge obsolete are a few of the sources of uncertainty and subjectivity that render the elicitation of utility non-trivial.
This thesis proposes a twofold solution for incremental self-adaptation of dynamic architectures. First, we introduce Venus, a solution that combines in its design a ruleand an optimization-based formalism enabling optimal and scalable adaptation of dynamic architectures. Venus incorporates rule-like constructs and relies on utility theory for decision-making. Using a graph-based representation of the architecture, Venus captures rules as graph patterns that represent architectural fragments, thus enabling runtime extensibility and, in turn, support for dynamic architectures; the architecture is evaluated by assigning utility values to fragments; pattern-based definition of rules and utility enables incremental computation of changes on the utility that result from rule executions, rather than evaluating the complete architecture, which supports scalability. Second, we introduce HypeZon, a hybrid solution for runtime coordination of multiple off-the-shelf adaptation policies, which typically offer only partial satisfaction of the quality and cost requirements. Realized based on meta-self-aware architectures, HypeZon complements Venus by re-using existing policies at runtime for balancing the quality-cost trade-off.
The twofold solution of this thesis is integrated in an adaptation engine that leverages state- and event-based principles for incremental execution, therefore, is scalable for large and dynamic software architectures with growing size and complexity. The utility elicitation challenge is resolved by defining a methodology to train utility-change prediction models. The thesis addresses the quality-cost trade-off in adaptation of dynamic software architectures via design-time combination (Venus) and runtime coordination (HypeZon) of rule- and optimization-based policy formalisms, while offering supporting mechanisms for optimal, cost-effective, scalable, and robust adaptation. The solutions are evaluated according to a methodology that is obtained based on our systematic literature review of evaluation in self-healing systems; the applicability and effectiveness of the contributions are demonstrated to go beyond the state-of-the-art in coverage of a wide spectrum of the problem space for software self-adaptation.
Sigmund Freud, the founder of psychoanalysis, began his intellectual life with the Jewish Bible and also ended it with it. He began by reading the Philippson Bible together, especially with his father Jacob Freud, and ended by studying the figure of Moses. This study systematically traces this preoccupation and shows that the Jewish Bible was a constant reference for Freud and determined his Jewish identity. This is shown by analysing family documents, religious instruction and references to the Bible in Freud's writings and correspondence.
The Central Andean region is characterized by diverse climate zones with sharp transitions between them. In this work, the area of interest is the South-Central Andes in northwestern Argentina that borders with Bolivia and Chile. The focus is the observation of soil moisture and water vapour with Global Navigation Satellite System (GNSS) remote-sensing methodologies. Because of the rapid temporal and spatial variations of water vapour and moisture circulations, monitoring this part of the hydrological cycle is crucial for understanding the mechanisms that control the local climate. Moreover, GNSS-based techniques have previously shown high potential and are appropriate for further investigation. This study includes both logistic-organization effort and data analysis. As for the prior, three GNSS ground stations were installed in remote locations in northwestern Argentina to acquire observations, where there was no availability of third-party data.
The methodological development for the observation of the climate variables of soil moisture and water vapour is independent and relies on different approaches. The soil-moisture estimation with GNSS reflectometry is an approximation that has demonstrated promising results, but it has yet to be operationally employed. Thus, a more advanced algorithm that exploits more observations from multiple satellite constellations was developed using data from two pilot stations in Germany. Additionally, this algorithm was slightly modified and used in a sea-level measurement campaign. Although the objective of this application is not related to monitoring hydrological parameters, its methodology is based on the same principles and helps to evaluate the core algorithm. On the other hand, water-vapour monitoring with GNSS observations is a well-established technique that is utilized operationally. Hence, the scope of this study is conducting a meteorological analysis by examining the along-the-zenith air-moisture levels and introducing indices related to the azimuthal gradient.
The results of the experiments indicate higher-quality soil moisture observations with the new algorithm. Furthermore, the analysis using the stations in northwestern Argentina illustrates the limits of this technology because of varying soil conditions and shows future research directions. The water-vapour analysis points out the strong influence of the topography on atmospheric moisture circulation and rainfall generation. Moreover, the GNSS time series allows for the identification of seasonal signatures, and the azimuthal-gradient indices permit the detection of main circulation pathways.
The remarkable antifouling properties of zwitterionic polymers in controlled environments are often counteracted by their delicate mechanical stability. In order to improve the mechanical stabilities of zwitterionic hydrogels, the effect of increased crosslinker densities was thus explored. In a first approach, terpolymers of zwitterionic monomer 3-[N -2(methacryloyloxy)ethyl-N,N-dimethyl]ammonio propane-1-sulfonate (SPE), hydrophobic monomer butyl methacrylate (BMA), and photo-crosslinker 2-(4-benzoylphenoxy)ethyl methacrylate (BPEMA) were synthesized. Thin hydrogel coatings of the copolymers were then produced and photo-crosslinked. Studies of the swollen hydrogel films showed that not only the mechanical stability but also, unexpectedly, the antifouling properties were improved by the presence of hydrophobic BMA units in the terpolymers.
Based on the positive results shown by the amphiphilic terpolymers and in order to further test the impact that hydrophobicity has on both the antifouling properties of zwitterionic hydrogels and on their mechanical stability, a new amphiphilic zwitterionic methacrylic monomer, 3-((2-(methacryloyloxy)hexyl)dimethylammonio)propane-1-sulfonate (M1), was synthesized in good yields in a multistep synthesis. Homopolymers of M1 were obtained by free-radical polymerization. Similarly, terpolymers of M1, zwitterionic monomer SPE, and photo-crosslinker BPEMA were synthesized by free-radical copolymerization and thoroughly characterized, including its solubilities in selected solvents.
Also, a new family of vinyl amide zwitterionic monomomers, namely 3-(dimethyl(2-(N -vinylacetamido)ethyl)ammonio)propane-1-sulfonate (M2), 4-(dimethyl(2-(N-vinylacetamido)ethyl)ammonio)butane-1-sulfonate (M3), and 3-(dimethyl(2-(N-vinylacetamido)ethyl)ammonio)propyl sulfate (M4), together with the new photo-crosslinker 4-benzoyl-N-vinylbenzamide (M5) that is well-suited for copolymerization with vinylamides, are introduced within the scope of the present work. The monomers are synthesized with good yields developing a multistep synthesis. Homopolymers of the new vinyl amide zwitterionic monomers are obtained by free-radical polymerization and thoroughly characterized. From the solubility tests, it is remarkable that the homopolymers produced are fully soluble in water, evidence of their high hydrophilicity. Copolymerization of the vinyl amide zwitterionic monomers, M2, M3, and M4 with the vinyl amide photo-crosslinker M5 proved to require very specific polymerization conditions. Nevertheless, copolymers were successfully obtained by free-radical copolymerization under appropriate conditions.
Moreover, in an attempt to mitigate the intrinsic hydrophobicity introduced in the copolymers by the photo-crosslinkers, and based on the proven affinity of quaternized diallylamines to copolymerize with vinyl amides, a new quaternized diallylamine sulfobetaine photo-crosslinker 3-(diallyl(2-(4-benzoylphenoxy)ethyl)ammonio)propane-1-sulfonate (M6) is synthesized. However, despite a priori promising copolymerization suitability, copolymerization with the vinyl amide zwitterionic monomers could not be achieved.
Efficiently managing large state is a key challenge for data management systems. Traditionally, state is split into fast but volatile state in memory for processing and persistent but slow state on secondary storage for durability. Persistent memory (PMem), as a new technology in the storage hierarchy, blurs the lines between these states by offering both byte-addressability and low latency like DRAM as well persistence like secondary storage. These characteristics have the potential to cause a major performance shift in database systems.
Driven by the potential impact that PMem has on data management systems, in this thesis we explore their use of PMem. We first evaluate the performance of real PMem hardware in the form of Intel Optane in a wide range of setups. To this end, we propose PerMA-Bench, a configurable benchmark framework that allows users to evaluate the performance of customizable database-related PMem access. Based on experimental results obtained with PerMA-Bench, we discuss findings and identify general and implementation-specific aspects that influence PMem performance and should be considered in future work to improve PMem-aware designs. We then propose Viper, a hybrid PMem-DRAM key-value store. Based on PMem-aware access patterns, we show how to leverage PMem and DRAM efficiently to design a key database component. Our evaluation shows that Viper outperforms existing key-value stores by 4–18x for inserts while offering full data persistence and achieving similar or better lookup performance. Next, we show which changes must be made to integrate PMem components into larger systems. By the example of stream processing engines, we highlight limitations of current designs and propose a prototype engine that overcomes these limitations. This allows our prototype to fully leverage PMem's performance for its internal state management. Finally, in light of Optane's discontinuation, we discuss how insights from PMem research can be transferred to future multi-tier memory setups by the example of Compute Express Link (CXL).
Overall, we show that PMem offers high performance for state management, bridging the gap between fast but volatile DRAM and persistent but slow secondary storage. Although Optane was discontinued, new memory technologies are continuously emerging in various forms and we outline how novel designs for them can build on insights from existing PMem research.
Actin is one of the most highly conserved proteins in eukaryotes and distinct actin-related proteins with filament-forming properties are even found in prokaryotes. Due to these commonalities, actin-modulating proteins of many species share similar structural properties and proposed functions. The polymerization and depolymerization of actin are critical processes for a cell as they can contribute to shape changes to adapt to its environment and to move and distribute nutrients and cellular components within the cell. However, to what extent functions of actin-binding proteins are conserved between distantly related species, has only been addressed in a few cases. In this work, functions of Coronin-A (CorA) and Actin-interacting protein 1 (Aip1), two proteins involved in actin dynamics, were characterized. In addition, the interchangeability and function of Aip1 were investigated in two phylogenetically distant model organisms. The flowering plant Arabidopsis thaliana (encoding two homologs, AIP1-1 and AIP1-2) and in the amoeba Dictyostelium discoideum (encoding one homolog, DdAip1) were chosen because the functions of their actin cytoskeletons may differ in many aspects. Functional analyses between species were conducted for AIP1 homologs as flowering plants do not harbor a CorA gene.
In the first part of the study, the effect of four different mutation methods on the function of Coronin-A protein and the resulting phenotype in D. discoideum was revealed in two genetic knockouts, one RNAi knockdown and a sudden loss-of-function mutant created by chemical-induced dislocation (CID). The advantages and disadvantages of the different mutation methods on the motility, appearance and development of the amoebae were investigated, and the results showed that not all observed properties were affected with the same intensity. Remarkably, a new combination of Selection-Linked Integration and CID could be established.
In the second and third parts of the thesis, the exchange of Aip1 between plant and amoeba was carried out. For A. thaliana, the two homologs (AIP1-1 and AIP1-2) were analyzed for functionality as well as in D. discoideum. In the Aip1-deficient amoeba, rescue with AIP1-1 was more effective than with AIP1-2. The main results in the plant showed that in the aip1-2 mutant background, reintroduced AIP1-2 displayed the most efficient rescue and A. thaliana AIP1-1 rescued better than DdAip1. The choice of the tagging site was important for the function of Aip1 as steric hindrance is a problem. The DdAip1 was less effective when tagged at the C-terminus, while the plant AIP1s showed mixed results depending on the tag position. In conclusion, the foreign proteins partially rescued phenotypes of mutant plants and mutant amoebae, despite the organisms only being very distantly related in evolutionary terms.
Relativistic pair beams produced in the cosmic voids by TeV gamma rays from blazars are expected to produce a detectable GeV-scale cascade emission missing in the observations. The suppression of this secondary cascade implies either the deflection of the pair beam by intergalactic magnetic fields (IGMFs) or an energy loss of the beam due to the electrostatic beam-plasma instability. IGMF of femto-Gauss strength is sufficient to significantly deflect the pair beams reducing the flux of secondary cascade below the observational limits. A similar flux reduction may result in the absence of the IGMF from the beam energy loss by the instability before the inverse Compton cooling. This dissertation consists of two studies about the instability role in the evolution of blazar-induced beams.
Firstly, we investigated the effect of sub-fG level IGMF on the beam energy loss by the instability. Considering IGMF with correlation lengths smaller than a few kpc, we found that such fields increase the transverse momentum of the pair beam particles, dramatically reducing the linear growth rate of the electrostatic instability and hence the energy-loss rate of the pair beam. Our results show that the IGMF eliminates beam plasma instability as an effective energy-loss agent at a field strength three orders of magnitude below that needed to suppress the secondary cascade emission by magnetic deflection. For intermediate-strength IGMF, we do not know a viable process to explain the observed absence of GeV-scale cascade emission and hence can be excluded.
Secondly, we probed how the beam-plasma instability feeds back on the beam, using a realistic two-dimensional beam distribution. We found that the instability broadens the beam opening angles significantly without any significant energy loss, thus confirming a recent feedback study on a simplified one-dimensional beam distribution. However, narrowing diffusion feedback of the beam particles with Lorentz factors less than 1e6 might become relevant even though initially it is negligible. Finally, when considering the continuous creation of TeV pairs, we found that the beam distribution and the wave spectrum reach a new quasi-steady state, in which the scattering of beam particles persists and the beam opening angle may increase by a factor of hundreds. This new intrinsic scattering of the cascade can result in time delays of around ten years, thus potentially mimicking the IGMF deflection. Understanding the implications on the GeV cascade emission requires accounting for inverse Compton cooling and simulating the beam-plasma system at different points in the IGM.
The icosahedral non-hydrostatic large eddy model (ICON-LEM) was applied around the drift track of the Multidisciplinary Observatory Study of the Arctic (MOSAiC) in 2019 and 2020. The model was set up with horizontal grid-scales between 100m and 800m on areas with radii of 17.5km and 140 km. At its lateral boundaries, the model was driven by analysis data from the German Weather Service (DWD), downscaled by ICON in limited area mode (ICON-LAM) with horizontal grid-scale of 3 km.
The aim of this thesis was the investigation of the atmospheric boundary layer near the surface in the central Arctic during polar winter with a high-resolution mesoscale model. The default settings in ICON-LEM prevent the model from representing the exchange processes in the Arctic boundary layer in accordance to the MOSAiC observations. The implemented sea-ice scheme in ICON does not include a snow layer on sea-ice, which causes a too slow response of the sea-ice surface temperature to atmospheric changes. To allow the sea-ice surface to respond faster to changes in the atmosphere, the implemented sea-ice parameterization in ICON was extended with an adapted heat capacity term.
The adapted sea-ice parameterization resulted in better agreement with the MOSAiC observations. However, the sea-ice surface temperature in the model is generally lower than observed due to biases in the downwelling long-wave radiation and the lack of complex surface structures, like leads. The large eddy resolving turbulence closure yielded a better representation of the lower boundary layer under strongly stable stratification than the non-eddy-resolving turbulence closure. Furthermore, the integration of leads into the sea-ice surface reduced the overestimation of the sensible heat flux for different weather conditions.
The results of this work help to better understand boundary layer processes in the central Arctic during the polar night. High-resolving mesoscale simulations are able to represent temporally and spatially small interactions and help to further develop parameterizations also for the application in regional and global models.