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Transitional Justice
(2022)
This publication deals with the topic of transitional justice. In six case studies, the authors link theoretical and practical implications in order to develop some innovative approaches. Their proposals might help to deal more effectively with the transition of societies, legal orders and political systems.
Young academics from various backgrounds provide fresh insights and demonstrate the relevance of the topic. The chapters analyse transitions and conflicts in Sierra Leone, Argentina, Nicaragua, Nepal, and South Sudan as well as Germany’s colonial genocide in Namibia. Thus, the book provides the reader with new insights and contributes to the ongoing debate about transitional justice.
Studies from several countries suggest that COVID-19 vaccination rates are lower among migrants compared to the general population. Urgent calls have been made to improve vaccine outreach to migrants, however, there is limited evidence on effective approaches, especially using social media. We assessed a targeted, low-cost, Facebook campaign disseminating COVID-19 vaccine information among Arabic, Turkish and Russian speakers in Germany (N = 888,994). As part of the campaign, we conducted two randomized, online experiments to assess the impact of the advertisement (1) language and (2) depicted messenger (government authority, religious leader, doctor or family). Key outcomes included reach, click-through rates, conversion rates and cost-effectiveness. Within 29 days, the campaign reached 890 thousand Facebook users. On average, 2.3 individuals accessed the advertised COVID-19 vaccination appointment tool for every euro spent on the campaign. Migrants were 2.4 (Arabic), 1.8 (Russian) and 1.2 (Turkish) times more likely to click on advertisements translated to their native language compared to German-language advertisements. Furthermore, findings showed that government representatives can be more successful in engaging migrants online compared to other messengers, despite common claims of lower trust in government institutions among migrants. This study highlights the potential of tailored, and translated, vaccination campaigns on social media for reaching migrants who may be left out by traditional media campaigns.
A decade ago, it became feasible to store multi-terabyte databases in main memory. These in-memory databases (IMDBs) profit from DRAM's low latency and high throughput as well as from the removal of costly abstractions used in disk-based systems, such as the buffer cache. However, as the DRAM technology approaches physical limits, scaling these databases becomes difficult. Non-volatile memory (NVM) addresses this challenge. This new type of memory is persistent, has more capacity than DRAM (4x), and does not suffer from its density-inhibiting limitations. Yet, as NVM has a higher latency (5-15x) and a lower throughput (0.35x), it cannot fully replace DRAM.
IMDBs thus need to navigate the trade-off between the two memory tiers. We present a solution to this optimization problem. Leveraging information about access frequencies and patterns, our solution utilizes NVM's additional capacity while minimizing the associated access costs. Unlike buffer cache-based implementations, our tiering abstraction does not add any costs when reading data from DRAM. As such, it can act as a drop-in replacement for existing IMDBs. Our contributions are as follows:
(1) As the foundation for our research, we present Hyrise, an open-source, columnar IMDB that we re-engineered and re-wrote from scratch. Hyrise enables realistic end-to-end benchmarks of SQL workloads and offers query performance which is competitive with other research and commercial systems. At the same time, Hyrise is easy to understand and modify as repeatedly demonstrated by its uses in research and teaching.
(2) We present a novel memory management framework for different memory and storage tiers. By encapsulating the allocation and access methods of these tiers, we enable existing data structures to be stored on different tiers with no modifications to their implementation. Besides DRAM and NVM, we also support and evaluate SSDs and have made provisions for upcoming technologies such as disaggregated memory.
(3) To identify the parts of the data that can be moved to (s)lower tiers with little performance impact, we present a tracking method that identifies access skew both in the row and column dimensions and that detects patterns within consecutive accesses. Unlike existing methods that have substantial associated costs, our access counters exhibit no identifiable overhead in standard benchmarks despite their increased accuracy.
(4) Finally, we introduce a tiering algorithm that optimizes the data placement for a given memory budget. In the TPC-H benchmark, this allows us to move 90% of the data to NVM while the throughput is reduced by only 10.8% and the query latency is increased by 11.6%. With this, we outperform approaches that ignore the workload's access skew and access patterns and increase the query latency by 20% or more.
Individually, our contributions provide novel approaches to current challenges in systems engineering and database research. Combining them allows IMDBs to scale past the limits of DRAM while continuing to profit from the benefits of in-memory computing.
Subdividing space through interfaces leads to many space partitions that are relevant to soft matter self-assembly. Prominent examples include cellular media, e.g. soap froths, which are bubbles of air separated by interfaces of soap and water, but also more complex partitions such as bicontinuous minimal surfaces.
Using computer simulations, this thesis analyses soft matter systems in terms of the relationship between the physical forces between the system's constituents and the structure of the resulting interfaces or partitions. The focus is on two systems, copolymeric self-assembly and the so-called Quantizer problem, where the driving force of structure formation, the minimisation of the free-energy, is an interplay of surface area minimisation and stretching contributions, favouring cells of uniform thickness.
In the first part of the thesis we address copolymeric phase formation with sharp interfaces. We analyse a columnar copolymer system "forced" to assemble on a spherical surface, where the perfect solution, the hexagonal tiling, is topologically prohibited. For a system of three-armed copolymers, the resulting structure is described by solutions of the so-called Thomson problem, the search of minimal energy configurations of repelling charges on a sphere. We find three intertwined Thomson problem solutions on a single sphere, occurring at a probability depending on the radius of the substrate.
We then investigate the formation of amorphous and crystalline structures in the Quantizer system, a particulate model with an energy functional without surface tension that favours spherical cells of equal size. We find that quasi-static equilibrium cooling allows the Quantizer system to crystallise into a BCC ground state, whereas quenching and non-equilibrium cooling, i.e. cooling at slower rates then quenching, leads to an approximately hyperuniform, amorphous state. The assumed universality of the latter, i.e. independence of energy minimisation method or initial configuration, is strengthened by our results. We expand the Quantizer system by introducing interface tension, creating a model that we find to mimic polymeric micelle systems: An order-disorder phase transition is observed with a stable Frank-Caspar phase.
The second part considers bicontinuous partitions of space into two network-like domains, and introduces an open-source tool for the identification of structures in electron microscopy images. We expand a method of matching experimentally accessible projections with computed projections of potential structures, introduced by Deng and Mieczkowski (1998). The computed structures are modelled using nodal representations of constant-mean-curvature surfaces. A case study conducted on etioplast cell membranes in chloroplast precursors establishes the double Diamond surface structure to be dominant in these plant cells. We automate the matching process employing deep-learning methods, which manage to identify structures with excellent accuracy.
While estimated numbers of past and future climate migrants are alarming, the growing empirical evidence suggests that the association between adverse climate-related events and migration is not universally positive. This dissertation seeks to advance our understanding of when and how climate migration emerges by analyzing heterogeneous climatic influences on migration in low- and middle-income countries. To this end, it draws on established economic theories of migration, datasets from physical and social sciences, causal inference techniques and approaches from systematic literature review. In three of its five chapters, I estimate causal effects of processes of climate change on inequality and migration in India and Sub-Saharan Africa. By employing interaction terms and by analyzing sub-samples of data, I explore how these relationships differ for various segments of the population. In the remaining two chapters, I present two systematic literature reviews. First, I undertake a comprehensive meta-regression analysis of the econometric climate migration literature to summarize general climate migration patterns and explain the conflicting findings. Second, motivated by the broad range of approaches in the field, I examine the literature from a methodological perspective to provide best practice guidelines for studying climate migration empirically. Overall, the evidence from this dissertation shows that climatic influences on human migration are highly heterogeneous. Whether adverse climate-related impacts materialize in migration depends on the socio-economic characteristics of the individual households, such as wealth, level of education, agricultural dependence or access to adaptation technologies and insurance. For instance, I show that while adverse climatic shocks are generally associated with an increase in migration in rural India, they reduce migration in the agricultural context of Sub-Saharan Africa, where the average wealth levels are much lower so that households largely cannot afford the upfront costs of moving. I find that unlike local climatic shocks which primarily enhance internal migration to cities and hence accelerate urbanization, shocks transmitted via agricultural producer prices increase migration to neighboring countries, likely due to the simultaneous decrease in real income in nearby urban areas. These findings advance our current understanding by showing when and how economic agents respond to climatic events, thus providing explicit contexts and mechanisms of climate change effects on migration in the future. The resulting collection of findings can guide policy interventions to avoid or mitigate any present and future welfare losses from climate change-related migration choices.
Writing travel, writing life
(2022)
The book compares the texts of three Swiss authors: Ella Maillart, Annemarie Schwarzenbach and Nicolas Bouvier. The focus is on their trip from Genève to Kabul that Ella Maillart and Annemarie Schwarzenbach made together in 1939/1940 and Nicolas Bouvier 1953/1954 with the artist Thierry Vernet. The comparison shows the strong connection between the journey and life and between ars vivendi and travel literature.
This book also gives an overview of and organises the numerous terms, genres, and categories that already exist to describe various travel texts and proposes the new term travelling narration. The travelling narration looks at the text from a narratological perspective that distinguishes the author, narrator, and protagonist within the narration.
In the examination, ten motifs could be found to characterise the travelling narration: Culture, Crossing Borders, Freedom, Time and Space, the Aesthetics of Landscapes, Writing and Reading, the Self and/as the Other, Home, Religion and Spirituality as well as the Journey. The importance of each individual motif does not only apply in the 1930s or 1950s but also transmits important findings for living together today and in the future.
Flares are magnetically driven explosions that occur in the atmospheres of all main sequence stars that possess an outer convection zone. Flaring activity is rooted in the magnetic dynamo that operates deep in the stellar interior, propagates through all layers of the atmosphere from the corona to the photosphere, and emits electromagnetic radiation from radio bands to X-ray. Eventually, this radiation, and associated eruptions of energetic particles, are ejected out into interplanetary space, where they impact planetary atmospheres, and dominate the space weather environments of young star-planet systems.
Thanks to the Kepler and the Transit Exoplanet Survey Satellite (TESS) missions, flare observations have become accessible for millions of stars and star-planet systems. The goal of this thesis is to use these flares as multifaceted messengers to understand stellar magnetism across the main sequence, investigate planetary habitability, and explore how close-in planets can affect the host star.
Using space based observations obtained by the Kepler/K2 mission, I found that flaring activity declines with stellar age, but this decline crucially depends on stellar mass and rotation. I calibrated the age of the stars in my sample using their membership in open clusters from zero age main sequence to solar age. This allowed me to reveal the rapid transition from an active, saturated flaring state to a more quiescent, inactive flaring behavior in early M dwarfs at about 600-800 Myr. This result is an important observational constraint on stellar activity evolution that I was able to de-bias using open clusters as an activity-independent age indicator.
The TESS mission quickly superseded Kepler and K2 as the main source of flares in low mass M dwarfs. Using TESS 2-minute cadence light curves, I developed a new technique for flare localization and discovered, against the commonly held belief, that flares do not occur uniformly across their stellar surface: In fast rotating fully convective stars, giant flares are preferably located at high latitudes. This bears implications for both our understanding of magnetic field emergence in these stars, and the impact on the exoplanet atmospheres: A planet that orbits in the equatorial plane of its host may be spared from the destructive effects of these poleward emitting flares.
AU Mic is an early M dwarf, and the most actively flaring planet host detected to date. Its innermost companion, AU Mic b is one of the most promising targets for a first observation of flaring star-planet interactions. In these interactions, the planet influences the star, as opposed to space weather, where the planet is always on the receiving side. The effect reflects the properties of the magnetosphere shared by planet and star, as well as the so far inaccessible magnetic properties of planets. In the about 50 days of TESS monitoring data of AU Mic, I searched for statistically robust signs of flaring interactions with AU Mic b as flares that occur in surplus of the star's intrinsic activity. I found the strongest yet still marginal signal in recurring excess flaring in phase with the orbital period of AU Mic b. If it reflects true signal, I estimate that extending the observing time by a factor of 2-3 will yield a statistically significant detection. Well within the reach of future TESS observations, this additional data may bring us closer to robustly detecting this effect than we have ever been.
This thesis demonstrates the immense scientific value of space based, long baseline flare monitoring, and the versatility of flares as a carrier of information about the magnetism of star-planet systems. Many discoveries still lay in wait in the vast archives that Kepler and TESS have produced over the years. Flares are intense spotlights into the magnetic structures in star-planet systems that are otherwise far below our resolution limits. The ongoing TESS mission, and soon PLATO, will further open the door to in-depth understanding of small and dynamic scale magnetic fields on low mass stars, and the space weather environment they effect.
Due to the major role of greenhouse gas emissions in global climate change, the development of non-fossil energy technologies is essential. Deep geothermal energy represents such an alternative, which offers promising properties such as a high base load capability and a large untapped potential. The present work addresses barite precipitation within geothermal systems and the associated reduction in rock permeability, which is a major obstacle to maintaining high efficiency. In this context, hydro-geochemical models are essential to quantify and predict the effects of precipitation on the efficiency of a system.
The objective of the present work is to quantify the induced injectivity loss using numerical and analytical reactive transport simulations. For the calculations, the fractured-porous reservoirs of the German geothermal regions North German Basin (NGB) and Upper Rhine Graben (URG) are considered.
Similar depth-dependent precipitation potentials could be determined for both investigated regions (2.8-20.2 g/m3 fluid). However, the reservoir simulations indicate that the injectivity loss due to barite deposition in the NGB is significant (1.8%-6.4% per year) and the longevity of the system is affected as a result; this is especially true for deeper reservoirs (3000 m). In contrast, simulations of URG sites indicate a minor role of barite (< 0.1%-1.2% injectivity loss per year). The key differences between the investigated regions are reservoir thicknesses and the presence of fractures in the rock, as well as the ionic strength of the fluids. The URG generally has fractured-porous reservoirs with much higher thicknesses, resulting in a greater distribution of precipitates in the subsurface. Furthermore, ionic strengths are higher in the NGB, which accelerates barite precipitation, causing it to occur more concentrated around the wellbore. The more concentrated the precipitates occur around the wellbore, the higher the injectivity loss.
In this work, a workflow was developed within which numerical and analytical models can be used to estimate and quantify the risk of barite precipitation within the reservoir of geothermal systems. A key element is a newly developed analytical scaling score that provides a reliable estimate of induced injectivity loss. The key advantage of the presented approach compared to fully coupled reservoir simulations is its simplicity, which makes it more accessible to plant operators and decision makers. Thus, in particular, the scaling score can find wide application within geothermal energy, e.g., in the search for potential plant sites and the estimation of long-term efficiency.
Climate change is one of the greatest challenges to humanity in this century, and most noticeable consequences are expected to be impacts on the water cycle – in particular the distribution and availability of water, which is fundamental for all life on Earth. In this context, it is essential to better understand where and when water is available and what processes influence variations in water storages. While estimates of the overall terrestrial water storage (TWS) variations are available from the GRACE satellites, these represent the vertically integrated signal over all water stored in ice, snow, soil moisture, groundwater and surface water bodies. Therefore, complementary observational data and hydrological models are still required to determine the partitioning of the measured signal among different water storages and to understand the underlying processes. However, the application of large-scale observational data is limited by their specific uncertainties and the incapacity to measure certain water fluxes and storages. Hydrological models, on the other hand, vary widely in their structure and process-representation, and rarely incorporate additional observational data to minimize uncertainties that arise from their simplified representation of the complex hydrologic cycle.
In this context, this thesis aims to contribute to improving the understanding of global water storage variability by combining simple hydrological models with a variety of complementary Earth observation-based data. To this end, a model-data integration approach is developed, in which the parameters of a parsimonious hydrological model are calibrated against several observational constraints, inducing GRACE TWS, simultaneously, while taking into account each data’s specific strengths and uncertainties. This approach is used to investigate 3 specific aspects that are relevant for modelling and understanding the composition of large-scale TWS variations.
The first study focusses on Northern latitudes, where snow and cold-region processes define the hydrological cycle. While the study confirms previous findings that seasonal dynamics of TWS are dominated by the cyclic accumulation and melt of snow, it reveals that inter-annual TWS variations on the contrary, are determined by variations in liquid water storages. Additionally, it is found to be important to consider the impact of compensatory effects of spatially heterogeneous hydrological variables when aggregating the contribution of different storage components over large areas. Hence, the determinants of TWS variations are scale-dependent and underlying driving mechanism cannot be simply transferred between spatial and temporal scales. These findings are supported by the second study for the global land areas beyond the Northern latitudes as well.
This second study further identifies the considerable impact of how vegetation is represented in hydrological models on the partitioning of TWS variations. Using spatio-temporal varying fields of Earth observation-based data to parameterize vegetation activity not only significantly improves model performance, but also reduces parameter equifinality and process uncertainties. Moreover, the representation of vegetation drastically changes the contribution of different water storages to overall TWS variability, emphasizing the key role of vegetation for water allocation, especially between sub-surface and delayed water storages. However, the study also identifies parameter equifinality regarding the decay of sub-surface and delayed water storages by either evapotranspiration or runoff, and thus emphasizes the need for further constraints hereof.
The third study focuses on the role of river water storage, in particular whether it is necessary to include computationally expensive river routing for model calibration and validation against the integrated GRACE TWS. The results suggest that river routing is not required for model calibration in such a global model-data integration approach, due to the larger influence other observational constraints, and the determinability of certain model parameters and associated processes are identified as issues of greater relevance. In contrast to model calibration, considering river water storage derived from routing schemes can already significantly improve modelled TWS compared to GRACE observations, and thus should be considered for model evaluation against GRACE data.
Beyond these specific findings that contribute to improved understanding and modelling of large-scale TWS variations, this thesis demonstrates the potential of combining simple modeling approaches with diverse Earth observational data to improve model simulations, overcome inconsistencies of different observational data sets, and identify areas that require further research. These findings encourage future efforts to take advantage of the increasing number of diverse global observational data.
Sustainable urban growth
(2022)
This dissertation explores the determinants for sustainable and socially optimalgrowth in a city. Two general equilibrium models establish the base for this evaluation, each adding its puzzle piece to the urban sustainability discourse and examining the role of non-market-based and market-based policies for balanced growth and welfare improvements in different theory settings. Sustainable urban growth either calls for policy actions or a green energy transition. Further, R&D market failures can pose severe challenges to the sustainability of urban growth and the social optimality of decentralized allocation decisions. Still, a careful (holistic) combination of policy instruments can achieve sustainable growth and even be first best.