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Skepticism
(2022)
This dissertation offers new and original readings of three major texts in the history of Western philosophy: Descartes’s “First Meditation,” Kant’s “Transcendental Deduction,” and his “Refutation of Idealism.” The book argues that each text addresses the problem of skepticism and posits that they have a hitherto underappreciated, organic relationship to one another. The dissertation begins with an analysis of Descartes’ “First Meditation,” which I argue offers two distinct and independent skeptical arguments that differ in both aim and scope. I call these arguments the “veil of ideas” argument and the “author of my origin” argument. My reading counters the standard interpretation of the text, which sees it as offering three stages of doubt, namely the occasional fallibility of the senses, the dream hypothesis, and the evil demon hypothesis. Building on this, the central argument of the dissertation is that Kant’s “Transcendental Deduction” actually transforms and radicalizes Descartes’s Author of My Origin argument, reconceiving its meaning within the framework of Kant’s own transcendental idealist philosophy. Finally, I argue that the Refutation of Idealism offers a similarly radicalized version of Descartes’s Veil of Ideas argument, albeit translated into the framework of transcendental idealism.
Organic solar cells offer an efficient and cost-effective alternative for solar energy harvesting. This type of photovoltaic cell typically consists of a blend of two organic semiconductors, an electron donating polymer and a low molecular weight electron acceptor to create what is known as a bulk heterojunction (BHJ) morphology. Traditionally, fullerene-based acceptors have been used for this purpose. In recent years, the development of new acceptor molecules, so-called non-fullerene acceptors (NFA), has breathed new life into organic solar cell research, enabling record efficiencies close to 19%. Today, NFA-based solar cells are approaching their inorganic competitors in terms of photocurrent generation, but lag in terms of open circuit voltage (V_OC). Interestingly, the V_OC of these cells benefits from small offsets of orbital energies at the donor-NFA interface, although previous knowledge considered large energy offsets to be critical for efficient charge carrier generation. In addition, there are several other electronic and structural features that distinguish NFAs from fullerenes.
My thesis focuses on understanding the interplay between the unique attributes of NFAs and the physical processes occurring in solar cells. By combining various experimental techniques with drift-diffusion simulations, the generation of free charge carriers as well as their recombination in state-of-the-art NFA-based solar cells is characterized. For this purpose, solar cells based on the donor polymer PM6 and the NFA Y6 have been investigated. The generation of free charge carriers in PM6:Y6 is efficient and independent of electric field and excitation energy. Temperature-dependent measurements show a very low activation energy for photocurrent generation (about 6 meV), indicating barrierless charge carrier separation. Theoretical modeling suggests that Y6 molecules have large quadrupole moments, leading to band bending at the donor-acceptor interface and thereby reducing the electrostatic Coulomb dissociation barrier. In this regard, this work identifies poor extraction of free charges in competition with nongeminate recombination as a dominant loss process in PM6:Y6 devices. Subsequently, the spectral characteristics of PM6:Y6 solar cells were investigated with respect to the dominant process of charge carrier recombination. It was found that the photon emission under open-circuit conditions can be almost entirely attributed to the occupation and recombination of Y6 singlet excitons. Nevertheless, the recombination pathway via the singlet state contributes only 1% to the total recombination, which is dominated by the charge transfer state (CT-state) at the donor-acceptor interface. Further V_OC gains can therefore only be expected if the density and/or recombination rate of these CT-states can be significantly reduced. Finally, the role of energetic disorder in NFA solar cells is investigated by comparing Y6 with a structurally related derivative, named N4. Layer morphology studies combined with temperature-dependent charge transport experiments show significantly lower structural and energetic disorder in the case of the PM6:Y6 blend. For both PM6:Y6 and PM6:N4, disorder determines the maximum achievable V_OC, with PM6:Y6 benefiting from improved morphological order. Overall, the obtained findings point to avenues for the realization of NFA-based solar cells with even smaller V_OC losses. Further reduction of nongeminate recombination and energetic disorder should result in organic solar cells with efficiencies above 20% in the future.
It is estimated that data scientists spend up to 80% of the time exploring, cleaning, and transforming their data. A major reason for that expenditure is the lack of knowledge about the used data, which are often from different sources and have heterogeneous structures. As a means to describe various properties of data, metadata can help data scientists understand and prepare their data, saving time for innovative and valuable data analytics. However, metadata do not always exist: some data file formats are not capable of storing them; metadata were deleted for privacy concerns; legacy data may have been produced by systems that were not designed to store and handle meta- data. As data are being produced at an unprecedentedly fast pace and stored in diverse formats, manually creating metadata is not only impractical but also error-prone, demanding automatic approaches for metadata detection.
In this thesis, we are focused on detecting metadata in CSV files – a type of plain-text file that, similar to spreadsheets, may contain different types of content at arbitrary positions. We propose a taxonomy of metadata in CSV files and specifically address the discovery of three different metadata: line and cell type, aggregations, and primary keys and foreign keys.
Data are organized in an ad-hoc manner in CSV files, and do not follow a fixed structure, which is assumed by common data processing tools. Detecting the structure of such files is a prerequisite of extracting information from them, which can be addressed by detecting the semantic type, such as header, data, derived, or footnote, of each line or each cell. We propose the supervised- learning approach Strudel to detect the type of lines and cells. CSV files may also include aggregations. An aggregation represents the arithmetic relationship between a numeric cell and a set of other numeric cells. Our proposed AggreCol algorithm is capable of detecting aggregations of five arithmetic functions in CSV files. Note that stylistic features, such as font style and cell background color, do not exist in CSV files. Our proposed algorithms address the respective problems by using only content, contextual, and computational features.
Storing a relational table is also a common usage of CSV files. Primary keys and foreign keys are important metadata for relational databases, which are usually not present for database instances dumped as plain-text files. We propose the HoPF algorithm to holistically detect both constraints in relational databases. Our approach is capable of distinguishing true primary and foreign keys from a great amount of spurious unique column combinations and inclusion dependencies, which can be detected by state-of-the-art data profiling algorithms.
Plant metabolism is the main process of converting assimilated carbon to different crucial compounds for plant growth and therefore crop yield, which makes it an important research topic. Although major advances in understanding genetic principles contributing to metabolism and yield have been made, little is known about the genetics responsible for trait variation or canalization although the concepts have been known for a long time. In light of a growing global population and progressing climate change, understanding canalization of metabolism and yield seems ever-more important to ensure food security. Our group has recently found canalization metabolite quantitative trait loci (cmQTL) for tomato fruit metabolism, showing that the concept of canalization applies on metabolism. In this work two approaches to investigate plant metabolic canalization and one approach to investigate yield canalization are presented.
In the first project, primary and secondary metabolic data from Arabidopsis thaliana and Phaseolus vulgaris leaf material, obtained from plants grown under different conditions was used to calculate cross-environment coefficient of variations or fold-changes of metabolite levels per genotype and used as input for genome wide association studies. While primary metabolites have lower CV across conditions and show few and mostly weak associations to genomic regions, secondary metabolites have higher CV and show more, strong metabolite to genome associations. As candidate genes, both potential regulatory genes as well as metabolic genes, can be found, albeit most metabolic genes are rarely directly related to the target metabolites, suggesting a role for both potential regulatory mechanisms as well as metabolic network structure for canalization of metabolism.
In the second project, candidate genes of the Solanum lycopersicum cmQTL mapping are selected and CRISPR/Cas9-mediated gene-edited tomato lines are created, to validate the genes role in canalization of metabolism. Obtained mutants appeared to either have strong aberrant developmental phenotypes or appear wild type-like. One phenotypically inconspicuous mutant of a pantothenate kinase, selected as candidate for malic acid canalization shows a significant increase of CV across different watering conditions. Another such mutant of a protein putatively involved in amino acid transport, selected as candidate for phenylalanine canalization shows a similar tendency to increased CV without statistical significance. This potential role of two genes involved in metabolism supports the hypothesis of structural relevance of metabolism for its own stability.
In the third project, a mutant for a putative disulfide isomerase, important for thylakoid biogenesis, is characterized by a multi-omics approach. The mutant was characterized previously in a yield stability screening and showed a variegated leaf phenotype, ranging from green leaves with wild type levels of chlorophyll over differently patterned variegated to completely white leaves almost completely devoid of photosynthetic pigments. White mutant leaves show wild type transcript levels of photosystem assembly factors, with the exception of ELIP and DEG orthologs indicating a stagnation at an etioplast to chloroplast transition state. Green mutant leaves show an upregulation of these assembly factors, possibly acting as overcompensation for partially defective disulfide isomerase, which seems sufficient for proper chloroplast development as confirmed by a wild type-like proteome. Likely as a result of this phenotype, a general stress response, a shift to a sink-like tissue and abnormal thylakoid membranes, strongly alter the metabolic profile of white mutant leaves. As the severity and pattern of variegation varies from plant to plant and may be effected by external factors, the effect on yield instability, may be a cause of a decanalized ability to fully exploit the whole leaf surface area for photosynthetic activity.
The post-antiretroviral therapy era has transformed HIV into a chronic disease and non-HIV comorbidities (i.e., cardiovascular and mental diseases) are more prevalent in PLWH. The source of these non-HIV comorbidities aside from traditional risk factor include HIV infection, inflammation, distorted immune activation, burden of chronic diseases, and unhealthy lifestyle like sedentarism. Exercise is known for its beneficial effects in mental and physical health; reasons why exercise is recommended to prevent and treat difference cardiovascular and mental diseases in the general population. This cumulative thesis aimed to comprehend the relation exercise has to non-HIV comorbidities in German PLWH. Four studies were conducted to 1) understand exercise effects in cardiorespiratory fitness and muscle strength on PLWH through a systematic review and meta-analyses and 2) determine the likelihood of German PLWH developing non-HIV comorbidities, in a cross-sectional study. Meta-analytic examination indicates PLWH cardiorespiratory fitness (VO2max SMD = 0.61 ml·kg·min-1, 95% CI: 0.35-0.88, z = 4.47, p < 0.001, I2 = 50%) and strength (of remark lowerbody strength by 16.8 kg, 95% CI: 13–20.6, p< 0.001) improves after an exercise intervention in comparison to a control group. Cross-sectional data suggest exercise has a positive effect on German PLWH mental health (less anxiety and depressive symptoms) and protects against the development of anxiety (PR: 0.57, 95%IC: 0.36 – 0.91, p = 0.01) and depression (PR: 0.62, 95%IC: 0.41 – 0.94, p = 0.01). Likewise, exercise duration is related to a lower likelihood of reporting heart arrhythmias (PR: 0.20, 95%IC: 0.10 – 0.60, p < 0.01) and exercise frequency to a lower likelihood of reporting diabetes mellitus (PR: 0.40, 95%IC: 0.10 – 1, p < 0.01) in German PLWH. A preliminary recommendation for German PLWH who want to engage in exercise can be to exercise ≥ 1 time per week, at an intensity of 5 METs per session or > 103 MET·min·day-1, with a duration ≥ 150 minutes per week. Nevertheless, further research is needed to comprehend exercise dose response and protective effect for cardiovascular diseases, anxiety, and depression in German PLWH.
We live in an aging society. The change in demographic structures poses a number of challenges, including an increase in age-associated diseases. Delirium, dementia, and depression are considered to be of particular interest in the field of aging and mental health. A common theory regarding healthy aging and mental health is that the highest satisfaction and best performance is achieved when a person's abilities match the demands of their environment. In this context, the person's environment includes both the physical and the social environment. Based on this assumption, this dissertation focuses on the investigation of non-pharmacological interventions that modify environmental factors in order to facilitate the prevention and treatment of mental disorders in older patients and their caregivers. The first part of this dissertation consists of two publications and deals with the prevention of postoperative delirium in elderly patients. The PAWEL study investigated the use of a multimodal, non-pharmacological intervention in the routine care of patients aged 70 years or older undergoing elective surgery. The intervention included an interdepartmental delirium prevention team, daily use of seven manualized “best practice” procedures, structured staff training on delirium, and the adaptation of the hospital environment to the patients’ needs. The second part of the dissertation used a meta-analysis to investigate whether technology-based interventions are a suitable form of support for informal caregivers of people with dementia. Subgroup analyses were conducted to examine the effect of different types of technology on caregiver burden and depressive symptoms. The following main results were found: The PAWEL study showed that the use of a multimodal, non-pharmacological intervention resulted in a significantly lower incidence rate of postoperative delirium and reduced days with delirium in the intervention group compared to the control group. However, this difference could not be observed in the group of patients undergoing elective cardiac surgery. The results of the meta-analysis showed that technology-based interventions offer a promising alternative to traditional “face-to-face” services. Significant effect sizes could be found in relation to both the burden and the depressive symptoms of caregiving relatives. These results provide further important information on the significant impact of non-pharmacological interventions that modify environmental factors on mental health, and support the consideration of such interventions in the prevention and treatment of mental disorders in both older patients and their caregivers.
X-rays are integral to furthering our knowledge of exoplanetary systems. In this work we discuss the use of X-ray observations to understand star-planet interac- tions, mass-loss rates of an exoplanet’s atmosphere and the study of an exoplanet’s atmospheric components using future X-ray spectroscopy.
The low-mass star GJ 1151 was reported to display variable low-frequency radio emission, which is an indication of coronal star-planet interactions with an unseen exoplanet. In chapter 5 we report the first X-ray detection of GJ 1151’s corona based on XMM-Newton data. Averaged over the observation, we detect the star with a low coronal temperature of 1.6 MK and an X-ray luminosity of LX = 5.5 × 1026 erg/s. This is compatible with the coronal assumptions for a sub-Alfvénic star- planet interaction origin of the observed radio signals from this star.
In chapter 6, we aim to characterise the high-energy environment of known ex- oplanets and estimate their mass-loss rates. This work is based on the soft X-ray instrument on board the Spectrum Roentgen Gamma (SRG) mission, eROSITA, along with archival data from ROSAT, XMM-Newton, and Chandra. We use these four X-ray source catalogues to derive X-ray luminosities of exoplanet host stars in the 0.2-2 keV energy band. A catalogue of the mass-loss rates of 287 exoplan- ets is presented, with 96 of these planets characterised for the first time using new eROSITA detections. Of these first time detections, 14 are of transiting exoplanets that undergo irradiation from their host stars that is of a level known to cause ob- servable evaporation signals in other systems, making them suitable for follow-up observations.
In the next generation of space observatories, X-ray transmission spectroscopy of an exoplanet’s atmosphere will be possible, allowing for a detailed look into the atmospheric composition of these planets. In chapter 7, we model sample spectra using a toy model of an exoplanetary atmosphere to predict what exoplanet transit observations with future X-ray missions such as Athena will look like. We then estimate the observable X-ray transmission spectrum for a typical Hot Jupiter-type exoplanet, giving us insights into the advances in X-ray observations of exoplanets in the decades to come.
Weather extremes pose a persistent threat to society on multiple layers. Besides an average of ~37,000 deaths per year, climate-related disasters cause destroyed properties and impaired economic activities, eroding people's livelihoods and prosperity. While global temperature rises – caused by anthropogenic greenhouse gas emissions – the direct impacts of climatic extreme events increase and will further intensify without proper adaptation measures. Additionally, weather extremes do not only have local direct effects. Resulting economic repercussions can propagate either upstream or downstream along trade chains causing indirect effects. One approach to analyze these indirect effects within the complex global supply network is the agent-based model Acclimate. Using and extending this loss-propagation model, I focus in this thesis on three aspects of the relation between weather extremes and economic repercussions.
First, extreme weather events cause direct impacts on local economic performance. I compute daily local direct output loss time series of heat stress, river floods, tropical cyclones, and their consecutive occurrence using (near-future) climate projection ensembles. These regional impacts are estimated based on physical drivers and local productivity distribution. Direct effects of the aforementioned disaster categories are widely heterogeneous concerning regional and temporal distribution. As well, their intensity changes differently under future warming. Focusing on the hurricane-impacted capital, I find that long-term growth losses increase with higher heterogeneity of a shock ensemble.
Second, repercussions are sectorally and regionally distributed via economic ripples within the trading network, causing higher-order effects. I use Acclimate to identify three phases of those economic ripples. Furthermore, I compute indirect impacts and analyze overall regional and global production and consumption changes. Regarding heat stress, global consumer losses double while direct output losses increase by a factor 1.5 between 2000 – 2039. In my research I identify the effect of economic ripple resonance and introduce it to climate impact research. This effect occurs if economic ripples of consecutive disasters overlap, which increases economic responses such as an enhancement of consumption losses. These loss enhancements can even be more amplified with increasing direct output losses, e.g. caused by climate crises.
Transport disruptions can cause economic repercussions as well. For this, I extend the model Acclimate with a geographical transportation route and expand the decision horizon of economic agents. Using this, I show that policy-induced sudden trade restrictions (e.g. a no-deal Brexit) can significantly reduce the longer-term economic prosperity of affected regions. Analyses of transportation disruptions in typhoon seasons indicate that severely affected regions must reduce production as demand falls during a storm. Substituting suppliers may compensate for fluctuations at the beginning of the storm, which fails for prolonged disruptions.
Third, possible coping mechanisms and adaptation strategies arise from direct and indirect economic responses to weather extremes. Analyzing annual trade changes due to typhoon-induced transport disruptions depict that overall exports rise. This trade resilience increases with higher network node diversification. Further, my research shows that a basic insurance scheme may diminish hurricane-induced long-term growth losses due to faster reconstruction in disasters aftermaths. I find that insurance coverage could be an economically reasonable coping scheme towards higher losses caused by the climate crisis. Indirect effects within the global economic network from weather extremes indicate further adaptation possibilities. For one, diversifying linkages reduce the hazard of sharp price increases. Next to this, close economic interconnections with regions that do not share the same extreme weather season can be economically beneficial in the medium run. Furthermore, economic ripple resonance effects should be considered while computing costs. Overall, an increase in local adaptation measures reduces economic ripples within the trade network and possible losses elsewhere. In conclusion, adaptation measures are necessary and potential present, but it seems rather not possible to avoid all direct or indirect losses.
As I show in this thesis, dynamical modeling gives valuable insights into how direct and indirect economic impacts arise from different categories of weather extremes. Further, it highlights the importance of resolving individual extremes and reflecting amplifying effects caused by incomplete recovery or consecutive disasters.
The Greenland Ice Sheet is the second-largest mass of ice on Earth. Being almost 2000 km long, more than 700 km wide, and more than 3 km thick at the summit, it holds enough ice to raise global sea levels by 7m if melted completely. Despite its massive size, it is particularly vulnerable to anthropogenic climate change: temperatures over the Greenland Ice Sheet have increased by more than 2.7◦C in the past 30 years, twice as much as the global mean temperature. Consequently, the ice sheet has been significantly losing mass since the 1980s and the rate of loss has increased sixfold since then. Moreover, it is one of the potential tipping elements of the Earth System, which might undergo irreversible change once a warming threshold is exceeded. This thesis aims at extending the understanding of the resilience of the Greenland Ice Sheet against global warming by analyzing processes and feedbacks relevant to its centennial to multi-millennial stability using ice sheet modeling.
One of these feedbacks, the melt-elevation-feedback is driven by the temperature rise with decreasing altitudes: As the ice sheet melts, its thickness and surface elevation decrease, exposing the ice surface to warmer air and thus increasing the melt rates even further. The glacial isostatic adjustment (GIA) can partly mitigate this melt-elevation feedback as the bedrock lifts in response to an ice load decrease, forming the negative GIA feedback. In my thesis, I show that the interaction between these two competing feedbacks can lead to qualitatively different dynamical responses of the Greenland Ice Sheet to warming – from permanent loss to incomplete recovery, depending on the feedback parameters. My research shows that the interaction of those feedbacks can initiate self-sustained oscillations of the ice volume while the climate forcing remains constant.
Furthermore, the increased surface melt changes the optical properties of the snow or ice surface, e.g. by lowering their albedo, which in turn enhances melt rates – a process known as the melt-albedo feedback. Process-based ice sheet models often neglect this melt-albedo feedback. To close this gap, I implemented a simplified version of the diurnal Energy Balance Model, a computationally efficient approach that can capture the first-order effects of the melt-albedo feedback, into the Parallel Ice Sheet Model (PISM). Using the coupled model, I show in warming experiments that the melt-albedo feedback almost doubles the ice loss until the year 2300 under the low greenhouse gas emission scenario RCP2.6, compared to simulations where the melt-albedo feedback is neglected,
and adds up to 58% additional ice loss under the high emission scenario RCP8.5. Moreover, I find that the melt-albedo feedback dominates the ice loss until 2300, compared to the melt-elevation feedback.
Another process that could influence the resilience of the Greenland Ice Sheet is the warming induced softening of the ice and the resulting increase in flow. In my thesis, I show with PISM how the uncertainty in Glen’s flow law impacts the simulated response to warming. In a flow line setup at fixed climatic mass balance, the uncertainty in flow parameters leads to a range of ice loss comparable to the range caused by different warming levels.
While I focus on fundamental processes, feedbacks, and their interactions in the first three projects of my thesis, I also explore the impact of specific climate scenarios on the sea level rise contribution of the Greenland Ice Sheet. To increase the carbon budget flexibility, some warming scenarios – while still staying within the limits of the Paris Agreement – include a temporal overshoot of global warming. I show that an overshoot by 0.4◦C increases the short-term and long-term ice loss from Greenland by several centimeters. The long-term increase is driven by the warming at high latitudes, which persists even when global warming is reversed. This leads to a substantial long-term commitment of the sea level rise contribution from the Greenland Ice Sheet.
Overall, in my thesis I show that the melt-albedo feedback is most relevant for the ice loss of the Greenland Ice Sheet on centennial timescales. In contrast, the melt-elevation feedback and its interplay with the GIA feedback become increasingly relevant on millennial timescales. All of these influence the resilience of the Greenland Ice Sheet against global warming, in the near future and on the long term.
The Antarctic ice sheet is the largest freshwater reservoir worldwide. If it were to melt completely, global sea levels would rise by about 58 m. Calculation of projections of the Antarctic contribution to sea level rise under global warming conditions is an ongoing effort which
yields large ranges in predictions. Among the reasons for this are uncertainties related to the physics of ice sheet modeling. These
uncertainties include two processes that could lead to runaway ice retreat: the Marine Ice Sheet Instability (MISI), which causes rapid grounding line retreat on retrograde bedrock, and the Marine Ice Cliff Instability (MICI), in which tall ice cliffs become unstable and calve off, exposing even taller ice cliffs.
In my thesis, I investigated both marine instabilities (MISI and MICI) using the Parallel Ice Sheet Model (PISM), with a focus on MICI.