Refine
Year of publication
Document Type
- Article (243)
- Doctoral Thesis (146)
- Conference Proceeding (122)
- Postprint (69)
- Working Paper (39)
- Monograph/Edited Volume (16)
- Review (15)
- Preprint (6)
- Master's Thesis (5)
- Habilitation Thesis (2)
Language
- English (665) (remove)
Keywords
- climate change (8)
- USA (7)
- United States (7)
- Arktis (6)
- moderne jüdische Geschichte (6)
- Arctic (5)
- COVID-19 (5)
- Fernerkundung (5)
- football (5)
- modern Jewish history (5)
Institute
- Extern (665) (remove)
Organisms often employ ecophysiological strategies to exploit environmental conditions and ensure bio-energetic success. However, the many complexities involved in the differential expression and flexibility of these strategies are rarely fully understood. Therefore, for the first time, using a three-part cross-disciplinary laboratory experimental analysis, we investigated the diversity and plasticity of photoresponsive traits employed by one family of environmentally contrasting, ecologically important phytoflagellates. The results demonstrated an extensive inter-species phenotypic diversity of behavioural, physiological, and compositional photoresponse across the Chlamydomonadaceae, and a multifaceted intra-species phenotypic plasticity, involving a broad range of beneficial photoacclimation strategies, often attributable to environmental predisposition and phylogenetic differentiation. Deceptively diverse and sophisticated strong (population and individual cell) behavioural photoresponses were observed, with divergence from a general preference for low light (and flexibility) dictated by intra-familial differences in typical habitat (salinity and trophy) and phylogeny. Notably, contrasting lower, narrow, and flexible compared with higher, broad, and stable preferences were observed in freshwater vs. brackish and marine species. Complex diversity and plasticity in physiological and compositional photoresponses were also discovered. Metabolic characteristics (such as growth rates, respiratory costs and photosynthetic capacity, efficiency, compensation and saturation points) varied elaborately with species, typical habitat (often varying more in eutrophic species, such as Chlamydomonas reinhardtii), and culture irradiance (adjusting to optimise energy acquisition and suggesting some propensity for low light). Considerable variations in intracellular pigment and biochemical composition were also recorded. Photosynthetic and accessory pigments (such as chlorophyll a, xanthophyll-cycle components, chlorophyll a:b and chlorophyll a:carotenoid ratios, fatty acid content and saturation ratios) varied with phylogeny and typical habitat (to attune photosystem ratios in different trophic conditions and to optimise shade adaptation, photoprotection, and thylakoid architecture, particularly in freshwater environments), and changed with irradiance (as reaction and harvesting centres adjusted to modulate absorption and quantum yield). The complex, concomitant nature of the results also advocated an integrative approach in future investigations. Overall, these nuanced, diverse, and flexible photoresponsive traits will greatly contribute to the functional ecology of these organisms, addressing environmental heterogeneity and potentially shaping individual fitness, spatial and temporal distribution, prevalence, and ecosystem dynamics.
Organisms often employ ecophysiological strategies to exploit environmental conditions and ensure bio-energetic success. However, the many complexities involved in the differential expression and flexibility of these strategies are rarely fully understood. Therefore, for the first time, using a three-part cross-disciplinary laboratory experimental analysis, we investigated the diversity and plasticity of photoresponsive traits employed by one family of environmentally contrasting, ecologically important phytoflagellates. The results demonstrated an extensive inter-species phenotypic diversity of behavioural, physiological, and compositional photoresponse across the Chlamydomonadaceae, and a multifaceted intra-species phenotypic plasticity, involving a broad range of beneficial photoacclimation strategies, often attributable to environmental predisposition and phylogenetic differentiation. Deceptively diverse and sophisticated strong (population and individual cell) behavioural photoresponses were observed, with divergence from a general preference for low light (and flexibility) dictated by intra-familial differences in typical habitat (salinity and trophy) and phylogeny. Notably, contrasting lower, narrow, and flexible compared with higher, broad, and stable preferences were observed in freshwater vs. brackish and marine species. Complex diversity and plasticity in physiological and compositional photoresponses were also discovered. Metabolic characteristics (such as growth rates, respiratory costs and photosynthetic capacity, efficiency, compensation and saturation points) varied elaborately with species, typical habitat (often varying more in eutrophic species, such as Chlamydomonas reinhardtii), and culture irradiance (adjusting to optimise energy acquisition and suggesting some propensity for low light). Considerable variations in intracellular pigment and biochemical composition were also recorded. Photosynthetic and accessory pigments (such as chlorophyll a, xanthophyll-cycle components, chlorophyll a:b and chlorophyll a:carotenoid ratios, fatty acid content and saturation ratios) varied with phylogeny and typical habitat (to attune photosystem ratios in different trophic conditions and to optimise shade adaptation, photoprotection, and thylakoid architecture, particularly in freshwater environments), and changed with irradiance (as reaction and harvesting centres adjusted to modulate absorption and quantum yield). The complex, concomitant nature of the results also advocated an integrative approach in future investigations. Overall, these nuanced, diverse, and flexible photoresponsive traits will greatly contribute to the functional ecology of these organisms, addressing environmental heterogeneity and potentially shaping individual fitness, spatial and temporal distribution, prevalence, and ecosystem dynamics.
Two-photon polymerization of hydrogels – versatile solutions to fabricate well-defined 3D structures
(2014)
Hydrogels are cross-linked water-containing polymer networks that are formed by physical, ionic or covalent interactions. In recent years, they have attracted significant attention because of their unique physical properties, which make them promising materials for numerous applications in food and cosmetic processing, as well as in drug delivery and tissue engineering. Hydrogels are highly water-swellable materials, which can considerably increase in volume without losing cohesion, are biocompatible and possess excellent tissue-like physical properties, which can mimic in vivo conditions. When combined with highly precise manufacturing technologies, such as two-photon polymerization (2PP), well-defined three-dimensional structures can be obtained. These structures can become scaffolds for selective cell-entrapping, cell/drug delivery, sensing and prosthetic implants in regenerative medicine. 2PP has been distinguished from other rapid prototyping methods because it is a non-invasive and efficient approach for hydrogel cross-linking. This review discusses the 2PP-based fabrication of 3D hydrogel structures and their potential applications in biotechnology. A brief overview regarding the 2PP methodology and hydrogel properties relevant to biomedical applications is given together with a review of the most important recent achievements in the field.
We study the time variability of emission lines in three WNE stars : WR 2 (WN2), WR 3 (WN3ha) and WR152 (WN3). While WR 2 shows no variability above the noise level, the other stars do show variation, which are like other WR stars in WR 152 but very fast in WR 3. From these motions, we deduce a value of β ∼1 for WR 3 that is like that seen in O stars and β ∼2–3 for WR 152, that is intermediate between other WR stars and WR 3.
The higher education structure in Malaysia has experienced significant changes since the implementation of the Private Higher Educational Institutions Act of 1996. The unprecedented expansion of the higher education sector and the increasing autonomy conferred to universities have created a huge demand for competent university leadership that supports the development of higher education in Malaysia. This article discusses the very first national multiplication training in Malaysia in 2014 and analyses such out-comes as the identification of good practices for future initiatives and applications in university leadership training.
A multitype Dawson-Watanabe process is conditioned, in subcritical and critical cases, on non-extinction in the remote future. On every finite time interval, its distribution is absolutely continuous with respect to the law of the unconditioned process. A martingale problem characterization is also given. Several results on the long time behavior of the conditioned mass process - the conditioned multitype Feller branching diffusion - are then proved. The general case is first considered, where the mutation matrix which models the interaction between the types, is irreducible. Several two-type models with decomposable mutation matrices are analyzed too .
The utilization of lignin as renewable electrode material for electrochemical energy storage is a sustainable approach for future batteries and supercapacitors. The composite electrode was fabricated from Kraft lignin and conductive carbon and the charge storage contribution was determined in terms of electrical double layer (EDL) and redox reactions. The important factors at play for achieving high faradaic charge storage capacity contribute to high surface area, accessibility of redox sites in lignin and their interaction with conductive additives. A thinner layer of lignin covering the high surface area of carbon facilitates the electron transfer process with a shorter pathway from the active sites of nonconductive lignin to the current collector leading to the improvement of faradaic charge storage capacity.
Composite electrodes from lignin and carbon would be even more sustainable if the fluorinated binder can be omitted. A new route to fabricate a binder-free composite electrode from Kraft lignin and high surface area carbon has been proposed by crosslinking lignin with glyoxal. A high molecular weight of lignin is obtained to enhance both electroactivity and binder capability in composite electrodes. The order of the processing step of crosslinking lignin on the composite electrode plays a crucial role in achieving a stable electrode and high charge storage capacity. The crosslinked lignin based electrodes are promising since they allow for more stable, sustainable, halogen-free and environmentally benign devices for energy storage applications. Furthermore, improvement of the amount of redox active groups (quinone groups) in lignin is useful to enhance the capacity in lithium battery applications. Direct oxidative demethylation by cerium ammonium nitrate has been carried out under mild conditions. This proves that an increase of quinone groups is able to enhance the performance of lithium battery. Thus, lignin is a promising material and could be a good candidate for application in sustainable energy storage devices.
We summarize Chandra observations of the emission line profiles from 17 OB stars. The lines tend to be broad and unshifted. The forbidden/intercombination line ratios arising from Helium-like ions provide radial distance information for the X-ray emission sources, while the H-like to He-like line ratios provide X-ray temperatures, and thus also source temperature versus radius distributions. OB stars usually show power law differential emission measure distributions versus temperature. In models of bow shocks, we find a power law differential emission measure, a wide range of ion stages, and the bow shock flow around the clumps provides transverse velocities comparable to HWHM values. We find that the bow shock results for the line profile properties, consistent with the observations of X-ray line emission for a broad range of OB star properties.
In the frame of a world fighting a dramatic global warming caused by human-related activities, research towards the development of renewable energies plays a crucial role. Solar energy is one of the most important clean energy sources and its role in the satisfaction of the global energy demand is set to increase. In this context, a particular class of materials captured the attention of the scientific community for its attractive properties: halide perovskites. Devices with perovskite as light-absorber saw an impressive development within the last decade, reaching nowadays efficiencies comparable to mature photovoltaic technologies like silicon solar cells. Yet, there are still several roadblocks to overcome before a wide-spread commercialization of this kind of devices is enabled. One of the critical points lies at the interfaces: perovskite solar cells (PSCs) are made of several layers with different chemical and physical features. In order for the device to function properly, these properties have to be well-matched.
This dissertation deals with some of the challenges related to interfaces in PSCs, with a focus on the interface between the perovskite material itself and the subsequent charge transport layer. In particular, molecular assemblies with specific properties are deposited on the perovskite surface to functionalize it. The functionalization results in energy level alignment adjustment, interfacial losses reduction, and stability improvement.
First, a strategy to tune the perovskite’s energy levels is introduced: self-assembled monolayers of dipolar molecules are used to functionalize the surface, obtaining simultaneously a shift in the vacuum level position and a saturation of the dangling bonds at the surface. A shift in the vacuum level corresponds to an equal change in work function, ionization energy, and electron affinity. The direction of the shift depends on the direction of the collective interfacial dipole. The magnitude of the shift can be tailored by controlling the deposition parameters, such as the concentration of the solution used for the deposition. The shift for different molecules is characterized by several non-invasive techniques, including in particular Kelvin probe. Overall, it is shown that it is possible to shift the perovskite energy levels in both directions by several hundreds of meV. Moreover, interesting insights on the molecules deposition dynamics are revealed.
Secondly, the application of this strategy in perovskite solar cells is explored. Devices with different perovskite compositions (“triple cation perovskite” and MAPbBr3) are prepared. The two resulting model systems present different energetic offsets at the perovskite/hole-transport layer interface. Upon tailored perovskite surface functionalization, the devices show a stabilized open circuit voltage (Voc) enhancement of approximately 60 meV on average for devices with MAPbBr3, while the impact is limited on triple-cation solar cells. This suggests that the proposed energy level tuning method is valid, but its effectiveness depends on factors such as the significance of the energetic offset compared to the other losses in the devices.
Finally, the above presented method is further developed by incorporating the ability to interact with the perovskite surface directly into a novel hole-transport material (HTM), named PFI. The HTM can anchor to the perovskite halide ions via halogen bonding (XB). Its behaviour is compared to that of another HTM (PF) with same chemical structure and properties, except for the ability of forming XB. The interaction of perovskite with PFI and PF is characterized through UV-Vis, atomic force microscopy and Kelvin probe measurements combined with simulations. Compared to PF, PFI exhibits enhanced resilience against solvent exposure and improved energy level alignment with the perovskite layer. As a consequence, devices comprising PFI show enhanced Voc and operational stability during maximum-power-point tracking, in addition to hysteresis reduction. XB promotes the formation of a high-quality interface by anchoring to the halide ions and forming a stable and ordered interfacial layer, showing to be a particularly interesting candidate for the development of tailored charge transport materials in PSCs.
Overall, the results exposed in this dissertation introduce and discuss a versatile tool to functionalize the perovskite surface and tune its energy levels. The application of this method in devices is explored and insights on its challenges and advantages are given. Within this frame, the results shed light on XB as ideal interaction for enhancing stability and efficiency in perovskite-based devices.
While a growing body of literature finds positive impacts of Start-Up Subsidies (SUS) on labor market outcomes of participants, little is known about how the design of these programs shapes their effectiveness and hence how to improve policy. As experimental variation in program design is unavailable, we exploit the 2011 reform of the current German SUS program for the unemployed which strengthened case-workers’ discretionary power, increased entry requirements and reduced monetary support. We estimate the impact of the reform on the program’s effectiveness using samples of participants and non-participants from before and after the reform. To control for time-constant unobserved heterogeneity as well as differential selection patterns based on observable characteristics over time, we combine Difference-in-Differences with inverse probability weighting using covariate balancing propensity scores. Holding participants’ observed characteristics as well as macroeconomic conditions constant, the results suggest that the reform was successful in raising employment effects on average. As these findings may be contaminated by changes in selection patterns based on unobserved characteristics, we assess our results using simulation-based sensitivity analyses and find that our estimates are highly robust to changes in unobserved characteristics. Hence, the reform most likely had a positive impact on the effectiveness of the program, suggesting that increasing entry requirements and reducing support in-creased the program’s impacts while reducing the cost per participant.
Subsidizing the geographical mobility of unemployed workers may improve welfare by relaxing their financial constraints and allowing them to find jobs in more prosperous regions. We exploit regional variation in the promotion of mobility programs along administrative borders of German employment agency districts to investigate the causal effect of offering such financial incentives on the job search behavior and labor market integration of unemployed workers. We show that promoting mobility – as intended – causes job seekers to increase their search radius, apply for and accept distant jobs. At the same time, local job search is reduced with adverse consequences for reemployment and earnings. These unintended negative effects are provoked by spatial search frictions. Overall, the unconditional provision of mobility programs harms the welfare of unemployed job seekers.
Predicting entrepreneurial development based on individual and business-related characteristics is a key objective of entrepreneurship research. In this context, we investigate whether the motives of becoming an entrepreneur influence the subsequent entrepreneurial development. In our analysis, we examine a broad range of business outcomes including survival and income, as well as job creation, expansion and innovation activities for up to 40 months after business formation. Using self-determination theory as conceptual background, we aggregate the start-up motives into a continuous motivational index. We show – based on a unique dataset of German start-ups from unemployment and non-unemployment – that the later business performance is better, the higher they score on this index. Effects are particularly strong for growth oriented outcomes like innovation and expansion activities. In a next step, we examine three underlying motivational categories that we term opportunity, career ambition, and necessity. We show that individuals driven by opportunity motives perform better in terms of innovation and business expansion activities, while career ambition is positively associated with survival, income, and the probability of hiring employees. All effects are robust to the inclusion of a large battery of covariates that are proven to be important determinants of entrepreneurial performance.
We investigate the effect of the COVID-19 pandemic on self-employed people’s mental health. Using representative longitudinal survey data from Germany, we reveal differential effects by gender: whereas self-employed women experienced a substantial deterioration in their mental health, self-employed men displayed no significant changes up to early 2021. Financial losses are important in explaining these differences. In addition, we find larger mental health responses among self-employed women who were directly affected by government-imposed restrictions and bore an increased childcare burden due to school and daycare closures. We also find that self-employed individuals who are more resilient coped better with the crisis.
We provide the first estimates of the impact of managers’ risk preferences on their training allocation decisions. Our conceptual framework links managers’ risk preferences to firms’ training decisions through the bonuses they expect to receive. Risk-averse managers are expected to select workers with low turnover risk and invest in specific rather than general training. Empirical evidence supporting these predictions is provided using a novel vignette study embedded in a nationally representative survey of firm managers. Risk-tolerant and risk-averse decision makers have significantly different training preferences. Risk aversion results in increased sensitivity to turnover risk. Managers who are risk-averse offer significantly less general training and, in some cases, are more reluctant to train workers with a history of job mobility. All managers, irrespective of their risk preferences, are sensitive to the investment risk associated with training, avoiding training that is more costly or targets those with less occupational expertise or nearing retirement. This suggests the risks of training are primarily due to the risk that trained workers will leave the firm (turnover risk) rather than the risk that the benefits of training do not outweigh the costs (investment risk).
In this paper we consider a simple syntactic extension of Answer Set Programming (ASP) for dealing with (nested) existential quantifiers and double negation in the rule bodies, in a close way to the recent proposal RASPL-1. The semantics for this extension just resorts to Equilibrium Logic (or, equivalently, to the General Theory of Stable Models), which provides a logic-programming interpretation for any arbitrary theory in the syntax of Predicate Calculus. We present a translation of this syntactic class into standard logic programs with variables (either disjunctive or normal, depending on the input rule heads), as those allowed by current ASP solvers. The translation relies on the introduction of auxiliary predicates and the main result shows that it preserves strong equivalence modulo the original signature.
Controlling interactions in synthetic polymers as precisely as in proteins would have a strong impact on polymer science. Advanced structural and functional control can lead to rational design of, integrated nano- and microstructures. To achieve this, properties of monomer sequence defined oligopeptides were exploited. Through their incorporation as monodisperse segments into synthetic polymers we learned in recent four years how to program the structure formation of polymers, to adjust and exploit interactions in such polymers, to control inorganic-organic interfaces in fiber composites and induce structure in Biomacromolecules like DNA for biomedical applications.
We introduce and discuss a number of issues that arise in the process of building a finite-state morphological analyzer for Urdu, in particular issues with potential ambiguity and non-concatenative morphology. Our approach allows for an underlyingly similar treatment of both Urdu and Hindi via a cascade of finite-state transducers that transliterates the very different scripts into a common ASCII transcription system. As this transliteration system is based on the XFST tools that the Urdu/Hindi common morphological analyzer is also implemented in, no compatibility problems arise.
The use of nano zerovalent iron (nZVI) for environmental remediation is a promising new technique for in situ remediation. Due to its high surface area and high reactivity, nZVI is able to dechlorinate organic contaminants and render them harmless. Limited mobility, due to fast aggregation and sedimentation of nZVI, limits the capability for source and plume remediation. Carbo-Iron is a newly developed material consisting of activated carbon particles (d50 = 0,8 µm) that are plated with nZVI particles. These particles combine the mobility of activated carbon and the reactivity of nZVI. This paper presents the first results of the transport experiments.
Floods continue to be the leading cause of economic damages and fatalities among natural disasters worldwide. As future climate and exposure changes are projected to intensify these damages, the need for more accurate and scalable flood risk models is rising. Over the past decade, macro-scale flood risk models have evolved from initial proof-of-concepts to indispensable tools for decision-making at global-, nationaland, increasingly, the local-level. This progress has been propelled by the advent of high-performance computing and the availability of global, space-based datasets. However, despite such advancements, these models are rarely validated and consistently fall short of the accuracy achieved by high-resolution local models. While capabilities have improved, significant gaps persist in understanding the behaviours of such macro-scale models, particularly their tendency to overestimate risk. This dissertation aims to address such gaps by examining the scale transfers inherent in the construction and application of coarse macroscale models. To achieve this, four studies are presented that, collectively, address exposure, hazard, and vulnerability components of risk affected by upscaling or downscaling.
The first study focuses on a type of downscaling where coarse flood hazard inundation grids are enhanced to a finer resolution. While such inundation downscaling has been employed in numerous global model chains, ours is the first study to focus specifically on this component, providing an evaluation of the state of the art and a novel algorithm. Findings demonstrate that our novel algorithm is eight times faster than existing methods, offers a slight improvement in accuracy, and generates more physically coherent flood maps in hydraulically challenging regions. When applied to a case study, the algorithm generated a 4m resolution inundation map from 30m hydrodynamic model outputs in 33 s, a 60-fold improvement in runtime with a 25% increase in RMSE compared with direct hydrodynamic modelling. All evaluated downscaling algorithms yielded better accuracy than the coarse hydrodynamic model when compared to observations, demonstrating similar limits of coarse hydrodynamic models reported by others. The substitution of downscaling into flood risk model chains, in place of high-resolution modelling, can drastically improve the lead time of impactbased forecasts and the efficiency of hazard map production. With downscaling, local regions could obtain high resolution local inundation maps by post-processing a global model without the need for expensive modelling or expertise.
The second study focuses on hazard aggregation and its implications for exposure, investigating implicit aggregations commonly used to intersect hazard grids with coarse exposure models. This research introduces a novel spatial classification framework to understand the effects of rescaling flood hazard grids to a coarser resolution. The study derives closed-form analytical solutions for the location and direction of bias from flood grid aggregation, showing that bias will always be present in regions near the edge of inundation. For example, inundation area will be positively biased when water depth grids are aggregated, while volume will be negatively biased when water elevation grids are aggregated. Extending the analysis to effects of hazard aggregation on building exposure, this study shows that exposure in regions at the edge of inundation are an order of magnitude more sensitive to aggregation errors than hazard alone. Among the two aggregation routines considered, averaging water surface elevation grids better preserved flood depths at buildings than averaging of water depth grids. The study provides the first mathematical proof and generalizeable treatment of flood hazard grid aggregation, demonstrating important mechanisms to help flood risk modellers understand and control model behaviour.
The final two studies focus on the aggregation of vulnerability models or flood damage functions, investigating the practice of applying per-asset functions to aggregate exposure models. Both studies extend Jensen’s inequality, a well-known 1906 mathematical proof, to demonstrate how the aggregation of flood damage functions leads to bias. Applying Jensen’s proof in this new context, results show that typically concave flood damage functions will introduce a positive bias (overestimation) when aggregated. This behaviour was further investigated with a simulation experiment including 2 million buildings in Germany, four global flood hazard simulations and three aggregation scenarios. The results show that positive aggregation bias is not distributed evenly in space, meaning some regions identified as “hot spots of risk” in assessments may in fact just be hot spots of aggregation bias. This study provides the first application of Jensen’s inequality to explain the overestimates reported elsewhere and advice for modellers to minimize such artifacts.
In total, this dissertation investigates the complex ways aggregation and disaggregation influence the behaviour of risk models, focusing on the scale-transfers underpinning macro-scale flood risk assessments. Extending a key finding of the flood hazard literature to the broader context of flood risk, this dissertation concludes that all else equal, coarse models overestimate risk. This dissertation goes beyond previous studies by providing mathematical proofs for how and where such bias emerges in aggregation routines, offering a mechanistic explanation for coarse model overestimates. It shows that this bias is spatially heterogeneous, necessitating a deep understanding of how rescaling may bias models to effectively reduce or communicate uncertainties. Further, the dissertation offers specific recommendations to help modellers minimize scale transfers in problematic regions. In conclusion, I argue that such aggregation errors are epistemic, stemming from choices in model structure, and therefore hold greater potential and impetus for study and mitigation. This deeper understanding of uncertainties is essential for improving macro-scale flood risk models and their effectiveness in equitable, holistic, and sustainable flood management.
Leadership plays an important role for the efficient and fair solution of social dilemmas but the effectiveness of a leader can vary substantially. Two main factors of leadership impact are the ability to induce high contributions by all group members and the (expected) fair use of power. Participants in our experiment decide about contributions to a public good. After all contributions are made, the leader can choose how much of the joint earnings to assign to herself; the remainder is distributed equally among the followers. Using machine learning techniques, we study whether the content of initial open statements by the group members predicts their behavior as a leader and whether groups are able to identify such clues and endogenously appoint a “good” leader to solve the dilemma. We find that leaders who promise fairness are more likely to behave fairly, and that followers appoint as leaders those who write more explicitly about fairness and efficiency. However, in their contribution decision, followers focus on the leader’s first-move contribution and place less importance on the content of the leader’s statements.
Strategic uncertainty is the uncertainty that players face with respect to the purposeful behavior of other players in an interactive decision situation. Our paper develops a new method for measuring strategic-uncertainty attitudes and distinguishing them from risk and ambiguity attitudes. We vary the source of uncertainty (whether strategic or not) across conditions in a ceteris paribus manner. We elicit certainty equivalents of participating in two strategic 2x2 games (a stag-hunt and a market-entry game) as well as certainty equivalents of related lotteries that yield the same possible payoffs with exogenously given probabilities (risk) and lotteries with unknown probabilities (ambiguity). We provide a structural model of uncertainty attitudes that allows us to measure a preference for or an aversion against the source of uncertainty, as well as optimism or pessimism regarding the desired outcome. We document systematic attitudes towards strategic uncertainty that vary across contexts. Under strategic complementarity [substitutability], the majority of participants tend to be pessimistic [optimistic] regarding the desired outcome. However, preferences for the source of uncertainty are distributed around zero.
Technological progress allows for producing ever more complex predictive models on the basis of increasingly big datasets. For risk management of natural hazards, a multitude of models is needed as basis for decision-making, e.g. in the evaluation of observational data, for the prediction of hazard scenarios, or for statistical estimates of expected damage. The question arises, how modern modelling approaches like machine learning or data-mining can be meaningfully deployed in this thematic field. In addition, with respect to data availability and accessibility, the trend is towards open data. Topic of this thesis is therefore to investigate the possibilities and limitations of machine learning and open geospatial data in the field of flood risk modelling in the broad sense. As this overarching topic is broad in scope, individual relevant aspects are identified and inspected in detail.
A prominent data source in the flood context is satellite-based mapping of inundated areas, for example made openly available by the Copernicus service of the European Union. Great expectations are directed towards these products in scientific literature, both for acute support of relief forces during emergency response action, and for modelling via hydrodynamic models or for damage estimation. Therefore, a focus of this work was set on evaluating these flood masks. From the observation that the quality of these products is insufficient in forested and built-up areas, a procedure for subsequent improvement via machine learning was developed. This procedure is based on a classification algorithm that only requires training data from a particular class to be predicted, in this specific case data of flooded areas, but not of the negative class (dry areas). The application for hurricane Harvey in Houston shows the high potential of this method, which depends on the quality of the initial flood mask.
Next, it is investigated how much the predicted statistical risk from a process-based model chain is dependent on implemented physical process details. Thereby it is demonstrated what a risk study based on established models can deliver. Even for fluvial flooding, such model chains are already quite complex, though, and are hardly available for compound or cascading events comprising torrential rainfall, flash floods, and other processes. In the fourth chapter of this thesis it is therefore tested whether machine learning based on comprehensive damage data can offer a more direct path towards damage modelling, that avoids explicit conception of such a model chain. For that purpose, a state-collected dataset of damaged buildings from the severe El Niño event 2017 in Peru is used. In this context, the possibilities of data-mining for extracting process knowledge are explored as well. It can be shown that various openly available geodata sources contain useful information for flood hazard and damage modelling for complex events, e.g. satellite-based rainfall measurements, topographic and hydrographic information, mapped settlement areas, as well as indicators from spectral data. Further, insights on damaging processes are discovered, which mainly are in line with prior expectations. The maximum intensity of rainfall, for example, acts stronger in cities and steep canyons, while the sum of rain was found more informative in low-lying river catchments and forested areas. Rural areas of Peru exhibited higher vulnerability in the presented study compared to urban areas. However, the general limitations of the methods and the dependence on specific datasets and algorithms also become obvious.
In the overarching discussion, the different methods – process-based modelling, predictive machine learning, and data-mining – are evaluated with respect to the overall research questions. In the case of hazard observation it seems that a focus on novel algorithms makes sense for future research. In the subtopic of hazard modelling, especially for river floods, the improvement of physical models and the integration of process-based and statistical procedures is suggested. For damage modelling the large and representative datasets necessary for the broad application of machine learning are still lacking. Therefore, the improvement of the data basis in the field of damage is currently regarded as more important than the selection of algorithms.
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.
Recent years witnessed a vast advent of stalagmites as palaeoclimate archives. The multitude of geochemical and physical proxies and a promise of a precise and accurate age model greatly appeal to palaeoclimatologists. Although substantial progress was made in speleothem-based palaeoclimate research and despite high-resolution records from low-latitudinal regions, proving that palaeo-environmental changes can be archived on sub-annual to millennial time scales our comprehension of climate dynamics is still fragmentary. This is in particular true for the summer monsoon system on the Indian subcontinent. The Indian summer monsoon (ISM) is an integral part of the intertropical convergence zone (ITCZ). As this rainfall belt migrates northward during boreal summer, it brings monsoonal rainfall. ISM strength depends however on a variety of factors, including snow cover in Central Asia and oceanic conditions in the Indic and Pacific. Presently, many of the factors influencing the ISM are known, though their exact forcing mechanism and mutual relations remain ambiguous. Attempts to make an accurate prediction of rainfall intensity and frequency and drought recurrence, which is extremely important for South Asian countries, resemble a puzzle game; all interaction need to fall into the right place to obtain a complete picture. My thesis aims to create a faithful picture of climate change in India, covering the last 11,000 ka. NE India represents a key region for the Bay of Bengal (BoB) branch of the ISM, as it is here where the monsoon splits into a northwestward and a northeastward directed arm. The Meghalaya Plateau is the first barrier for northward moving air masses and receives excessive summer rainfall, while the winter season is very dry. The proximity of Meghalaya to the Tibetan Plateau on the one hand and the BoB on the other hand make the study area a key location for investigating the interaction between different forcings that governs the ISM. A basis for the interpretation of palaeoclimate records, and a first important outcome of my thesis is a conceptual model which explains the observed pattern of seasonal changes in stable isotopes (d18O and d2H) in rainfall. I show that although in tropical and subtropical regions the amount effect is commonly called to explain strongly depleted isotope values during enhanced rainfall, alone it cannot account for observed rainwater isotope variability in Meghalaya. Monitoring of rainwater isotopes shows no expected negative correlation between precipitation amount and d18O of rainfall. In turn I find evidence that the runoff from high elevations carries an inherited isotopic signature into the BoB, where during the ISM season the freshwater builds a strongly depleted plume on top of the marine water. The vapor originating from this plume is likely to memorize' and transmit further very negative d18O values. The lack of data does not allow for quantication of this plume effect' on isotopes in rainfall over Meghalaya but I suggest that it varies on seasonal to millennial timescales, depending on the runoff amount and source characteristics. The focal point of my thesis is the extraction of climatic signals archived in stalagmites from NE India. High uranium concentration in the stalagmites ensured excellent age control required for successful high-resolution climate reconstructions. Stable isotope (d18O and d13C) and grey-scale data allow unprecedented insights into millennial to seasonal dynamics of the summer and winter monsoon in NE India. ISM strength (i. e. rainfall amount) is recorded in changes in d18Ostalagmites. The d13C signal, reflecting drip rate changes, renders a powerful proxy for dry season conditions, and shows similarities to temperature-related changes on the Tibetan Plateau. A sub-annual grey-scale profile supports a concept of lower drip rate and slower stalagmite growth during dry conditions. During the Holocene, ISM followed a millennial-scale decrease of insolation, with decadal to centennial failures resulting from atmospheric changes. The period of maximum rainfall and enhanced seasonality corresponds to the Holocene Thermal Optimum observed in Europe. After a phase of rather stable conditions, 4.5 kyr ago, the strengthening ENSO system dominated the ISM. Strong El Nino events weakened the ISM, especially when in concert with positive Indian Ocean dipole events. The strongest droughts of the last 11 kyr are recorded during the past 2 kyr. Using the advantage of a well-dated stalagmite record at hand I tested the application of laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) to detect sub-annual to sub-decadal changes in element concentrations in stalagmites. The development of a large ablation cell allows for ablating sample slabs of up to 22 cm total length. Each analyzed element is a potential proxy for different climatic parameters. Combining my previous results with the LAICP- MS-generated data shows that element concentration depends not only on rainfall amount and associated leaching from the soil. Additional factors, like biological activity and hydrogeochemical conditions in the soil and vadose zone can eventually affect the element content in drip water and in stalagmites. I present a theoretical conceptual model for my study site to explain how climatic signals can be transmitted and archived in stalagmite carbonate. Further, I establish a first 1500 year long element record, reconstructing rainfall variability. Additionally, I hypothesize that volcanic eruptions, producing large amounts of sulfuric acid, can influence soil acidity and hence element mobilization.
Recurrences in past climates
(2023)
Our ability to predict the state of a system relies on its tendency to recur to states it has visited before. Recurrence also pervades common intuitions about the systems we are most familiar with: daily routines, social rituals and the return of the seasons are just a few relatable examples. To this end, recurrence plots (RP) provide a systematic framework to quantify the recurrence of states. Despite their conceptual simplicity, they are a versatile tool in the study of observational data. The global climate is a complex system for which an understanding based on observational data is not only of academical relevance, but vital for the predurance of human societies within the planetary boundaries. Contextualizing current global climate change, however, requires observational data far beyond the instrumental period. The palaeoclimate record offers a valuable archive of proxy data but demands methodological approaches that adequately address its complexities. In this regard, the following dissertation aims at devising novel and further developing existing methods in the framework of recurrence analysis (RA). The proposed research questions focus on using RA to capture scale-dependent properties in nonlinear time series and tailoring recurrence quantification analysis (RQA) to characterize seasonal variability in palaeoclimate records (‘Palaeoseasonality’).
In the first part of this thesis, we focus on the methodological development of novel approaches in RA. The predictability of nonlinear (palaeo)climate time series is limited by abrupt transitions between regimes that exhibit entirely different dynamical complexity (e.g. crossing of ‘tipping points’). These possibly depend on characteristic time scales. RPs are well-established for detecting transitions and capture scale-dependencies, yet few approaches have combined both aspects. We apply existing concepts from the study of self-similar textures to RPs to detect abrupt transitions, considering the most relevant time scales. This combination of methods further results in the definition of a novel recurrence based nonlinear dependence measure. Quantifying lagged interactions between multiple variables is a common problem, especially in the characterization of high-dimensional complex systems. The proposed ‘recurrence flow’ measure of nonlinear dependence offers an elegant way to characterize such couplings. For spatially extended complex systems, the coupled dynamics of local variables result in the emergence of spatial patterns. These patterns tend to recur in time. Based on this observation, we propose a novel method that entails dynamically distinct regimes of atmospheric circulation based on their recurrent spatial patterns. Bridging the two parts of this dissertation, we next turn to methodological advances of RA for the study of Palaeoseasonality. Observational series of palaeoclimate ‘proxy’ records involve inherent limitations, such as irregular temporal sampling. We reveal biases in the RQA of time series with a non-stationary sampling rate and propose a correction scheme.
In the second part of this thesis, we proceed with applications in Palaeoseasonality. A review of common and promising time series analysis methods shows that numerous valuable tools exist, but their sound application requires adaptions to archive-specific limitations and consolidating transdisciplinary knowledge. Next, we study stalagmite proxy records from the Central Pacific as sensitive recorders of mid-Holocene El Niño-Southern Oscillation (ENSO) dynamics. The records’ remarkably high temporal resolution allows to draw links between ENSO and seasonal dynamics, quantified by RA. The final study presented here examines how seasonal predictability could play a role for the stability of agricultural societies. The Classic Maya underwent a period of sociopolitical disintegration that has been linked to drought events. Based on seasonally resolved stable isotope records from Yok Balum cave in Belize, we propose a measure of seasonal predictability. It unveils the potential role declining seasonal predictability could have played in destabilizing agricultural and sociopolitical systems of Classic Maya populations.
The methodological approaches and applications presented in this work reveal multiple exciting future research avenues, both for RA and the study of Palaeoseasonality.
The Milky Way is only one out of billions of galaxies in the universe. However, it is a special galaxy because it allows to explore the main mechanisms involved in its evolution and formation history by unpicking the system star-by-star. Especially, the chemical fingerprints of its stars provide clues and evidence of past events in the Galaxy’s lifetime. These information help not only to decipher the current structure and building blocks of the Milky Way, but to learn more about the general formation process of galaxies.
In the past decade a multitude of stellar spectroscopic Galactic surveys have scanned millions of stars far beyond the rim of the solar neighbourhood. The obtained spectroscopic information provide unprecedented insights to the chemo-dynamics of the Milky Way. In addition analytic models and numerical simulations of the Milky Way provide necessary descriptions and predictions suited for comparison with observations in order to decode the physical properties that underlie the complex system of the Galaxy.
In the thesis various approaches are taken to connect modern theoretical modelling of galaxy formation and evolution with observations from Galactic stellar surveys. With its focus on the chemo-kinematics of the Galactic disk this work aims to determine new observational constraints on the formation of the Milky Way providing also proper comparisons with two different models. These are the population synthesis model TRILEGAL based on analytical distribution functions, which aims to simulate the number and distribution of stars in the Milky Way and its different components, and a hybrid model (MCM) that combines an N-body simulation of a Milky Way like galaxy in the cosmological framework with a semi-analytic chemical evolution model for the Milky Way. The major observational data sets in use come from two surveys, namely the “Radial Velocity Experiment” (RAVE) and the “Sloan Extension for Galactic Understanding and Exploration” (SEGUE).
In the first approach the chemo-kinematic properties of the thin and thick disk of the Galaxy as traced by a selection of about 20000 SEGUE G-dwarf stars are directly compared to the predictions by the MCM model. As a necessary condition for this, SEGUE's selection function and its survey volume are evaluated in detail to correct the spectroscopic observations for their survey specific selection biases. Also, based on a Bayesian method spectro-photometric distances with uncertainties below 15% are computed for the selection of SEGUE G-dwarfs that are studied up to a distance of 3 kpc from the Sun.
For the second approach two synthetic versions of the SEGUE survey are generated based on the above models. The obtained synthetic stellar catalogues are then used to create mock samples best resembling the compiled sample of observed SEGUE G-dwarfs. Generally, mock samples are not only ideal to compare predictions from various models. They also allow validation of the models' quality and improvement as with this work could be especially achieved for TRILEGAL. While TRILEGAL reproduces the statistical properties of the thin and thick disk as seen in the observations, the MCM model has shown to be more suitable in reproducing many chemo-kinematic correlations as revealed by the SEGUE stars. However, evidence has been found that the MCM model may be missing a stellar component with the properties of the thick disk that the observations clearly show. While the SEGUE stars do indicate a thin-thick dichotomy of the stellar Galactic disk in agreement with other spectroscopic stellar studies, no sign for a distinct metal-poor disk is seen in the MCM model.
Usually stellar spectroscopic surveys are limited to a certain volume around the Sun covering different regions of the Galaxy’s disk. This often prevents to obtain a global view on the chemo-dynamics of the Galactic disk. Hence, a suitable combination of stellar samples from independent surveys is not only useful for the verification of results but it also helps to complete the picture of the Milky Way. Therefore, the thesis closes with a comparison of the SEGUE G-dwarfs and a sample of RAVE giants. The comparison reveals that the chemo-kinematic relations agree in disk regions where the samples of both surveys show a similar number of stars. For those parts of the survey volumes where one of the surveys lacks statistics they beautifully complement each other. This demonstrates that the comparison of theoretical models on the one side, and the combined observational data gathered by multiple surveys on the other side, are key ingredients to understand and disentangle the structure and formation history of the Milky Way.
Deductive databases need general formulas in rule bodies, not only conjuctions of literals. This is well known since the work of Lloyd and Topor about extended logic programming. Of course, formulas must be restricted in such a way that they can be effectively evaluated in finite time, and produce only a finite number of new tuples (in each iteration of the TP-operator: the fixpoint can still be infinite). It is also necessary to respect binding restrictions of built-in predicates: many of these predicates can be executed only when certain arguments are ground. Whereas for standard logic programming rules, questions of safety, allowedness, and range-restriction are relatively easy and well understood, the situation for general formulas is a bit more complicated. We give a syntactic analysis of formulas that guarantees the necessary properties.
Background: The COVID-19 pandemic has highlighted the importance of scientific endeavors. The goal of this systematic review is to evaluate the quality of the research on physical activity (PA) behavior change and its potential to contribute to policy-making processes in the early days of COVID-19 related restrictions.
Methods: We conducted a systematic review of methodological quality of current research according to PRISMA guidelines using Pubmed and Web of Science, of articles on PA behavior change that were published within 365 days after COVID-19 was declared a pandemic by the World Health Organization (WHO). Items from the JBI checklist and the AXIS tool were used for additional risk of bias assessment. Evidence mapping is used for better visualization of the main results. Conclusions about the significance of published articles are based on hypotheses on PA behavior change in the light of the COVID-19 pandemic.
Results: Among the 1,903 identified articles, there were 36% opinion pieces, 53% empirical studies, and 9% reviews. Of the 332 studies included in the systematic review, 213 used self-report measures to recollect prepandemic behavior in often small convenience samples. Most focused changes in PA volume, whereas changes in PA types were rarely measured. The majority had methodological reporting flaws. Few had very large samples with objective measures using repeated measure design (pre and during the pandemic). In addition to the expected decline in PA duration, these studies show that many of those who were active prepandemic, continued to be active during the pandemic.
Conclusions: Research responded quickly at the onset of the pandemic. However, most of the studies lacked robust methodology, and PA behavior change data lacked the accuracy needed to guide policy makers. To improve the field, we propose the implementation of longitudinal cohort studies by larger organizations such as WHO to ease access to data on PA behavior, and suggest those institutions set clear standards for this research. Researchers need to ensure a better fit between the measurement method and the construct being measured, and use both objective and subjective measures where appropriate to complement each other and provide a comprehensive picture of PA behavior.
Cognitive resources contribute to balance control. There is evidence that mental fatigue reduces cognitive resources and impairs balance performance, particularly in older adults and when balance tasks are complex, for example when trying to walk or stand while concurrently performing a secondary cognitive task.
We conducted a systematic literature search in PubMed (MEDLINE), Web of Science and Google Scholar to identify eligible studies and performed a random effects meta-analysis to quantify the effects of experimentally induced mental fatigue on balance performance in healthy adults. Subgroup analyses were computed for age (healthy young vs. healthy older adults) and balance task complexity (balance tasks with high complexity vs. balance tasks with low complexity) to examine the moderating effects of these factors on fatigue-mediated balance performance.
We identified 7 eligible studies with 9 study groups and 206 participants. Analysis revealed that performing a prolonged cognitive task had a small but significant effect (SMDwm = −0.38) on subsequent balance performance in healthy young and older adults. However, age- and task-related differences in balance responses to fatigue could not be confirmed statistically.
Overall, aggregation of the available literature indicates that mental fatigue generally reduces balance in healthy adults. However, interactions between cognitive resource reduction, aging and balance task complexity remain elusive.
We have analyzed the spectra of seven Galactic O4 supergiants, with the NLTE wind code CMFGEN. For all stars, we have found that clumped wind models match well lines from different species spanning a wavelength range from FUV to optical, and remain consistent with Hα data. We have achieved an excellent match of the P V λλ1118, 1128 resonance doublet and N IV λ1718, as well as He II λ4686 suggesting that our physical description of clumping is adequate. We find very small volume filling factors and that clumping starts deep in the wind, near the sonic point. The most crucial consequence of our analysis is that the mass loss rates of O stars need to be revised downward significantly, by a factor of 3 and more compared to those obtained from smooth-wind models.
Eye fixation durations during normal reading correlate with processing difficulty but the specific cognitive mechanisms reflected in these measures are not well understood. This study finds support in German readers’ eyefixations for two distinct difficulty metrics: surprisal, which reflects the change in probabilities across syntactic analyses as new words are integrated, and retrieval, which quantifies comprehension difficulty in terms of working memory constraints. We examine the predictions of both metrics using a family of dependency parsers indexed by an upper limit on the number of candidate syntactic analyses they retain at successive words. Surprisal models all fixation measures and regression probability. By contrast, retrieval does not model any measure in serial processing. As more candidate analyses are considered in parallel at each word, retrieval can account for the same measures as surprisal. This pattern suggests an important role for ranked parallelism in theories of sentence comprehension.
Parsing costs as predictors of reading difficulty: An evaluation using the Potsdam Sentence Corpus
(2008)
The surprisal of a word on a probabilistic grammar constitutes a promising complexity metric for human sentence comprehension difficulty. Using two different grammar types, surprisal is shown to have an effect on fixation durations and regression probabilities in a sample of German readers’ eye movements, the Potsdam Sentence Corpus. A linear mixed-effects model was used to quantify the effect of surprisal while taking into account unigram and bigram frequency, word length, and empirically-derived word predictability; the so-called “early” and “late” measures of processing difficulty both showed an effect of surprisal. Surprisal is also shown to have a small but statistically non-significant effect on empirically-derived predictability itself. This work thus demonstrates the importance of including parsing costs as a predictor of comprehension difficulty in models of reading, and suggests that a simple identification of syntactic parsing costs with early measures and late measures with durations of post-syntactic events may be difficult to uphold.
Strong as a hippo’s heart
(2021)
The heart is comprised of multiple tissues that contribute to its physiological functions. During development, the growth of myocardium and endocardium is coupled and morphogenetic processes within these separate tissue layers are integrated. Here, we discuss the roles of mechanosensitive Hippo signaling in growth and morphogenesis of the zebrafish heart. Hippo signaling is involved in defining numbers of cardiac progenitor cells derived from the secondary heart field, in restricting the growth of the epicardium, and in guiding trabeculation and outflow tract formation. Recent work also shows that myocardial chamber dimensions serve as a blueprint for Hippo signaling-dependent growth of the endocardium. Evidently, Hippo pathway components act at the crossroads of various signaling pathways involved in embryonic zebrafish heart development. Elucidating how biomechanical Hippo signaling guides heart morphogenesis has direct implications for our understanding of cardiac physiology and pathophysiology.
Metals are often used in environments that are conducive to corrosion, which leads to a reduction in their mechanical properties and durability. Coatings are applied to corrosion-prone metals such as aluminum alloys to inhibit the destructive surface process of corrosion in a passive or active way. Standard anticorrosive coatings function as a physical barrier between the material and the corrosive environment and provide passive protection only when intact. In contrast, active protection prevents or slows down corrosion even when the main barrier is damaged. The most effective industrially used active corrosion inhibition for aluminum alloys is provided by chromate conversion coatings. However, their toxicity and worldwide restriction provoke an urgent need for finding environmentally friendly corrosion preventing systems. A promising approach to replace the toxic chromate coatings is to embed particles containing nontoxic inhibitor in a passive coating matrix. This work presents the development and optimization of effective anticorrosive coatings for the industrially important aluminum alloy, AA2024-T3 using this approach. The protective coatings were prepared by dispersing mesoporous silica containers, loaded with the nontoxic corrosion inhibitor 2-mercaptobenzothiazole, in a passive sol-gel (SiOx/ZrOx) or organic water-based layer. Two types of porous silica containers with different sizes (d ≈ 80 and 700 nm, respectively) were investigated. The studied robust containers exhibit high surface area (≈ 1000 m² g-1), narrow pore size distribution (dpore ≈ 3 nm) and large pore volume (≈ 1 mL g-1) as determined by N2 sorption measurements. These properties favored the subsequent adsorption and storage of a relatively large amount of inhibitor as well as its release in response to pH changes induced by the corrosion process. The concentration, position and size of the embedded containers were varied to ascertain the optimum conditions for overall anticorrosion performance. Attaining high anticorrosion efficiency was found to require a compromise between delivering an optimal amount of corrosion inhibitor and preserving the coating barrier properties. This study broadens the knowledge about the main factors influencing the coating anticorrosion efficiency and assists the development of optimum active anticorrosive coatings doped with inhibitor loaded containers.
Urban pollution
(2022)
We use worldwide satellite data to analyse how population size and density affect urban pollution. We find that density significantly increases pollution exposure. Looking only at urban areas, we find that population size affects exposure more than density. Moreover, the effect is driven mostly by population commuting to core cities rather than the core city population itself. We analyse heterogeneity by geography and income levels. By and large, the influence of population on pollution is greatest in Asia and middle-income countries. A counterfactual simulation shows that PM2.5 exposure would fall by up to 36% and NO2 exposure up to 53% if within countries population size were equalized across all cities.
Property tax competition
(2022)
We develop a model of property taxation and characterize equilibria under three alternative taxa-tion regimes often used in the public finance literature: decentralized taxation, centralized taxation, and “rent seeking” regimes. We show that decentralized taxation results in inefficiently high tax rates, whereas centralized taxation yields a common optimal tax rate, and tax rates in the rent-seeking regime can be either inefficiently high or low. We quantify the effects of switching from the observed tax system to the three regimes for Japan and Germany. The decentralized or rent-seeking regime best describes the Japanese tax system, whereas the centralized regime does so for Germany. We also quantify the welfare effects of regime changes.
We study the effect of energy and transport policies on pollution in two developing country cities. We use a quantitative equilibrium model with choice of housing, energy use, residential location, transport mode, and energy technology. Pollution comes from commuting and residential energy use. The model parameters are calibrated to replicate key variables for two developing country cities, Maputo, Mozambique, and Yogyakarta, Indonesia. In the counterfactual simulations, we study how various transport and energy policies affect equilibrium pollution. Policies may be induce rebound effects from increasing residential energy use or switching to high emission modes or locations. In general, these rebound effects tend to be largest for subsidies to public transport or modern residential energy technology.
Finite state methods for natural language processing often require the construction and the intersection of several automata. In this paper, we investigate the question of determining the best order in which these intersections should be performed. We take as an example lexical disambiguation in polarity grammars. We show that there is no efficient way to minimize the state complexity of these intersections.
In the first section of the thesis graphitic carbon nitride was for the first time synthesised using the high-temperature condensation of dicyandiamide (DCDA) – a simple molecular precursor – in a eutectic salt melt of lithium chloride and potassium chloride. The extent of condensation, namely next to complete conversion of all reactive end groups, was verified by elemental microanalysis and vibrational spectroscopy. TEM- and SEM-measurements gave detailed insight into the well-defined morphology of these organic crystals, which are not based on 0D or 1D constituents like known molecular or short-chain polymeric crystals but on the packing motif of extended 2D frameworks. The proposed crystal structure of this g-C3N4 species was derived in analogy to graphite by means of extensive powder XRD studies, indexing and refinement. It is based on sheets of hexagonally arranged s-heptazine (C6N7) units that are held together by covalent bonds between C and N atoms. These sheets stack in a graphitic, staggered fashion adopting an AB-motif, as corroborated by powder X-ray diffractometry and high-resolution transmission electron microscopy. This study was contrasted with one of many popular – yet unsuccessful – approaches in the last 30 years of scientific literature to perform the condensation of an extended carbon nitride species through synthesis in the bulk. The second section expands the repertoire of available salt melts introducing the lithium bromide and potassium bromide eutectic as an excellent medium to obtain a new phase of graphitic carbon nitride. The combination of SEM, TEM, PXRD and electron diffraction reveals that the new graphitic carbon nitride phase stacks in an ABA’ motif forming unprecedentedly large crystals. This section seizes the notion of the preceding chapter, that condensation in a eutectic salt melt is the key to obtain a high degree of conversion mainly through a solvatory effect. At the close of this chapter ionothermal synthesis is seen established as a powerful tool to overcome the inherent kinetic problems of solid state reactions such as incomplete polymerisation and condensation in the bulk especially when the temperature requirement of the reaction in question falls into the proverbial “no man’s land” of classical solvents, i.e. above 250 to 300 °C. The following section puts the claim to the test, that the crystalline carbon nitrides obtained from a salt melt are indeed graphitic. A typical property of graphite – namely the accessibility of its interplanar space for guest molecules – is transferred to the graphitic carbon nitride system. Metallic potassium and graphitic carbon nitride are converted to give the potassium intercalation compound, K(C6N8)3 designated according to its stoichiometry and proposed crystal structure. Reaction of the intercalate with aqueous solvents triggers the exfoliation of the graphitic carbon nitride material and – for the first time – enables the access of singular (or multiple) carbon nitride sheets analogous to graphene as seen in the formation of sheets, bundles and scrolls of carbon nitride in TEM imaging. The thus exfoliated sheets form a stable, strongly fluorescent solution in aqueous media, which shows no sign in UV/Vis spectroscopy that the aromaticity of individual sheets was subject to degradation. The final section expands on the mechanism underlying the formation of graphitic carbon nitride by literally expanding the distance between the covalently linked heptazine units which constitute these materials. A close examination of all proposed reaction mechanisms to-date in the light of exhaustive DSC/MS experiments highlights the possibility that the heptazine unit can be formed from smaller molecules, even if some of the designated leaving groups (such as ammonia) are substituted by an element, R, which later on remains linked to the nascent heptazine. Furthermore, it is suggested that the key functional groups in the process are the triazine- (Tz) and the carbonitrile- (CN) group. On the basis of these assumptions, molecular precursors are tailored which encompass all necessary functional groups to form a central heptazine unit of threefold, planar symmetry and then still retain outward functionalities for self-propagated condensation in all three directions. Two model systems based on a para-aryl (ArCNTz) and para-biphenyl (BiPhCNTz) precursors are devised via a facile synthetic procedure and then condensed in an ionothermal process to yield the heptazine based frameworks, HBF-1 and HBF-2. Due to the structural motifs of their molecular precursors, individual sheets of HBF-1 and HBF-2 span cavities of 14.2 Å and 23.0 Å respectively which makes both materials attractive as potential organic zeolites. Crystallographic analysis confirms the formation of ABA’ layered, graphitic systems, and the extent of condensation is confirmed as next-to-perfect by elemental analysis and vibrational spectroscopy.
Background: Biological age markers are a crucial indicator whether children are decelerated in growth tempo. Skeletal maturation is the standard measure. Yet, it relies on exposing children to x-radiation. Dental eruption is a potential, but highly debated, radiation free alternative.
Objectives: We assess the interrelationship between dental eruption and other maturational markers. We hypothesize that dental age correlates with body height and skeletal age. We further evaluate how the three different variables behave in cohorts from differing social backgrounds.
Sample and Method: Dental, skeletal and height data from the 1970s to 1990s from Guatemalan boys were converted into standard deviation scores, using external references for each measurement. The boys, aged between 7 and 12, derived from different social backgrounds (middle SES (N = 6529), low-middle SES (N = 736), low SES Ladino (N = 3653) and low SES Maya (N = 4587).
Results: Dental age shows only a weak correlation with skeletal age (0.18) and height (0.2). The distinction between cohorts differs according to each of the three measurements. All cohorts differ significantly in height. In skeletal maturation, the middle SES cohort is significantly advanced compared to all other cohorts. The periodically malnourished cohorts of low SES Mayas and Ladinos are significantly delayed in dental maturation compared to the well-nourished low-middle and middle class Ladino children.
Conclusion: Dental development is an independent system, that is regulated by different mechanisms than skeletal development and growth. Tooth eruption is sensitive to nutritional status, whereas skeletal age is more sensitive to socioeconomic background.
Emotions are a complex concept and they are present in our everyday life. Persons on the autism spectrum are said to have difficulties in social interactions, showing deficits in emotion recognition in comparison to neurotypically developed persons. But social-emotional skills are believed to be positively augmented by training. A new adaptive social cognition training tool “E.V.A.” is introduced which teaches emotion recognition from face, voice and body language. One cross-sectional and one longitudinal study with adult neurotypical and autistic participants were conducted. The aim of the cross-sectional study was to characterize the two groups and see if differences in their social-emotional skills exist. The longitudinal study, on the other hand, aimed for detecting possible training effects following training with the new training tool. In addition, in both studies usability assessments were conducted to investigate the perceived usability of the new tool for neurotypical as well as autistic participants. Differences were found between autistic and neurotypical participants in their social-emotional and emotion recognition abilities. Training effects for neurotypical participants in an emotion recognition task were found after two weeks of home training. Similar perceived usability was found for the neurotypical and autistic participants. The current findings suggest that persons with ASC do not have a general deficit in emotion recognition, but are in need for more time to correctly recognize emotions. In addition, findings suggest that training emotion recognition abilities is possible. Further studies are needed to verify if the training effects found for neurotypical participants also manifest in a larger ASC sample.
The self-employed faced strong income losses during the Covid-19 pandemic. Many governments introduced programs to financially support the self-employed during the pandemic, including Germany. The German Ministry for Economic Affairs announced a €50bn emergency-aid program in March 2020, offering one-off lump-sum payments of up to €15,000 to those facing substantial revenue declines. By reassuring the self- employed that the government ‘would not let them down’ during the crisis, the program had also the important aim of motivating the self-employed to get through the crisis. We investigate whether the program affected the confidence of the self-employed to survive the crisis using real-time online-survey data comprising more than 20,000 observations. We employ propensity score matching, making use of a rich set of variables that influence the subjective survival probability as main outcome measure. We observe that this program had significant effects, with the subjective survival probability of the self- employed being moderately increased. We reveal important effect heterogeneities with respect to education, industries, and speed of payment. Notably, positive effects only occur among those self-employed whose application was processed quickly. This suggests stress-induced waiting costs due to the uncertainty associated with the administrative processing and the overall pandemic situation. Our findings have policy implications for the design of support programs, while also contributing to the literature on the instruments and effects of entrepreneurship policy interventions in crisis situations.
Background: As the number of cardiac diseases continuously increases within the last years in modern society, so does cardiac treatment, especially cardiac catheterization. The procedure of a cardiac catheterization is challenging for both patients and practitioners. Several potential stressors of psychological or physical nature can occur during the procedure. The objective of the study is to develop and implement a stress management intervention for both practitioners and patients that aims to reduce the psychological and physical strain of a cardiac catheterization.
Methods: The clinical study (DRKS00026624) includes two randomized controlled intervention trials with parallel groups, for patients with elective cardiac catheterization and practitioners at the catheterization lab, in two clinic sites of the Ernst-von-Bergmann clinic network in Brandenburg, Germany. Both groups received different interventions for stress management. The intervention for patients comprises a psychoeducational video with different stress management technics and additional a standardized medical information about the cardiac catheterization examination. The control condition includes the in hospitals practiced medical patient education before the examination (usual care). Primary and secondary outcomes are measured by physiological parameters and validated questionnaires, the day before (M1) and after (M2) the cardiac catheterization and at a postal follow-up 6 months later (M3). It is expected that people with standardized information and psychoeducation show reduced complications during cardiac catheterization procedures, better pre- and post-operative wellbeing, regeneration, mood and lower stress levels over time. The intervention for practitioners includes a Mindfulness-based stress reduction program (MBSR) over 8 weeks supervised by an experienced MBSR practitioner directly at the clinic site and an operative guideline. It is expected that practitioners with intervention show improved perceived and chronic stress, occupational health, physical and mental function, higher effort-reward balance, regeneration and quality of life. Primary and secondary outcomes are measured by physiological parameters (heart rate variability, saliva cortisol) and validated questionnaires and will be assessed before (M1) and after (M2) the MBSR intervention and at a postal follow-up 6 months later (M3). Physiological biomarkers in practitioners will be assessed before (M1) and after intervention (M2) on two work days and a two days off. Intervention effects in both groups (practitioners and patients) will be evaluated separately using multivariate variance analysis.
Discussion: This study evaluates the effectiveness of two stress management intervention programs for patients and practitioners within cardiac catheter laboratory. Study will disclose strains during a cardiac catheterization affecting both patients and practitioners. For practitioners it may contribute to improved working conditions and occupational safety, preservation of earning capacity, avoidance of participation restrictions and loss of performance. In both groups less anxiety, stress and complications before and during the procedures can be expected. The study may add knowledge how to eliminate stressful exposures and to contribute to more (psychological) security, less output losses and exhaustion during work. The evolved stress management guidelines, training manuals and the standardized patient education should be transferred into clinical routines
Since Harris’ parser in the late 50s, multiword units have been progressively integrated in parsers. Nevertheless, in the most part, they are still restricted to compound words, that are more stable and less numerous. Actually, language is full of semi-fixed expressions that also form basic semantic units: semi-fixed adverbial expressions (e.g. time), collocations. Like compounds, the identification of these structures limits the combinatorial complexity induced by lexical ambiguity. In this paper, we detail an experiment that largely integrates these notions in a finite-state procedure of segmentation into super-chunks, preliminary to a parser.We show that the chunker, developped for French, reaches 92.9% precision and 98.7% recall. Moreover, multiword units realize 36.6% of the attachments within nominal and prepositional phrases.
In the living cell, the organization of the complex internal structure relies to a large extent on molecular motors. Molecular motors are proteins that are able to convert chemical energy from the hydrolysis of adenosine triphosphate (ATP) into mechanical work. Being about 10 to 100 nanometers in size, the molecules act on a length scale, for which thermal collisions have a considerable impact onto their motion. In this way, they constitute paradigmatic examples of thermodynamic machines out of equilibrium. This study develops a theoretical description for the energy conversion by the molecular motor myosin V, using many different aspects of theoretical physics. Myosin V has been studied extensively in both bulk and single molecule experiments. Its stepping velocity has been characterized as a function of external control parameters such as nucleotide concentration and applied forces. In addition, numerous kinetic rates involved in the enzymatic reaction of the molecule have been determined. For forces that exceed the stall force of the motor, myosin V exhibits a 'ratcheting' behaviour: For loads in the direction of forward stepping, the velocity depends on the concentration of ATP, while for backward loads there is no such influence. Based on the chemical states of the motor, we construct a general network theory that incorporates experimental observations about the stepping behaviour of myosin V. The motor's motion is captured through the network description supplemented by a Markov process to describe the motor dynamics. This approach has the advantage of directly addressing the chemical kinetics of the molecule, and treating the mechanical and chemical processes on equal grounds. We utilize constraints arising from nonequilibrium thermodynamics to determine motor parameters and demonstrate that the motor behaviour is governed by several chemomechanical motor cycles. In addition, we investigate the functional dependence of stepping rates on force by deducing the motor's response to external loads via an appropriate Fokker-Planck equation. For substall forces, the dominant pathway of the motor network is profoundly different from the one for superstall forces, which leads to a stepping behaviour that is in agreement with the experimental observations. The extension of our analysis to Markov processes with absorbing boundaries allows for the calculation of the motor's dwell time distributions. These reveal aspects of the coordination of the motor's heads and contain direct information about the backsteps of the motor. Our theory provides a unified description for the myosin V motor as studied in single motor experiments.
We present a new analysis of illocutionary forces in dialogue. We analyze them as complex conversational moves involving two dimensions: what Speaker commits herself to and what she calls on Addressee to perform. We start from the analysis of speech acts such as confirmation requests or whimperatives, and extend the analysis to seemingly simple speech acts, such as statements and queries. Then, we show how to integrate our proposal in the framework of the Grammar for Conversation (Ginzburg, to app.), which is adequate for modelling agents' information states and how they get updated.
In the most abstract definition of its operational semantics, the declarative and concurrent programming language CHR is trivially non-terminating for a significant class of programs. Common refinements of this definition, in closing the gap to real-world implementations, compromise on declarativity and/or concurrency. Building on recent work and the notion of persistent constraints, we introduce an operational semantics avoiding trivial non-termination without compromising on its essential features.