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The aim of this work was the generation of carbon materials with high surface area, exhibiting a hierarchical pore system in the macro- and mesorange. Such a pore system facilitates the transport through the material and enhances the interaction with the carbon matrix (macropores are pores with diameters > 50 nm, mesopores between 2 – 50 nm). Thereto, new strategies for the synthesis of novel carbon materials with designed porosity were developed that are in particular useful for the storage of energy. Besides the porosity, it is the graphene structure itself that determines the properties of a carbon material. Non-graphitic carbon materials usually exhibit a quite large degree of disorder with many defects in the graphene structure, and thus exhibit inherent microporosity (d < 2nm). These pores are traps and oppose reversible interaction with the carbon matrix. Furthermore they reduce the stability and conductivity of the carbon material, which was undesired for the proposed applications. As one part of this work, the graphene structures of different non-graphitic carbon materials were studied in detail using a novel wide-angle x-ray scattering model that allowed precise information about the nature of the carbon building units (graphene stacks). Different carbon precursors were evaluated regarding their potential use for the synthesis shown in this work, whereas mesophase pitch proved to be advantageous when a less disordered carbon microstructure is desired. By using mesophase pitch as carbon precursor, two templating strategies were developed using the nanocasting approach. The synthesized (monolithic) materials combined for the first time the advantages of a hierarchical interconnected pore system in the macro- and mesorange with the advantages of mesophase pitch as carbon precursor. In the first case, hierarchical macro- / mesoporous carbon monoliths were synthesized by replication of hard (silica) templates. Thus, a suitable synthesis procedure was developed that allowed the infiltration of the template with the hardly soluble carbon precursor. In the second case, hierarchical macro- / mesoporous carbon materials were synthesized by a novel soft-templating technique, taking advantage of the phase separation (spinodal decomposition) between mesophase pitch and polystyrene. The synthesis also allowed the generation of monolithic samples and incorporation of functional nanoparticles into the material. The synthesized materials showed excellent properties as an anode material in lithium batteries and support material for supercapacitors.
Dieser Aufsatz verknüpft die komparative Wohlfahrtsforschung mit der traditionellen Einstellungsforschung auf Basis von quantitativen empirischen Erhebungen. Inhalt: 1 Wohlfahrtskonzepte 1.1 Die Zieldimension 1.2 Die Wohlfahrtsproduzenten 2 Determinanten von Unterstützungen 2.1 Die Makroebene: Wohlfahrtsregimetypologien 2.2 Die Mikroebene: Interessen und Werte 3 Operationalisierung 3.1 Wohlfahrtskonzepte 3.2 Indikatoren der Mikroebene 4 Unterstützungspotentiale für die Wohlfahrtskonzepte 4.1 Die Unterstützung des Egalitarismus-Etatismus 4.1.1 Innerstaatliche Einstellungsvariationen 4.1.2 Zwischenstaatliche Einstellungsvariation 4.2 Die Unterstützung des Funktionalismus 4.3 Die Unterstützung des Investitionsprinzips 4.4 Die Unterstützung des reinen Leistungsprinzips 4.5 Die Unterstützung des Familialismus 4.6 Die Unterstützung des Chancengleichheit-Marktliberalismus-Index 4.7 Kompositionen von Wohlfahrtskonzepten
We investigate how inviting students to set task-based goals affects usage of an online learning platform and course performance. We design and implement a randomized field experiment in a large mandatory economics course with blended learning elements. The low-cost treatment induces students to use the online learning system more often, more intensively, and to begin earlier with exam preparation. Treated students perform better in the course than the control group: they are 18.8% (0.20 SD) more likely to pass the exam and earn 6.7% (0.19 SD) more points on the exam. There is no evidence that treated students spend significantly more time, rather they tend to shift to more productive learning methods. The heterogeneity analysis suggests that higher treatment effects are associated with higher levels of behavioral bias but also with poor early course behavior.
High growth firms (HGFs) are important for job creation and considered to be precursors of economic growth. We investigate how formal institutions, like product- and labor-market regulations, as well as the quality of regional governments that implement these regulations, affect HGF development across European regions. Using data from Eurostat, OECD, WEF, and Gothenburg University, we show that both regulatory stringency and the quality of the regional government influence the regional shares of HGFs. More importantly, we find that the effect of labor- and product-market regulations ultimately depends on the quality of regional governments: in regions with high quality of government, the share of HGFs is neither affected by the level of product market regulation, nor by more or less flexibility in hiring and firing practices. Our findings contribute to the debate on the effects of regulations by showing that regulations are not, per se, “good, bad, and ugly”, rather their impact depends on the efficiency of regional governments. Our paper offers important building blocks to develop tailored policy measures that may influence the development of HGFs in a region.
Adsorption layers of soluble surfactants enable and govern a variety of phenomena in surface and colloidal sciences, such as foams. The ability of a surfactant solution to form wet foam lamellae is governed by the surface dilatational rheology. Only systems having a non-vanishing imaginary part in their surface dilatational modulus, E, are able to form wet foams. The aim of this thesis is to illuminate the dissipative processes that give rise to the imaginary part of the modulus. There are two controversial models discussed in the literature. The reorientation model assumes that the surfactants adsorb in two distinct states, differing in their orientation. This model is able to describe the frequency dependence of the modulus E. However, it assumes reorientation dynamics in the millisecond time regime. In order to assess this model, we designed a SHG pump-probe experiment that addresses the orientation dynamics. Results obtained reveal that the orientation dynamics occur in the picosecond time regime, being in strong contradiction with the two states model. The second model regards the interface as an interphase. The adsorption layer consists of a topmost monolayer and an adjacent sublayer. The dissipative process is due to the molecular exchange between both layers. The assessment of this model required the design of an experiment that discriminates between the surface compositional term and the sublayer contribution. Such an experiment has been successfully designed and results on elastic and viscoelastic surfactant provided evidence for the correctness of the model. Because of its inherent surface specificity, surface SHG is a powerful analytical tool that can be used to gain information on molecular dynamics and reorganization of soluble surfactants. They are central elements of both experiments. However, they impose several structural elements of the model system. During the course of this thesis, a proper model system has been identified and characterized. The combination of several linear and nonlinear optical techniques, allowed for a detailed picture of the interfacial architecture of these surfactants.
This paper sheds new light on the role of communication for cartel formation. Using machine learning to evaluate free-form chat communication among firms in a laboratory experiment, we identify typical communication patterns for both explicit cartel formation and indirect attempts to collude tacitly. We document that firms are less likely to communicate explicitly about price fixing and more likely to use indirect messages when sanctioning institutions are present. This effect of sanctions on communication reinforces the direct cartel-deterring effect of sanctions as collusion is more difficult to reach and sustain without an explicit agreement. Indirect messages have no, or even a negative, effect on prices.
The concept of hydrologic connectivity summarizes all flow processes that link separate regions of a landscape. As such, it is a central theme in the field of catchment hydrology, with influence on neighboring disciplines such as ecology and geomorphology. It is widely acknowledged to be an important key in understanding the response behavior of a catchment and has at the same time inspired research on internal processes over a broad range of scales. From this process-hydrological point of view, hydrological connectivity is the conceptual framework to link local observations across space and scales.
This is the context in which the four studies this thesis comprises of were conducted. The focus was on structures and their spatial organization as important control on preferential subsurface flow. Each experiment covered a part of the conceptualized flow path from hillslopes to the stream: soil profile, hillslope, riparian zone, and stream.
For each study site, the most characteristic structures of the investigated domain and scale, such as slope deposits and peat layers were identified based on preliminary or previous investigations or literature reviews. Additionally, further structural data was collected and topographical analyses were carried out. Flow processes were observed either based on response observations (soil moisture changes or discharge patterns) or direct measurement (advective heat transport). Based on these data, the flow-relevance of the characteristic structures was evaluated, especially with regard to hillslope to stream connectivity.
Results of the four studies revealed a clear relationship between characteristic spatial structures and the hydrological behavior of the catchment. Especially the spatial distribution of structures throughout the study domain and their interconnectedness were crucial for the establishment of preferential flow paths and their relevance for large-scale processes. Plot and hillslope-scale irrigation experiments showed that the macropores of a heterogeneous, skeletal soil enabled preferential flow paths at the scale of centimeters through the otherwise unsaturated soil. These flow paths connected throughout the soil column and across the hillslope and facilitated substantial amounts of vertical and lateral flow through periglacial slope deposits.
In the riparian zone of the same headwater catchment, the connectivity between hillslopes and stream was controlled by topography and the dualism between characteristic subsurface structures and the geomorphological heterogeneity of the stream channel. At the small scale (1 m to 10 m) highest gains always occurred at steps along the longitudinal streambed profile, which also controlled discharge patterns at the large scale (100 m) during base flow conditions (number of steps per section). During medium and high flow conditions, however, the impact of topography and parafluvial flow through riparian zone structures prevailed and dominated the large-scale response patterns.
In the streambed of a lowland river, low permeability peat layers affected the connectivity between surface water and groundwater, but also between surface water and the hyporheic zone. The crucial factor was not the permeability of the streambed itself, but rather the spatial arrangement of flow-impeding peat layers, causing increased vertical flow through narrow “windows” in contrast to predominantly lateral flow in extended areas of high hydraulic conductivity sediments.
These results show that the spatial organization of structures was an important control for hydrological processes at all scales and study areas. In a final step, the observations from different scales and catchment elements were put in relation and compared. The main focus was on the theoretical analysis of the scale hierarchies of structures and processes and the direction of causal dependencies in this context. Based on the resulting hierarchical structure, a conceptual framework was developed which is capable of representing the system’s complexity while allowing for adequate simplifications.
The resulting concept of the parabolic scale series is based on the insight that flow processes in the terrestrial part of the catchment (soil and hillslopes) converge. This means that small-scale processes assemble and form large-scale processes and responses. Processes in the riparian zone and the streambed, however, are not well represented by the idea of convergence. Here, the large-scale catchment signal arrives and is modified by structures in the riparian zone, stream morphology, and the small-scale interactions between surface water and groundwater. Flow paths diverge and processes can better be represented by proceeding from large scales to smaller ones. The catchment-scale representation of processes and structures is thus the conceptual link between terrestrial hillslope processes and processes in the riparian corridor.
Die Konferenz „International Conference for the 10th Anniversary of the Institute of Comparative Law” hat am 24. Mai 2013 in Szeged stattgefunden. Im Rahmen der viersprachigen Konferenz haben mehr als dreißig Teilnehmer ihre Forschungsergebnisse präsentiert. Der Essay von Zoltán Péteri blickt auf die Disziplin aus der Perspektive der Wissenschaftsgeschichte. Katalin Kelemen und Balázs Fekete gehen in ihrem Aufsatz der Frage nach, welchen Weg die Versuche der Klassifikation der Rechtssysteme von Osteuropa in der späten Phase der Umbrüche der 1980/90er Jahren genommen haben. Die historische Betrachtungsweise mit Bezug auf Rechtsgeschichte und Rechtsvergleichung spiegelt sich auch in anderen Essays wider, vor allem in den Aufsätzen von Szilvia Bató, Magdolna Gedeon und Béla Szabó P. sowie auch in den Aufsätzen von Péter Mezei und Tünde Szűcs. Attila Badó analysiert die Rechtsvergleichung aus der Sicht des Rechts, der Soziologie und der Politikwissenschaft anhand von Untersuchungen über das Sanktionsystem der Richter in den USA. Diese politikwissenschaftliche Seite wird auch in den Aufsätzen über die aktuellen Fragen der europäischen Integration von Carine Guemar und Laureline Congnard betont. Eine Reihe von Aufsätzen behandeln die konventionelle normative Komparatistik auf dem Gebiet des Verfassungsrechts (Jordane Arlettaz und Péter Kruzslicz), Gesellschaftsrechts (Kitti Bakos-Kovács), Urheberrechts (Dóra Hajdú) und Steuerrechts (Judit Jacsó). Daneben bilden eine weitere Gruppe die Aufsätze von János Bóka und Erzsébet Csatlós, die die Verwendung der vergleichenden Methode in der Praxis der Rechtsprechung untersuchen. Die Rechtsvergleichung ist eine sich dynamisch entwickelnde Disziplin. Die Konferenz und dieser Band dienen nicht nur der Würdigung der bisherigen Arbeit des Instituts für Rechtsvergleichung, sondern zeigen gleichzeitig neue Ziele auf. Die wichtigsten Grundsätze bleiben aber fest verankert auch in einem sich stets verändernden rechtlichen und geistigen Umfeld. Das Motto des Instituts lautet „instruere et docere omnes qui edoceri desiderant“ – „alle lehren, die lernen wollen.“ Auch in den folgenden Jahrzehnten werden uns der Wille des Lernens und Lehrens, die Freiheit der Forschung sowie die Übertragung und Weiterentwicklung der ungarischen wie globalen Rechtskultur leiten.
The Arctic is the hot spot of the ongoing, global climate change. Over the last decades, near-surface temperatures in the Arctic have been rising almost four times faster than on global average. This amplified warming of the Arctic and the associated rapid changes of its environment are largely influenced by interactions between individual components of the Arctic climate system. On daily to weekly time scales, storms can have major impacts on the Arctic sea-ice cover and are thus an important part of these interactions within the Arctic climate. The sea-ice impacts of storms are related to high wind speeds, which enhance the drift and deformation of sea ice, as well as to changes in the surface energy budget in association with air mass advection, which impact the seasonal sea-ice growth and melt.
The occurrence of storms in the Arctic is typically associated with the passage of transient cyclones. Even though the above described mechanisms how storms/cyclones impact the Arctic sea ice are in principal known, there is a lack of statistical quantification of these effects. In accordance with that, the overarching objective of this thesis is to statistically quantify cyclone impacts on sea-ice concentration (SIC) in the Atlantic Arctic Ocean over the last four decades. In order to further advance the understanding of the related mechanisms, an additional objective is to separate dynamic and thermodynamic cyclone impacts on sea ice and assess their relative importance. Finally, this thesis aims to quantify recent changes in cyclone impacts on SIC. These research objectives are tackled utilizing various data sets, including atmospheric and oceanic reanalysis data as well as a coupled model simulation and a cyclone tracking algorithm.
Results from this thesis demonstrate that cyclones are significantly impacting SIC in the Atlantic Arctic Ocean from autumn to spring, while there are mostly no significant impacts in summer. The strength and the sign (SIC decreasing or SIC increasing) of the cyclone impacts strongly depends on the considered daily time scale and the region of the Atlantic Arctic Ocean. Specifically, an initial decrease in SIC (day -3 to day 0 relative to the cyclone) is found in the Greenland, Barents and Kara Seas, while SIC increases following cyclones (day 0 to day 5 relative to the cyclone) are mostly limited to the Barents and Kara Seas.
For the cold season, this results in a pronounced regional difference between overall (day -3 to day 5 relative to the cyclone) SIC-decreasing cyclone impacts in the Greenland Sea and overall SIC-increasing cyclone impacts in the Barents and Kara Seas. A cyclone case study based on a coupled model simulation indicates that both dynamic and thermodynamic mechanisms contribute to cyclone impacts on sea ice in winter. A typical pattern consisting of an initial dominance of dynamic sea-ice changes followed by enhanced thermodynamic ice growth after the cyclone passage was found. This enhanced ice growth after the cyclone passage most likely also explains the (statistical) overall SIC-increasing effects of cyclones in the Barents and Kara Seas in the cold season.
Significant changes in cyclone impacts on SIC over the last four decades have emerged throughout the year. These recent changes are strongly varying from region to region and month to month. The strongest trends in cyclone impacts on SIC are found in autumn in the Barents and Kara Seas. Here, the magnitude of destructive cyclone impacts on SIC has approximately doubled over the last four decades. The SIC-increasing effects following the cyclone passage have particularly weakened in the Barents Sea in autumn. As a consequence, previously existing overall SIC-increasing cyclone impacts in this region in autumn have recently disappeared. Generally, results from this thesis show that changes in the state of the sea-ice cover (decrease in mean sea-ice concentration and thickness) and near-surface air temperature are most important for changed cyclone impacts on SIC, while changes in cyclone properties (i.e. intensity) do not play a significant role.
The COVID-19 pandemic created the largest experiment in working from home. We study how persistent telework may change energy and transport consumption and costs in Germany to assess the distributional and environmental implications when working from home will stick. Based on data from the German Microcensus and available classifications of working-from-home feasibility for different occupations, we calculate the change in energy consumption and travel to work when 15% of employees work full time from home. Our findings suggest that telework translates into an annual increase in heating energy expenditure of 110 euros per worker and a decrease in transport expenditure of 840 euros per worker. All income groups would gain from telework but high-income workers gain twice as much as low-income workers. The value of time saving is between 1.3 and 6 times greater than the savings from reduced travel costs and almost 9 times higher for high-income workers than low-income workers. The direct effects on CO₂ emissions due to reduced car commuting amount to 4.5 millions tons of CO₂, representing around 3 percent of carbon emissions in the transport sector.
Zwischen den Juristischen Fakultäten der Universität Szeged und der Universität Potsdam besteht seit etlichen Jahren eine fruchtbare Kooperation in der Lehre. Durch sie entwickelt sich allmählich eine wissenschaftliche Zusammenarbeit. Gemeinsame Konferenzen und Publikationen sind dafür ein Beweis. Der vorliegende Band ist das Resultat dieser Kooperation. Der Buchtitel kennzeichnet das Engagement der ungarischen und der deutschen Juristen sowie die gemeinsamen Werte, welche der europäischen Rechtsentwicklung im 21. Jahrhundert zugrunde liegen und die Dogmatik der verschiedenen Rechtsgebiete verknüpfen. Die einzelnen Beiträge legen dabei Zeugnis ab von der ganzen Breite der Interessen der ungarischen und deutschen Juristen.
Antarctic glacier forfields are extreme environments and pioneer sites for ecological succession. The Antarctic continent shows microbial community development as a natural laboratory because of its special environment, geographic isolation and little anthropogenic influence. Increasing temperatures due to global warming lead to enhanced deglaciation processes in cold-affected habitats and new terrain is becoming exposed to soil formation and accessible for microbial colonisation. This study aims to understand the structure and development of glacier forefield bacterial communities, especially how soil parameters impact the microorganisms and how those are adapted to the extreme conditions of the habitat. To this effect, a combination of cultivation experiments, molecular, geophysical and geochemical analysis was applied to examine two glacier forfields of the Larsemann Hills, East Antarctica. Culture-independent molecular tools such as terminal restriction length polymorphism (T-RFLP), clone libraries and quantitative real-time PCR (qPCR) were used to determine bacterial diversity and distribution. Cultivation of yet unknown species was carried out to get insights in the physiology and adaptation of the microorganisms. Adaptation strategies of the microorganisms were studied by determining changes of the cell membrane phospholipid fatty acid (PLFA) inventory of an isolated bacterium in response to temperature and pH fluctuations and by measuring enzyme activity at low temperature in environmental soil samples. The two studied glacier forefields are extreme habitats characterised by low temperatures, low water availability and small oligotrophic nutrient pools and represent sites of different bacterial succession in relation to soil parameters. The investigated sites showed microbial succession at an early step of soil formation near the ice tongue in comparison to closely located but rather older and more developed soil from the forefield. At the early step the succession is influenced by a deglaciation-dependent areal shift of soil parameters followed by a variable and prevalently depth-related distribution of the soil parameters that is driven by the extreme Antarctic conditions. The dominant taxa in the glacier forefields are Actinobacteria, Acidobacteria, Proteobacteria, Bacteroidetes, Cyanobacteria and Chloroflexi. The connection of soil characteristics with bacterial community structure showed that soil parameter and soil formation along the glacier forefield influence the distribution of certain phyla. In the early step of succession the relative undifferentiated bacterial diversity reflects the undifferentiated soil development and has a high potential to shift according to past and present environmental conditions. With progressing development environmental constraints such as water or carbon limitation have a greater influence. Adapting the culturing conditions to the cold and oligotrophic environment, the number of culturable heterotrophic bacteria reached up to 108 colony forming units per gram soil and 148 isolates were obtained. Two new psychrotolerant bacteria, Herbaspirillum psychrotolerans PB1T and Chryseobacterium frigidisoli PB4T, were characterised in detail and described as novel species in the family of Oxalobacteraceae and Flavobacteriaceae, respectively. The isolates are able to grow at low temperatures tolerating temperature fluctuations and they are not specialised to a certain substrate, therefore they are well-adapted to the cold and oligotrophic environment. The adaptation strategies of the microorganisms were analysed in environmental samples and cultures focussing on extracellular enzyme activity at low temperature and PLFA analyses. Extracellular phosphatases (pH 11 and pH 6.5), β-glucosidase, invertase and urease activity were detected in the glacier forefield soils at low temperature (14°C) catalysing the conversion of various compounds providing necessary substrates and may further play a role in the soil formation and total carbon turnover of the habitat. The PLFA analysis of the newly isolated species C. frigidisoli showed that the cold-adapted strain develops different strategies to maintain the cell membrane function under changing environmental conditions by altering the PLFA inventory at different temperatures and pH values. A newly discovered fatty acid, which was not found in any other microorganism so far, significantly increased at decreasing temperature and low pH and thus plays an important role in the adaption of C. frigidisoli. This work gives insights into the diversity, distribution and adaptation mechanisms of microbial communities in oligotrophic cold-affected soils and shows that Antarctic glacier forefields are suitable model systems to study bacterial colonisation in connection to soil formation.
Inhalt: Umweltbeziehungen und Verantwortungsbewußtsein von Unternehmen -Ökologische Anliegen der Unternehmensumwelt -Verantwortliches Verhalten von Unternehmen -Der Risiko- und Krisenbegriff Subjektive Wahrnehmung und Bewertung von Risiken und Krisen -Subjektive Risikowahrnehmung: Wahrnehmung quantitativer Risikofaktoren,die Wahrnehmung qualitativer Merkmale der Risikoquelle, individuelle Risikodeterminanten -Empirische Studie zur Risikowahrnehmung -Subjektive Krisenbewältigung: Prozesse der Krisenentstehung und –entwicklung, Merkmale der Krisenwahrnehmung durch Manager, Verhaltenswissenschaftliche Theorien zur Krisenbewältigung Risiko- und Krisenmanagement
Im Rahmen dieser Studie soll ermittelt werden, welche Bedeutung sowohl quantitative, als auch qualitative Kriterien für die Beurteilung von Risiken in den Akteursgruppen Manager, Verwaltungsangehörige, Experten und Laien einnehmen. Bei der Beurteilung von Umweltrisiken spielen neben quantitativen Beurteilungskriterien (z.B. der Schadenswahrscheinlichkeit) auch qualitative Kriterien (z.B. die ausgelösten Emotionen) eine zentrale Rolle, wie insbesondere die Ergebnisse der psychometrischen Risikoforschung zeigen.
Gegenstand der Studie sind Risikobewertungen als Teil von Entscheidungen über die Akzeptanz von Risiken. Dabei handelt es sich nicht nur um einen Problembereich für Experten. Auch die Wirtschaft, die Verwaltung, die Politik und die interessierte Öffentlichkeit ist an solchen Entscheidungen beteiligt. Inhalt: Gegenstand der Studie Ziele der Studie Theoretischer Rahmen Hypothesen Methodische Grundlagen Ergebnisse -Das Kategorienschema -Häufigkeit und Inhalte kognitiver Kategorien: Zentrale Kategorien -Graphische Auswertung: Hierarchical Value Maps: HVM für gentechnisch verändertes Getreidesaatgut, HVM für Kunststoffzusatz, HVM für neues Arzneimittel, Gesamtanalysen
Im Rahmen dieser Studie soll ermittelt werden, welche Urteilsmodelle bzw. -konzepte Manager, Verwaltungsangehörige, Experten und Laien zur Bewertung von Risiken verwenden. Dazu wird eine Untersuchungsmethode, die Conjoint-Analyse, verwendet, die mit spezifischen Problemen der psychometrischen Risikoforschung besser umzugehen vermag und die u.E. noch nicht in der Risikowahrnehmungsforschung eingesetzt wurde. Inhalt: Ziel der Untersuchung Fragestellung Die Conjoint-Analyse Das Design der Studie Stichprobe und Datenerhebungsprozedur Ergebnisse: -Risikoakzeptanzwerte -Entscheidungswichtigkeit der Risiken -Gruppenanalyse
Data dependencies, or integrity constraints, are used to improve the quality of a database schema, to optimize queries, and to ensure consistency in a database. In the last years conditional dependencies have been introduced to analyze and improve data quality. In short, a conditional dependency is a dependency with a limited scope defined by conditions over one or more attributes. Only the matching part of the instance must adhere to the dependency. In this paper we focus on conditional inclusion dependencies (CINDs). We generalize the definition of CINDs, distinguishing covering and completeness conditions. We present a new use case for such CINDs showing their value for solving complex data quality tasks. Further, we define quality measures for conditions inspired by precision and recall. We propose efficient algorithms that identify covering and completeness conditions conforming to given quality thresholds. Our algorithms choose not only the condition values but also the condition attributes automatically. Finally, we show that our approach efficiently provides meaningful and helpful results for our use case.
Data obtained from foreign data sources often come with only superficial structural information, such as relation names and attribute names. Other types of metadata that are important for effective integration and meaningful querying of such data sets are missing. In particular, relationships among attributes, such as foreign keys, are crucial metadata for understanding the structure of an unknown database. The discovery of such relationships is difficult, because in principle for each pair of attributes in the database each pair of data values must be compared. A precondition for a foreign key is an inclusion dependency (IND) between the key and the foreign key attributes. We present with Spider an algorithm that efficiently finds all INDs in a given relational database. It leverages the sorting facilities of DBMS but performs the actual comparisons outside of the database to save computation. Spider analyzes very large databases up to an order of magnitude faster than previous approaches. We also evaluate in detail the effectiveness of several heuristics to reduce the number of necessary comparisons. Furthermore, we generalize Spider to find composite INDs covering multiple attributes, and partial INDs, which are true INDs for all but a certain number of values. This last type is particularly relevant when integrating dirty data as is often the case in the life sciences domain - our driving motivation.
Hyperspectral remote sensing of the spatial and temporal heterogeneity of low Arctic vegetation
(2019)
Arctic tundra ecosystems are experiencing warming twice the global average and Arctic vegetation is responding in complex and heterogeneous ways. Shifting productivity, growth, species composition, and phenology at local and regional scales have implications for ecosystem functioning as well as the global carbon and energy balance. Optical remote sensing is an effective tool for monitoring ecosystem functioning in this remote biome. However, limited field-based spectral characterization of the spatial and temporal heterogeneity limits the accuracy of quantitative optical remote sensing at landscape scales. To address this research gap and support current and future satellite missions, three central research questions were posed:
• Does canopy-level spectral variability differ between dominant low Arctic vegetation communities and does this variability change between major phenological phases?
• How does canopy-level vegetation colour images recorded with high and low spectral resolution devices relate to phenological changes in leaf-level photosynthetic pigment concentrations?
• How does spatial aggregation of high spectral resolution data from the ground to satellite scale influence low Arctic tundra vegetation signatures and thereby what is the potential of upcoming hyperspectral spaceborne systems for low Arctic vegetation characterization?
To answer these questions a unique and detailed database was assembled. Field-based canopy-level spectral reflectance measurements, nadir digital photographs, and photosynthetic pigment concentrations of dominant low Arctic vegetation communities were acquired at three major phenological phases representing early, peak and late season. Data were collected in 2015 and 2016 in the Toolik Lake Research Natural Area located in north central Alaska on the North Slope of the Brooks Range. In addition to field data an aerial AISA hyperspectral image was acquired in the late season of 2016. Simulations of broadband Sentinel-2 and hyperspectral Environmental and Mapping Analysis Program (EnMAP) satellite reflectance spectra from ground-based reflectance spectra as well as simulations of EnMAP imagery from aerial hyperspectral imagery were also obtained.
Results showed that canopy-level spectral variability within and between vegetation communities differed by phenological phase. The late season was identified as the most discriminative for identifying many dominant vegetation communities using both ground-based and simulated hyperspectral reflectance spectra. This was due to an overall reduction in spectral variability and comparable or greater differences in spectral reflectance between vegetation communities in the visible near infrared spectrum.
Red, green, and blue (RGB) indices extracted from nadir digital photographs and pigment-driven vegetation indices extracted from ground-based spectral measurements showed strong significant relationships. RGB indices also showed moderate relationships with chlorophyll and carotenoid pigment concentrations. The observed relationships with the broadband RGB channels of the digital camera indicate that vegetation colour strongly influences the response of pigment-driven spectral indices and digital cameras can track the seasonal development and degradation of photosynthetic pigments.
Spatial aggregation of hyperspectral data from the ground to airborne, to simulated satel-lite scale was influenced by non-photosynthetic components as demonstrated by the distinct shift of the red edge to shorter wavelengths. Correspondence between spectral reflectance at the three scales was highest in the red spectrum and lowest in the near infra-red. By artificially mixing litter spectra at different proportions to ground-based spectra, correspondence with aerial and satellite spectra increased. Greater proportions of litter were required to achieve correspondence at the satellite scale.
Overall this thesis found that integrating multiple temporal, spectral, and spatial data is necessary to monitor the complexity and heterogeneity of Arctic tundra ecosystems. The identification of spectrally similar vegetation communities can be optimized using non-peak season hyperspectral data leading to more detailed identification of vegetation communities. The results also highlight the power of vegetation colour to link ground-based and satellite data. Finally, a detailed characterization non-photosynthetic ecosystem components is crucial for accurate interpretation of vegetation signals at landscape scales.
Revisiting public investment
(2004)
The consumption equivalence method is the theoretical basis of public cost-benefit analysis. Consumption equivalence public capital prices are explicitly introduces in order to sufficiently care for the opportunity cost of public expenditure. This can solve the dispute about the social rate of discount within public cost-benefit analysis witch was generated on a criterion looking similar to the capital value formula, known as Lind’s approach. The social rate of discount is liberated from opportunity costs considerations and the discounting away of the effects for future welfare vanishes. The corresponding question whether one should accept a positive value of the pure rate of social time preference is an old issue. Its current state between the prescriptive and descriptive view can also be interpreted as a consequence of the oversimplification of standard cost– benefit analysis. But apart from an economic self-process the pure rate of social time preference is also defined as a business-as-usual value of social distance discounting. Hence, a political choice has to be made about this rate which is free in principal.
An exhaustive and disjoint decomposition of social choice situations is derived in a general set theoretical framework using the new tools of the Lifted Pareto relation on the power set of social states representing a pre-choice comparison of choice option sets. The main result is the classification of social choice situations which include three types of social choice problems. First, we usually observe the common incompleteness of the Pareto relation. Second, a kind of non-compactness problem of a choice set of social states can be generated. Finally, both can be combined. The first problem root can be regarded as natural everyday dilemma of social choice theory whereas the second may probably be much more due to modeling technique implications. The distinction is enabled at a very general set theoretical level. Hence, the derived classification of social choice situations is applicable on almost every relevant economic model.
Thermoresponsive Zellkultursubstrate für zeitlich-räumlich gesteuertes Auswachsen neuronaler Zellen
(2019)
Ein wichtiges Ziel der Neurowissenschaften ist das Verständnis der komplexen und zugleich faszinierenden, hochgeordneten Vernetzung der Neurone im Gehirn, welche neuronalen Prozessen, wie zum Beispiel dem Wahrnehmen oder Lernen wie auch Neuropathologien zu Grunde liegt. Für verbesserte neuronale Zellkulturmodelle zur detaillierten Untersuchung dieser Prozesse ist daher die Rekonstruktion von geordneten neuronalen Verbindungen dringend erforderlich. Mit Oberflächenstrukturen aus zellattraktiven und zellabweisenden Beschichtungen können neuronale Zellen und ihre Neuriten in vitro strukturiert werden. Zur Kontrolle der neuronalen Verbindungsrichtung muss das Auswachsen der Axone zu benachbarten Zellen dynamisch gesteuert werden, zum Beispiel über eine veränderliche Zugänglichkeit der Oberfläche.
In dieser Arbeit wurde untersucht, ob mit thermoresponsiven Polymeren (TRP) beschichtete Zellkultursubstrate für eine dynamische Kontrolle des Auswachsens neuronaler Zellen geeignet sind. TRP können über die Temperatur von einem zellabweisenden in einen zellattraktiven Zustand geschaltet werden, womit die Zugänglichkeit der Oberfläche für Zellen dynamisch gesteuert werden kann. Die TRP-Beschichtung wurde mikrostrukturiert, um einzelne oder wenige neuronale Zellen zunächst auf der Oberfläche anzuordnen und das Auswachsen der Zellen und Neuriten über definierte TRP-Bereiche in Abhängigkeit der Temperatur zeitlich und räumlich zu kontrollieren. Das Protokoll wurde mit der neuronalen Zelllinie SH-SY5Y etabliert und auf humane induzierte Neurone übertragen. Die Anordnung der Zellen konnte bei Kultivierung im zellabweisenden Zustand des TRPs für bis zu 7 Tage aufrecht erhalten werden. Durch Schalten des TRPs in den zellattraktiven Zustand konnte das Auswachsen der Neuriten und Zellen zeitlich und räumlich induziert werden. Immunozytochemische Färbungen und Patch-Clamp-Ableitungen der Neurone demonstrierten die einfache Anwendbarkeit und Zellkompatibilität der TRP-Substrate.
Eine präzisere räumliche Kontrolle des Auswachsens der Zellen sollte durch lokales Schalten der TRP-Beschichtung erreicht werden. Dafür wurden Mikroheizchips mit Mikroelektroden zur lokalen Jouleschen Erwärmung der Substratoberfläche entwickelt. Zur Evaluierung der generierten Temperaturprofile wurde eine Temperaturmessmethode entwickelt und die erhobenen Messwerte mit numerisch simulierten Werten abgeglichen. Die Temperaturmessmethode basiert auf einfach zu applizierenden Sol-Gel-Schichten, die den temperatursensitiven Fluoreszenzfarbstoff Rhodamin B enthalten. Sie ermöglicht oberflächennahe Temperaturmessungen in trockener und wässriger Umgebung mit hoher Orts- und Temperaturauflösung. Numerische Simulationen der Temperaturprofile korrelierten gut mit den experimentellen Daten. Auf dieser Basis konnten Geometrie und Material der Mikroelektroden hinsichtlich einer lokal stark begrenzten Temperierung optimiert werden. Ferner wurden für die Kultvierung der Zellen auf den Mikroheizchips eine Zellkulturkammer und Kontaktboard für die elektrische Kontaktierung der Mikroelektroden geschaffen.
Die vorgestellten Ergebnisse demonstrieren erstmalig das enorme Potential thermoresponsiver Zellkultursubstrate für die zeitlich und räumlich gesteuerte Formation geordneter neuronaler Verbindungen in vitro. Zukünftig könnte dies detaillierte Studien zur neuronalen Informationsverarbeitung oder zu Neuropathologien an relevanten, humanen Zellmodellen ermöglichen.
Cargo transport by molecular motors is ubiquitous in all eukaryotic cells and is typically driven cooperatively by several molecular motors, which may belong to one or several motor species like kinesin, dynein or myosin. These motor proteins transport cargos such as RNAs, protein complexes or organelles along filaments, from which they unbind after a finite run length. Understanding how these motors interact and how their movements are coordinated and regulated is a central and challenging problem in studies of intracellular transport. In this thesis, we describe a general theoretical framework for the analysis of such transport processes, which enables us to explain the behavior of intracellular cargos based on the transport properties of individual motors and their interactions. Motivated by recent in vitro experiments, we address two different modes of transport: unidirectional transport by two identical motors and cooperative transport by actively walking and passively diffusing motors. The case of cargo transport by two identical motors involves an elastic coupling between the motors that can reduce the motors’ velocity and/or the binding time to the filament. We show that this elastic coupling leads, in general, to four distinct transport regimes. In addition to a weak coupling regime, kinesin and dynein motors are found to exhibit a strong coupling and an enhanced unbinding regime, whereas myosin motors are predicted to attain a reduced velocity regime. All of these regimes, which we derive both by analytical calculations and by general time scale arguments, can be explored experimentally by varying the elastic coupling strength. In addition, using the time scale arguments, we explain why previous studies came to different conclusions about the effect and relevance of motor-motor interference. In this way, our theory provides a general and unifying framework for understanding the dynamical behavior of two elastically coupled molecular motors. The second mode of transport studied in this thesis is cargo transport by actively pulling and passively diffusing motors. Although these passive motors do not participate in active transport, they strongly enhance the overall cargo run length. When an active motor unbinds, the cargo is still tethered to the filament by the passive motors, giving the unbound motor the chance to rebind and continue its active walk. We develop a stochastic description for such cooperative behavior and explicitly derive the enhanced run length for a cargo transported by one actively pulling and one passively diffusing motor. We generalize our description to the case of several pulling and diffusing motors and find an exponential increase of the run length with the number of involved motors.
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.
Zentrale Aufgaben von Kommunen in Deutschland umfassen die Gewährleistung der Daseinsvorsorge, des öffentlichen Verkehrs, Wirtschaftsförderung, Zugang zu ausreichender Breitbandinfrastruktur, gesundheitliche und soziale Betreuung und Zugang zu kulturellem Leben. Kommunen in ländlichen Regionen stehen gleichzeitig vor zahlreichen gesellschaftlichen, wirtschaftlichen, sozialen und politischen Herausforderungen. Neuartige Ansätze und innovative Akteure und Netzwerke werden daher im Kontext der Schaffung von sozialen oder digitalen Innovationen von den Kommunen als Antwort auf diese Herausforderungen begrüßt, stoßen aber auch teilweise auf Barrieren.
In dem Sammelband wird von den Herausgebenden die Frage untersucht, wie sich digitale Vorreiter:innen, die wir „Digitale Pioniere“ nennen, in ländlichen Regionen vernetzen, um einen positiven Beitrag zur ländlichen Regionalentwicklung zu leisten. Dabei liegt der Fokus hauptsächlich auf der kommunalpolitischen Ebene und auf der Frage, wie Digitale Pioniere als Schlüsselakteure in der ländlichen Governance agieren. Die Forschungsergebnisse kommunaler Governance sind anhand ländlicher Untersuchungsteilregionen in Baden-Württemberg und Mecklenburg-Vorpommern im Rahmen des Forschungsprojekts „DigPion – Digitale Pioniere in der ländlichen Regionalentwicklung“ (2020–2023) erarbeitet worden. Abschließend wird überprüft, wie die Erkenntnisse und erarbeiteten Handlungsempfehlungen für das Bundesland Brandenburg zu übertragen sind.
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.
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.
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.
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.
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.
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.
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.
Die zunehmende Erosion der veranlagten Einkommensteuer wirft die Frage auf, inwieweit die Möglichkeit der Verechnung positiver mit negativen Einkünften dafür verantwortlich ist. Auf der Basis eines Mikrosimulationsmodells analysiert der Beitrag die Wirkungen dieser im deutschen Einkommensteuerrecht möglichen Verrechenbarkeit. Zum einen werden die aus der Abschaffung der Verrechnungsmöglichkeiten resultierenden Wanderungen von Steuerpflichtigen in höhere Einkommensklassen und zum anderen die Auswirkungen auf das Steueraufkommen untersucht. Insgesamt vermindern ca. 5 % der Steuerpflichtigen ihre positiven Einkünfte durch negative Einkünfte einer anderen Einkunftsart. Im Hinblick auf das Steueraufkommen zeichnen sich beträchtliche Steuerausfälle in Höhe von ca. 33 Mrd. DM durch diese Verrechnungsmöglichkeit ab.
Inhalt: Theoretische Begründung einer Besteuerung von Alterseinkünften Kritische Überprüfung einiger Argumente Die Reformvorschläge und das Simulationsmodell Die Simulationsergebnisse Kritische Bewertung der Ergebnisse Mit einem Kommentar von Hans-Peter Weikard: Rentenbesteuerung und Korrespondenzprinzip: 1. Wie selbstverständlich ist das Korrespondenzprinzip? 2. Die zeitliche Dimension des Korrespondenzprinzips 3. Eine unzulässige Interpretation 4. Fazit
Kaum einem anderen Unterrichtsfach ist das Fachübergreifende so immanent wie dem Fach Musik, das durch seine Themen- und Inhaltsfelder vielfältige Bezüge zu anderen Fächern und Wissenschaftsdisziplinen aufweist. Dennoch lässt sich bezüglich der Literatur- und Forschungslage konstatieren, dass zwar theoretische Ansätze und Modelle für einen fachübergreifenden Musikunterricht existieren, sich die musikpädagogische Forschung jedoch mit dem fachübergreifenden Musikunterricht und dessen Umsetzung durch die Musiklehrkräfte noch nicht befasst hat. Auch die Zahl der praxisbezogenen Publikationen für einen fachübergreifenden Musikunterricht ist überschaubar, ebenso das Fortbildungsangebot für Musiklehrende.
Aus diesem Grund widmet sich der vorliegende Band 9 der „Potsdamer Schriftenreihe zur Musikpädagogik“ dem Thema „Fachübergreifender Musikunterricht“ aus verschiedenen Perspektiven. Zum einen bilden die derzeit aktuellen theoretischen Grundlagen eine wichtige Basis. Zum anderen fließen auch ausbildungsrelevante und methodische Aspekte zur Umsetzung eines fachübergreifenden Musikunterrichts in die Texte ein. In bewährter Tradition der Schriftenreihe werden dabei sowohl Beiträge von Lehrenden am Lehrstuhl für Musikpädagogik und Musikdidaktik der Universität Potsdam als auch von Studierenden sowie von Kooperationspartnern des Lehrstuhls in der Musiklehrer*innenbildung berücksichtigt. Ziel ist es, auf der Basis verschiedener theoretischer Ansätze Umsetzungsmöglichkeiten eines fachübergreifenden Musikunterrichts als Beitrag zum Erreichen der im Teil B des Rahmenlehrplans für Berlin und Brandenburg angeführten fachübergreifenden Kompetenzziele aufzuzeigen.
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.
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.
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.
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.
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.
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.
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.
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 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).
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.
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.
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.
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.