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Die Elektrosprayionisation (ESI) ist eine der weitverbreitetsten Ionisationstechniken für flüssige Pro-ben in der Massen- und Ionenmobilitäts(IM)-Spektrometrie. Aufgrund ihrer schonenden Ionisierung wird ESI vorwiegend für empfindliche, komplexe Moleküle in der Biologie und Medizin eingesetzt. Überdies ist sie allerdings für ein sehr breites Spektrum an Substanzklassen anwendbar. Die IM-Spektrometrie wurde ursprünglich zur Detektion gasförmiger Proben entwickelt, die hauptsächlich durch radioaktive Quellen ionisiert werden. Sie ist die einzige analytische Methode, bei der Isomere in Echtzeit getrennt und über ihre charakteristische IM direkt identifiziert werden können. ESI wurde in den 90ger Jahren durch die Hill Gruppe in die IM-Spektrometrie eingeführt. Die Kombination wird bisher jedoch nur von wenigen Gruppen verwendet und hat deshalb noch ein hohes Entwick-lungspotential. Ein vielversprechendes Anwendungsfeld ist der Einsatz in der Hochleistungs-flüssigkeitschromatographie (HPLC) zur mehrdimensionalen Trennung. Heutzutage ist die HPLC die Standardmethode zur Trennung komplexer Proben in der Routineanalytik. HPLC-Trennungsgänge sind jedoch häufig langwierig und der Einsatz verschiedener Laufmittel, hoher Flussraten, von Puffern, sowie Laufmittelgradienten stellt hohe Anforderungen an die Detektoren. Die ESI-IM-Spektrometrie wurde in einigen Studien bereits als HPLC-Detektor eingesetzt, war dort bisher jedoch auf Flussratensplitting oder geringe Flussraten des Laufmittels beschränkt.
In dieser kumulativen Doktorarbeit konnte daher erstmals ein ESI IM-Spektrometer als HPLC-Detektor für den Flussratenbereich von 200-1500 μl/min entwickelt werden. Anhand von fünf Publi-kationen wurden (1) über eine umfassende Charakterisierung die Eignung des Spektrometers als HPLC-Detektor festgestellt, (2) ausgewählte komplexe Trenngänge präsentiert und (3) die Anwen-dung zum Reaktionsmonitoring und (4, 5) mögliche Weiterentwicklungen gezeigt.
Erfolgreich konnten mit dem selbst-entwickelten ESI IM-Spektrometer typische HPLC-Bedingungen wie Wassergehalte im Laufmittel von bis zu 90%, Pufferkonzentrationen von bis zu 10 mM, sowie Nachweisgrenzen von bis zu 50 nM erreicht werden. Weiterhin wurde anhand der komplexen Trennungsgänge (24 Pestizide/18 Aminosäuren) gezeigt, dass die HPLC und die IM-Spektrometrie eine hohe Orthogonalität besitzen. Eine effektive Peakkapazität von 240 wurde so realisiert. Auf der HPLC-Säule koeluierende Substanzen konnten über die Driftzeit getrennt und über ihre IM identifi-ziert werden, sodass die Gesamttrennzeiten erheblich minimiert werden konnten. Die Anwend-barkeit des ESI IM-Spektrometers zur Überwachung chemischer Synthesen wurde anhand einer dreistufigen Reaktion demonstriert. Es konnten die wichtigsten Edukte, Zwischenprodukte und Produkte aller Stufen identifiziert werden. Eine quantitative Auswertung war sowohl über eine kurze HPLC-Vortrennung als auch durch die Entwicklung eines eigenen Kalibrierverfahrens, welches die Ladungskonkurrenz bei ESI berücksichtigt, ohne HPLC möglich. Im zweiten Teil der Arbeit werden zwei Weiterentwicklungen des Spektrometers präsentiert. Eine Möglichkeit ist die Reduzierung des Drucks in den intermediären Bereich (300 - 1000 mbar) mit dem Ziel der Verringerung der benötigten Spannungen. Mithilfe von Streulichtbildern und Strom-Spannungs-Kurven wurden für geringe Drücke eine verminderte Freisetzung der Analyt-Ionen aus den Tropfen festgestellt. Die Verluste konnten jedoch über höhere elektrische Feldstärken ausgeglichen werden, sodass gleiche Nachweisgrenzen bei 500 mbar und bei 1 bar erreicht wurden. Die zweite Weiterentwicklung ist ein neuartiges Ionentors mit Pulsschaltung, welches eine Verdopplung der Auflösung auf bis zu R > 100 bei gleicher Sensitivität ermöglichte. Eine denkbare Anwendung im Bereich der Peptidanalytik wurde mit beachtlichen Auflösungen der Peptide von R = 90 gezeigt.
With recent advances in the area of information extraction, automatically extracting structured information from a vast amount of unstructured textual data becomes an important task, which is infeasible for humans to capture all information manually. Named entities (e.g., persons, organizations, and locations), which are crucial components in texts, are usually the subjects of structured information from textual documents. Therefore, the task of named entity mining receives much attention. It consists of three major subtasks, which are named entity recognition, named entity linking, and relation extraction.
These three tasks build up an entire pipeline of a named entity mining system, where each of them has its challenges and can be employed for further applications. As a fundamental task in the natural language processing domain, studies on named entity recognition have a long history, and many existing approaches produce reliable results. The task is aiming to extract mentions of named entities in text and identify their types. Named entity linking recently received much attention with the development of knowledge bases that contain rich information about entities. The goal is to disambiguate mentions of named entities and to link them to the corresponding entries in a knowledge base. Relation extraction, as the final step of named entity mining, is a highly challenging task, which is to extract semantic relations between named entities, e.g., the ownership relation between two companies.
In this thesis, we review the state-of-the-art of named entity mining domain in detail, including valuable features, techniques, evaluation methodologies, and so on. Furthermore, we present two of our approaches that focus on the named entity linking and relation extraction tasks separately.
To solve the named entity linking task, we propose the entity linking technique, BEL, which operates on a textual range of relevant terms and aggregates decisions from an ensemble of simple classifiers. Each of the classifiers operates on a randomly sampled subset of the above range. In extensive experiments on hand-labeled and benchmark datasets, our approach outperformed state-of-the-art entity linking techniques, both in terms of quality and efficiency.
For the task of relation extraction, we focus on extracting a specific group of difficult relation types, business relations between companies. These relations can be used to gain valuable insight into the interactions between companies and perform complex analytics, such as predicting risk or valuating companies. Our semi-supervised strategy can extract business relations between companies based on only a few user-provided seed company pairs. By doing so, we also provide a solution for the problem of determining the direction of asymmetric relations, such as the ownership_of relation. We improve the reliability of the extraction process by using a holistic pattern identification method, which classifies the generated extraction patterns. Our experiments show that we can accurately and reliably extract new entity pairs occurring in the target relation by using as few as five labeled seed pairs.
Information on the contemporary in-situ stress state of the earth’s crust is essential for geotechnical applications and physics-based seismic hazard assessment. Yet, stress data records for a data point are incomplete and their availability is usually not dense enough to allow conclusive statements. This demands a thorough examination of the in-situ stress field which is achieved by 3D geomechanicalnumerical models. However, the models spatial resolution is limited and the resulting local stress state is subject to large uncertainties that confine the significance of the findings. In addition, temporal variations of the in-situ stress field are naturally or anthropogenically induced. In my thesis I address these challenges in three manuscripts that investigate (1) the current crustal stress field orientation, (2) the 3D geomechanical-numerical modelling of the in-situ stress state, and (3) the phenomenon of injection induced temporal stress tensor rotations. In the first manuscript I present the first comprehensive stress data compilation of Iceland with 495 data records. Therefore, I analysed image logs from 57 boreholes in Iceland for indicators of the orientation of the maximum horizontal stress component. The study is the first stress survey from different kinds of stress indicators in a geologically very young and tectonically active area of an onshore spreading ridge. It reveals a distinct stress field with a depth independent stress orientation even very close to the spreading centre. In the second manuscript I present a calibrated 3D geomechanical-numerical modelling approach of the in-situ stress state of the Bavarian Molasse Basin that investigates the regional (70x70x10km³) and local (10x10x10km³) stress state. To link these two models I develop a multi-stage modelling approach that provides a reliable and efficient method to derive from the larger scale model initial and boundary conditions for the smaller scale model. Furthermore, I quantify the uncertainties in the models results which are inherent to geomechanical-numerical modelling in general and the multi-stage approach in particular. I show that the significance of the models results is mainly reduced due to the uncertainties in the material properties and the low number of available stress magnitude data records for calibration. In the third manuscript I investigate the phenomenon of injection induced temporal stress tensor rotation and its controlling factors. I conduct a sensitivity study with a 3D generic thermo-hydro-mechanical model. I show that the key control factors for the stress tensor rotation are the permeability as the decisive factor, the injection rate, and the initial differential stress. In particular for enhanced geothermal systems with a low permeability large rotations of the stress tensor are indicated. According to these findings the estimation of the initial differential stress in a reservoir is possible provided the permeability is known and the angle of stress rotation is observed. I propose that the stress tensor rotations can be a key factor in terms of the potential for induced seismicity on pre-existing faults due to the reorientation of the stress field that changes the optimal orientation of faults.
Self-adaptive data quality
(2017)
Carrying out business processes successfully is closely linked to the quality of the data inventory in an organization. Lacks in data quality lead to problems: Incorrect address data prevents (timely) shipments to customers. Erroneous orders lead to returns and thus to unnecessary effort. Wrong pricing forces companies to miss out on revenues or to impair customer satisfaction. If orders or customer records cannot be retrieved, complaint management takes longer. Due to erroneous inventories, too few or too much supplies might be reordered.
A special problem with data quality and the reason for many of the issues mentioned above are duplicates in databases. Duplicates are different representations of same real-world objects in a dataset. However, these representations differ from each other and are for that reason hard to match by a computer. Moreover, the number of required comparisons to find those duplicates grows with the square of the dataset size. To cleanse the data, these duplicates must be detected and removed. Duplicate detection is a very laborious process. To achieve satisfactory results, appropriate software must be created and configured (similarity measures, partitioning keys, thresholds, etc.). Both requires much manual effort and experience.
This thesis addresses automation of parameter selection for duplicate detection and presents several novel approaches that eliminate the need for human experience in parts of the duplicate detection process.
A pre-processing step is introduced that analyzes the datasets in question and classifies their attributes semantically. Not only do these annotations help understanding the respective datasets, but they also facilitate subsequent steps, for example, by selecting appropriate similarity measures or normalizing the data upfront. This approach works without schema information.
Following that, we show a partitioning technique that strongly reduces the number of pair comparisons for the duplicate detection process. The approach automatically finds particularly suitable partitioning keys that simultaneously allow for effective and efficient duplicate retrieval. By means of a user study, we demonstrate that this technique finds partitioning keys that outperform expert suggestions and additionally does not need manual configuration. Furthermore, this approach can be applied independently of the attribute types.
To measure the success of a duplicate detection process and to execute the described partitioning approach, a gold standard is required that provides information about the actual duplicates in a training dataset. This thesis presents a technique that uses existing duplicate detection results and crowdsourcing to create a near gold standard that can be used for the purposes above. Another part of the thesis describes and evaluates strategies how to reduce these crowdsourcing costs and to achieve a consensus with less effort.
To what extent cities can be made sustainable under the mega-trends of urbanization and climate change remains a matter of unresolved scientific debate. Our inability in answering this question lies partly in the deficient knowledge regarding pivotal humanenvironment interactions. Regarded as the most well documented anthropogenic climate modification, the urban heat island (UHI) effect – the warmth of urban areas relative to the rural hinterland – has raised great public health concerns globally. Worse still, heat waves are being observed and are projected to increase in both frequency and intensity, which further impairs the well-being of urban dwellers. Albeit with a substantial increase in the number of publications on UHI in the recent decades, the diverse urban-rural definitions applied in previous studies have remarkably hampered the general comparability of results achieved. In addition, few studies have attempted to synergize the land use data and thermal remote sensing to systematically assess UHI and its contributing factors.
Given these research gaps, this work presents a general framework to systematically quantify the UHI effect based on an automated algorithm, whereby cities are defined as clusters of maximum spatial continuity on the basis of land use data, with their rural hinterland being defined analogously. By combining land use data with spatially explicit surface skin temperatures from satellites, the surface UHI intensity can be calculated in a consistent and robust manner. This facilitates monitoring, benchmarking, and categorizing UHI intensities for cities across scales. In light of this innovation, the relationship between city size and UHI intensity has been investigated, as well as the contributions of urban form indicators to the UHI intensity.
This work delivers manifold contributions to the understanding of the UHI, which have complemented and advanced a number of previous studies. Firstly, a log-linear relationship between surface UHI intensity and city size has been confirmed among the 5,000 European cities. The relationship can be extended to a log-logistic one, when taking a wider range of small-sized cities into account. Secondly, this work reveals a complex interplay between UHI intensity and urban form. City size is found to have the strongest influence on the UHI intensity, followed by the fractality and the anisometry. However, their relative contributions to the surface UHI intensity depict a pronounced regional heterogeneity, indicating the importance of considering spatial patterns of UHI while implementing UHI adaptation measures.
Lastly, this work presents a novel seasonality of the UHI intensity for individual clusters in the form of hysteresis-like curves, implying a phase shift between the time series of UHI intensity and background temperatures. Combining satellite observation and urban boundary layer simulation, the seasonal variations of UHI are assessed from both screen and skin levels. Taking London as an example, this work ascribes the discrepancies between the seasonality observed at different levels mainly to the peculiarities of surface skin temperatures associated with the incoming solar radiation. In addition, the efforts in classifying cities according to their UHI characteristics highlight the important role of regional climates in determining the UHI.
This work serves as one of the first studies conducted to systematically and statistically scrutinize the UHI. The outcomes of this work are of particular relevance for the overall spatial planning and regulation at meso- and macro levels in order to harness the benefits of rapid urbanization, while proactively minimizing its ensuing thermal stress.
Die zerstörungsfreien Prüfungen von Bauwerken mit Hilfe von Ultraschallmessverfahren haben in den letzten Jahren an Bedeutung gewonnen. Durch Ultraschallmessungen können die Geometrien von Bauteilen bestimmt sowie von außen nicht sichtbare Fehler wie Delaminationen und Kiesnester erkannt werden.
Mit neuartigen, in das Betonbauteil eingebetteten Ultraschallprüfköpfen sollen nun Bauwerke dauerhaft auf Veränderungen überprüft werden. Dazu werden Ultraschallsignale direkt im Inneren eines Bauteils erzeugt, was die Möglichkeiten der herkömmlichen Methoden der Bauwerksüberwachung wesentlich erweitert. Ein Ultraschallverfahren könnte mit eingebetteten Prüfköpfen ein Betonbauteil kontinuierlich integral überwachen und damit auch stetig fortschreitende Gefügeänderungen, wie beispielsweise Mikrorisse, registrieren.
Sicherheitsrelevante Bauteile, die nach dem Einbau für Messungen unzugänglich oder mittels Ultraschall, beispielsweise durch zusätzliche Beschichtungen der Oberfläche, nicht prüfbar sind, lassen sich mit eingebetteten Prüfköpfen überwachen. An bereits vorhandenen Bauwerken können die Ultraschallprüfköpfe mithilfe von Bohrlöchern und speziellem Verpressmörtel auch nachträglich in das Bauteil integriert werden. Für Fertigbauteile bieten sich eingebettete Prüfköpfe zur Herstellungskontrolle sowie zur Überwachung der Baudurchführung als Werkzeug der Qualitätssicherung an. Auch die schnelle Schadensanalyse eines Bauwerks nach Naturkatastrophen, wie beispielsweise einem Erdbeben oder einer Flut, ist denkbar.
Durch die gute Ankopplung ermöglichen diese neuartigen Prüfköpfe den Einsatz von empfindlichen Auswertungsmethoden, wie die Kreuzkorrelation, die Coda-Wellen-Interferometrie oder die Amplitudenauswertung, für die Signalanalyse. Bei regelmäßigen Messungen können somit sich anbahnende Schäden eines Bauwerks frühzeitig erkannt werden.
Da die Schädigung eines Bauwerks keine direkt messbare Größe darstellt, erfordert eine eindeutige Schadenserkennung in der Regel die Messung mehrerer physikalischer Größen die geeignet verknüpft werden. Physikalische Größen können sein: Ultraschalllaufzeit, Amplitude des Ultraschallsignals und Umgebungstemperatur. Dazu müssen Korrelationen zwischen dem Zustand des Bauwerks, den Umgebungsbedingungen und den Parametern des gemessenen Ultraschallsignals untersucht werden.
In dieser Arbeit werden die neuartigen Prüfköpfe vorgestellt. Es wird beschrieben, dass sie sich, sowohl in bereits errichtete Betonbauwerke als auch in der Konstruktion befindliche, einbauen lassen. Experimentell wird gezeigt, dass die Prüfköpfe in mehreren Ebenen eingebettet sein können da ihre Abstrahlcharakteristik im Beton nahezu ungerichtet ist. Die Mittenfrequenz von rund 62 kHz ermöglicht Abstände, je nach Betonart und SRV, von mindestens 3 m zwischen Prüfköpfen die als Sender und Empfänger arbeiten. Die Empfindlichkeit der eingebetteten Prüfköpfe gegenüber Veränderungen im Beton wird an Hand von zwei Laborexperimenten gezeigt, einem Drei-Punkt-Biegeversuch und einem Versuch zur Erzeugung von Frost-Tau-Wechsel Schäden. Die Ergebnisse werden mit anderen zerstörungsfreien Prüfverfahren verglichen. Es zeigt sich, dass die Prüfköpfe durch die Anwendung empfindlicher Auswertemethoden, auftretende Risse im Beton detektieren, bevor diese eine Gefahr für das Bauwerk darstellen. Abschließend werden Beispiele von Installation der neuartigen Ultraschallprüfköpfe in realen Bauteilen, zwei Brücken und einem Fundament, gezeigt und basierend auf dort gewonnenen ersten Erfahrungen ein Konzept für die Umsetzung einer Langzeitüberwachung aufgestellt.
Background: Consumption of whole-grain, coffee, and red meat were consistently related to the risk of developing type 2 diabetes in prospective cohort studies, but potentially underlying biological mechanisms are not well understood. Metabolomics profiles were shown to be sensitive to these dietary exposures, and at the same time to be informative with respect to the risk of type 2 diabetes. Moreover, graphical network-models were demonstrated to reflect the biological processes underlying high-dimensional metabolomics profiles.
Aim: The aim of this study was to infer hypotheses on the biological mechanisms that link consumption of whole-grain bread, coffee, and red meat, respectively, to the risk of developing type 2 diabetes. More specifically, it was aimed to consider network models of amino acid and lipid profiles as potential mediators of these risk-relations.
Study population: Analyses were conducted in the prospective EPIC-Potsdam cohort (n = 27,548), applying a nested case-cohort design (n = 2731, including 692 incident diabetes cases). Habitual diet was assessed with validated semiquantitative food-frequency questionnaires. Concentrations of 126 metabolites (acylcarnitines, phosphatidylcholines, sphingomyelins, amino acids) were determined in baseline-serum samples. Incident type 2 diabetes cases were assed and validated in an active follow-up procedure. The median follow-up time was 6.6 years.
Analytical design: The methodological approach was conceptually based on counterfactual causal inference theory. Observations on the network-encoded conditional independence structure restricted the space of possible causal explanations of observed metabolomics-data patterns. Given basic directionality assumptions (diet affects metabolism; metabolism affects future diabetes incidence), adjustment for a subset of direct neighbours was sufficient to consistently estimate network-independent direct effects. Further model-specification, however, was limited due to missing directionality information on the links between metabolites. Therefore, a multi-model approach was applied to infer the bounds of possible direct effects. All metabolite-exposure links and metabolite-outcome links, respectively, were classified into one of three categories: direct effect, ambiguous (some models indicated an effect others not), and no-effect.
Cross-sectional and longitudinal relations were evaluated in multivariable-adjusted linear regression and Cox proportional hazard regression models, respectively. Models were comprehensively adjusted for age, sex, body mass index, prevalence of hypertension, dietary and lifestyle factors, and medication.
Results: Consumption of whole-grain bread was related to lower levels of several lipid metabolites with saturated and monounsaturated fatty acids. Coffee was related to lower aromatic and branched-chain amino acids, and had potential effects on the fatty acid profile within lipid classes. Red meat was linked to lower glycine levels and was related to higher circulating concentrations of branched-chain amino acids. In addition, potential marked effects of red meat consumption on the fatty acid composition within the investigated lipid classes were identified.
Moreover, potential beneficial and adverse direct effects of metabolites on type 2 diabetes risk were detected. Aromatic amino acids and lipid metabolites with even-chain saturated (C14-C18) and with specific polyunsaturated fatty acids had adverse effects on type 2 diabetes risk. Glycine, glutamine, and lipid metabolites with monounsaturated fatty acids and with other species of polyunsaturated fatty acids were classified as having direct beneficial effects on type 2 diabetes risk.
Potential mediators of the diet-diabetes links were identified by graphically overlaying this information in network models. Mediation analyses revealed that effects on lipid metabolites could potentially explain about one fourth of the whole-grain bread effect on type 2 diabetes risk; and that effects of coffee and red meat consumption on amino acid and lipid profiles could potentially explain about two thirds of the altered type 2 diabetes risk linked to these dietary exposures.
Conclusion: An algorithm was developed that is capable to integrate single external variables (continuous exposures, survival time) and high-dimensional metabolomics-data in a joint graphical model. Application to the EPIC-Potsdam cohort study revealed that the observed conditional independence patterns were consistent with the a priori mediation hypothesis: Early effects on lipid and amino acid metabolism had the potential to explain large parts of the link between three of the most widely discussed diabetes-related dietary exposures and the risk of developing type 2 diabetes.
The motivation of this work was to investigate the self-assembly of a block copolymer species that attended little attraction before, double hydrophilic block copolymers (DHBCs). DHBCs consist of two linear hydrophilic polymer blocks. The self-assembly of DHBCs towards suprastructures such as particles and vesicles is determined via a strong difference in hydrophilicity between the corresponding blocks leading to a microphase separation due to immiscibility. The benefits of DHBCs and the corresponding particles and vesicles, such as biocompatibility, high permeability towards water and hydrophilic compounds as well as the large amount of possible functionalizations that can be addressed to the block copolymers make the application of DHBC based structures a viable choice in biomedicine. In order to assess a route towards self-assembled structures from DHBCs that display the potential to act as cargos for future applications, several block copolymers containing two hydrophilic polymer blocks were synthesized. Poly(ethylene oxide)-b-poly(N-vinylpyrrolidone) (PEO-b-PVP) and Poly(ethylene oxide)-b-poly(N-vinylpyrrolidone-co-N-vinylimidazole) (PEO-b-P(VP-co-VIm) block copolymers were synthesized via reversible deactivation radical polymerization (RDRP) techniques starting from a PEO-macro chain transfer agent. The block copolymers displayed a concentration dependent self-assembly behavior in water which was determined via dynamic light scattering (DLS). It was possible to observe spherical particles via laser scanning confocal microscopy (LSCM) and cryogenic scanning electron microscopy (cryo SEM) at highly concentrated solutions of PEO-b-PVP. Furthermore, a crosslinking strategy with (PEO-b-P(VP-co-VIm) was developed applying a diiodo derived crosslinker diethylene glycol bis(2-iodoethyl) ether to form quaternary amines at the VIm units. The formed crosslinked structures proved stability upon dilution and transfer into organic solvents. Moreover, self-assembly and crosslinking in DMF proved to be more advantageous and the crosslinked structures could be successfully transferred to aqueous solution. The afforded spherical submicron particles could be visualized via LSCM, cryo SEM and Cryo TEM.
Double hydrophilic pullulan-b-poly(acrylamide) block copolymers were synthesized via copper catalyzed alkyne azide cycloaddition (CuAAC) starting from suitable pullulan alkyne and azide functionalized poly(N,N-dimethylacrylamide) (PDMA) and poly(N-ethylacrylamide) (PEA) homopolymers. The conjugation reaction was confirmed via SEC and 1H-NMR measurements. The self-assembly of the block copolymers was monitored with DLS and static light scattering (SLS) measurements indicating the presence of hollow spherical structures. Cryo SEM measurements could confirm the presence of vesicular structures for Pull-b-PEA block copolymers. Solutions of Pull-b-PDMA displayed particles in cryo SEM. Moreover, an end group functionalization of Pull-b-PDMA with Rhodamine B allowed assessing the structure via LSCM and hollow spherical structures were observed indicating the presence of vesicles, too.
An exemplified pathway towards a DHBC based drug delivery vehicle was demonstrated with the block copolymer Pull-b-PVP. The block copolymer was synthesized via RAFT/MADIX techniques starting from a pullulan chain transfer agent. Pull-b-PVP displayed a concentration dependent self-assembly in water with an efficiency superior to the PEO-b-PVP system, which could be observed via DLS. Cryo SEM and LSCM microscopy displayed the presence of spherical structures. In order to apply a reversible crosslinking strategy on the synthesized block copolymer, the pullulan block was selectively oxidized to dialdehydes with NaIO4. The oxidation of the block copolymer was confirmed via SEC and 1H-NMR measurements. The self-assembled and oxidized structures were subsequently crosslinked with cystamine dihiydrochloride, a pH and redox responsive crosslinker resulting in crosslinked vesicles which were observed via cryo SEM. The vesicular structures of crosslinked Pull-b-PVP could be disassembled by acid treatment or the application of the redox agent tris(2-carboxyethyl)-phosphin-hydrochloride. The successful disassembly was monitored with DLS measurements.
To conclude, self-assembled structures from DHBCs such as particles and vesicles display a strong potential to generate an impact on biomedicine and nanotechnologies. The variety of DHBC compositions and functionalities are very promising features for future applications.
In this thesis, I develop a theoretical implementation of prosodic reconstruction and apply it to the empirical domain of German sentences in which part of a focus or contrastive topic is fronted.
Prosodic reconstruction refers to the idea that sentences involving syntactic movement show prosodic parallels with corresponding simpler structures without movement. I propose to model this recurrent observation by ordering syntax-prosody mapping before copy deletion.
In order to account for the partial fronting data, the idea is extended to the mapping between prosody and information structure. This assumption helps to explain why object-initial sentences containing a broad focus or broad contrastive topic show similar prosodic and interpretative restrictions as sentences with canonical word order.
The empirical adequacy of the model is tested against a set of gradient acceptability judgments.
Start-up incentives targeted at unemployed individuals have become an important tool of the Active Labor Market Policy (ALMP) to fight unemployment in many countries in recent years. In contrast to traditional ALMP instruments like training measures, wage subsidies, or job creation schemes, which are aimed at reintegrating unemployed individuals into dependent employment, start-up incentives are a fundamentally different approach to ALMP, in that they intend to encourage and help unemployed individuals to exit unemployment by entering self-employment and, thus, by creating their own jobs. In this sense, start-up incentives for unemployed individuals serve not only as employment and social policy to activate job seekers and combat unemployment but also as business policy to promote entrepreneurship. The corresponding empirical literature on this topic so far has been mainly focused on the individual labor market perspective, however. The main part of the thesis at hand examines the new start-up subsidy (“Gründungszuschuss”) in Germany and consists of four empirical analyses that extend the existing evidence on start-up incentives for unemployed individuals from multiple perspectives and in the following directions:
First, it provides the first impact evaluation of the new start-up subsidy in Germany. The results indicate that participation in the new start-up subsidy has significant positive and persistent effects on both reintegration into the labor market as well as the income profiles of participants, in line with previous evidence on comparable German and international programs, which emphasizes the general potential of start-up incentives as part of the broader ALMP toolset. Furthermore, a new innovative sensitivity analysis of the applied propensity score matching approach integrates findings from entrepreneurship and labor market research about the key role of an individual’s personality on start-up decision, business performance, as well as general labor market outcomes, into the impact evaluation of start-up incentives. The sensitivity analysis with regard to the inclusion and exclusion of usually unobserved personality variables reveals that differences in the estimated treatment effects are small in magnitude and mostly insignificant. Consequently, concerns about potential overestimation of treatment effects in previous evaluation studies of similar start-up incentives due to usually unobservable personality variables are less justified, as long as the set of observed control variables is sufficiently informative (Chapter 2).
Second, the thesis expands our knowledge about the longer-term business performance and potential of subsidized businesses arising from the start-up subsidy program. In absolute terms, the analysis shows that a relatively high share of subsidized founders successfully survives in the market with their original businesses in the medium to long run. The subsidy also yields a “double dividend” to a certain extent in terms of additional job creation. Compared to “regular”, i.e., non-subsidized new businesses founded by non-unemployed individuals in the same quarter, however, the economic and growth-related impulses set by participants of the subsidy program are only limited with regard to employment growth, innovation activity, or investment. Further investigations of possible reasons for these differences show that differential business growth paths of subsidized founders in the longer run seem to be mainly limited by higher restrictions to access capital and by unobserved factors, such as less growth-oriented business strategies and intentions, as well as lower (subjective) entrepreneurial persistence. Taken together, the program has only limited potential as a business and entrepreneurship policy intended to induce innovation and economic growth (Chapters 3 and 4).
And third, an empirical analysis on the level of German regional labor markets yields that there is a high regional variation in subsidized start-up activity relative to overall new business formation. The positive correlation between regular start-up intensity and the share among all unemployed individuals who participate in the start-up subsidy program suggests that (nascent) unemployed founders also profit from the beneficial effects of regional entrepreneurship capital. Moreover, the analysis of potential deadweight and displacement effects from an aggregated regional perspective emphasizes that the start-up subsidy for unemployed individuals represents a market intervention into existing markets, which affects incumbents and potentially produces inefficiencies and market distortions. This macro perspective deserves more attention and research in the future (Chapter 5).