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Institute
- Institut für Biochemie und Biologie (51)
- Institut für Chemie (35)
- Institut für Geowissenschaften (28)
- Institut für Physik und Astronomie (26)
- Institut für Ernährungswissenschaft (17)
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- Wirtschaftswissenschaften (16)
- Department Psychologie (10)
- Historisches Institut (10)
Heutzutage ist es üblich, die Ehre als einen obsoleten Begriff zu betrachten, der nur einem archaischen Denkmodell zuzuordnen ist und keine handlungsprägende Größe in der Gegenwartsgesellschaft darstellt. Die Ehrenmorde, die heute noch in unterschiedlichen Teilen der Welt verübt werden, scheinen diese Behauptung zu bestätigen. In diesem Buch wird jedoch die These vertreten, dass nicht der Ehrbegriff, sondern seine Deutungen archaischer Natur und daher in Frage zu stellen sind. Die Ehre ist die Bezeichnung des sozialen Werts eines Menschen, den er infolge seiner achtenswerten Handlungen erlangt. Also kann sie kein Motiv für moralisch fragwürdige Praktiken bilden. Vor diesem Hintergrund werden die Formen und die Voraussetzungen der Ehre dargestellt, die sowohl in Bezug auf unsere Zeit anpassungsfähig als auch ethisch tragbar sind.
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.