Within this thesis the hedging behaviour of airlines from 2005 to 2019 is analysed by using an unbalanced panel dataset consisting of a total of 78 airlines from 39 countries. The focus of the analysis is on financial and operational hedging as well as the influence of both on CO2 emissions and the development of emitted CO2 emissions. For the analysis Probit models with random effects and OLS models with fixed effects were used. The results regarding the relationship between leverage and financial hedging indicate a negative relationship between leverage and financial fuel hedging and a non-linear convex relationship for highly leveraged airlines, which is contrary to the theory of financial distress. In addition, the study provides evidence that airlines using other types of derivatives, such as interest rate derivatives, engage in more fuel hedging. In terms of operational hedging, the analysis suggests that operating a diversified fleet is a complement to, rather than a substitute for, financial hedging. With regard to alliance membership, the results do not show that alliance membership is a substitute for financial hedging, as members of alliances are more likely to engage in hedging transactions and to a greater extent. The analysis shows that the relative CO2 emissions fall in the period under review, but this does not apply to the absolute amount. No general statement can be made about the influence of financial and operational hedging on CO2 emissions, as the results are mixed.

When natural phenomena and data-based relations are driven by dynamics which are not purely local, they cannot be described satisfactorily by partial differential equations. As a consequence, mathematical models governed by nonlocal operators are of interest. This thesis is concerned with nonlocal operators of the form $\mathcal{L}u(x) = PV \int_{\mathbb{R}^d} (u(x)-u(y)) K(x,dy), x \in \mathbb{R}^d$, which are determined through a family of Borel measures $K=(K(x, \cdot))_{x \in \mathbb{R}^d}$ on $\mathbb{R}^d$ and which act on the vector space of Borel measurable functions $u: \mathbb{R}^d \rightarrow \mathbb{R}$. For a large class of families $K$, namely those where $K$ is a symmetric transition kernel satisfying a specific non-degeneracy condition, a variational theory for nonlocal equations of the type $\mathcal{L}u=f$ is established which builds upon gadgets from both measure theory and classical analysis. While measure theory is used to provide a nonlocal integration by parts formula that allows to set up a reasonable variational formulation of the above equation in dependency of the particular boundary condition (Dirichlet, Robin, Neumann) considered, Hilbert space theory and fixed-point approaches are utilized to develop sufficient conditions for the existence of variational solutions. This theory is then applied to two specific realizations of $\mathcal{L}$ of interest before a weak maximum principle is established which is finally used to study overlapping domain decomposition methods for the nonlocal and homogeneous Dirichlet problem.

Immer wieder tauchen Fragen nach dem Stüve-Diagramm und seiner Benutzung auf. Es gibt zwar neben der Vorlesung “Einführung in die Meteorologie” auch erklärende Darstellungen in den empfohlenen Lehrbüchern und im Internet. Diese scheinen aber offenbar nicht zufriedenstellend zu sein. Deshalb habe ich nachfolgend versucht, die Antworten auf die häufigsten Fragen in Form einer Anleitung zusammen zu fassen. Ich danke em. Prof. Dr. Alfred Helbig, der im Rahmen seiner früheren Tätigkeit im operationellen Dienst umfangreiche praktische Erfahrung mit Radiosonden-Aufstiegen erworben hat, sowie Dr. Micha Gryschka (Institut für Meteorologie und Klimatologie, Leibniz Universität Hannover) für die hilfreichen Kommentare zum Manuskript.

Mit Fokus auf historischen Liedern aus Deutschland, Frankreich, England, Irland, den USA, Österreich, den Niederlanden, Slowenien, Polen, Italien, Neuseeland und der Schweiz zeichnet dieses Buch ein breites und lebendiges Panorama der Frühen Neuzeit. Die Sammlung spannt den Bogen von klassischen Lerninhalten bis hin zu aktuellen Forschungsperspektiven: Reformation, Amerikanische und Französische Revolution, Höfische Kultur, Kriminalität, Seefahrt, militärische und diplomatische Konflikte sind ebenso Thema wie die Geschlechterordnung, globale Migration oder historische Identitäts- und Fremdheitsvorstellungen. Eine systematische Verschlagwortung erleichtert den Zugriff und erlaubt vielfältige Kombinationen für die akademische und schulische Lehre. Jedes der 101 Lieder wird mit Informationen zum historischen Kontext, zur Überlieferung und zu online verfügbaren Vertonungen präsentiert. Hinzu kommen Aufgabenstellungen und Anregungen für die Diskussion im Kurs oder Seminar. Auch das lange und mitunter ambivalente Nachleben neuzeitlicher Lieder im 19. und 20. Jahrhundert wird beleuchtet. Darüber hinaus bietet der Band methodische Hinweise und Anregungen zur eigenständigen Recherche und Analyse historischer Lieder, beispielsweise in Seminar- und Abschlussarbeiten.

Case-Based Reasoning (CBR) is a symbolic Artificial Intelligence (AI) approach that has been successfully applied across various domains, including medical diagnosis, product configuration, and customer support, to solve problems based on experiential knowledge and analogy. A key aspect of CBR is its problem-solving procedure, where new solutions are created by referencing similar experiences, which makes CBR explainable and effective even with small amounts of data. However, one of the most significant challenges in CBR lies in defining and computing meaningful similarities between new and past problems, which heavily relies on domain-specific knowledge. This knowledge, typically only available through human experts, must be manually acquired, leading to what is commonly known as the knowledge-acquisition bottleneck. One way to mitigate the knowledge-acquisition bottleneck is through a hybrid approach that combines the symbolic reasoning strengths of CBR with the learning capabilities of Deep Learning (DL), a sub-symbolic AI method. DL, which utilizes deep neural networks, has gained immense popularity due to its ability to automatically learn from raw data to solve complex AI problems such as object detection, question answering, and machine translation. While DL minimizes manual knowledge acquisition by automatically training models from data, it comes with its own limitations, such as requiring large datasets, and being difficult to explain, often functioning as a "black box". By bringing together the symbolic nature of CBR and the data-driven learning abilities of DL, a neuro-symbolic, hybrid AI approach can potentially overcome the limitations of both methods, resulting in systems that are both explainable and capable of learning from data. The focus of this thesis is on integrating DL into the core task of similarity assessment within CBR, specifically in the domain of process management. Processes are fundamental to numerous industries and sectors, with process management techniques, particularly Business Process Management (BPM), being widely applied to optimize organizational workflows. Process-Oriented Case-Based Reasoning (POCBR) extends traditional CBR to handle procedural data, enabling applications such as adaptive manufacturing, where past processes are analyzed to find alternative solutions when problems arise. However, applying CBR to process management introduces additional complexity, as procedural cases are typically represented as semantically annotated graphs, increasing the knowledge-acquisition effort for both case modeling and similarity assessment. The key contributions of this thesis are as follows: It presents a method for preparing procedural cases, represented as semantic graphs, to be used as input for neural networks. Handling such complex, structured data represents a significant challenge, particularly given the scarcity of available process data in most organizations. To overcome the issue of data scarcity, the thesis proposes data augmentation techniques to artificially expand the process datasets, enabling more effective training of DL models. Moreover, it explores several deep learning architectures and training setups for learning similarity measures between procedural cases in POCBR applications. This includes the use of experience-based Hyperparameter Optimization (HPO) methods to fine-tune the deep learning models. Additionally, the thesis addresses the computational challenges posed by graph-based similarity assessments in CBR. The traditional method of determining similarity through subgraph isomorphism checks, which compare nodes and edges across graphs, is computationally expensive. To alleviate this issue, the hybrid approach seeks to use DL models to approximate these similarity calculations more efficiently, thus reducing the computational complexity involved in graph matching. The experimental evaluations of the corresponding contributions provide consistent results that indicate the benefits of using DL-based similarity measures and case retrieval methods in POCBR applications. The comparison with existing methods, e.g., based on subgraph isomorphism, shows several advantages but also some disadvantages of the compared methods. In summary, the methods and contributions outlined in this work enable more efficient and robust applications of hybrid CBR and DL in process management applications.