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In the last years, the public interest in epidemiology and mathematical modeling of disease spread has increased - mainly caused by the COVID-19 pandemic, which has emphasized the urgent need for accurate and timely modelling of disease transmission. However, even prior to that, mathematical modelling has been used for describing the dynamics and spread of infectious diseases, which is vital for developing effective interventions and controls, e.g., for vaccination campaigns and social restrictions like lockdowns. The forecasts and evaluations provided by these models influence political actions and shape the measures implemented to contain the virus.
This research contributes to the understanding and control of disease spread, specifically for Dengue fever and COVID-19, making use of mathematical models and various data analysis techniques. The mathematical foundations of epidemiological modelling, as well as several concepts for spatio-temporal diffusion like ordinary differential equation (ODE) models, are presented, as well as an originally human-vector model for Dengue fever, and the standard (SEIR)-model (with the potential inclusion of an equation for deceased persons), which are suited for the description of COVID-19. Additionally, multi-compartment models, fractional diffusion models, partial differential equations (PDE) models, and integro-differential models are used to describe spatial propagation of the diseases.
We will make use of different optimization techniques to adapt the models to medical data and estimate the relevant parameters or finding optimal control techniques for containing diseases using both Metropolis and Lagrangian methods. Reasonable estimates for the unknown parameters are found, especially in initial stages of pandemics, when little to no information is available and the majority of the population has not got in contact with the disease. The longer a disease is present, the more complex the modelling gets and more things (vaccination, different types, etc.) appear and reduce the estimation and prediction quality of the mathematical models.
While it is possible to create highly complex models with numerous equations and parameters, such an approach presents several challenges, including difficulties in comparing and evaluating data, increased risk of overfitting, and reduced generalizability. Therefore, we will also consider criteria for model selection based on fit and complexity as well as the sensitivity of the model with respect to specific parameters. This also gives valuable information on which political interventions should be more emphasized for possible variations of parameter values.
Furthermore, the presented models, particularly the optimization using the Metropolis algorithm for parameter estimation, are compared with other established methods. The quality of model calculation, as well as computational effort and applicability, play a role in this comparison. Additionally, the spatial integro-differential model is compared with an established agent-based model. Since the macroscopic results align very well, the computationally faster integro-differential model can now be used as a proxy for the slower and non-traditionally optimizable agent-based model, e.g., in order to find an apt control strategy.
In a world where language defines the boundaries of one's understanding, the words of Austrian philosopher Ludwig Wittgenstein resonate profoundly. Wittgenstein's assertion that "Die Grenzen meine Sprache bedeuten die Grenzen meiner Welt" (Wittgenstein 2016: v. 5.6) underscores the vital role of language in shaping our perceptions. Today, in a globalized and interconnected society, fluency in foreign languages is indispensable for individual success. Education must break down these linguistic barriers, and one promising approach is the integration of foreign languages into content subjects.
Teaching content subjects in a foreign language, a practice known as Content Language Integrated Learning (CLIL), not only enhances language skills but also cultivates cognitive abilities and intercultural competence. This approach expands horizons and aligns with the core principles of European education (Leaton Gray, Scott & Mehisto 2018: 50). The Kultusministerkonferenz (KMK) recognizes the benefits of CLIL and encourages its implementation in German schools (cf. KMK 2013a).
With the rising popularity of CLIL, textbooks in foreign languages have become widely available, simplifying teaching. However, the appropriateness of the language used in these materials remains an unanswered question. If textbooks impose excessive linguistic demands, they may inadvertently limit students' development and contradict the goal of CLIL.
This thesis focuses on addressing this issue by systematically analyzing language requirements in CLIL teaching materials, emphasizing receptive and productive skills in various subjects based on the Common European Framework of Reference. The aim is to identify a sequence of subjects that facilitates students' language skill development throughout their school years. Such a sequence would enable teachers to harness the full potential of CLIL, fostering a bidirectional approach where content subjects facilitate language learning.
While research on CLIL is extensive, studies on language requirements for bilingual students are limited. This thesis seeks to bridge this gap by presenting findings for History, Geography, Biology, and Mathematics, allowing for a comprehensive understanding of language demands. This research endeavors to enrich the field of bilingual education and CLIL, ultimately benefiting the academic success of students in an interconnected world.
The trends of industry 4.0 and the further enhancements toward an ever changing factory lead to more mobility and flexibility on the factory floor. With that higher need of mobility and flexibility the requirements on wireless communication rise. A key requirement in that setting is the demand for wireless Ultra-Reliability and Low Latency Communication (URLLC). Example use cases therefore are cooperative Automated Guided Vehicles (AGVs) and mobile robotics in general. Working along that setting this thesis provides insights regarding the whole network stack. Thereby, the focus is always on industrial applications. Starting on the physical layer, extensive measurements from 2 GHz to 6 GHz on the factory floor are performed. The raw data is published and analyzed. Based on that data an improved Saleh-Valenzuela (SV) model is provided. As ad-hoc networks are highly depended onnode mobility, the mobility of AGVs is modeled. Additionally, Nodal Encounter Patterns (NEPs) are recorded and analyzed. A method to record NEP is illustrated. The performance by means of latency and reliability are key parameters from an application perspective. Thus, measurements of those two parameters in factory environments are performed using Wireless Local Area Network (WLAN) (IEEE 802.11n), private Long Term Evolution (pLTE) and 5G. This showed auto-correlated latency values. Hence, a method to construct confidence intervals based on auto-correlated data containing rare events is developed. Subsequently, four performance improvements for wireless networks on the factory floor are proposed. Of those optimization three cover ad-hoc networks, two deal with safety relevant communication, one orchestrates the usage of two orthogonal networks and lastly one optimizes the usage of information within cellular networks.
Finally, this thesis is concluded by an outlook toward open research questions. This includes open questions remaining in the context of industry 4.0 and further the ones around 6G. Along the research topics of 6G the two most relevant topics concern the ideas of a network of networks and overcoming best-effort IP.
Focusing on the triangulation of detective fiction, masculinity studies and disability studies, "Investigating the Disabled Detective – Disabled Masculinity and Masculine Disability in Contemporary Detective Fiction" shows that disability challenges common ideals of (hegemonic) masculinity as represented in detective fiction. After a theoretical introduction to the relevant focal points of the three research fields, the dissertation demonstrates that even the archetypal detectives Dupin and Holmes undermine certain nineteenth-century masculine ideals with their peculiarities. Shifting to contemporary detective fiction and adopting a literary disability studies perspective, the dissertation investigates how male detectives with a form of neurodiversity or a physical impairment negotiate their masculine identity in light of their disability in private and professional contexts. It argues that the occupation as a detective supports the disabled investigator to achieve ‘masculine disability’. Inversing the term ‘disabled masculinity’, predominantly used in research, ‘masculine disability’ introduces a decisively gendered reading of neurodiversity and (acquired) physical impairment in contemporary detective fiction. The term implies that the disabled detective (re)negotiates his masculine identity by implementing the disability in his professional investigations and accepting it as an important, yet not defining, characteristic of his (gender) identity. By applying this approach to five novels from contemporary British and American detective fiction, the dissertation demonstrates that masculinity and disability do not negate each other, as commonly assumed. Instead, it emphasises that disability allows the detective, as much as the reader, to rethink masculinity.
Empirische Studien in der Softwaretechnik verwenden Software Repositories als Datenquellen, um die Softwareentwicklung zu verstehen. Repository-Daten werden entweder verwendet, um Fragen zu beantworten, die die Entscheidungsfindung in der Softwareentwicklung leiten, oder um Werkzeuge bereitzustellen, die bei praktischen Aspekten der Entwicklung helfen. Studien werden in die Bereiche Empirical Software Engineering (ESE) und Mining Software Repositories (MSR) eingeordnet. Häufig konzentrieren sich Studien, die mit Repository-Daten arbeiten, auf deren Ergebnisse. Ergebnisse sind aus den Daten abgeleitete Aussagen oder Werkzeuge, die bei der Softwareentwicklung helfen. Diese Dissertation konzentriert sich hingegen auf die Methoden und High-Order-Methoden, die verwendet werden, um solche Ergebnisse zu erzielen. Insbesondere konzentrieren wir uns auf inkrementelle Methoden, um die Verarbeitung von Repositories zu skalieren, auf deklarative Methoden, um eine heterogene Analyse durchzuführen, und auf High-Order-Methoden, die verwendet werden, um Bedrohungen für Methoden, die auf Repositories arbeiten, zu operationalisieren. Wir fassen dies als technische und methodische Verbesserungen zusammen um zukünftige empirische Ergebnisse effektiver zu produzieren. Wir tragen die folgenden Verbesserungen bei. Wir schlagen eine Methode vor, um die Skalierbarkeit von Funktionen, welche über Repositories mit hoher Revisionszahl abstrahieren, auf theoretisch fundierte Weise zu verbessern. Wir nutzen Erkenntnisse aus abstrakter Algebra und Programminkrementalisierung, um eine Kernschnittstelle von Funktionen höherer Ordnung zu definieren, die skalierbare statische Abstraktionen eines Repositorys mit vielen Revisionen berechnen. Wir bewerten die Skalierbarkeit unserer Methode durch Benchmarks, indem wir einen Prototyp mit MSR/ESE Wettbewerbern vergleichen. Wir schlagen eine Methode vor, um die Definition von Funktionen zu verbessern, die über ein Repository mit einem heterogenen Technologie-Stack abstrahieren, indem Konzepte aus der deklarativen Logikprogrammierung verwendet werden, und mit Ideen zur Megamodellierung und linguistischen Architektur kombiniert werden. Wir reproduzieren bestehende Ideen zur deklarativen Logikprogrammierung mit Datalog-nahen Sprachen, die aus der Architekturwiederherstellung, der Quellcodeabfrage und der statischen Programmanalyse stammen, und übertragen diese aus der Analyse eines homogenen auf einen heterogenen Technologie-Stack. Wir liefern einen Proof-of-Concept einer solchen Methode in einer Fallstudie. Wir schlagen eine High-Order-Methode vor, um die Disambiguierung von Bedrohungen für MSR/ESE Methoden zu verbessern. Wir konzentrieren uns auf eine bessere Disambiguierung von Bedrohungen durch Simulationen, indem wir die Argumentation über Bedrohungen operationalisieren und die Auswirkungen auf eine gültige Datenanalysemethodik explizit machen. Wir ermutigen Forschende, „gefälschte“ Simulationen ihrer MSR/ESE-Szenarien zu erstellen, um relevante Erkenntnisse über alternative plausible Ergebnisse, negative Ergebnisse, potenzielle Bedrohungen und die verwendeten Datenanalysemethoden zu operationalisieren. Wir beweisen, dass eine solche Art des simulationsbasierten Testens zur Disambiguierung von Bedrohungen in der veröffentlichten MSR/ESE-Forschung beiträgt.