The 10 most recently published documents
The rapid evolution of wireless communication technologies, particularly the introduction
of Fifth-Generation (5G) networks and the anticipated transition to Sixth-Generation (6G)
systems, ushers in a new era of connectivity, enabling transformative applications across
industrial automation, the Internet of Everything (IoE), and the Industrial Internet of Things
(IIoT). However, the exponential growth in the number of connected devices, stringent reliability
requirements, and increasing security challenges pose significant hurdles for current network
architectures. This dissertation addresses these challenges by proposing innovative frameworks
and mechanisms that enhance reliability, optimize resource utilization, and strengthen security
and trust management in next-generation mobile networks.
The first contribution of this dissertation focuses on reliability enhancements in 5G networks.
While existing mechanisms, such as Dual Connectivity (DC) and Network Function (NF)
redundancy, provide partial solutions, they do not fully resolve application-layer reliability
and dynamic server failover. To bridge this gap, this work introduces the Make-Before-Break-
Reliability (MBBR) and enhanced Make-Before-Break-Reliability (eMBBR) mechanisms. These
frameworks proactively establish redundant communication paths, ensuring seamless failovers
with minimal latency and service disruption. By extending reliability to the application layer
and integrating adaptive path selection and dynamic failover capabilities, these mechanisms
offer robust solutions for latency-sensitive and mission-critical applications.
The second major contribution addresses bandwidth optimization for industrial networks.
The black channel paradigm, widely adopted for industrial safety applications, relies heavily on
cyclic keep-alive messages to detect connection loss, leading to significant signaling overhead.
This dissertation proposes a novel solution leveraging 5G Channel State Information (CSI)
to replace cyclic messaging with real-time connection quality monitoring. By exposing CSI
metrics, such as Signal-to-Noise Ratio (SNR) and Channel Quality Indicator (CQI), to the
application layer, the proposed mechanism reduces bandwidth consumption while maintaining
the safety and reliability requirements of industrial networks.
Addressing the growing complexity of security requirements in IIoT, the third contribution
introduces the AF-based Security Framework (AERO) framework. This framework empowers
application providers to dynamically apply cryptographic mechanisms to the user plane,
overcoming the limitations of legacy protocols and eliminating the need for redundant security
layers. By ensuring backward compatibility and enabling both static and dynamic configuration
of user plane encryption, AERO enhances security while minimizing computational overhead
and reducing transmission delays.
The fourth and final contribution redefines trust management in mobile networks through
the SecUre deleGAtion of tRust (SUGAR) framework. Traditional trust models, which rely
on identity chips for each connected device, are becoming increasingly impractical in the
IoE era, where billions of devices require connectivity. The SUGAR framework introduces a
delegation-based trust model, allowing Parent Devices (PaDs) to delegate trust to multiple
Child Devices (ChDs) securely. This approach eliminates the need for individual identity chips,
significantly reducing costs and enhancing scalability. Integration with System-on-a-Chip
(SoC)-based identity enclaves further strengthens the security of trust credentials.
The findings of this dissertation offer substantial contributions to both academia and
industry. The proposed frameworks effectively address critical gaps in current 5G standards
and provide valuable contributions for developing the 6G framework. By enhancing reliability,
optimizing bandwidth, and redefining security and trust management, this dissertation provides
a comprehensive foundation for the design and deployment of next-generation mobile networks.
Furthermore, the solutions presented are adaptable to a wide range of applications, including
industrial automation, autonomous systems, and smart city infrastructures.
In conclusion, this dissertation represents a significant step toward realizing the full
potential of next-generation mobile networks. By addressing key challenges in reliability,
resource optimization, security, and trust management, the proposed frameworks pave the way
for scalable, secure, and efficient mobile ecosystems that are essential for the dynamic and
interconnected world of the future.
This thesis investigates the potential of LLMs to provide personalized and context aware feedback in data science education. Traditional automated feedback systems often face challenges related to adaptiveness, scalability, and pedagogical alignment. To address these limitations, an experimental study was conducted using a custom-built AI tutor based on GPT-4o, which guided students through six clustering assignments designed around k-means and DBSCAN concepts. Data were collected from pre- and post experiment questionnaires and 516 dialogue exchanges recorded across ten individual tutoring sessions. A mixed-methods approach was adopted. Quantitative analysis compared pre and post-survey results to measure normalized learning gain (g = 0.375), effect size (Cohen’s d = 0.321), and statistical significance (t(9) = 0.811, p > 0.05). Qualitative analysis involved manual coding of AI responses for feedback type, adaptiveness, and student engagement. Results showed that students generally perceived the AI tutor positively, emphasizing its clear explanations, step-by-step guidance, and timely feedback. While moderate conceptual improvement was observed, statistical effects remained small, suggesting that perceived learning gains may exceed measured performance improvements. Conversational analysis revealed that adaptive responses and interactive questioning supported engagement, though occasional inconsistencies and reliance on predefined solutions limited deeper adaptiveness. The study contributes to educational technology research by providing empirical insight into both the capabilities and current constraints of LLM-based tutoring. Although student satisfaction was high, findings highlight the need for more sophisticated scaffolding, enhanced contextual adaptiveness, and hybrid human-AI feedback frameworks. Overall, this research demonstrates the promise of LLMs in delivering scalable, personalized support in data science education, while emphasizing the importance of continued evaluation to ensure pedagogical reliability and meaningful learning outcomes.
This study examines student housing experiences in Koblenz through a mixed-methods approach that integrates surveys, geospatial analysis, and quantitative modeling to explore affordability, accessibility, satisfaction, and equity. By analyzing both objective factors—like rent, distance to campus, and travel times—and subjective measures such as satisfaction and sentiment, it identifies disparities across student groups, especially affecting international students. The findings suggest that housing outcomes stem from both structural conditions and lived experiences, revealing possible biases within the housing system. The study advocates for targeted interventions, including expanding affordable residences, enhancing transport connectivity, and promoting transparency in housing allocation to ensure equitable access in Germany’s higher education context.
Improving patient care is an ongoing process, evolving from early evidence-based practices to modern AI-driven approaches. This thesis explores three key research directions aimed at improving clinical decisionmaking through AI. Adverse events, defined as negative and harmful outcomes that occur during medical care, present major challenges for hospitals. Most data-driven research using electronic health records relies on data from tertiary referral hospitals, but their patient population differs from those in hospitals of medium level of care. The first major contribution of this thesis is a data-driven Trigger Tool for predicting adverse events trained on data from a hospital of medium level of care. This tool uses a concise set of laboratory values measured within the first 24 hours of hospitalization. In addition to models using numerical features, we devised models using dichotomized features that indicate whether a laboratory value falls below or above a reference threshold. Our findings show that models using numerical features achieve high accuracy in predicting acute kidney injury and the COVID-19 associated adverse events in-hospital mortality and transfer to the ICU. Models using dichotomous features performonly slightly worse but offer better interpretability.
The second major contribution is the online-updateable AI model OptAB for selecting optimal antibiotics in sepsis patients. OptAB aims to minimize the sepsis-related organ failure score (SOFA-Score) while accounting for nephrotoxic and hepatotoxic side effects. OptAB relies on a hybrid neural network differential equation algorithm tailored to the special properties of patient data, including irregular measurements, missing values, and time-dependent confounding. Time-dependent confounding describes a dependence between time-varying covariates and treatment decisionsmade by physicians, often leading to biased treatment effect estimates. OptAB generates disease course forecasts for (combinations of ) the antibiotics vancomycin, ceftriaxone, and piperacillin/tazobactam and learns realistic treatment effects on the SOFA-Score and side effect indicative laboratory values. Results indicate that OptAB’s recommendations achieve faster efficacy than the administered antibiotics while reducing side effects.
The third major contribution is DoseAI, an online-updateable AI model that extends OptAB to optimize dosing regimens. DoseAI mitigates time-dependent confounding in dosage selection by minimizing the absolute spearman correlation between predicted and future treatment dosages. It forecasts disease progression under alternative dosing regimens and proposes optimal chemotherapy and radiotherapy dosing regimens for synthetic cancer patients. These regimens effectively reduce the tumor volume while adhering to varying maximum allowed weight loss constraints, used as a measure of toxicity.
Mathematiklehrkräfte sind bisher unzureichend auf die Digitalisierung des Mathematikunterrichts vorbereitet. Daher ist es notwendig bereits im Studium passende Lernangebote zur Entwicklung professioneller Kompetenzen für den Einsatz digitaler Mathematikwerkzeuge zu schaffen. Im Rahmen der vorliegenden Arbeit wird eine fachdidaktische Lehrveranstaltung für angehende Mathematiklehrkräfte der Sekundarstufen
konzipiert, um deren professionellen Kompetenzen mit Blick auf den Einsatz digitaler Mathematikwerkzeuge im Kontext der Leitidee Strukturen und funktionaler Zusammenhang zu fördern. Neben Werkzeugkompetenzen zur Nutzung der digitalen Mathematikwerkzeuge sollen die Kompetenzen zur Planung und Gestaltung von Mathematikunterricht entwickelt werden. Für das Themenfeld funktionale Zusammenhänge sind die beiden digitalen Mathematikwerkzeuge GeoGebra und Tabellenkalkulationsprogramm besonders relevant. Deren Potentiale für das funktionale Denken werden insbesondere in Bezug auf Darstellungsformen und Repräsentationswechsel sowie Lernschwierigkeiten thematisiert. Ergänzend zu den genannten Kompetenzen werden zudem Überzeugungen zum Einsatz digitaler Mathematikwerkzeuge in den Blick genommen. Diese haben einen erheblichen Einfluss darauf, ob die erworbenen Kompetenzen im eigenen Unterricht eingesetzt werden oder nicht.
Zur Ermittlung des Ist-Zustands bei Kompetenzen und Überzeugungen der angehenden
Mathematiklehrkräfte vor dem Besuch der Lehrveranstaltung wurden zwei Erhebungsinstrumente entwickelt und eingesetzt. Neben einem Fragebogen zur Selbsteinschätzung erfolgte die Datenerhebung mittels eines neu entwickelten Kompetenztests
im Sinne eines Leistungstests. Es zeigte sich, dass die professionellen Kompetenzen und Vorerfahrungen bei den Studierenden äußerst heterogen sind.
Um Rückschlüsse auf die Wirksamkeit der Lehrveranstaltung ziehen zu können, wurden die beiden Erhebungsinstrumente im Pre-Post-Design eingesetzt. Die Ergebnisse nach Besuch der Lehrveranstaltung lassen auf positive Effekte schließen. Gleichzeitig wird deutlich, dass eine einzelne Lehrveranstaltungen im Studium nicht ausreicht. Zudem besteht Bedarf an weiterer Forschung in diesem Bereich, insbesondere was die Untersuchung langfristiger Effekte und die Zusammenhänge zwischen den verschiedenen Kompetenzfacetten und Überzeugungen betrifft.
In view of the requirement that students (e.g. in Rhineland-Palatinate) from grade 7 onwards
have to work independently with dynamic geometry software (e.g. GeoGebra) and a
spreadsheet program, teachers have a special role in the integration of digital mathematics
toolsin mathematics lessons. For example this can be implemented, with regard to the guiding
idea L4 (Functional connection), since the use of digital mathematics tools is recommended to
promote functional thinking. Within the qualitive offensive for pre-service-teacher training,
which has been established by the Ministry of Education, the project MoSAiK provided a
Digital Research Workshop Multiple Representations in Mathematics Education using the
topic of elementary functions on secondary school level. On the one hand this workshop
examined tool skills of students in relation to the two softwares GeoGebra and spreadsheets
that apply for secondary schools, high schools and vocational schools. On the other hand
(technology-related) beliefs of the students were researched according to the function
concept.
For that reason a specific didactic workshop, which included on the one hand the fostering of
operating skills for the tools GeoGebra and spreadsheet and on the other hand the
subsequent planning and teaching of lessons had been conceived. Before and after attending
the workshop the operating and selection skills in both tools have been measured in a sample
of 55 students using a self-developed competency test. In addition, the sub-areas TK, TPK and
TCK of the TPACK-framework, self-efficacy beliefs and technology-related beliefs regarding
the advantages and disadvantages of using digital mathematics tools were collected using a
student questionnaire. The workshop intervention achieved a significant effect on the
development of the students' operating skills for both tools – GeoGebra and spreadsheet. The
results of a qualitative content analysis with quantitative elements show that in particular the
visualizing of functions and the checking of results were viewed as advantages of the digital
tools when teaching functional thinking. The most frequently cited disadvantage was a danger
to manual calculation methods. With regard to the time required, on the one hand, savings
were expected when using digital tools, but on the other hand, there were fears of high
expenditure if pupils do not have the necessary tool skills.
Mit der Demographischen Dividende (DD) wird ein wirtschaftlicher Vorteil beschrieben, der aus einer Verschiebung der Altersstruktur einer Gesellschaft hin zu einem höheren Anteil erwerbsfähiger Bevölkerung resultiert. Ursprünglich am Beispiel der so genannten ‚Tigerstaaten‘ Südostasiens entwickelt, gilt die DD als ein Modell für wirtschaftliches Wachstum durch Fertilitätsrückgänge. Die vorliegende Dissertation befasst sich diskursanalytisch mit der Verwendung des Konzepts in Entwicklungszusammenarbeit, Politikberatung und Bevölkerungsforschung sowie dessen Übertragung auf den Globalen Süden als ein leitendes Paradigma in der internationalen Entwicklungszusammenarbeit. Hierzu wurden teilstrukturierte Interviews mit Expertinnen und Experten geführt.
Die Debatte um die DD ist von uneinheitlichen Definitionen darüber geprägt, was die DD genau ist. Eine technisch-mathematische Perspektive sieht die DD als reine Folge der Altersstrukturverschiebung, während eine behavioristische Sicht zusätzlich die Auswirkungen individueller Faktoren wie Bildung, Emanzipation und wirtschaftlichen Handelns mit einbezieht. Ein Wachstumseffekt durch Altersstrukturverschiebungen wird im Globalen Südens voraussichtlich weniger stark ausfallen als in Südostasien. Gründe hierfür sind langsamere Fertilitätsrückgänge, geringere Arbeitsmarktanteile und unterschiedliche sozioökonomische Rahmenbedingungen. Hinzu kommt, dass heute die Prinzipien von sexueller und reproduktiver Gesundheit und Rechte (SRGR) zu respektieren sind, die während des südostasiatischen Wirtschaftswunders nicht etabliert waren. Dieses Leitbild wurde im Jahr 1994 auf der Konferenz für Bevölkerung und Entwicklung in Kairo hegemonial. Faktoren wie Bildung, soziale Sicherungssysteme, politische Stabilität und globale Abhängigkeiten sind jedoch ebenso entscheidend für den sozialen und wirtschaftlichen Fortschritt.
Entwicklungspolitische Programme propagieren vielfach Familienplanung als Schlüssel zur DD, doch stoßen diese Programme auch auf Kritik. Denn die Institutionen, welche den Diskurs um die DD dominieren, haben zum Teil eine problematische historische Verbindung zu eugenischen oder rassistischen Programmen, was der Glaubwürdigkeit ihrer Positionen schaden kann. Zudem werden die Programme in großen Teilen von eben diesen privaten Stiftungen und Initiativen mit fragwürdiger Historie finanziert, so dass die Macht über die Ausgestaltung derartiger Programme von staatlichen Akteurinnen und Akteuren hin zu privaten Akteuren verschoben wird. Die Interviews und die weiterhin verwendete Literatur zeigen auf, dass langfristige Investitionen in Bildung, Gesundheitsversorgung und soziale Sicherungssysteme größere und nachhaltigere wirtschaftliche Fortschritte bewirken als rein demographische Ansätze. Und trotz ihrer Einschränkungen bleibt die DD ein nützliches Konzept, sofern sie in einen breiteren Kontext eingebettet wird. Die Herausforderung besteht darin das Konzept so anzuwenden, dass es nicht nur in einem technokratischen Verständnis hegemonial wird, sondern dass die Verbindungen zwischen der Verschiebung von Bevölkerungsanteilen, Bildung, Stärkung des Gesundheitswesens und Wirtschafts- wie Arbeitsmarktpolitik verstanden werden.
The integration of the different stakeholder needs and environmental constraints is the key goal of requirements engineering. This demands collaborations between involved parties, to reach “understandability of the system”, what is particularly challenging for collaborations over different organisations. High quality requirements engineering is the key factor to address these challenges. Requirements are input to all development steps and carry the knowledge to exchange—requirements engineering is an overall life-cycle spanning and in its essence a knowledge management task.
The main goal of the T-Reqs framework presented in this thesis is to enable semantic interoperability and to sustain the knowledge by conceptualization of the requirements engineering process applied to European space projects. T-Reqs’ objective is to formally capture the information carried by the requirements to provide top-shelf inputs for the consecutive system and discipline-specific development tasks, in particular within model-based systems engineering. Emphasis is placed on the nature of relationships that exist among requirements and requirement documents. The T-Reqs formalism addresses the structuring of requirements as well as their potential reuse, e.g., in product line development or even between different projects. This implies an overall System Requirements Specification that is distributed in many specifications documents and involves requirements of different levels of abstractions from abstract goals to implementation details. This thesis especially focuses the specification and validation of such requirements documents.
The T-Reqs traceability model provides a means to trace not only individual requirements,
but also consider relations among views such as documents, taking into account the role
they play for stakeholders, especially in reuse. It is shown, how formalization of dependencies, such as for tailoring of standards, enables automated quality checks to facilitate reviews and enhance completeness and consistency of the overall specification.
Towards the structuring of requirements itself, different syntactic template systems aim to
increase the quality of requirement documentation. Within this thesis a comparative evaluation of these notations is conducted, supporting that claim and differentiating the strength and weaknesses of different approaches. Special emphasis is not only laid on documentation quality, but also the usefulness of these semi-formal notations for integration with model-based development methods. This is achieved through the representation of concepts, which can be managed in special contextualised glossaries.
Overall it can be shown that conceptualization of requirements engineering knowledge can support requirements engineering in different aspects and a holistic approach to integrate different tasks lays the foundation for semantic interoperability spanning organizations and life cycle phases.
The production and use of polymeric materials have been increasing continuously for
years. At the same time, the entry of microplastics (MP) – tiny particles resulting from the
wear and tear of these materials – into our environment is growing as well. By now,
awareness of MP has reached broad sections of the population and also research and
development on this field are similarly becoming increasingly important. However,
insufficient standardization and the lack of suitable analytical methods still make
recording and tracking of MP difficult, so that it remains largely unregulated. Mass-based
analytical methods are particularly advantageous for the establishment of legal
regulations. Apart from thermogravimetric methods, however, there are currently few
alternatives in this field. In this context, the use of nuclear magnetic resonance
spectroscopy (NMR), previously only qualitatively applied to MP, has now also been
examined for its quantitative benefits. This work deals with the current state of
quantitative NMR spectroscopy (qNMR) and tests possibilities for optimization and further
development for this purpose. Initially, the reduction of sample volumes and thus
minimized effort and measurement time of the method will be examined by combining
different polymer types into simultaneously measurable groups, as well as the suitability
of homopolymer calibrations for the detection of copolymers. Existing restrictions during
measurement will be adopted, and thus extractive procedures for sample preparation are
implemented. Finally, the influence of real environmental samples will be assessed, and
measures to reduce interfering factors will be taken in to account. As a result, the method
encompasses at least six polymer types, from PMMA, PS, BR, and PVC to PA and PET, as
well as separate approaches for polymers such as PAN and LDPE. A modular sample
preparation protocol, including extractive fractionation into measurement groups and a
chemical digestion method for matrix reduction, will be established and expanded to
include options for diffusion measurement and application to low-field instruments.
Practical application will be presented using real-world examples, such as freshwater
biofilms, as well as the use for quality control of certified reference materials.
Furthermore, initial insights into future development possibilities, like for the detection of
tire abrasion, will be provided.
Many factors predict adolescents' school grades, two of which may be character strengths and physical activity. We investigated 339 adolescents (130 boys, 185 girls, two diverse, 22 missing data) between 10 and 21 years old (M =14.92, SD =2.28). They filled out questionnaires regarding their character strengths, extracurricular physical activity, math, language, and sports grades. The 24 character strengths were summarized into the
virtues of wisdom, courage, humanity, justice, temperance, and transcendence. Math grades were positively predicted by the virtues of courage and temperance and negatively by age and justice. Girls and younger pupils reported better language grades. Sports grades were predicted by extracurricular physical activity and courage. Better sports grades were found in pupils who were members of a sports club and practiced their sports longer. In particular, courage, which consists of bravery, perseverance, honesty, and zest, is an essential predictor of adolescents' math and sports grades.