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Positron Emission Tomography (PET) is becoming more and more important in clinical routine
applications. One of the major limitations is the sensitivity to patient motion especially in the thorax
to periodic respiratory movement. Another open point of discussion is the method how to define the
tumor volume, especially when the precise knowledge of the tumor borders is important as in
radiation treatment planning. Therefore, in this work these two topics to improve quantification in
PET imaging have been addressed. First a new motion correction algorithm was implemented using
image deblurring including movement information of a 4D Computed Tomography (CT). This method,
which has the advantage of not increasing the PET acquisition time as other motion correction
techniques, was applied to phantom and patient data and showed promising result in improvement of lesion quantification. In phantom studies an improvement of up to 49% in lesion volume and in
patient studies of up to 33.3% could be demonstrated.
In the second part of this work, a new segmentation method based on textural parameters was
implemented and validated as well in phantom and patient data. In the latter a validation with
histopathological data was performed showing a very good performance of the new algorithms,
especially in larger lesions. Best result could be shown in phantom data and patient data for the
segmentation algorithm based in the local entropy.
In summary, two algorithms were implemented and validated which can improve quantification of
PET imaging furthermore
Seit mehr als 10 Jahren forscht und lehrt das Team des Centers for Enterprise Research (CEIR) an der Universität Koblenz im Bereich der digitalen Unterstützung kollaborativer Arbeit in Unternehmen (Enterprise Collaboration) und untersucht den Aufbau und die Nutzung von Kollaborationstechnologie für den Digitalen Arbeitsplatz.
Der vorliegende CEIR Report mit dem Titel „Collaborative Actions on Documents Ontology (ColActDOnt)“ beschreibt die Entwicklung einer Ontologie für Benutzeraktionen an Content in Kollaborationssystemen (englisch: Enterprise Collaboration Systems). Die Arbeit an der Ontologie erfolgte im Rahmen eines DFG-Projekts mit dem Titel „Social Process Mining“. In diesem Projekt wurde eine Methode für das sogenannte „Cross-System Process Mining“ entwickelt. Cross-System Process Mining erlaubt die Analyse von Arbeitsprozessen, deren Ablauf von mehreren, heterogenen Kollaborationslösungen (z.B. HCL Connections, Alfresco, Skype) unterstützt werden. Aufgrund der Heterogenität der involvierten Systeme müssen die Logfiles der unterschiedlichen Softwaresysteme zunächst in einen gemeinsamen, harmonisierten Event-Log überführt werden. Die ColActDOnt stellt die notwendige Systematik für diese Harmonisierung zur Verfügung.
Die Ontologie wurde entwickelt in einer Reihe von interaktiven Workshops mit den Projektbeteiligten (Prof. Dr. Petra Schubert, Dr. Florian Schwade, Julian Mosen) unter der Leitung von Martin Just, der die Ergebnisse anschließend im Rahmen seiner Masterarbeit dokumentierte. Anschließend wurden die wichtigsten Teile in gekürzter Form in dem vorliegenden CEIR-Report veröffentlicht.
Mit den CEIR-Reports möchten wir ausgewählte wissenschaftliche Ergebnisse einem breiteren Publikum zur Verfügung stellen. Die in dieser Arbeit dargestellten Informationen sind für Unternehmen interessant, die an einer gezielten (Weiter-)Entwicklung ihrer Enterprise Collaboration Platform interessiert sind.
In recent decades, there has been a growing awareness in our society of the impor-
tance of medicines that are personalized to the needs of patients. This dissertation
contributes to the research on personalized dosage forms with controlled drug release.
The basis of our investigations is the simulation of the expected diffusion properties
of these personalized dosage forms using computer-aided statistical methods and the
subsequent adaptation of suitable models to experimentally obtained data. This
novel approach makes it possible to verify the parameters determined from exper-
imental data, such as the diffusion coefficient. A key finding is that, for instance,
the homogeneity of the sample, the precision of the measurement data collection
and the consideration of the measurement environment have a greater influence on
the validity of the diffusion coefficient than the choice of the diffusion model. The
experimental part of this thesis comprises the development and characterization of
drug-loaded polymer resins, the implant fabrication, and the pharmaceutical and
physical investigation of the polymer-drug implants. The formulation of polymer
resins and their suitability for 3D printing (3DP), as well as their use in pharmaceu-
tical applications, are extensively addressed. The release properties and, in partic-
ular, the polymer network’s mesh size influence on the active ingredient’s diffusion
rate in aqueous solution are studied. In addition, the production of polymer sam-
ples by means of UV photopolymerization in a molding process developed for this
purpose, as well as by using stereolithographic 3DP, are examined comparatively.
The polymerization process and the polymer properties resulting from the different
production methods are presented. The major findings from these studies include
the successful development of a polymer resin formulation whose release and swelling
properties are comprehensively demonstrated using a model drug. Furthermore, the
optimization of this resin for the use of the active pharmaceutical ingredient (API)
testosterone was achieved. A comparison of the two developed resin formulations
clearly shows the limitations and the possibilities of transferability of both systems.
In addition, the results concerning the release of API from the polymer, as well as
the diffusion of the solvent into the polymer and the resulting increase in the volume,
provide insight into the changed interfacial diffusion resistance in 3D printed poly-
mers compared to molded polymers. This work thus contributes to the development
of personalized drug forms and paves the way for the production of release-controlled
polymer resin implants for future follow-up work.
Satzung der Studierendenschaft der Universität Koblenz zur Änderung
von Vorschriften der Studierendenschaft der Universität Koblenz
Fünfundzwanzigste Ordnung zur Änderung der Prüfungsordnung
für die Prüfung im Zwei-Fach-Bachelorstudiengang an der Universität Koblenz
Erste Ordnung zur Änderung der Masterprüfungsordnung für
den weiterbildenden Fernstudiengang „Master of Business Administration“
des Fachbereichs 4: Informatik der Universität Koblenz
Erste Ordnung zur Änderung der Masterprüfungsordnung für
den weiterbildenden Fernstudiengang Energiemanagement
des Fachbereichs 3: Mathematik / Naturwissenschaften der
Universität Koblenz
Prüfungsordnung für den Bachelorstudiengang „Angewandte
Naturwissenschaften“ und den Masterstudiengang „Material
Science“ an der Universität Koblenz (Studiengangs-PO Angewandte
Naturwissenschaften / Material Science)
Prüfungsordnung für das Studienmodell uk-Master an der
Universität Koblenz (Studiengangs-PO uk-Master)
Einschreibeordnung der Universität Koblenz
This thesis tackles a common bottleneck in data-science courses: students struggle
to turn a broad interest into a focused, workable project idea. This thesis set out to
design and evaluate a compact assistant—EduIDEAtor—that makes this first mile
simpler and more intentional. The tool uses a text-first interface with plain inputs,
a small set of clearly different directions, and quick, reversible edits so students can
steer ideas without losing momentum. After building and iterating the web appli-
cation, The thesis evaluated how students experienced it and how it compared with
familiar, non-AI brainstorming. The findings are consistent: navigation and input
clarity were strong; students felt more able to generate and shape ideas; overall sat-
isfaction and willingness to continue using the tool were high. Two practical refine-
ments emerged—make back navigation clearly visible and give users finer control
over how broad or specific the suggestions are both achievable without changing
the core design. The contribution is a concrete pattern for first-mile ideation and a
set of actionable guidelines for course-level adoption.
Globally billions of dollars are invested on information systems and technology (IS/IT) to achieve business change. Understanding how value is generated and captured from these investments has been a key theme in information systems (IS) research for over 25 years. However, despite significant theoretical progress, organisations are still failing to achieve the full value of their investments and identifying and realising the benefits of IS/IT-enabled business change remains a challenge for both research and practice.
Our research is concerned with the business change associated with the introduction and use of new forms of enterprise collaboration system (ECS) that incorporate social software functionality (e.g. social profiles, blogs, wikis, activity streams, collaborative tagging etc). ECS represent a significant business investment; however, there remains uncertainty around the benefits and value arising from the introduction of these new types of ECS. Existing research studies on IS/IT benefits are focused primarily on traditional enterprise systems such as ERP systems. This article summarises the existing work that directly, or indirectly addresses IS benefits, to reveal four broad themes (i) evaluations of IS/IT investments (ii) measuring IS success (iii) classifying and measuring IS benefits and (iv) benefits realisation management.
The article concludes with an overview of the research on benefits management conducted in the Center for Enterpise Information Research at the University of Koblenz and the current research project investigating the benefit of enterprise collaboration systems (BECS).
The BECS project investigates the benefits arising from the adoption and use of Enterprise Collaboration Systems (ECS).
ECS are large-scale collaboration technology infrastructures that provide the software functionality to enable workgroups to organise online team meetings, to create and share information, to coordinate workflows and to collaborate on joint projects, regardless of the location and timing of work activities.
When ECS are introduced into organisations there are initial expectations about what can be gained from the system, e.g. improved collaboration, improved communication across silos, etc. Over time, as users gain experience using the system, ideas about what can be achieved change and the ECS become embedded into organisational work practices. However, identifying and understanding the expected benefits of ECS, how they evolve over time, and how they contribute to organisational performance is challenging due to a lack of suitable methods and tools to describe (profile), measure and monitor ECS benefits.
The BECS project addresses this challenge; the primary focus is on identifying, measuring and monitoring the benefits that arise from ECS implementation and use over time. Through the development of in-depth longitudinal case studies of ECS adoption in leading organisations in the DACH region and empirical analyses of collaboration system use, the research:
i) developed practical tools and methods for the measurement of ECS benefits and benefits profiling;
ii) provides greater insights into how benefits management is experienced and constituted in practice; and
iii) developed a novel and integrated framework that assists researchers and practitioners to coordinate their efforts in developing, implementing and evaluating ECS benefits.
The project delivered both practical and theoretical outcomes. The methods and tools developed in the BECS project have been applied in organisations and delivered useful and useable results enabling organisations to understand and monitor the evolving benefits of their ECS. Following the COVID-19 pandemic, this work became of even greater importance as new uses of ECS emerged when organisations adopted large-scale support for hybrid and remote working initiatives.
The research findings also provide key theoretical concepts and analytical methods, including the MoBeC framework, Social Collaboration Analytics and Benefits Scorecards. These provide the foundation for subse-quent research projects to examine transformation to digital work and the development of a new stream of research into trace analysis and collaboration analytics more broadly.
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.