Pediatric Intensive Care Unit (PICU) capacity management plays a vital role in healthcare systems, especially during dynamic and high-demand situations. However, current planning approaches rely on static models and retrospective hospital data, which are limited in their ability to respond to sudden changes in demand. These models largely depend on internal patient flow and length of stay predictions, without considering external influences or real-time developments, leading to delayed responses and inefficient resource allocation. This research explores how new approaches to patient data analytics can improve PICU utilization planning and what kind of system architecture could enable this shift. A qualitative approach was employed, involving interviews with various PICU capacity management work-related professionals. The research process was structured using the Double Diamond model to guide the progression from problem exploration to solution development. Thematic analysis was used to identify key themes related to data use, legal and technical constraints, and opportunities for integrating new data sources such as wearable devices, remote monitoring systems, and data from other departments. This research was conducted in the context of PICU capacity management in the Netherlands. The findings show that while relevant data is being generated, it remains fragmented and unintegrated in current systems, limiting forecasting capabilities. Legal frameworks such as GDPR and the EU AI Act further complicate data exchange but also provide a basis for designing compliant systems. The study proposes a new system architecture that integrates diverse data sources while taking into account legal constraints, to enhance forecasting accuracy and operational decision-making. This research contributes to PICU capacity management by bringing together data analytics and automation while addressing legal constraints. It offers a scientific contribution by exploring how predictive modeling from various data sources can be integrated within a compliant system architecture and by addressing the challenge of fragmented data for effective PICU planning.

Federated learning is a promising method of distributed machine learning that can allow industries such as aviation to utilize their data without actively sharing it, but has issues regarding cooperation if proper incentives are not utilized. This thesis explores the problem and builds from a previously designed FL Framework by designing an IT system that can be used to control FL Tasks and Rewards. Using an institutional analysis to set the playing field and based on previous work, an IT artefact consisting of a set of smart contracts on a private blockchain were designed and implemented as an MVP. These contracts perform the role of organising and controlling the participants within FL tasks and can be used to distribute rewards in the form of two types of Tokens: The Reward Token which also can be utilized for voting in proposals and reward distributing as a representation of ownership, and the Model Access Token, which can be used to represent Model access.

The Architecture, Engineering, and Construction (AEC) industry is grappling with the growing challenge of improving communication and collaboration. As projects become more intricate, the need for seamless interoperability—where systems and teams can easily exchange and understand information—becomes even more critical. This research delves into a socio-technical approach designed to address both the technical and non-technical obstacles that hinder seamless integration and interaction across various systems and stakeholders. By conducting an in-depth review of existing literature and engaging with industry professionals, the study identifies key challenges such as fragmented information sources, misaligned organizational strategies, and complex contractual relationships.

The framework proposed in this research aims to bridge these gaps by combining ontology development and semantic web technologies with middleware integration, which should enhance data exchange and improve standardization. On the non-technical side, the framework emphasizes the importance of collaboration across organizations through coalition-building, the development of standardized contracts, and ensuring strategic alignment.
Ultimately, this research not only presents a practical solution to the interoperability challenges in the AEC industry, but it also contributes to the larger conversation on digital transformation within the sector. By aligning technical innovations with organizational strategies, the proposed framework has the potential to improve productivity by encourage innovation. The study highlights the importance of integrating both technical and organizational perspectives in order to create scalable and effective solutions.

Executive Summary
Current Status The aviation industry is transitioning from traditional maintenance practices, typically scheduled after a specified number of flight hours, to more advanced, data-driven approaches. These predictive maintenance techniques leverage machine learning models to enhance the accuracy of assessments. These models can reduce the frequency of unscheduled maintenance and optimize inventory management. However, such models rely heavily on large datasets, which are challenging to compile in the aviation sector due to the rarity of specific operational incidents and the diverse types of data collected by different companies. When collaborating, a tradeoff arises between the level of security measures, trust in partners, and ensuring the system still functions. Federated Learning emerges as a promising solution to these challenges. Federated Learning is a novel form of machine learning that allows multiple entities to collaboratively develop a shared model while keeping their data localized, thus maintaining privacy and data sovereignty. Question The primary objective of this research is to identify and validate the critical architectural features necessary for the acceptance of Federated Learning in the aviation maintenance industry. Architectural features refer to design choices such as security protocols and governance frameworks. By focusing on these aspects, this study addresses the technical and collaborative challenges that must be overcome to develop a Federated Learning system for predictive maintenance in aviation. Resulting in the following research question:
‘What architectural features should be included in the design of the ‘Federated Learning for aircrafts’ predictive maintenance system’ to be accepted by the stakeholders in the aviation industry?’ Approach This study employs a Design Science Research methodology. The sub-questions for this research follow the steps in the Design Science Research methodology. This is not the case for the demonstration phase which was deemed not possible due to the conceptual nature of the design.
1.
What are the challenges in sharing maintenance data, and how do they impact data safety and collaboration? Through a literature review and interviews with stakeholders, the study identified concerns about data privacy, security, and competition. These challenges significantly limit data-sharing initiatives. (Problem Identification and Motivation)
2.
What specific technical requirements should the Federated Learning system have to address these challenges? Based on the interviews and thematic analysis, a list of requirements was developed. These include robust privacy mechanisms, transparent governance, ensuring model explainability, and clear accountability mechanisms. (Defining the Objectives for a Solution)
3.
What architectural features should be included in the Federated Learning design to meet these requirements? ArchiMate modeling was used to design a system incorporating these requirements. Federated Learning, combined with encryption techniques and a consortium-managed system, was the result. (Design and Development)
4.
How do stakeholders perceive the acceptability of the designed system? After the validation interviews and the expert session, three changes were implemented: Switching from
5
differentiating on the model version to differentiating on the usages. Improving explainability
by switching to Trusted Executive Environments and removing differential privacy. Traditional contracts to increase trust towards each other were also added. (Evaluation)
5.
What lessons can be learned from the development and evaluation of the Federated Learning system for future improvements? The research highlights the importance of continuously building trust among stakeholders. Furthermore, the token-based reward system based on contribution is a good incentive to be adaptable and develop long-term collaboration. (Communication) Results
To build trust, this study employs traditional methods like legal contracts and appoints a consortium as a neutral party. Transparent governance and involving a neutral, trusted entity is necessary to gain stakeholder acceptance and ensure long-term collaboration. Blockchain technology enhances the transparency of the consortium's operations, ensuring all transactions and data exchanges are recorded on an immutable database...

The aviation industry is poised for substantial expansion, with forecasts predicting that passenger demand will double by the mid-2030s. This increase in demand presents both opportunities and challenges for various stakeholders within the industry. One sector anticipated to encounter difficulties is Maintenance, Repair, and Overhaul (MRO), particularly regarding data sharing within the supply chain for part tracking. Effective part tracking is essential as it entails crucial data that confirms the 'airworthiness' of parts, demonstrating their safety for operation. Currently, the absence of comprehensive inter-organizational maintenance record software impedes accurate tracking of aircraft parts. This lack leads to increased maintenance and communication costs for all stakeholders involved, as each party must independently verify parts history, often resulting in redundant work and additional pressure on profit margins.

With profit margins already under strain, aviation companies must innovate. One promising solution is blockchain technology, which has the potential to serve as an inter-organizational maintenance record system, facilitating the tracking and tracing of part histories and offering significant cost reductions. However, effective lifecycle tracking of parts requires a collective effort since multiple entities are involved at different stages of the lifecycle, making the formation of a data-sharing consortium necessary. The creation of such a consortium is further incentivized by the implementation of the Corporate Sustainability Reporting Directive (CSRD), which requires companies to disclose all activities related to both downstream and upstream value chains and assess their sustainability impacts. Despite the potential for cost reduction, increased efficiency in inventory management, and legal mandates, companies remain hesitant to join a data-sharing blockchain consortium. Research into similar industry data-sharing consortia indicates that one persistent barrier to success is the design of governance mechanisms within the consortium. Although extensive research on governance mechanisms exists, stakeholder participation in these data-sharing blockchain consortia remains low.

The objective of this research is to develop governance mechanisms that enhance stakeholder participation by taking into account stakeholder values. To achieve this, a value-focused thinking methodology is utilized, ensuring that stakeholder values are integral to the governance design process rather than relying on arbitrary arrangements. In addition to conducting a literature review to discover values, contact was established with the Independent Data Consortium for Aviation (IDCA). IDCA is a global consortium of leading aviation companies at all levels of the industry coming together to develop the foundation for allowing data to be shared in a non-competitive manner. Its objective is to create a more efficient marketplace where both waste and the time required to get to a common solution are minimized. A combination of participatory observations within the part-tracking work group of IDCA and interviews with high-level executives from the aviation industry is conducted to discover stakeholder values. Three contexts, 21 values and 11 sub-values, were identified for joining a data-sharing blockchain consortium. The three contexts are the data that will be shared and received, the stakeholders that will join the consortium, and the blockchain platform that will be used.

Adopting such a broad perspective on values has necessitated the prioritization of these values to create a clear roadmap for the governance design process. This approach ensures that the final governance designs are not only comprehensive but also practical, enabling effective and sustainable blockchain implementation for part tracking in the aviation industry. To obtain this ranking, a survey employing the Bayesian Best-Worst Method (BWM) was used to systematically analyse stakeholder values where 12 aviation stakeholders pertaining to the eight different stakeholder groups encompassing the full life-cycle of a part were received.

Survey takers ranked the data context as the most important, underscoring the value companies place on data as their primary resource, which was followed by stakeholder context. On the other hand, the blockchain platform was ranked the lowest in importance, which was also justified by the credal ranking calculations indicating it is seen merely as a tool for data sharing. Within the data context, the values of integrity, access control, confidentiality, and ownership emerged not only as the highest priorities but also as the most important values overall. This underscores the critical importance stakeholders place on data. These were followed by the values of trust, legitimacy, neutrality, compliance, and benefit equality within the stakeholder context. Completing the top ten, security within the blockchain context was followed by adaptability and tangibility.

Once these stakeholder values are identified, governance designs can be proposed to ensure their fulfilment. A benchmarking study was conducted on existing governance research to understand its definition, scope, and blockchain data-sharing initiatives in other industries. The literature review revealed that no unified definition or established mechanisms for blockchain governance currently exist. Therefore, the researcher established the following definition: "Consortium blockchain governance refers to the process and mechanisms that ensure the direction, control, and coordination of a blockchain platform to which stakeholders jointly contribute."

Given that consortium blockchain governance is a broad term, it is essential to keep its definition straightforward while supplementing it with specific mechanisms. To supplement this definition, a comprehensive table was developed that combines all previously identified mechanisms.Finally, five real-world blockchain consortia were investigated to understand how these mechanisms are designed and implemented in projects that have undergone piloting or advanced further.

The research was finalized by linking the eight most important stakeholders' values to governance mechanisms and, subsequently, governance designs. One example is Executive neutrality, which concerns the impartiality of the entity controlling a platform's direction. Stakeholders also, during interviews, have stated from past experiences that the executive body might not remain unbiased, leading to decisions that favour specific parties. Several governance mechanisms can ensure executive neutrality, including legal compliance, where decisions are based on industry laws and standards. However, due to the underdevelopment of data-sharing policies, industry standards, policies cannot yet serve as reliable benchmarks. Another approach is relying on ethical responsibilities, assuming all participants will adhere to principles of the greater good, though this might be overly optimistic in a profit-driven ecosystem. Since this value primarily revolves around the decisions made by the governing body, decision rights are identified as the most suitable governance mechanism and governance designs can be established.

Based on similar projects like VeChainThor and PharmaLedger, four governance designs were considered: Ecosystem members-led, Service Provider-led, Foundation-led, Independently Governed, and Foundation-led Ecosystem Governed. It was decided, based on the stakeholder value, that the foundation-led ecosystem-governed model was the most suitable, where a non-profit foundation directs the network's operations while being accountable to a board comprising ecosystem stakeholders. This model ensures stakeholder involvement and collective decision-making, maintaining executive neutrality. Finally, it is acknowledged that design inherently involves trade-offs. Therefore, selecting a particular design to address one stakeholder’s value can have ramifications on other values. Because of that, the design decision on other values is investigated. One potential downside is its impact on growth, as involving more stakeholders in decision-making can be challenging and may lead to resistance to adding new entities, potentially hindering growth. This method was employed for the other seven most important values that were identified through the BWM. However, it is important to note that this recommendation is based on the values identified through stakeholder interviews. If stakeholders prefer not to participate actively in the execution of governance duties, then a foundation-led, independently governed model would be more suitable. In this model, an independent non-profit entity would take on the responsibility of directing and managing the network, creating technology roadmaps, and providing necessary services, with limited direct involvement from the stakeholders.


In conclusion, to address the primary research objective of designing governance mechanisms that enhance participation in blockchain-based aviation part-tracking consortia, it can be stated that \textbf{there is no universally optimal governance mechanism design that enhances stakeholder participation; rather, this varies according to the values held by stakeholders within their specific context. As these values change depending on the context, it is recommended that decision-makers begin by investigating stakeholder values.} A value-focused thinking approach constitutes the primary contribution of this research, as it introduces a novel method of blockchain governance that prioritizes stakeholder values. By integrating these values into governance designs, a mutual understanding among consortium members is established, promoting more effective collaboration and decision-making. Furthermore, this research addresses a literature gap within the aviation industry, where blockchain consortiums have not been extensively examined.


For the initial phase of implementation, it is advisable to engage regulators to establish credibility, thereby fostering increased participation. Once a stable level of participation is achieved, the focus can then shift to examining the values of the stakeholders involved. Future research could assess the applicability of identified values and governance mechanisms in contexts beyond the aviation industry. Similar studies across various industries are important to ensure external validity. Additionally, further research could be conducted into how different mechanisms besides the one suggested in this research could fulfil stakeholder values and investigate the interaction among those mechanisms.

The internet's infrastructure relies heavily on Autonomous System Numbers (ASNs) for efficient and reliable data routing across complex networks. This thesis investigates the policies governing the allocation and management of ASNs across the five Regional Internet Registries by employing a mixed-method approach, including content analysis, surveys, and interviews. The study examines the potential impacts of recent policy changes, particularly the introduction of maintenance fees at RIPE NCC and APNIC as these fees could disproportionately affect smaller stakeholders.

The research highlights regional differences in ASN policies and the effects on stakeholders. It also explores the broader consequences of these policies for the global internet infrastructure. Based on the findings, several recommendations are proposed to harmonize policies, improve transparency, and ensure that the governance of ASNs remains responsive to the evolving needs of the Internet. The study concludes by addressing the critical balance between financial sustainability for RIRs and the accessibility of resources for diverse stakeholders.