The global energy transition necessitates the rapid decarbonisation of industrial clusters, with hydrogen emerging as a key solution (kim2023). However, ongoing global crises have led to a shift in policy priorities, potentially delaying decarbonisation efforts and increasing the urgency for strategic and efficient resource planning (Stemerding2025). As climate targets approach, minimising delays becomes essential. This underscores the need for models that can capture the complex behaviour of firms and their mutual influence under uncertainty. Yet, most existing models do not account for how firm level behaviour and interdependencies shape infrastructure adoption and investment decisions in uncertain environments.

This study investigates how different firm characteristics, interdependencies, and scenario conditions influence the development of hydrogen infrastructure over time. The main objective is to understand how early investment decisions affect network formation and spatial outcomes in industrial clusters. To achieve this, a dynamic modelling framework was developed that combines a threshold based adoption model with the Optimal Network Layout Tool (ONLT). This approach incorporates firm level attributes such as hydrogen trade volume, grid connection capacity, plot size, and company type, and uses scenario analyses that vary hydrogen demand, import volumes, and early adopter configurations to simulate firm behaviour.

The results show that network development is highly sensitive to firm interdependencies, adoption behaviour, and external conditions. The timing of adoption depends on each firm's characteristics, with emerging strategic hubs such as Air Liquide, Eneco, and BP accelerating the rollout. In contrast, scenarios with high hydrogen demand might promote more integrated networks, whereas low demand scenarios often lead to fragmentation. Furthermore, the delayed adoption by Air Products, driven by relatively unfavourable characteristics, resulted in inefficient connections that were both long and costly.

The findings inform infrastructure planners and project developers on where to prioritise early incentives. The model provides guidance on investment priorities, supporting a more coordinated and cost effective infrastructure planning process, while also contributing to risk mitigation. By analysing different network layouts, robust segments can be identified that perform consistently across a range of scenario configurations, thereby reducing the risk of stranded assets. This study focuses on the Rotterdam Industrial Cluster as an illustrative case, but the approach could be adapted for application in other clusters beyond Rotterdam.

This thesis investigates the comparative performance of three Multi-Objective Evolutionary Algorithms, Epsilon-NSGA-II, Borg, and Generational Borg, within the Evolutionary Multi-Objective Direct Policy Search framework, applied to the optimisation of the JUSTICE Integrated Assessment Model. By combining benchmark problems (DTLZ2 and DTLZ3) with the high-dimensional, multi-modal JUSTICE model, the study evaluates convergence dynamics, computational efficiency, and solution quality across varying levels of parallelisation. Results show that Borg consistently outperforms the other algorithms, particularly under high problem complexity, due to its asynchronous, steady-state architecture and adaptive operator features. Generational Borg performs better than Epsilon-NSGA-II on complex problems, but both suffer from scalability limitations due to synchronous execution. These findings underscore the importance of algorithm selection in climate-economy policy modelling, offering guidance on how MOEA design and computational resources interact to affect optimisation outcomes.

With the rise of cloud computing, remote work, and interconnected systems, the traditional model of trusting everything inside the network perimeter has become increasingly risky. In response, Zero Trust Architecture (ZTA) has emerged as a model where no user or device is inherently trusted, regardless of their location. Micro-segmentation, one of ZTA’s key mechanisms, aims to limit lateral movement and enforce fine-grained access control. However, implementing micro-segmentation in environments with legacy systems remains particularly challenging due to outdated technologies, rigid configurations, and limited visibility.

Although the literature offers many advanced segmentation models, these are often designed for cloud-native or modern infrastructures and fail to address the constraints inherent in legacy-heavy environments. This research investigates how organizations with legacy systems can identify and evaluate appropriate micro-segmentation strategies based on their network characteristics and technical constraints. The central research question is:

How can organizations choose and implement micro-segmentation strategies that align with their network architectures and the technical constraints posed by legacy systems?

To address this question, the study began with a structured literature review, which revealed a lack of detailed and context-aware guidance for applying micro-segmentation in constrained environments. To fill this gap, a series of semi-structured interviews were conducted with cybersecurity professionals experienced in industrial and legacy-heavy systems. The interview data were analysed using thematic coding to uncover recurring patterns, practical challenges, and the decision-making logic used in real-world settings.

The findings reveal three principal categories of micro-segmentation strategies: network-based, agent-based, and hybrid. The choice between these depends on technical factors such as system compatibility, network architecture, and the level of administrative control available. In addition to strategy selection, the study identifies a phased implementation approach typically followed by organizations. The study also defines key criteria for evaluating segmentation outcomes, including enforcement of access control, operational continuity, system performance, network visibility, and many more. These insights form the basis of a structured, step-by-step implementation guide and a decision-support framework. Together, they enable organizations to assess their technical constraints and select appropriate strategies. The main contribution of this research is the development of a practice-oriented framework that supports the secure integration of legacy systems into Zero Trust environments.

Schiphol Airport aims to achieve fully autonomous airside operations by 2050, driven by the goals of reducing human presence in high-risk environments and enhancing operational efficiency. Within this vision, drones present a promising innovation for airside tasks, offering autonomous inspection capabilities and adaptability to dynamic conditions. National developments, such as the Dutch Ministry of Infrastructure and Water Management's push for drone integration, reinforce this ambition. Schiphol's participation in the Dutch Drone Delta consortium highlights its commitment to exploring drone use cases in operational settings.

To explore drone integration at Schiphol, Runway Condition Reporting (RCR) was selected as a case study due to its repetitive, linear nature and critical safety function. RCR involves collecting runway surface data using a MARWIS sensor system. Currently performed manually by airside vehicles, the study proposes an autonomous drone solution flying between hubs at either end of the Polderbaan runway to perform RCR.

This research addressed autonomous drone-based RCR through three dimensions: reliability, effectiveness, and safety, leading to the central question:

"How can drones be utilized reliably, effectively and safely within Schiphol Airport's operational environment to automate runway condition reporting?"

The research followed a staged case study approach validated through stakeholder input. It began by assessing technical feasibility and system constraints, followed by designing a flight trajectory and operational protocol. A safety risk assessment was then conducted using a HAZOP analysis, and a visual simulation model was developed to communicate system insights.

Reliability was evaluated through performance assessment of commercial drones against Schiphol-specific criteria. A weighted decision-making process, supported by expert interviews, identified the MRTK drone system as suitable. However, wind gusts above 12 m/s—common in afternoons—would limit annual operability to approximately 64%, making wind resistance a key constraint.

Effectiveness was assessed by designing a geofenced, constrained flight path performing RCR in 3 minutes and 41 seconds. The protocol included emergency procedures such as Return-to-Home and Emergency Landing. An agent-based simulation integrated operational data, showing 27 drone inspections per day totaling 1 hour and 45 minutes of flight time. Results suggest potential efficiency improvements, but highlight airspace regulation bottlenecks during simultaneous drone operations.

Safety was assessed via HAZOP with six key stakeholders. The analysis identified 51 potential system deviations, four of which posed high risks: compromised airspace due to wildlife, loss of drone control, emergency protocol failure, and ground collisions. These risks could render runways unusable or halt airside operations. Root causes include wind gusts, system failures, and external interference. Mitigation measures such as stricter geofencing, redundancies, and improved coordination were identified, but require further research.

Despite the risks, this case study provides Schiphol with a reference framework for exploring autonomous drone innovation. It recommends starting with low-risk, non-runway use cases and engaging with regulators and stakeholders to build experience. Scientifically, the study offers a replicable framework combining technical analysis, stakeholder input, and risk assessment for early-stage exploration of autonomous systems.

In conclusion, while drones can perform RCR under certain conditions, safety challenges currently prevent implementation. However, this research provides a structured foundation for advancing autonomous drone integration at Schiphol and similar environments.

In an era shaped by systemic risks and long-term challenges such as climate change, technological disruption and geopolitical instability, policymakers must make consequential decisions without knowing what the future holds. This condition, known as deep uncertainty, arises when the relationships between actions and outcomes are contested and traditional prediction methods no longer apply. To address this, analysts have turned to computational tools such as scenario discovery, which explores thousands of simulated futures to identify combinations of factors that lead to policy success or failure. It provides decision-makers with understandable scenarios: data-grounded narratives of the future that highlight critical vulnerabilities and opportunities. These scenarios help enable the design of strategies that are robust and adaptive...

Executive Summary

Situation
Dutch first-line healthcare still faces major problems with how its information systems work together. Many of these systems are outdated, don't follow the same standards, and can't easily share data with each other. This makes it difficult for doctors, pharmacists, and nurses to work with complete and up-to-date patient information. It also creates obstacles for third-party developers, like those building telehealth apps or digital tools, who need to connect to these systems to offer new services.

Even though most healthcare providers use digital tools, these tools don’t speak the same language. That means that when one provider enters data, another might not be able to read or use it properly. This lack of data interoperability slows down care, increases paperwork, and limits innovation.

To address these issues, this thesis introduces a modular platform architecture that follows shared rules and standards. The goal is to help different systems work together more smoothly and allow developers to build useful tools without having to create custom connections for each system. The platform uses common APIs and reusable services to exchange data in a secure and structured way, while staying in line with Dutch healthcare policies.

Research Question
This research focuses on the following main question:
How can a platform architecture be designed to enhance data interoperability in first-line healthcare, enabling seamless integration for third-party complementors to support healthcare practitioners?

The question comes from the need to reduce technical complexity and help third-party developers deliver value to healthcare professionals faster and more reliably.

Approach
To answer this question, a systems engineering approach was used. The research started with interviews, where one was completed with a general practitioner and one with an expert from a third-party healthcare IT company. These conversations revealed key issues, such as outdated systems, mismatched data meanings, and a lack of shared standards.

From these insights, a list of requirements was created to guide the platform’s design. Then, several UML diagrams were developed to model how different components of the platform should interact. Based on these models, a working prototype was built using Python (Flask). This included core services like data validation, transformation, and user authentication.

Finally, expert software architects reviewed the prototype. Their feedback confirmed that the design made sense and had potential for real-world use. They also highlighted areas that still need improvement, especially in terms of security and handling data meaningfully and correctly.

Results and Conclusions
The research confirmed that fragmentation, outdated technology, and poor standard usage are the main barriers to data sharing in Dutch first-line healthcare. To address this, a modular platform was developed and tested.

The prototype showed that using shared components for data transformation and validation makes it easier to connect systems. Two services, the DataTransformationModule and the ExternalSystemConnector were seen as essential for creating an interoperable data platform.

The proposed design uses a layered structure and modular services. This makes it scalable, flexible, and easier for external developers to integrate with. While the technical foundation is strong, more work is needed—especially on semantic interoperability (ensuring data has the same meaning everywhere), user access control, and real-life testing in clinics.

Contribution
This thesis adds to both academic knowledge and practical system development.

From a theoretical perspective, it applies platform thinking to healthcare IT, based on Tiwana’s core–periphery model. It also builds on Deshmukh’s layered model of interoperability by turning abstract ideas into working components where each deals with structure, meaning, or security in healthcare data.

On the practical side, it provides a working system design that others can build on. The prototype shows how developers can use reusable components, like data validation or transformation services, to save time and improve data quality. This approach can help reduce technical effort and improve care delivery by making systems more connected.

Recommendations
To make this platform usable in real healthcare environments, several improvements are needed. First, authentication should be upgraded from basic API keys to more secure methods like OAuth2 or federated identity systems. These changes will better protect patient data and support real-world compliance without making the system harder to use.

Second, semantic interoperability needs more attention. Although the platform handles structure and syntax well, it still lacks tools to make sure that medical data means the same thing across systems. Future versions should include support for terminology mapping and validation using Dutch coding systems like SNOMED CT, ICPC, and LOINC.

Third, flexibility should remain a core design principle. Some services, like conflict detection, may only be useful in specific cases and should be added as optional modules rather than built-in features. This keeps the platform adaptable for different types of healthcare organizations.

Next, the platform should be tested in live clinical environments. Real-world deployment would uncover workflow issues, performance bottlenecks, and user experience gaps that are not visible in lab tests. Feedback from actual users, namely: GPs, nurses and pharmacists, is vital to make the system both effective and usable.

Finally, as data standards evolve, the platform must stay up-to-date. Future versions should be able to work with new releases of HL7 FHIR and support multiple profiles. This ensures long-term compatibility and encourages broader adoption.

In summary, this research lays the groundwork for a modular, standards-based platform that makes healthcare data more interoperable. It supports both better patient care and more efficient innovation by reducing the technical barriers that developers and healthcare professionals currently face.

This thesis examines how AI-generated content labels are applied on two major short-form video platforms: TikTok and YouTube Shorts. As generative AI becomes more integrated into social media, platforms have introduced disclosure labels to support transparency and help users distinguish between real and synthetic content. However, little is known about how these labels are implemented in practice, whether they align with platform policies, and how they affect user engagement.

Using a dataset of 12,315 videos collected through hybrid scraping and API-based methods, this study compares labeling prevalence, source attribution, policy alignment, and potential effects on user metrics such as likes, comments, and views. The findings reveal notable differences: TikTok relies primarily on creator-applied labels with minimal platform enforcement, while YouTube Shorts applies more labels but does not disclose their source. In both cases, labeling practices are often inconsistent and not fully aligned with platform-specific guidelines or the technical capabilities of C2PA, the standard used for AI content detection.

Although some statistically significant differences in engagement were observed, especially on TikTok, the overall effect of labeling on user behavior was limited. The findings highlight key challenges in current AI labeling practices and point to a growing need for more consistent, transparent, and accountable moderation policies. While regulatory frameworks such as the EU’s AI Act and Digital Services Act aim to improve transparency in digital environments, this study shows that current platform practices often fall short of these goals, especially in terms of visibility, attribution, and enforcement of AI labels.

Electric mobility has emerged as a central strategy for decarbonising the transport sector, especially in emerging economies like India. While national-level initiatives such as FAME provide policy momentum, subnational governments play a critical role in implementing Electric Vehicle (EV) transitions. This thesis evaluates the effectiveness of Karnataka’s EV policy mix using the Policy Mix Framework, focusing on four characteristics: consistency, coherence, comprehensiveness, and credibility. Through a qualitative content analysis of 18 key policy documents and 40 state- and municipal-level instruments issued between 2017 and 2025, the study maps interactions and misalignments across manufacturing incentives, infrastructure support, and consumer-facing measures.
Findings reveal moderate consistency, with some reinforcing interactions, such as those between subsidies and concessional tariffs, alongside contradictions in areas like metering standards and tax exemptions. Coherence is hindered by institutional fragmentation, particularly between state and municipal actors. Comprehensiveness is stronger on the supply side (manufacturing and infrastructure) but lacks equitable support for low-income users and rural areas. Due to the document-based methodology, credibility was not assessed.
This study contributes to the literature on sustainability transitions and multi-level governance by applying the Policy Mix Framework to a developing country context. It offers evaluations of a subnational EV ecosystem in India. The findings underscore that effective EV transitions require not only well-designed instruments but also strong coordination and inclusive implementation. Recommendations include creating a dedicated EV task force, expanding digital infrastructure, and introducing inclusive business models. The results provide insights for other Indian states and federal systems aiming to scale sustainable mobility.

Fifth generation district heating and cooling (5GDHC) networks enable the integration of low temperature sources and the utilization of synergies between heating and cooling demands. To implement them in urban areas a modular approach is suitable which implies the partition of the respective area into smaller cluster networks. This study addresses the effects of participation uncertainty in potential networks and the lack of comparison of clustering methods in the context of 5GDHC networks. Within this work the performance of different clustering methods is measured by optimization metrics that capture the requirements for a suitable partition, such as compact and balanced clusters with sufficient energy supply. This enables the comparison of the selected methods K-Means, K-Medians, DBSCAN and Single Linkage Clustering (SLC). To investigate the robustness with respect to participation uncertainty, a scenario-based approach that combines the minimax regret method with a cluster viability analysis is developed. The developed methodology is applied to the city centre of Amsterdam. The findings indicate that the participation rate influences the viability of the cluster networks. Nevertheless, K-Means, K-Medians and SLC are robust in their performance measured by the averages of the optimization metrics. Additionally, K-Means leads to the lowest variance in performance and is the best performing and most robust method in terms of average cluster compactness and demand fulfilment. Moreover, promising starting clusters for the implementation in the city centre of Amsterdam are identified. The study gives insights into the behaviour of the tested clustering methods and how they may be an advantageous alternative to state-of-the-art planning tools. Finally, the proposed approach to analyse the robustness of clustering methods contributes to the planning of district heating and cooling networks.

Cybercrime has rapidly evolved into a global threat, facilitated by advancements in digital technology and the emergence of illicit online marketplaces. Among these, Telegram has become a key platform for criminal activity, offering an environment where illicit goods and services are traded with relative ease and anonymity. Its minimal content moderation, group and search functionalities have made it attractive for criminals. However, despite its increasing role in cybercrime, law enforcement struggles to effectively disrupt illicit activities, as traditional enforcement strategies face challenges to address the resilience and adaptability of such networks.

Despite extensive research on cybercrime, illicit Telegram marketplaces remain poorly understood, particularly in how they sustain resilience and adapt modi operandi despite enforcement. The role of automation and commoditisation in shaping these exchanges is largely overlooked, while reliance on outdated data limits insight into recent adaptive strategies. A community-centric approach is needed to examine marketplace dynamics, the impact of disruptions, and assess the impact of specified targeting on the adaptation of crime. This study answers these knowledge gaps by using the following research question: What ecosystemic mechanisms stimulate the resilience of Telegram as an illicit market? To answer this research question, a mixed-methods approach is used, where an extensive literature study and expert interviews are enriched with a large-scale data analysis with 1.4M independently sourced Telegram messages. These messages stem from 2021 to 2024 and are sent by 14,856 unique Telegram users in 45 Dutch Telegram channels. Using machine learning-based classification (SVM), topic modelling (LDA) and automated indexing, this study identifies modi operandi, payment preferences and patterns in communication. Ultimately, this research aims to uncover the systemic mechanisms driving adaptive cybercrime behaviours on Telegram by analysing the impact of automation, commoditisation, platform regulations, and law enforcement interventions. It examines how illicit markets sustain resilience, whether enforcement efforts lead to disruption or displacement, and how vendor specialisation and payment preferences shape underground economies. Thereby, this study provides insights to improve law enforcement strategies, inform policy development, and establish a scalable framework for future illicit ecosystems understandings.

The findings highlight automation and self-regulation as key factors in Telegram’s resilience. Thereby, bots play a central role in message auto-deletion, channel moderation, scam detection, and mass message distribution, reducing exposure to law enforcement and increasing efficiency. Auto-deletion is widely used, with most channels setting messages to expire, effectively erasing digital footprints. The dataset shows that only 5.3% of messages are unique, a stark contrast to the 21.8% uniqueness rate in the reference dataset (2017-2021), underscoring the dominance of mass-distributed, repetitive advertisements. Additionally, vendors increasingly rely on outsourced advertising services, where intermediaries automate message forwarding across multiple channels (multi-homing), ensuring that criminal listings remain visible and accessible and minimising the impact of platform takedowns. This transformation shifts Telegram from a socially driven chat platform to an automated and heavily commoditised illicit marketplace.

Despite Telegram’s recent increased moderation efforts, the study finds no empirical evidence of mass migration to alternative platforms. During the analyses, 33 channels ceased existence, resulting in only 38 of the 71 initially identified channels remaining active, indicating a deterrence effect, but no full elimination. This study further presents internal displacement mechanisms, such as invitation-based access systems and broadcasting channels for fast internal displacement.

Market specialisation primarily occurs at the vendor level, rather than the channel level. While channels present mixed products and services interchangeably, 67.6% of vendors specialise in offering a single crime category, whereas 32.4% engage in multi-category sales. Multi-category vendors display professionalism to an extent, as indicated by their broader range of accepted payment methods (1.63 on average, compared to 1.37 for single-category vendors). Furthermore, scarcity-driven demand is evident, observed in categories with fewer advertisements, such as firearms and explosives, where user requests are more frequent. These observations suggest that law enforcement actions targeting specific markets do not eliminate demand but instead push buyers toward more proactive search behaviours. Besides, a shift in financial strategies is also observed. Cash has become the dominant payment method for drug-related transactions, replacing cryptocurrencies, likely driven by the physical nature of exchanges and pre-existing in-person contact. Cybercrime and financial fraud remain dominated by PayPal, Paysafecard, and cryptocurrencies due to their pseudo-anonymity and the online nature of the committed crimes. Last, a decline in Tikkie and bank transfers is observed, impacted by the traceability of these payments.

This study contributes to society and literature by revealing how Telegram has transformed into an automated, self-regulating and heavily commoditised illicit marketplace, challenging conventional views on cybercrime communities. It demonstrates that automation, self-regulation, and vendor specialisation drive market resilience, reducing reliance on social trust and replacing it with bot-driven enforcement mechanisms. Unlike assumptions that enforcement actions lead to cross-platform migration, findings suggest that crime displacement occurs within Telegram itself, facilitated by broadcasting channels and invitation-based access systems for wide automatic dispersion of invitation links. Additionally, the study uncovers scarcity-driven demand, where illicit products with limited supply generate increased user requests, shifting markets towards demand-based interactions rather than reducing crime. The commoditisation of services, including outsourced advertising and scam prevention, lowers barriers to entry, allowing even low-skilled actors to engage in cybercrime.

These insights provide a new perspective on digital illicit markets, emphasizing the need for enforcement strategies targeting the disruption of technological enablers in the ecosystem, namely automation, bot networks, and broadcasting channels, as these infrastructural mechanisms sustain illicit trade. Disrupting bots, scam registries, and invitation-based access can destabilise criminal networks. Instead of forcing criminals off Telegram, increased enforcement often pushes them towards demand-based interactions or into private invite-only spaces, (infiltration) strategies should therefore adapt accordingly. Besides, a differentiated approach is needed: casual users may be deterred through awareness campaigns, while professional criminals require direct disruption of their security measures. Proactive monitoring using Telegram’s API by tracking high-risk keywords, administrator activities, and advertising patterns could provide real-time intelligence to disrupt criminal networks before they scale. For policymakers, legal clarity is needed on passive group membership and dual-use automation tools that enable crime. Strengthening regulatory frameworks and forcing providers to monitor misuse can curb illicit activities. Future research should focus on tracking internal crime displacement within Telegram, identifying central network hubs, and understanding how bot-driven automation influences illicit trade dynamics, including more research into the distribution techniques, executive actors, rates and the reach of these advertisements. Furthermore, future research can build on the applied methods and the intermediate outcomes, as it has some unresolved aspects. Last, establishing benchmark datasets will help track long-term market evolution and measure the true impact of enforcement measures.

In today's digitally connected world, the spread of conspiracy theories on social media poses a significant challenge to societal trust and public discourse. This thesis aims to develop a model for identifying conspiracy theories on Telegram, a platform known for its private nature and the use of channels to disseminate information. The main research question guiding this study is: *How can conspiracy theories be identified on Telegram?* To address this, the research leverages graph theory, machine learning, and topic modeling.

The first step involved modeling the network structure of Telegram channels using graph theory. Channels were represented as nodes, and forwarded messages as directed, weighted edges, allowing for the analysis of network structures and the identification of influential channels. Various centrality measures, including weighted degree centrality, betweenness centrality, and viral message centrality, were computed to assess the influence of channels within the network.

A fine-tuned multilingual BERT (m-BERT) model was used to classify Telegram messages as either conspiracy-related or not. This model was trained on a manually labeled dataset and demonstrated robust performance.

To identify specific conspiracy theories, BERTopic, a topic modeling technique, was applied to the messages classified as conspiracy-related. The resulting topics were then analyzed using the OpenAI API, which linked them to known conspiracy theories. The study found that all identified topics could be connected to existing conspiracy theories, suggesting that the model is effective in detecting these narratives on Telegram.

The research also included the validation of the model using a fictive conspiracy theory, "The Verdant Shadow Conspiracy," created specifically for this purpose. This validation demonstrated the model's ability to detect even simulated conspiracies, though it highlighted the importance of continuous refinement and expansion of the dataset.

The contributions of this thesis are twofold: scientifically, it advances the understanding of how to model and analyze Telegram networks to detect conspiracy theories, and societally, it offers a framework that can be used to monitor and potentially mitigate the spread of harmful misinformation. Future research should focus on expanding the dataset, refining the model, and exploring interdisciplinary approaches to further enhance the detection and understanding of conspiracy theories on Telegram.

This research focuses on designing a digital platform that contributes to reducing food waste in Saudi Arabia. While a digital platform may not be an efficient and viable option to tackle this problem in countries with advanced waste management infrastructure, it is a feasible solution for tackling food waste in Saudi Arabia, given the current lack of infrastructure. It offers a quick and scalable approach that can be rapidly deployed to bridge existing gaps. Unlike traditional infrastructure projects requiring significant time and resources, a digital platform leverages existing technology to create immediate connections between retailers, consumers, and other stakeholders. The retail stage includes restaurants, cafes, and bakeries. The main stakeholders of interest are retail businesses. The main research question that guided this research is: What digital platform design can contribute to reducing food waste in the retail sector in the eastern province of Saudi Arabia in alignment with circular economy principles?...

Ransomware attacks, orchestrated by cybercriminal organizations, pose a global threat in our digital era by exploiting system vulnerabilities and demanding ransoms for seized and encrypted data. Conti, a Russian ransomware group, ceased to exist after a 2022 data leak, offering a unique opportunity to study their modus operandi. The leak includes chat transcripts containing indicators of value, like compensation agreements and digital transaction details. By using the value chain lens, these indicators can be used to determine how ransomware groups create and allocate value within their operations. This understanding is essential for law enforcement aiming to disrupt ransomware activities more effectively, as targeting the most valuable components of their operations can result in significant disruptions to the organization. This value attribution is currently unknown. The question this thesis attempts to answer is: How do ransomware groups allocate value to the activities of the ransomware value chain, and how can this inform law enforcement in developing effective intervention strategies? The methodology involves an exploratory research approach to Conti's public chat transcript data, supported by blockchain analysis. The final deliverable includes a value analysis through the value chain lens and recommendations for the FIOD. These recommendations include insights gained from studying the value creation, compensation, and allocation of ransomware groups, highlighting strategic points along the value chain where disruption would result in the most significant impact. These insights are crucial for enhancing criminal investigations and guiding authorities to disrupt valuable and critical activities within ransomware operations effectively.

As the Netherlands aims to phase out gas by 2050, alternative heating solutions are urgently needed, especially in densely populated areas like Amsterdam's city center. Solutions like hydrogen and green gas are infeasible due to limited availability.
A potential solution is a fifth-generation district heating and cooling (5GDHC) network, an energy system that provides both heating and cooling to buildings using external energy. This is possible due to a low temperature heat carrier in combination with bidirectional operation. Heating and cooling can be exchanged between buildings within the network, reducing the need for external energy. A 5GDHC network for a large city requires clustering of the buildings to create smaller networks, which can later be connected. Clustering allows for a bottom-up approach, reducing both the investment risks and the overload risk. A benefit of 5GDHC is the possibility to use aquifer thermal energy storage (ATES), where thermal energy is stored underground. However, research on 5GDHC, particularly on large-scale implementation, is scarce. Dividing buildings into clusters is crucial for large-scale implementation, since it reduces the investment and overload risk, but the best methods are unknown.
Additionally, the possibility of implementing ATES is not considered in research during the clustering process. This research investigates a method to cluster buildings and integrate ATES systems within these clusters. The goal is to create compact clusters to minimize the use of space in the already crowded subsurface. The clusters must also have a high demand fulfillment, meaning the heating and cooling demands must be fulfilled as much as possible within the cluster, either through energy
exchange between buildings or through the use of storage. A higher demand fulfillment reduces the need for external energy. The developed method clusters buildings based on their geographical location using both k-means and equal size k-means to ensure compactness.After this, ATES systems are implemented in the clusters, which can only be placed in available open space. The model results indicate that implementing ATES systems can increase the demand fulfillment from 1.4% to 58.6% for the entire center of Amsterdam, if buildings are insulated to a level fit for low temperature heating. The results show that very compact areas struggle more to meet heating demand. k-means outperforms equal size k-means in both compactness and demand fulfillment, making it the recommended method. The findings suggest that a 5GDHC network with ATES implementation has significant potential, with the most potential for clusters in the east and northwest. The identified method is robust, adaptable to various geographical regions, and particularly effective in densely populated urban areas with limited space and high demand. In conclusion, this research contributes to the fields of aquifer thermal energy storage and fifth generation district heating and cooling. It not only fills critical gaps in existing literature but also provides a practical methodology for optimizing urban energy systems. This approach holds promise for supporting initiatives like the ‘High-hanging fruit’ project by the AMS Institute, assisting sustainable urban heating solutions in Amsterdam and beyond.

The Affordable Rent Act (Wet Betaalbare Huur), enacted on July 1, 2024, establishes regulations on rent control in the mid-market rental sector. This is accomplished by establishing maximum rents in the mid-market rental sector based on the quality of housing, as determined by the WWS points system. The study critically examines the impact of this legislation on the governance of housing associations in the West-Brabant and Tholen regions through a combination of literature review and semi-structured interviews with key stakeholders.

The findings are threefold: First, the Act has a limited direct impact on housing associations, as their rents typically fall below the new limits. Second, the enforcement of the WWS points system introduces significant administrative challenges for housing associations. Third, the anticipated exit of investors from the mid-market sector may increase demand for social housing, thereby placing additional pressure on housing associations.

This study addresses a gap in the existing literature by focusing on the effects of the Affordable Rent Act on housing associations, as previous research has primarily concentrated on investors. The study concludes with policy recommendations, including the establishment of a central housing agency, the creation of a mid-market rental fund, and a continued focus on social housing by housing associations. The recommendations are designed to assist housing associations in navigating the dynamic housing market.

Responding rapidly to epidemic outbreaks presents significant challenges, due to resource, capacity and time limitations. Anticipatory Action (AA) is a newly emerging strategy in the field of humanitarian aid, designed to preemptively address potential crises. By taking impact-reducing actions before a disaster strikes, AA seeks to minimize human loss. However, AA frameworks currently use static prepared-in-advance plans. As a result, AA is not sufficiently able to deal with the uncertainty levels in the onset and spread of epidemics. Effective epidemic control requires plans that can adapt to a constantly changing environment and incoming information, such as the number and location of suspected cases, weather forecasts and population movement, while balancing flexibility with an effective management approach. We show how the (DAPP) framework for decisionmaking under deep uncertainty can be adapted to enhance the common anticipatory action approach with flexibility and effective management for epidemic control. More specifically, we show how DAPP allows to take into account newly available information and change the strategy to minimize human loss. We illustrate it with a case study of cholera in Cameroon, for which the French, Netherlands, and Cameroon Red Cross, supported by EHESP, are developing an early action protocol and a model that assesses the cost-effectiveness of actions for different risk levels and external shocks. Our results suggest that DAPP increase flexibility and coordination in anticipatory action for epidemics and helps optimizing early action strategies, which could have larger implications for global disease control.

Access to affordable, reliable, and sustainable energy is a key goal of the Sustainable Development Goals (SDG). Despite this, 675 million people globally, especially in rural areas, remain without access to electricity. Sierra Leone exemplifies this issue, with only 4.9% of its rural population having electricity access. The lack of access hinders economic and social development, impacting healthcare, education, and overall quality of life. While grid extension is costly and difficult in remote areas, solar-based micro-grids offer a promising solution for rural electrification, leveraging the country's solar potential.

However, literature reports performance issues with these micro-grids, and while some factors influencing the performance of micro-grids are identified, their impact and mitigation strategies are underexplored in rural contexts. This research aims to identify and classify the factors affecting micro-grid performance and assess their impact on rural developing areas. The study provides insights into mitigation strategies, considering technical, social, economic, and governmental contexts, bridging the gap between qualitative and quantitative research to improve access to electricity.

The research uses a case study approach combined with modelling. Data collection includes site visits, literature reviews, and semi-structured interviews. The modelling uses Python for Power System Analysis (PyPSA) to assess the impact of identified factors on micro-grid performance. The case study focuses on four small communities in Sierra Leone with varying levels of user satisfaction. Findings reveal that micro-grids face economic constraints, technical limitations, and dependency on government support.

The study identifies several key factors affecting micro-grid performance, including high appliance use, low-quality battery design, and a lack of skilled technicians. Battery performance is determined as the most critical factor, directly affecting electricity availability. The research evaluates several mitigation strategies, such as Demand Control (DC), air conditioning, and additional battery capacity. DC is found to be an effective mitigation strategy, especially in evening hours, enhancing electricity access in rural areas.

Demand Control is a promising mitigation strategy and can enhance micro-grid performance, within the rural developing context. Further research is recommended to refine these strategies and explore their broader applicability to other micro-grid systems in rural developing areas.

Indonesia started its journey towards a net-zero energy system. Research and developments towards these targets are currently well underway. However, present-day prediction models show that the rising demand of the island will not be met with the renewable energy technology potential. One solution for this problem is the development of power interconnections between the power systems of the Archipelago. However, short-term planning and speculations about the feasibility of inter-island interconnections block their construction. This study aims to explore the feasible options for inter-island interconnections and recommends the next steps that can be researched. The method to answer this research question is through the combination of a techno-economical model and an institutional analysis. The techno-economical model utilizes the energy planning model ’Calliope’, as the foundation of this model was already developed by researchers at TU Delft. To include geographical features, a GIS (Geographic Information System analysis) is performed and its outcomes are used to analyse the results coming from the Calliope model. The results from this techno-economical analysis are then used as input for the institutional analysis. The latter is built upon the existing Williamson framework. Due to the limited scope and timeline of this study, the aspects planning of transmission infrastructure, financing of transmission infrastructure and contractual arrangements between public and private parties are chosen as focus points in the institutional analysis. The state-of-the-art technology used for inter-island interconnections, or submarine power cables in general, are High-Voltage Direct Current (HVDC) cables. The advantage of HVDC over HVAC (High-Voltage Alternating Current) cables is their ability to transmit power over longer distances while creating less envi- ronmental impact due to limited electromagnetic interference. The planning of submarine power cables is a relatively new subject in academic literature. The impor- tant geographical features to consider in the design process of submarine power cable routing found in this study are the depth of water (bathymetry), seabed slope, seismic regions, and protected zones. These ge- ographical features can be categorized in terms of their impact on the cost and risk of breakage of the cables. This study found through a techno-economical analysis that there are multiple inter-island intercon- nections that are found to be needed in a Calliope power model of Indonesia and feasible considering the aforementioned geographical features. The visual shortest-path routing of these feasible cables has been determined through an AHP (Analytic Hierarchy Process), analysis combined with a Dijkstra shortest-path algorithm. On the institutional side of the result, this study found that there are multiple barriers that could form a barrier to the development of inter-island interconnections. The main issues found have to do with the planning, financing and contraction of the institutional side of transmission infrastructure development. In order to soothe these barriers the following recommendations are constructed; using an integrated transmission capacity planning approach, institutional strengthening for investment and creating a governance body dedicated to interconnectivity. The conclusion of this study is that there are feasible interconnections for accommodating renewable energy technology integration in Indonesia. The found feasible routes of this methodology consist of Bali- Nusa Tenggara Barat, Kalimantan Barat-Daerah Khusus, Jakarta-Kalimantan Tengah, Jawa Tengah-Kalimantan Selatan, Jawa Timur-Kalimantan Tengah, Jawa Barat-Lampung Banten, and Kepulauan Riau-Riau and between Kalimantan Barat and Jawa Barat. Institutional adjustments are found to be crucial for fostering this interconnected system. Institutional recommendations involve integrating interconnections into the RUPTL (Electricity Supply Business Plan), strengthening foreign investment with clear policies, and ensuring transparent contractual arrangements between public and private entities. Moreover, prioritizing grid balancing and exploring sustainable financing methods like green bonds further support these efforts. The most important limitations of this study are the minimal use of scenarios, the use of only one energy system modelling software (Calliope) and the limited scoping for the institutional analysis. As for future research, this study invites researchers to think about the specific power flow of the system, the operational challenges, social engagement and public perception and comparison to other archipelago countries

Critical infrastructures, such as the energy sector, are vital for the proper functioning of society. It is crucial to protect these infrastructures against cyberattacks on their operational technology, ensuring their operational performance, and safeguarding energy safety. There is a lack of existing research on improving cybersecurity in operational technology for increasing operational performance in liquefied natural gas infrastructure in the Netherlands, specifically when it comes to increasing redundancy in the system. In an attempt to fill this knowledge gap, this thesis implements agent-based modelling to answer the following main research question:

“To what extent does the implementation of redundancy enhance the operational performance of operational technology in the industrial processes of Gate terminal in the face of cyber threats, in order to maintain the availability of business services?”

The thesis shows that the key determinants for the effectiveness of a redundancy strategy are the criticality of the redundantly implemented element, and the degree of diversity applied. Additionally, a trade-off between the advantages of redundancy the inevitably increased attack surface can be observed: when implementing an ineffective redundancy strategy, overall system performance may degrade in comparison to the initial system conditions. The importance of an effective incident response cycle must not be overlooked. A redundancy strategy is only as effective as the capability of the system operator to prevent and deal with incidents.
Future research can build on this thesis, exploring more explicit redundancy strategies, types of attacks, and strategic defense systems, as well as the optimization of the redundancy trade-off. These research directions can help in further analyzing and understanding the impact of redundancy on system operational performance.