The rise of hybrid work in the Netherlands has increased the number of meetings where some participants are remote and others on-site. In agile software development, this change affects high-interaction processes such as requirements elicitation. While prior research addresses hybrid collaboration in general, there is limited insight into how it specifically impacts requirements elicitation. Existing studies mainly focus on tools or broad barriers without exploring how collaboration breaks down when elicitation techniques are applied in hybrid settings. This leaves requirements engineers without structured guidance to address hybrid-specific challenges, risking reduced participation, engagement, and facilitation quality. This thesis applies a design science methodology in five phases: problem identification, design objectives, design and development, demonstration and evaluation, and communication. Literature review and interviews with requirements engineers, product owners, and clients identified three key challenges: unequal participation, insufficient focus, and facilitation difficulty. Based on these findings, three alternative approaches were developed and integrated into a three-pillar intervention strategy: Setup (meeting environment), Structure (meeting design and leadership), and Awareness (participant behavior and mindset). The strategy was tested in three real-world hybrid meetings. Evaluations showed improved focus, more balanced participation, and positive participant reception. The final output is a practical roadmap with three levels of intervention: quick wins, facilitator behavioral adjustments, and collective behavioral adjustments. This research contributes scientifically by identifying hybrid-specific elicitation challenges and offering a layered, tool-independent strategy tested in practice. It contributes to practice by providing a low-barrier approach that does not require major system changes. While results are promising, generalizability depends on context factors such as digital infrastructure, team culture, and facilitation readiness. Future research should validate the roadmap across different organizations and assess its long-term impact.

Green hydrogen is increasingly recognised as an essential component for achieving global climate neutrality, offering a versatile and sustainable alternative to fossil fuels in hard-to-abate sectors such as industry, transport, and energy storage. Among the production pathways, water electrolysis using renewable electricity stands out as the most scalable and environmentally viable method. However, a major barrier to large-scale adoption is the uncertainty surrounding future electrolyser costs and deployment trajectories. Most existing cost projections rely on deterministic approaches that provide single-point estimates without explicitly addressing uncertainty, often leading to overly conservative cost decline assumptions and potentially misguided policy and investment decisions.

To address these shortcomings, this thesis develops a probabilistic, empirically grounded framework to forecast both the deployment and capital expenditure (CAPEX) of alkaline electrolysis cells (AEC) and proton exchange membrane (PEM) electrolysers. The framework integrates a logistic S-curve model to simulate technology deployment and a stochastic implementation of Wright’s Law to model cost reductions as a function of cumulative capacity, explicitly incorporating uncertainty through Monte Carlo simulations. Hindcasting validation is used to assess the robustness and predictive performance of the framework, ensuring alignment with historical trends.

The results reveal that achieving the IEA Net Zero Emissions (NZE) 2050 targets would require average annual growth rates of 46% for AEC and 49% for PEM technologies, significantly higher than historical growth trends of around 39%. Even under a more conservative industrial-use scenario, focusing only on sectors such as refining, ammonia, and methanol production, substantial acceleration remains necessary, with required growth rates of 41% for AEC and 45% for PEM. These findings highlight the immense scale of the deployment challenge and the value of explicitly addressing uncertainty when evaluating policy pathways.

Cost forecasts demonstrate a clear divergence between the two technologies. AEC shows a positive experience exponent, suggesting a negative learning rate, implying potential cost increases with expanded deployment. This trend is not statistically significant (p = 0.41), and the model exhibits low explanatory power (R2 = 0.04), indicating that historical data do not support a strong cost-deployment relationship for AEC. In contrast, PEM electrolysers display a statistically significant learning rate of 3.3% (p < 0.01), with an experience exponent of –0.0480 and a higher model fit (R2 = 0.62). Under the reference scenario, AEC median CAPEX is projected to rise to 1,921 EUR/kW by 2030 and 2,076 EUR/kW by 2050, with a wide interquartile range reflecting large uncertainties. For PEM, median CAPEX declines to 1,800 EUR/kW by 2030 and further to 1,533 EUR/kW by 2050, with more pronounced cost reductions under high deployment scenarios.

This transparent and modular framework not only improves cost forecasting for electrolysers but is also adaptable to other emerging energy technologies facing similar learning and deployment uncertainties. By relying on openly available data and explicitly quantifying uncertainty, it provides a robust foundation for analysts, policymakers, and scenario developers. Moreover, its structure makes it suitable for integration into Integrated Assessment Models (IAMs), supporting more realistic and adaptive long-term energy transition planning. Future research should focus on expanding its application across technologies, refining empirical learning rates, and assessing policy impacts within comprehensive system-level analyses, ultimately enabling more confident and informed decisions towards a net-zero future.
The data and the code are available at: https://doi.org/10.4121/82988dc7-099b-45e2-81e2-3850cee1b940

This thesis explores the development of lightweight neural network models for predicting perceptual soundscape attributes and environmental sound sources, with the goal of enabling real-time, sensor-based soundscape monitoring in urban environments. Recognizing the limitations of conventional noise metrics, the study adopts a perceptual approach that accounts for how individuals experience urban soundscapes. The work leverages the Affective Responses to Augmented Urban Soundscapes (ARAUS) dataset to replicate and extend the training pipeline of the baseline AD_CNN model. Four parameter-reduction strategies are implemented - shrinking convolutional and dense layers, reducing temporal resolution, and increasing pooling - to produce compact models suitable for low-cost, resource-constrained sensors. Two optimized variants, AD_CNN_dense_layer and AD_CNN_hop_length, are combined into an ensemble model with only ~160K parameters, which outperforms the original AD_CNN in perceptual attribute prediction. A generalisation study evaluates model robustness on a separate urban parks dataset, revealing that while the lightweight ensemble performs well overall, it struggles with certain attributes such as pleasantness. Larger models like SoundAQnet generalise better but remain imperfect. The findings demonstrate the technical feasibility of lightweight perceptual models but also highlight the need for localised fine-tuning to account for subjective and context-dependent factors shaping soundscape perception. This work contributes to scalable, context-aware soundscape monitoring solutions for healthier urban environments.

Courthouses in the Netherlands are grappling with substantial challenges stemming from escalating workloads and high burnout rates among court clerks. This strain not only compromises the well-being of essential judicial personnel but also hinders the overall efficiency and timeliness of the justice system. In response to this pressing issue, this research has delved into the potential application of Large Language Models (LLMs), a form of artificial intelligence like ChatGPT, as a means to alleviate these burdens. In order to investigate the potential of LLMs in the juridical field, the following main question has been drafted:
“How effective is an LLM in lowering the workload of the Dutch court clerks?”
The research employs a mixed research approach. The literature and desk review examines the tasks of the clerks, information about the available LLMs and prompt engineering technique. In addition to this, semi-structured interviews have been held to explore the different tasks of the clerks. Since the scope of this research is only big enough for one task, The task selection process utilized the elimination by aspects technique. After this, a targeted experiment has been conducted to analyse and evaluate the possible LLMs and select the most suitable model for this research. The effectiveness of the LLM-based prompt support have been evaluated using a mixed-methods approach, combining quantitative and qualitative data analysis, which are a within-subject experiment, semi-structured interviews, DeepEval Evaluation, expert evaluation, and a statistical analysis.
The results and the conclusion of this research are still under embargo.

Visual Studio Code (VS Code) has become the dominant development environment worldwide, used by the majority of software professionals. Its success largely stems from its highly extensible architecture, supported by an extensive marketplace hosting more than 100,000 extensions that allow developers to tailor their workflows. However, recent research and industry investigations have revealed that these extensions can be exploited to execute arbitrary code, exfiltrate data, or compromise build environments.

Despite growing attention to these technical threats, little is known about how such risks are perceived and managed within organizational settings, where developer autonomy intersects with organizational governance and policy. Using a qualitative approach, interviews were conducted with 21 professionals from 19 companies across five countries to explore how developers perceive and manage the security of VS Code extensions in organizational contexts.

The findings reveal that extension management practices are largely convenience-driven, with developers relying on surface-level Marketplace signals, such as publisher verification, ratings, and download counts, that can easily be manipulated, as shown in prior research. These cues provide reassurance but not assurance, leading developers to conflate popularity or verified status with safety. In most organizations, extension governance is minimal or informal, resulting in fragmented practices where developers must independently assess security risks despite operating in managed environments.

The study concludes that secure extension use in VS Code is not merely a technical issue but a socio-technical and governance challenge that requires coordination across multiple levels. At the marketplace level, clearer communication of verification criteria, greater visibility of permissions or a modified permission model, and stronger mechanisms for signaling risk are needed. At the organizational level, structured allowlist policies, internal vetting workflows, and targeted awareness programs can bridge the gap between platform safeguards and developer behavior. At the developer level, improved understanding and interpretation of trust cues should be supported, not assumed, through organizational policy and education. Together, these measures align platform design, organizational governance, and developer practice toward a shared framework of accountability and safer extension use within professional environments.

As digitalization advances, cybersecurity departments are increasingly overwhelmed by alerts and potential threats, leading to decision fatigue among security analysts. In response, many are adopting Artificial Intelligence (AI) to automate routine tasks, prioritize alerts, and accelerate incident response. However, the pace of AI adoption in cybersecurity lags behind that of threat actors, revealing underlying challenges. This thesis explores these challenges through a case study of a large European bank's cybersecurity department, using a sociotechnical systems (STS) approach. Semi-structured interviews with security analysts, data scientists, and leadership revealed four key themes: (1) mixed perceptions of AI, (2) the influence of organizational factors on AI adoption, (3) the importance of interdisciplinary collaboration, and (4) the critical role of trust in AI systems. While AI offers potential benefits like improved threat detection and reduced decision fatigue, challenges such as data quality issues and lack of transparency persist. Organizational readiness, leadership support, and effective change management are crucial for successful AI integration. Building trust through transparency and active user involvement is essential for adoption. The thesis proposes a conceptual model that addresses these challenges by integrating technical and social factors, offering practical recommendations for enhancing AI adoption in cybersecurity. Future research should expand on these findings through diverse case studies, human-centered AI frameworks, and longitudinal studies to better understand AI’s impact on trust and collaboration in cybersecurity.

In the modern digital age, software vulnerabilities pose significant threats to security and privacy. These vulnerabilities are weaknesses in software products that can be exploited for malicious purposes. To manage and coordinate information about these vulnerabilities, the Common Vulnerabilities and Exposures (CVE) system is widely used. Although a lot of research has been done on coordinated CVEs, there is less focus on vulnerabilities that did not receive a CVE number. This research aims to estimate the number of vulnerabilities in open-source projects that do not receive CVE identification numbers and are consequently overlooked by the community. The focus of the research is on quantifying the prevalence of security issues in the issue tracker of GitHub projects, examining these security issues to find hidden CVEs and determining their severity based on the CVSS 3.1 scoring methodology. The methods used for the research are data extraction and analysis, transformer-based models for the classification of security issues, and expert validation for CVE identification and severity scoring. The findings of the research reveal crucial insights into the scale and nature of security threats in open-source software. The DeBERTaV3 model fine-tuned on the Chromium dataset demonstrated good performance metrics for the task of classifying security issues, achieving an f1 score of 0.9 with threshold modification. The case-study application of the model on the gRPC project demonstrated that in the issue tracker, 2.4\% of issues have been predicted as being security issues, out of which 52 were validated as CVEs with an average severity score CVSS 3.1 score of 5.3, compared to 11 CVEs attributed to the gRPC project on NVD. These findings suggest a need for revising current practices in vulnerability reporting and management in open-source projects, potentially influencing future security protocols and policies. The results of the study serve as information for the decision-making process of improving the coordinated vulnerability disclosure process in open-source software. An initial intervention suggestion is provided to induce behavior change and incentivize discoverers to disclose sensitive vulnerability information in private communication channels. Future work on understanding the motivations of discoverers to post such sensitive information in public trackers is required to create well-informed and effective policies for coordinated vulnerability disclosure.

This thesis explores the sociotechnical challenges surrounding the implementation of black-box AI in the Dutch healthcare sector, with a focus on aligning AI implementation frameworks to the needs of key stakeholders. The research addresses the growing demand for AI in healthcare as a potential solution to challenges like staff shortages, operational inefficiencies, and resource limitations. Despite its potential, the adoption of black-box AI, characterized by its opaque decision-making processes, faces resistance due to issues such as trust, lack of transparency, and difficulties in technical integration. To better understand these barriers, the study conducted a series of interviews with Dutch healthcare professionals, including technical, clinical, and business stakeholders, and analyzed existing AI implementation frameworks from the literature. The research identifies three primary challenges: trust in AI systems, technological integration with existing workflows, and organizational readiness, particularly regarding AI literacy.

In response to these findings, the study proposes a combined framework that bridges the sociotechnical gap in AI implementation, drawing from established implementation models and stakeholder insights. Key propositions include the use of Computational Reliabilism (CR) to build trust in AI systems without requiring full transparency, and the introduction of explainability tools to help end-users, particularly clinicians, trust the AI's outputs and ensure responsible use. Additionally, a maturity model is suggested to assess and improve organizational readiness for AI adoption, focusing on enhancing AI literacy and preparing healthcare institutions for more complex AI integrations. This framework and its propositions aim to foster a collaborative approach to AI adoption in Dutch healthcare, addressing both social and technical barriers to pave the way for ethical, reliable, and effective use of black-box AI.