This thesis examines the effects of safety notices on the utilization of knee implants in orthopedic practices. Utilizing a blend of correlation analysis, machine learning techniques, and interrupted time series analysis, this study evaluates the impact of safety notices on the market share of diverse knee implant models. The analysis leverages a dataset comprising safety notices, market share figures, and analytical model outputs to investigate both the direct and indirect influences of these notices on clinical decision-making processes.
Although safety notices are integral to regulatory compliance and patient safety under the Medical Device Regulation (MDR), which mandates continuous monitoring of device performance, findings indicate that these notices seldom significantly alter clinical practices or market shares. Notable exceptions include specific models such as Advance, Evolution, and Journey, which display variable impacts, suggesting the presence of other complex factors at play in clinical decision-making. These findings illustrate the complex dynamics of clinical responses to regulatory initiatives and underscore the need for more robust monitoring and evaluation strategies to determine the effectiveness of safety notices in post-market surveillance.
The thesis underscores the need to enhance post-market surveillance systems to more accurately assess the influence of safety notices on clinical practice, thereby addressing a crucial knowledge gap. By recommending directions for future research aimed at elucidating the subtle effects of safety notices, this work strives to maximize their utility and foster improved outcomes in patient care within the realm of medical devices. This research establishes a foundation for understanding the practical implications of safety notices in clinical environments, ensuring that regulatory measures are more closely aligned with clinical requirements, which will be useful for future research.

We live in the era of data. Data stands as the new essential resource, proving crucial not only for sophisticated mathematical models but also for commonplace applications. Regardless the field or domain, data needs to be shared in order to improve these tools, which means that systems must speak the same language. To address this challenge, there is a growing popularity in utilizing ontologies. These are formal structures of knowledge that enable a common understanding of concepts related to a specific domain, both by humans and by machines. But their generation is a complex task that requires knowledge from both the domain and the ontology engineering field. Many ontologies already exist, and extending those with new concepts and paradigms is a common practice. It is also essential to promote reusability of standards and standardization, and to avoid “reinventing the wheel”.
In parallel, the fast evolution of Artificial Intelligence is leading the emergence of infinite possibilities in terms of automation of tasks that have hitherto been completely manual. In particular, the use of Large Language Models is booming, though its use to help in the extension of existing ontologies has not been exploited yet. This research proposal aims to answer the question of: How can LLMs be integrated into a semi-automated and domain-independent process for the extension of existing ontologies?
This master’s thesis, carried out in collaboration with TNO, follows the Design Science Research Approach to analyze the problems associated with ontology engineering and the integration of LLMs in the ontology extension process by interviewing 11 ontology engineers and experts in the field. The analysis of the interviews is used to generate a model of the current ontology extension process and to produce a set of 22 high-level design requirements to guide the design a process framework for human-LLM collaboration for the ontology extension process. The design prototype proposed consists of an extended version of the current ontology extension process model augmented by the assistance of LLMs on various ontology extension tasks and incorporating additional ontology engineering tools and stakeholders in the process. The design includes a set of prompt templates that can be customized by the ontology engineer to extend an ontology in any domain. The final design is demonstrated and evaluated with a real use case, which consists of extending the Common Greenhouse Ontology (CGO), developed by TNO, with the use case Semantic Explanation and Navigation System (SENS), previously executed by TNO. The demonstration and evaluation is performed using OpenAI’s model GPT-4 Omni, through the creation of a GPT Assistant.
The generated ontology extension using the process framework and the GPT assistant is similar to the manually crafted extension. Remarkably good results are achieved in tasks such as defining business scenarios and creating a glossary of terms, generating Competency Questions, formalizing the ontology (in OWL) and the CQs (in SPARQL queries), and verifying the generated ontology extension using mock data and CQs. An end-user (ontology engineer) is asked to use the proposed process framework prototype and qualitatively evaluate the tasks, concluding that this approach is very useful for ontology engineers with various level of expertise to structure and increase the quality of the current ontology extension process.
Although the proposed design holds a great potential to be implemented and integrated with the current methodologies and tools used by ontology engineers, several challenges need to be tackled before we see a wide adoption and integration of LLMs in the ontology extension process. These challenges go beyond the technical performance of the LLMs, but revolve around the societal implications of the use of this technology instead. The loss of enriching human interactions and expertise, and the environmental and ethical impact are big concerns of the ontology engineers. These must be addressed before the potential of LLMs for the ontology extension process can be unveiled.

The growing population in the Netherlands is straining the accessibility and liveability of cities. To address these issues, new mobility policies prioritize quiet, low-emission, and space-efficient modes of transport, creating a conflict with the characteristics of private fuel-engine cars, which are noisy, polluting, and space-inefficient. For example, a private car trip in Amsterdam requires about 95m² of public space, including 15m² for parking. Consequently, Dutch cities are considering measures to reduce car mobility, with parking regulations viewed as a potential tool to decrease car ownership and enhance urban liveability. However, there is a lack of evidence supporting the effectiveness of these policies, as car ownership research typically focuses on sociodemographic and household factors.

This study aims to fill this knowledge gap and assist municipalities in balancing car accessibility with urban liveability. It begins by defining urban parking availability, influenced not only by the number of parking spaces but also by various regulations. Two main types of parking are identified: private parking on premises and public on-street parking. Additionally, parking permits, which subsidize parking costs for residents, are considered.

To understand the influence of parking availability on household car ownership, a multinomial logistic regression (MNL) model was used. Data from 80,527 urban households over three years from the Dutch National Traffic Survey (ODiN) was analyzed. The study operationalized the number of on-street parking bays per household, parking places on premises per household, and maximum parking costs in the municipality.

The results showed that parking availability in a neighborhood does affect car ownership. Both types of parking were positively related to car ownership, while higher permit costs in a municipality decreased the likelihood of car ownership. However, household composition and income were the most influential factors. The number of driving licenses in a household, closely linked to the number of adults, and disposable income were the strongest predictors of car ownership.

The study also examined the combined influence of multiple parking availability dimensions. It compared car ownership probabilities in three neighborhoods for average households and predicted car ownership levels for a potential new neighborhood under different parking availability scenarios. The findings indicated significant increases in car ownership with higher parking availability, emphasizing the need for balanced parking strategies.

The relationship between parking availability and car ownership is crucial for policymakers, as it supports the formulation of parking regulations aimed at reducing car ownership. Municipalities are advised to develop parking policies that consider the identified dimensions of parking availability to optimize public space use.

Future research should focus on proving causality, improving spatial data, and exploring additional factors of car ownership and parking availability. The study's limitations include the inability to establish causality and the need for more reliable parking data. Further research should also consider attitudinal factors towards car ownership and investigate the relationship between parking availability and car ownership in rural areas.

Political and societal attention on transport inequality, transport vulnerabilities and transport poverty risk has been increasing the last five years. The Dutch government has committed multiple large sums of resources to abate the issue, 120 million euros to cancel the increase in price of train tickets, 300 million to cancel the increase in price and preserve the provision of the regional public transit and 6 million to start a pilot of ‘innovative public transport’ in the Zeeland region. A review of the literature on transport poverty risk reveals that past studies analyze the concept using a limited number of pre-selected variables. For example, car ownership and residence area. This carries the risk for misinterpretation as an accurate representation can only be achieved by considering all relevant variables. Such a comprehensive analysis could allow policymakers to formulate tailor-made policies according to the specific problems and needs of certain groups in society. This study aims to include the entire complexity of the concept of transport poverty risk in the analysis. Relevant factors include affordability, accessibility and mobility. Then there are individual differences, and for every individual changing over time, between needs of participating in society and social life. This study aims to incorporate this comprehensive definition in a Latent Class Analysis approach to construct groups of individuals based on the MPN and ODiN national datasets. The different groups of individuals can be then used to indicate the effect of policies on transport inequality. Results show three groups that are most transport ‘fortunate’, with high income, high car ownership, high accessibility and the potential to use different travel modes. Results also show one group with substantial transport vulnerabilities due to higher car dependency, lower car ownership and lower household income. A policy thought experiment is established through the application of mobility policy packages of three political parties. Car policies that reduce costs per driven kilometer such as reduction in excise tax will increase transport inequality due to the three groups that are the most transport ‘fortunate’, which also have the highest car use. This study provides the scientific community with a comprehensive definition and conceptual model of transport poverty risk and for policymakers contributes a comprehensive understanding of transport inequality in the Netherlands. Future research needs to focus on identifying specific minority groups that are not or less captured in the national datasets. Transport problems are experiences by an individual, while transport inequality is represented by the differences in the extent of transport problems between certain individuals or groups. In this case, certain groups in the Netherlands. This calls for the need to differentiate the government levels at which transport inequality and transport problems are addressed. While this study presents the transport inequality at the national level, municipalities will be able to identify specific individuals and neighborhoods who are more prone to transport problems.

Objective: The aim of this study was to investigate the effect of autonomous-vehicle-to-pedestrian (AV2P) communication through augmented reality (AR) interfaces on the road crossing behaviour of pedestrians, and research whether subjective results from a previous Cave Automatic Virtual Environment (CAVE) study replicated in a real world AR experiment.
Background: Previous studies investigating the effects of AV2P communication have mostly been conducted through virtual reality (VR) providing researchers with safe experimentation methods and high experimental control, but also resulting in a common limitation: the lack of ecological validity and realism, thereby affecting participants’ behaviour and causing distractions. This study therefore introduces AR experiments that have been conducted in a real world environment to increase ecological validity.
Methods: An AR experiment was conducted in which 28 participants were situated in the real world with the objective to cross the road. The virtual vehicle, that was projected through a Varjo XR-3 head mounted display, approached from the right at a speed of 30 km/h while 4 interfaces (2x world-locked, head-locked, and vehicle-locked) appeared to communicate the vehicle’s intention towards the participants, in addition to a no-interface baseline. Participants were tasked with indicating when they were willing to cross through the push of a remote button from which their Willingness to cross and Decision certainty could be derived. Subjective data was collected after the trials and after the experiment through interviews and a questionnaire respectively.
Results: Results suggest a positive effect of the AV2P interfaces on the Willingness to cross and Decision certainty, although statistically not significant. In other words, Willingness to cross increases when the vehicle indicates that it will yield, and decreases when the vehicle communicates that it will not yield. Decision certainty also increases when an interface is present compared to the no-interface baseline. Moreover, participants indicated using the interfaces as a tool to validate their own decisions. Compared to the CAVE study, subjective intuitiveness ratings replicate in terms of observing higher intuitiveness of the interfaces than the no-interface baseline. However, the intuitiveness ratings were higher in the CAVE study than the real world AR experiment. Furthermore, the order of the top 3 most preferred interfaces ranking is in the opposite order. Both differences suggest that the increased ecological validity of the real world AR experiment introduces new insights into participants’ perception of interfaces. The Van der Laan acceptance scale shows that participants believe interfaces to be useful and satisfying overall.
Conclusion: The experiments suggest that AV2P interfaces have a positive effect on the crossing behaviour of pedestrians. Furthermore, participants indicate using the interfaces as a tool to validate their own decision, which increases confidence in their decisions. Although results partially replicate a previous virtual environment study, there are differences that suggest that real world AR experiments provide valuable insights into participants’ perception of interfaces in a more realistic experiment.

The increase in online retail demand has stimulated automation in order picking systems, leading to new challenges and opportunities in task assignment and scheduling. In partially automated order picking systems, such challenges and opportunities exist regarding human factors implementation in the job-shop scheduling problem, an optimisation problem essential in operations. Workplace fatigue is a human factor often overlooked in scheduling research and application, despite hurting employees’ well-being and costing U.S. employers up to €127 billion annually. With the opportunities that automation offers, cobotic order picking systems could actively consider human fatigue development, mitigating its negative effects in operation.
This thesis investigates the possibility and potential benefits of fatigue consideration in the job-shop scheduling problem for a partially automated order picking system. We present a new bi-objective mixed integer nonlinear programming problem formulation to represent system constraints and a predictive fatigue model while considering worker fatigue and productivity during schedule optimisation. To put the results of simulated optimisation in perspective, we experimentally validate the fatigue model predictions and fatigue mitigation capabilities of the scheduling approach using heart rate measurements and qualitative fatigue ratings. These experiments occur with employees in a real-life partially automated order picking system.
Our mathematical model can find solutions that the conventional single-objective optimisation approach cannot, allowing fractional energy expenditure distribution improvements more than 4x larger than the decrease in productivity they require in 53% of the considered virtual cases. This is a promising result for fatigue mitigation in operations only by altering operational decision-making. However, the validation experiments show that our predictive fatigue model has an average RMSE of 2.20 kcal/min in estimating energy expenditure rates compared to heart rate measurements while also showing a low correlation. When assessing 10 minute intervals, a time span that fits a scheduling scope, the estimations improve slightly (avg. deviation of -1.85 kcal/min, avg. correlation of 0.17) but still underestimate the measured values. The experiments also show no significant differences in experienced fatigue between existing schedules and those with fatigue mitigation measures applied.
We conclude that the current scheduling formulation is not yet fit for application with a predictive fatigue model. However, real-life operations can benefit from energy expenditure estimation via heart rate measurements and a different approach for implementation is proposed. Research opportunities lie in further fatigue model development and validation, extension to indirect fatigue effects and other human factors, and further development of the mathematical formulation.