Introduction
Cancer-related pain is a prevalent and under-assessed issue in oncological care. Despite the availability of effective pain treatments, challenges in pain assessment - such as communication barriers, subjective interpretation, and workflow constraints - continue to hinder accurate pain recognition and management. In recent years, advances in artificial intelligence (AI), sensor technology and mobile health have offered promising new opportunities for more consistent, continuous and context-aware assessment strategies. Facial expressions and vocal cues have been identified as promising behavioural indicators of pain, and their use in automatic pain assessment (APA) is gaining increasing attention.

To explore how such technologies could support cancer pain assessment and management, the SENSAI project was initiated. The project focuses on the development of a human-centred, AI-empowered tool for APA in cancer care, based on facial expressions and vocal cues captured through a mobile application.

This thesis presents the initial steps of the SENSAI project. Its objectives were to examine current pain assessment practices and challenges in oncology, investigate end-user perspectives on AI-supported assessment, translate research insights into a conceptual tool framework, support the design of a user-centred APA application, and initiate the development of a multimodal cancer-related pain database.

Methodology
A multiphase, design-oriented research approach was used, guided by user-centred design, the Double Diamond framework, and a translational AI development model for healthcare. This thesis encompassed the Research and Conceptualisation phases and initiated the Development phase.

In the research phase, background studies and an exploratory interview study with oncologists were conducted to understand current pain assessment practices and challenges. This informed the problem definition and supported the development of an initial concept for the tool In the conceptualisation phase, this concept was refined into was structured framework. The second part of the interview study explored oncologists’ attitudes towards the proposed APA tool, guided by the mobile health Technology Acceptance Model. These findings, along with expert consultations, a brainstorm session and feasibility considerations, shaped the envisioned design and functionalities of the tool. In the development phase, agile application development was initiated in collaboration with software company Innovattic, with the author serving as product owner. In parallel, a clinical study protocol was prepared for the creation of a multimodal cancer-related pain dataset to support future AI model training.

Results
The interview study identified five key challenges in current cancer pain assessment: the complexity and subjectivity of pain, ambiguity in responsibility, communication barriers, balancing all pain information with clinical judgement to come to decision-making, and practical constraints. Oncologists expressed conditional interest in an APA tool, emphasising the importance of clinical validation, interpretability, and seamless integration. The conceptual framework proposed three core functionalities: multimodal data collection via a mobile application, AI-based pain classification, and feedback for both patients and clinicians. The mobile application is currently under development and includes measurement and login/authentication modules. A clinical protocol for database development has been submitted for ethical review.

Conclusion
This thesis establishes a solid interdisciplinary foundation for the development of a human-centred APA tool tailored to cancer care. While the AI model itself lies beyond the scope of this thesis, the design process, stakeholder engagement, and technical groundwork contribute to a clinically grounded and ethically aligned vision for future development. The work advances efforts to improve how cancer-related pain is recognised, communicated, and managed in clinical practice. Building on this foundation, future research should focus on user testing with patients, iterative refinement of the application and measurement protocol, technical development of the AI model, and broader clinical evaluation to assess its impact on care delivery and patient experience.

Shifting towards sustainable mobility is essential to reduce global emissions and alleviate congested transport systems, while the transport sector is responsible for more than a third of all carbon dioxide emissions from end-use sectors. The car has a much larger carbon footprint than alternatives such as the train, cycling, or the bus, and in the Netherlands, more than half of all kilometres driven annually come from business traffic and commuting. Employers therefore have a major role in a shift towards sustainable mobility by stimulating employees to travel less, more efficiently, or differently. But this shift is complicated, while a universal solution to achieve sustainable mobility is lacking. There is no “one-size-fits-all” solution, and research measuring effects after policy implementation remains limited.

This research investigates the relationships between day-to-day changing factors such as weather conditions and seasonality and the commuting behaviour of ASML’s employees. Also, implemented mobility policies are evaluated. Using a two-year dataset capturing daily commuting volumes by car, bike, bus, and office presence, this quantitative case study applies a range of methods to explore day-to-day variability in travel behaviour. These include Ordinary Least Squares (OLS) regression, Multinomial Logistic Regression (MLR), and Time Series Analysis techniques such as decomposition, SARIMAX modelling, and Interrupted Time Series (ITS) analysis.

Findings show that weather has a significant influence on commuting behaviour, particularly cycling. Darkness, which is generally understudied, appears to be one of the prevailing factors, especially as many employees commute during rush hours when darkness is present. The results also reveal that commuters tend to fall back on unsustainable practices when opportunities arise—for example, on Fridays and during school holidays when office attendance is lower, resulting in more available car parking and fewer cycling commuters.

Based on the findings, two categories of policy recommendations are proposed:

The first category involves improving policy assessment. To strengthen data-driven evaluation, it is essential not only to collect but also to validate and verify data. In addition to quantitative analysis, qualitative methods are needed to gather commuter perceptions. Enhanced collaboration across organizations and stakeholders is also vital for better data-sharing and evaluation. Given the high day-to-day variability in commuting, both short-term fluctuations and long-term effects should be considered.

The second category concerns promoting sustainable commuting behaviour. Incentives for travelling during non-peak hours and cycling during dark periods are important. Alongside ‘pull’ measures, ‘push’ strategies discouraging car use should be explored. Though the Netherlands has strong cycling infrastructure, weather and seasonality remain influential, requiring attention to facilities such as better-lit cycle paths, favourable traffic lights for cyclists, and drying services at work, which are part of an inexhaustible list of possibilities.

This research highlights that a mix of policy measures is necessary to achieve a meaningful shift towards sustainable commuting and offers valuable insights for policymakers, researchers, and commuters.

The intersection of fossil fuel industry funding and climate change research is a contentious area, raising significant ethical and practical concerns. Fossil fuel companies have historically played a substantial role in funding research, which has sparked debates about potential biases in climate research due to financial ties. Understanding the extent and impact of this funding is crucial for ensuring unbiased and accurate information that forms the basis of climate policies.

This thesis investigates the terminology and methodology in climate research funded by the fossil fuel industry through a comprehensive scientometric analysis, as compared to general research and a pool of research from universities with ties to fossil fuel industry. The study aims to identify patterns of industry influence on scientific outcomes and the dissemination of climate-related information. Data is collected by extracting research paper abstracts and other relevant information from Scopus database over the period of 2014-2024, divided by subject areas and funding sources, leading to over a 100,000 papers analysed with 80\% relevance score word maps produced in VOSViewer. Thus, employing methodologies such as keyword-based data collection, Natural Language Processing (NLP), and topic modeling, the research identifies significant patterns in studies funded by fossil fuel companies, such as lack of focus on ESG terms, increased focus on carbon capture and hydrogen technologies, and more technical analysis methods. To ensure the robustness of these findings, the study employs validation techniques, including cross-referencing with independent literature and applying advanced statistical methods to control for potential confounding factors via topic modelling. This multifaceted approach enhances the credibility of the results and provides a clearer understanding of the extent and nature of industry-induced biases in climate research.

The analysis spans multiple disciplines, including energy, environmental science, earth and planetary science, and engineering, revealing that industry funded research outcomes are often tied directly to fossil fuel sector sphere of interests, while finding no significant influence patterns in university research. The findings of lack of ESG terms and increased focus on specific climate-mitigation technologies underscore the need for increased ESG interest and diversity in the industry funding of climate research to ensure its integrity and objectivity.

The thesis concludes with a discussion of the implications of these findings for policy and practice. It emphasizes the need for greater transparency in the disclosure of research funding sources and calls for the implementation of stricter guidelines to mitigate bias in scientific research, or for fossil fuel industry to ensure a larger diversity in the research funding. Additionally, it offers recommendations for future research, highlighting the importance of independent funding to preserve the objectivity and integrity of climate science.

By shedding light on the intersection of industry interests and scientific research, this thesis contributes to the ongoing debate about the ethical implications of industry-funded research. It underscores the critical importance of unbiased, evidence-based decision-making in addressing global climate change and supports the development of policies that ensure the reliability of scientific knowledge.