Fostering disaster resilience depends on effective coordination between diverse groups. At the same time, the volatile conditions of a disaster require different actors to self-organize in a decentralized way. While information sharing has been described as central to both coordination and self-organization, effective information management remains challenging during disasters, given shifting organizational patterns, evolving roles, changing information needs, and the high information load caused by volatility. This thesis addresses these challenges by analyzing coordinated self-organization through effective inter-group information exchange between communities and professional responders during disasters (1).

This thesis puts forward a way to systematically study disaster information sharing from an actor-centered perspective through a combination agent-based modeling and empirical case study research. It does so by developing a methodology to create Agent-Based Models (ABMs) for studying disaster information sharing through qualitative inquiry (2). This approach is chosen given the challenges of gathering quantitative data about disaster response (e.g., through surveys and disaster simulation exercises).

The methodology was applied to the case of Jakarta to develop an empirical descriptive ABM. Findings from case study research and simulations with this ABM show that, when actors are unaware of the information they need (i.e., their needs are latent), delivering information on time to them becomes particularly challenging. Further, the results show that communities tend to address a higher portion of their information needs compared to professional responders (3,2). Communities’ highly localized situational awareness needs to be combined with a more global perspective concerning a disaster and its development to foster effective coordination. To achieve this, a two-way communication between professional response organizations and communities is essential.

The empirical ABM was then extended and abstracted from the Jakarta case to develop a theoretical ABM to study the emergence of Informational Boundary Spanners (IBSs); i.e. of actors that facilitate inter-group information exchange. Findings from simulations with this ABM suggest that individually learning who provides high-quality information is a mechanism that fosters the emergence of IBSs (4). This collectively intelligent behavior is contingent on stable information sources and a high number of trusted inter-group connections among communities and professional response organizations, especially at high levels of volatility (4).

The methodology proposed in this thesis provided the means to systematically study actor-centered disaster information sharing. First, the methodology was found to be rigorous in translating qualitative data into ABMs, and to provide a way to balance cross-case comparability with the flexibility to capture the nuances of specific cases (1,2). Second, the methodology was found to be versatile in developing both empirical and theoretical ABMs to study actor centered disaster information sharing, including mechanisms leading to the emergence of intergroup information exchange during disasters (2,4).

Future research will focus on exploring synergies among coordinated self-organization, collective intelligence, and resilience beyond the context of disaster response, concentrating on how collective learning, sensing, and remembering can foster adaptive, transformative, and absorptive resilience capacities in both the short and long term.

The Dutch drinking water utilities are legally responsible for supplying drinking water to their assigned customer base. Continuing to supply drinking water for the long term is coming under pressure from challenges on the supply side, related to water quality and quantity on the one hand and developments due to increasing demand for drinking water on the other. Both developments are expected to be negatively influenced by the effects of climate change.
The Dutch drinking water companies face three major challenges regarding strategic investment decisions. First, the current sourcing and production capacity must be expanded to meet future drinking water demand. Second, there is a great demand for End-of-Life replacement of pipes in the drinking water infrastructure. Third, an investment challenge of a lesser financial magnitude but with an expected great impact on business operations is related to gaining operational control over the drinking water distribution network by integrating state-of-the-art sensor technology.

The outcomes of the internal decision-making processes of the drinking water utilities regarding these three strategic challenges will affect the stakeholders of the drinking water utilities. In addition, it offers possibilities for alignment with the goals of the other stakeholders. The main problem that this research seeks to address is a lack of engagement with drinking water utilities' stakeholders in the decision-making processes. A way to engage with stakeholders is by using Participatory Modelling, a technique that is not commonly applied by drinking water utilities.

These possibilities to engage stakeholders in the decision-making process are further backed by the development of new resources that have become available in recent years. These resources are new modelling techniques that have been applied in the field of drinking water research, in recent years. And, a novel perspective on multi-modelling e.g. the Multi-Model Ecology (MME) with Multi-Model Interface (MMI). In the current practice of research for Water Resource Management and other research for drinking water utilities, an MME and MMI (MME+I) have not yet materialised. This study aims to determine if an MME+I can benefit research for drinking water utilities and facilitate Participatory Modelling.

The Participatory Systems Design methodology (PSD methodology) is applied to generate a design for the conceptual model of the MME+I and the logical architecture for the MMI. A Proof of Concept (PoC) use case of model-coupling was applied. Here, an ABM model for Water Demand generates water demand patterns for an EPANET hydraulic model. This is a novel approach in hydraulic modelling for Dutch drinking water utility Oasen, since it introduces agents' behaviour from the ABM model to the modelling of hydraulic networks. It demonstrated that the outcomes of an ABM model affect the performance of the EPANET hydraulic model. In addition, It provided insight into how changes in water demand from scenario studies can affect strategic investment decisions for drinking water utilities.

The Green Deal (Fetting, 2020), initiated by the European Commission, aims to reduce greenhouse gas emissions within the EU to limit global warming to 1.5 degrees Celsius compared to pre-industrial levels (International Panel of Climate Change, 2022). The EU focuses on energy that is acceptable, applicable, available, and affordable – referring to sustainability, technological readiness, energy security, and cost-effectiveness. Energy security involves meeting energy needs using domestic sources to avoid reliance on imported energy, which could pose threats to energy security due to the political power of supplying countries (Asia Pacific Energy Research Centre, 2007). A potential proposed by researchers is the Desertec project (Van Wijk & Wouters, 2021).

In this idea the strong solar radiation in African countries is used to supply Europe with hydrogen The Desertec project, despite its promising potential, never materialized. Studies examining the reasons for its non-realization concur that the primary hindrance was not technological limitations but rather the complexities arising from multi-country politics (Schmitt, 2018; Scheer, 2012; Lilliestam & Ellenbeck, 2011). Scheer (2012) aptly described the plan as "practically impossible for obvious political, economic, and sociological reasons" (Schmitt, 2018). He emphasized that coordinating an energy system involving over forty different goverments, each with their own energy grids and territories for power transmission, inevitably led to unrealistic expectations.

In response, this research presents a system that addresses key barriers that impeded the Desertec project's success. By focusing on a specific geographical area with fewer national governments involved, integrated energy grids, and no energy transport crossing other countries' territories, the research proposes a solution to the challenges identified by Scheer (2012) and Schmitt (2018). Portugal and Spain, with an integrated energy grid and limited European energy grid connection, are considered, while Spain's existing natural gas pipelines to Algeria offer a paved path for hydrogen transport. With this more manageable consortium of four national governments and fewer complexities, the research seeks to evaluate various technological design options using a cost model to test their feasibility and impact on energy security. The aim of this research is to provide an answer to: How does a technologically feasible Maghreb-Iberian green hydrogen system (MIGHS) impact the Iberian energy cost and energy security?

The Netherlands is experiencing a significant increase in its aging population. Multi morbidity and a rapidly growing shortage of healthcare personnel are putting pressure on the healthcare system, leading to capacity issues. The capacity problems of care have medical, safety, social, and economic consequences. Policy measures are needed to address these challenges and improve the transfer care system. Therefore, the research question is: On which factors should policy interventions by the Ministry of Health, Welfare, and Sport be aimed, to improve the performance of the Dutch transfer care system? This study uses qualitative System Dynamics (SD), a method that uses causal loop diagrams, to analyze the relationships between factors that influence the transfer care system performance, such as the demographic changes and workforce shortage. The goal is to find strategies and solutions for a sustainable healthcare system. A visual SD model is used to highlight the complexity of the transfer care system and to reveal inter dependencies, interactions, and feedback loops, aiming to understand system dynamics. A literature review, multiple interviews and case studies have revealed factors and interactions that are modeled in a causal loop diagram. This qualitative System Dynamics analysis revealed both unidirectional (+ or -) and different polarity (+/-) interactions/feedback loops between/among the indicators, which implicates that certain policy interventions (mentioned in Dutch policy notes and other publications) may not achieve the intended results. Based on the interactions found, the Ministry of Health, Welfare and Sport can start using a bundle of interventions aiming on reducing workload/number of admissions and simultaneously keeping availability of aftercare constant. Additionally, the Ministry can improve other indicators, when keeping workload constant. However, to determine how many additional interventions are needed to keep workload and availability aftercare constant and to determine the net total effect of interactions with different polarities, quantitative System Dynamics is needed. In addition, quantitative System Dynamics is needed to be able to prioritize policy interventions, because qualitative System Dynamics is not able to determine the net effect of feedback loops. However, this qualitative System Dynamics study provides a solid foundation with which researchers (and the Ministry of Health, Welfare and Sport) can proceed to construct a quantitative model.

Games have been widely used for purposes other than entertainment due to their engaging nature. However, the concept of game engagement is still not well-defined, which limits its use in analysis and game design. The primary objective of this dissertation is to conceptualize game engagement to guide the analysis and design of applied games.

The dissertation first explores the requirements for conceptualizing applied game engagement, identified through an analysis of three applied gaming projects and an empirical study. It then uses these requirements to develop the Applied Games Engagement Model (AGEM). The AGEM posits that engagement is the process of focusing attention on a task and that attention can be purposefully directed through design.

The practical use of the AGEM is then explored by analyzing applied games. The theory is extended with relevant game design knowledge and applied to game design practice. This results in the Lens of Engagement for Applied Games, a unique way to view the design of an applied game.

Overall, this dissertation provides a comprehensive perspective on applied game engagement, emphasizing the role of attention and its relation to game design. It offers a practical and workable method of considering and discussing game engagement, which can be used by anyone creating or studying applied games.

Power systems are large-scale, complex socio-technical systems that provide modern society with one of its most indispensible assets: electricity. Electricity supply is not only an irreplaceable asset in daily activities, it is also vital for operation of other critical infrastructures of the technological age. Crucial socio-economic systems depend on electricity supply to support infrastructures such as telecommunications, transportation, water and natural gas supply, as well as financial and healthcare services. Therefore, ensuring secure and reliable operation of power systems as an enabling infrastructure is crucial....

In addition to the physiological alterations that occur relatively predictably during pregnancy, psychological transformations also occur at the same time. The baby is developing, but so are a mother's fears and imaginations about her unborn child. Such imaginations about the unborn child and parenting methods are sure to evoke a range of emotions, including happiness, excitement, curiosity, anxiety, frustration, and maybe depression. Social comparison and intense emotional expression are two of the numerous opportunities offered by social networking platforms. Social comparison is a personality characteristic. Individuals who are highly social comparison-oriented make excessive social comparisons and are more affected by the outcomes. Social comparisons can be done intentionally, but more frequently they are done involuntarily. Social networking sites play a key role in the contemporary digital age in terms of personal experience sharing and constructive debate. However, research has found that the need for social comparison increases at times of stress, uncertainty, or adjustment. As a result, in unexpected and upsetting settings like pregnancy, social comparison may become more significant. The social comparison, psychology, and linguistics aspects of pregnancy had all previously been the subject of very few research, however, they had been done so independently. The objective of this research is to jointly comprehend the social, psychological, and linguistic aspects of pregnancy. In this research, a survey that was published on Reddit communities collected responses based on three questionnaires (Iowa-Netherlands Comparison Orientation Measure, Gratitude During Pregnancy Scale, and Tilburg Pregnancy Distress Scale), as well as a textual question on using social media while being pregnant. A correlation and linear regression analysis were used in this study to investigate the association between the three sub-scales and the selected LIWC (Linguistic Inquiry and Word Count) categories. In conclusion, considering the general inadequacy of information in the domain of pregnancy care and the imminent transition to digital care, the findings of this study could make a significant contribution to social and psychological research.

Nowadays, mental illnesses are universal around the globe. However, compared with developed countries, the health budget assigned to mental health is much lower in low-income or developing countries (Mnookin et al., 2016). Even in China, a country with a large population, the epidemiological data on mental health (anxiety and depression) are too few to have sufficient understanding. Despite the current research that is to identify GAD and Depression from natural language, and seeks to explore the relationships of personality and GAD and Depression has until now not conducted in Chinese language. Besides, university students are a very noteworthy students, who are going through the transition from adolescence to adulthood, and facing a lot of challenges in this process. Therefore, this research explored the relationships of Generalized Anxiety Disorders, Depression, Natural Language and Personality Traits among Chinese university students.
To reach the objective of the present research, an online survey was conducted among Chinese university students, which consists of GAD-7, PHQ-8, BIS/BAS, and PANAS questionnaires, followed by a writing task to describe their feelings about university life and one anxious moment happened in the university. Finally, demographics were assessed. All the sample data were exported into .csv format and imported into the software JASP (JASP, 2017). And the text of the writing task was uploaded on LIWC 2022 software (LIWC, 2022) package, and on the statistical software SPSS for additional analyses.
Through the experiment, the main Research Question and Sub-Research Questions were answered and all hypotheses were accepted. By reflecting on the whole thesis, some limitations of the current research were pointed out and some suggestions were proposed for future work.

Mental disorders are now becoming prevalent illnesses. More than 1 billion people globally were affected by mental and addictive disorders in 2016, which caused 7% of all global burden of disease (Rehm & Shield, 2019). Among those mental disorders, anxiety disorders and depression rank at the top. GAD is the most common anxiety disorder form in primary care, and the burden caused by GAD is severe in terms of decreased work productivity and increased health care utilization. The present study pivoted around GAD, studying the intersection between motivational arousal, language, and sympathetic activation. A Randomized Controlled Trial (RCT) was designed and undertaken. The findings were satisfactory: Different arousal manipulation led to different language patterns of students. For example, negative (avoidance) arousal triggered fatigue-related words use. GAD inhibited motivational arousal effect on language, which extended the previous study that GAD people have ’diminished physiological flexibility’. Furthermore, sympathetic activation was observed during arousal manipulation, and the variability of Heart Rate (HR) and Heart Rate Variability (HRV) was significantly affected. Lastly, the ’diminished physiological flexibility’ of GAD people was also shown in the present study: GAD students experienced little change in Electrodermal Activity (EDA), HR, and HRV under motivational arousal manipulation

In this research the influence of induced stress with a virtual reality (VR) game on moral dilemmas was investigated. VR is a topic that is becoming more and more researched, but studies about the induction of stress and the effects are scarce. By placing participants in a VR environment, where they have to defuse a bomb with the help of another participant, the influence could be analysed. A test group was selected which would perform relaxation exercises after the VR game to relax them. Afterwards, they filled in a survey which measured their stress/anxiety level and gave them social dilemmas to answer. During the whole experiment, participants wore wristbands that measured their biometric data to see if there were any physical signs of stress.

From the data it was confirmed that the VR induced stress in the participants. The data for heart rate, electrodermal activity, and heart rate variability showed an increase in stress level during the VR; Temperature did not show a difference. From the linear regression analysis done, no significant influence of stress on responses to moral dilemmas and a significant influence of personality type was confirmed.

The possibilities of VR in the field of psychology has the potential to improve research regarding stress. VR can be used as a coping method but also as a stress inducing activity. Future research should focus on discovering new possibilities for VR to be used. By improving the research about stress, an improvement of our lifes will follow.

Place-making initiatives have gained momentum in recent years to establish stronger urban communities. Through place-making, people attach meaning to spaces and they become places. While place-making initiatives have traditionally been designed and implemented from top-down, more and more scholars call for a participatory and bottom-up approach, for place-making to realise its full potential in creating strong neighbourhood communities. In this context, the thesis explored how the knowledge from Participatory Design and place-making may confluence to move from spaces to places in a more inclusive and community-driven way. The main research question was: How can Participatory Design facilitate place-making in urban settings across physical space, social connections, and institutional support? This thesis presents a framework for participatory place-making, build from place-making and Participatory Design literature and evaluated using six participatory place-making interventions that were designed, implemented, and evaluated in neighbourhoods in The Hague, Rotterdam, and Cork (Ireland). The framework contains five principles (emergent, empowering, inclusive, playful, reflective) that should guide the design of participatory place-making interventions and is complemented with five guidelines to design for participatory place-making.

The IPCC report of August 2021 calls for an immediately reduction in greenhouse gas emissions. Without changing the world’s diets, even if we stop all fossil fuel emissions today, we would still not be able to maintain global warming below 1.5°C. The EU has set out their strategy for reducing agricultural emissions in the Farm to Fork Strategy, as part of the European Green Deal. This strategy, however, falls short of addressing meat consumption. Individual meat consumption is shaped by a range of factors, including social norms. The influence of these norms is investigated in this thesis, which addresses the research question: “how do social norms influence meat consumption and to what extent can European policy influence these to reduce meat consumption?”. This thesis takes the Netherlands as case study, and constructs an explorative agent-based model, based on the Theory of Planned Behaviour, where social norms spread through social networks. Individual meat consumption is broken into beef, pork, poultry, processed and substitute meat consumption. The factors of influence are analysed in this research through a correlation analysis of various surveys, and the likelihood to eat specific meat types is calculated through a least-squares multiple regression analysis. This research finds that social norms play a tangible role in shaping consumption, and targeting these can emissions from meat consumption by 0.4-4%. Fiscal policies are a more effective policy measure for the EU to follow, as a 20% tax on all meat can reduce emissions by 10%. A tax on beef was found to redistribute consumption in such a way that overall emissions do not decrease. The findings from this research are discussed in the political, policy and socio-economic contexts regarding meat consumption.

Increasing flexibility requirements and skill gaps resulting from today’s world of globalisation and digitisation pose constant challenges for manufacturing companies. Augmented Reality (AR) applications offer an efficient way to overcome these tensions by enhancing the interaction between people and technology. However, individual models in the scientific literature show ambiguous findings, and a statistically powerful empirical assessment is still missing. Hence, this research project aims to understand the potential of AR applications in manufacturing environments by aggregating the empirical findings. For this purpose, the following research question is posed: ’Can the use of AR solutions benefit manufacturing activities and if so, how?’. Following the media naturalness theory by Kock [2005], this research hypothesises that AR solutions in comparison to classical instructions have a reducing effect on processing times, errors rates, and cognitive load levels of workers during manufacturing activities. To answer the research question and prove the hypotheses, this research project conducts three meta-analyses in which several small studies are synthesised into one large study. Specifically, the meta-analyses address the evaluation criteria ’time’, ’errors’, and ’cognitive load’. The underlying systematic literature search to collect and evaluate relevant data follows the framework by Vom Brocke et al. [2009]. What is more, this research project examines the interrelationships between the evaluation criteria and moderating variables using meta-regressions. Finally, surveys with industrial experts in a consumer goods and chemical company support and expand the findings from the meta-analyses and the meta-regressions. The meta-analyses show that AR applications in comparison to classical instructions indeed have a reducing effect on the described evaluation criteria. In particular, based on the studies, a small, reducing effect can be achieved for ’time’, a medium, reducing effect for ’errors’, and a small to medium, reducing effect for ’cognitive load’. For this reason, all three previously formulated hypotheses are accepted. Furthermore, in line with the media naturalness hypothesis by Kock [2005, p. 122], the meta-regressions show that ’cognitive load’ moderates the evaluation criterion ’time’. The results are validated with the help of the expert surveys in the company context, with time savings being identified as the greatest potential and lack of proven profitable business models as the greatest challenge. Further research could, on the one hand, focus on repeating the meta-analyses as soon as new empirical studies are available and on the analysis of moderating variables. On the other hand, a long-term validation in manufacturing environments across industries is still missing and could show further scientific and practical relevance.

In the wake of more inclusive and sustainable cities, as targeted in the UN’s Sustainable Development Goal 11, public administrators and urban planners aspire to incorporate the pluralism of public values into decision making. Although theoretical work exists since the 1990, public values and their conflicts are yet to be empirically identified and applied in a context of relational urban space. This study makes use of a case study-mixed methods approach combining a quantitative strand to identify public values and their conflicts from geo-located citizen contributions using natural language processing with a qualitative strand that leverages expert workshops. Integrating the findings from both strands embedded in the case study of Hamburg, Germany, there is evidence for a broader conceptualization of public values. In a new conceptual tool named "public value spheres", the main identified public values of social equity, livability, economic opportunity, ecologic quality, safety, health and conservatism are displayed. A total of nine archetypical spatial public value conflicts were found within Hamburg, among them the newly identified "externality conflict", "dangers of nature conflict" and the "drawback of beauty conflict". The findings provide urban planners and practitioners with a new tool to account for the value-laden nature of relational urban space in future participatory processes. The citizenry itself could profit from a better discourse due to aggregation of voices in participatory tools and a transparent mapping of public values that creates a sense of the pluralism of public values within a city. Future research is needed to expand the conceptual tool of public value spheres with case studies of different cities and to investigate possible constituting socioeconomic factors of public values within urban planning.

Depression is one of the most common mental health disorders affecting people from different age groups, societies, communities, and countries. Many countries lack awareness about psychological disorders and there is a scarcity of good mental healthcare facilities available globally. Medical practitioners recognise depression by analysing the patient’s behavioural patterns like speech levels, facial expression, body language and language patterns during therapy. Previous research has shown that behavioural studies are effective means for depression recognition. To explore this relationship, the automated depression recognition dataset called Distress Analysis Interview Corpus (DAIC) was evaluated. This dataset was chosen as it consists of paralinguistic (vocal), linguistic (verbal/text) and extralinguistic (visual) features from the dyadic interviews between participants and a virtual human. Prior research has evaluated the relationship between paralinguistics, linguistics and depression but many researchers failed to analyse the relationship between personality and depression for the DAIC database. The present study explores how different paralinguistic and linguistic features and personality types differentiate between high and low levels of depression. This study was exploratory in nature and used the LIWC software for linguistic and personality analysis, Pandas software for pre-processing the audio and text data files and lastly correlational analysis using JASP software to answer the research questions. The main findings concluded that linguistic features like emotion (sad and negative), feeling and health related words are used most often by depressed people. Additionally, paralinguistic features like high pitch and breathy voice as well as the personality trait neuroticism were characteristic identifiers of depressed people. These results showed that linguistics, paralinguistics, and personality traits help in depression recognition. These research findings have the scope for broader and cross-disciplinary applications in the future. Further research and development for automated detection technologies is required in the field of behavioural studies, to enable people globally to easily access and use artificial healthcare platforms for mental health diagnosis.

Evacuation strategies are critical in preventing casualties during emergency evacuations in buildings. As large-scale gatherings and high crowd densities in buildings occur more often, the need of relevant and effective evacuation strategies emerges. However, the domain of research that tries to identify possible ways to improve evacuation, i.e. prescriptive domain, is underlooked. Several studies successfully improve evacuation by optimizing existing evacuation scenarios in buildings. A shortcoming of these studies is that they often focus on one strategy and scenario in particular. Therefore, one should opt for a more generic approach to evaluate the effectiveness of evacuation strategies under different circumstances. A way to mitigate uncertainties in evacuation is by using data. Recent studies use a data-driven approach, in which data is used as an input to calibrate and enhance the evacuation strategy. A promising source of data is WiFi data. WiFi data captures movement patterns of building occupants and can be translated to population and building characteristics. Therefore, WiFi data offers the creation of evacuation scenarios in which evacuation strategies can be practically tested. This study aims to (1) evaluate the efficiency of evacuation strategies in buildings under different circumstances, and (2) determine effective evacuation strategies given WiFi data as an input. Therefore, this study presents a new exploratory agent-based approach to evaluate evacuation strategies, and moreover, presents an approach to incorporate input data to practically test evacuation strategies in a given building. To do so, this study used three methodological approaches, namely ExploratoryModeling and Analysis (EMA), Agent- Based Modeling (ABM) and Data Mining. EMA is used to experiment with the created agent-based evacuation model. EMA addresses the effect of uncertainties on the evacuation time, and if evacuation strategies are effective and robust in different circumstances. This study showed that in the created model of the TU Delft TPM faculty building, guiding evacuation strategies, such as dynamic signs and using evacuee staff members turned out to be an effective option if the familiarity in the building is low. However, as the familiarity increases the relative effectiveness of these strategies becomes negligible. In case of increasing familiarity, bottleneck improvement strategies, such as wider exits or stairs and obstacle placement, decrease the total evacuation time consequently. Moreover, this study concluded that an exploratory approach for evacuation models is promising, as the effectiveness of evacuation strategies is very dependent on the evacuation scenario. As a result, this study is able to evaluate these scenarios beforehand and to determine the effect on the total evacuation time. In this study the uncertainties crowd density, familiarity with the building, compliance with given instructions, and the exit capacity are leading in influencing the total evacuation time. The latter was found as a newly modelled uncertainty for evacuation scenarios.

Emerging technologies are revolutionizing organizational growth, productivity and in- vestments more than ever before. One such technology that has come into the limelight in the past few years is the Digital Twin. The so-called ’digital twin’ is a real-time virtual replica (representation) of any given physical asset/object. The full-potential of a digital twin lies in its ability to not only communicate with the physical asset, but also control it remotely. Although the concept of digital twins is more than a decade old, digital twin initiatives are now been deployed in the manufacturing, automotive and healthcare industries among others. One such industry, experimenting with digital twins is the Energy sector. The objective of this research was to perform an exploratory investigation into the adoption of Digital Twins in the Dutch Energy sector predominately by Trans- mission System Operators (TSOs) and Distribution System Operators (DSOs). The investigation comprised of four elements: establishing the most fitting technology adoption model when it comes to digital twins, determining the relevant adoption variables, exploring the perception of digital twins in the industry and investigating the relationship between absorptive capacity and organizational characteristics. The research method deployed for the former two elements was desk research, whereas the latter elements were probed by conducting (semi-structured) interviews and targeted questionnaires respectively. There were a total of nine participants involved in this research which included technology adoption decision-makers having a wide range of work experience (1-5 years to 20+ years) from the five of the eight TSO/DSOs of the Netherlands.
The research found that the most fitting technology adoption model when it comes to digital twins in the Dutch energy sector was the Technology-Organization-Environment (TOE) Framework with the following relevant (non-exhaustive) adoption variables: Complexity, Compatibility, Perception, Technological Characteristics, Availability, Organizational culture, Organizational size, Budget size, Incentives, Management support, Ab- sorptive capacity, (decision-maker’s) Demographics, Attitude towards technology, Regulations, Competitive pressure and Network effects. In addition, the overall perception of digital twins was found to be positive across the Dutch Energy sector, however, there was no consistent relationship established between organizational characteristics and the levels of digital twin perception. Similarly, the research suggested that organizational characteristics and absorptive capacity were not correlated. Nonetheless given the limitations of having a low number of study participants and the potential of bias amongst respondents towards their employer, the strength (significance) of these discovered relationships are indicative and should be further investigated in future research prior to making any additional claims that are conclusive.

Solutions to contemporary urban challenges are increasingly the outcome of the complex interactions between formal and informal actors that take part in a variety of networks. Resilience-thinking has become a global popular perspective for the governance of urban systems, concerning the ability of communities to deal with urban challenges, that requires the development of the self-organizing capacity of informal actors such as residents.
This study aims to determine how the extent to which formal and informal actors are resilient in interaction with one another can be determined. Therefore, a literature study was conducted that led to the development of the Institutional Resilience Analysis and Development (IRAD) framework, that is a modification of the Institutional Analysis and Development (IAD) framework that is complemented with variables influencing decision-making processes that are extracted from the literature on social resilience and adaptive governance.
The IRAD framework is applied to an exploratory case study with the Resilient Bospolder Tussendijken 2028 programme in the neighbourhood Bospolder-Tussendijken in Rotterdam as the research context. Hereby, desk research and semi-structured interviews were held with formal and informal actors to study interactions resulting from a conflict concerning the management of societal real estate in the neighborhood.
The application of the developed IRAD framework thereby identified both enablers and barriers for resilient interactions between formal and informal actors. Access to social networks has been identified as an enabler for resilient actions, whereas the lack of trust of residents in the municipality, lack of communication between formal and informal actors and a lack of a political base for new insights have been identified as barriers to resilient actions.

The overwhelming majority of doctor visits are related to stress. When stress is studied the data is often not disaggregated based on gender, creating a gender data gap. Literature about mHealth apps with data analysis being disaggregated based on gender does not exist. In order to amend that, an experiment has been created where the perceived stress of participants is measured in the pre­test, followed by a manipulation video that presents stress as debilitating or enhancing in nature. A post­test measures the perceived stress again, collects demographics as well as life satisfaction, self­-control, and grit. Participants that joined via mHealth app Brainjam had their HRV measured as well. Manipulation videos have been found to be ineffective and were removed from data analysis. One statistically sig­nificant result depended on gender has been found. The duration of the experiment was affected by the perceived level of stress men reported, however, no significant difference was found in women.