The new Medical Device Regulation (MDR) addresses patient safety by mandating supporting clinical evidence for the sale and clinical use of medical devices. However, it places pressure on medical device manufacturers to supply regulators with robust clinical evidence to obtain regulatory approval of new devices they develop. Regulatory compliance is therefore more stringent, requiring more effort on part of both regulators and manufacturers. This thesis project explored the possibility of developing a new method to be used as a regulatory decision-making tool for the approval of newly developed knee implants. A Bayesian probabilistic method was developed and proposed. This method utilises existing arthroplasty registry and arthroplasty registry annual report data to determine if a newly released implant variant is “good”, where a “good” implant is one which meets standard regulatory thresholds in term of revision risk. This method was implemented on two datasets which were selected based on conditions of granularity, measures of implant performance, and accessibility for the thesis project. Some advantages and limitations of this method as a regulatory tool were proposed, the limitations being based on a clinical appraisal framework. Based on this it was concluded that the method indeed showed potential as such a regulatory tool, but needs further improvement due to considerations of reliability, precision, and the probabilistic nature of the results it yields. Both surgeons and patients can also potentially stand to benefit from this method. Aside from the regulatory context, also, the method has limitations in the fact that it does not account for other factors that may be responsible for complications related to knee arthroplasty surgery. Overall, however, this thesis presents a novel approach towards estimating the performance of knee implants from existing clinical data.

Transboundary river basins are increasingly subjected to pressures from climate change, economic expansion, and population growth. These challenges are compounded by Deep Uncertainties and the complexities of managing water resources that cross administrative borders. The thesis aims to improve decision support for river basin management under Deep Uncertainty. Robustness Analysis is leveraged to improve the adaptive policy design of reservoir operating policies. Open Exploration generates future states of the world. Using Feature Scoring, PRIM, and Logistic Regression Modeling, Scenario Discovery pinpoints vulnerabilities that result in Adaptation Tipping Points described by streamflow patterns. Results identified decreasing precipitation and low seasonal amplitudes as the most significant uncertainty factors influencing system performance. In combination with the evaporation rate, they accurately predict policy failure. A precipitation threshold of 0.965 and a seasonal amplitude threshold of 1.05 effectively describe streamflow patterns that describe the Adaptation Tipping Point across the Best Hydropower, Best Environment, and Best Tradeoff policy. Specifically, a resultant streamflow pattern at these thresholds accurately signals imminent policy inefficiencies that necessitate policy adaptation.

Gaining insights into anticipated future expenses is essential for both the planning and construction phases of a project. A significant factor in future costs, particularly over extended periods, is the variability of material and labor prices. the change in costs over time, known as ’cost escalation,’ has been the focus of numerous research efforts. These studies primarily aim to forecast the Construction Cost Index (CCI), a composite index representing a standardized array of materials and labor typical in construction projects. This research presents a novel approach to forecast cost escalations, tailored to individual construction projects, addressing the shortcomings of predictions of a generic Construction Cost Index (CCI). Traditional CCI predictions, while providing some foresight, are limited by their generic nature and often overlook the specific material and labor variations within different projects. Additionally, current research generally fails to account for the uncertainty in the forecasts of the change in the material price and the estimate of the construction cost. In response to these challenges, this research pivots around the following research question: How can cost escalation for various types of construction projects be predicted, accounting for uncertainties in final construction costs and the forecast? Addressing this question involves selecting distinct indices for various project resources, such as steel, concrete, and labor, and combining these predicted indices in line with each resource’s cost. To achieve this, the study evaluates several time-series forecasting models, namely the vector error correction model (VECM), the vector autoregression (VAR) model, and the Holt-Winters model. For each model an automatic forecasting process is created, which automatically checks the suitability of the model, select the appropriate variables (in case of a multivariate model) and selects parameters. These models are evaluated for their accuracy across different time-frames and forecasting horizons. The Holt-Winters model, in particular, showed promise in providing reliable confidence intervals and point forecasts. The study’s testing phase revealed varying degrees of success, as volatile indices like copper and steel proved to be challenging, whereas forecasts for less volatile indices achieved higher accuracy. This outcome suggests room for improvement in refining these forecasting methods. The project-specific forecasts, including cost uncertainties, are developed by inputting cost estimates associated to the material price at current day value. The cost estimates input includes material and labor costs and their uncertainties. The uncertainties are represented through a three v point estimate (optimistic, pessimistic, and most likely values). This input is then transformed into a PERT distribution which is a transformation of the Beta distribution. The final stage involves combining the PDFs of each project activities cost (considering the cost of each specific resource) with the monthly forecast PDFs in a Monte Carlo simulation, providing a detailed cost distribution histogram of total cost and the individual resources cost. The tool’s functionality was demonstrated through a case study on a highway construction project. In this demonstration, specific project data, including material and labor cost estimates, were inputted into a hybrid web and Excel interface. This setup facilitates visualization and manipulation of project information. The tool processes these inputs via the AFP and Monte Carlo simulation, yielding comprehensive outputs such as histograms and statistical properties of the output. This is visible for total project costs and resource-specific escalations. This demonstration effectively showcased the tool’s capability to offer detailed insights into cost escalation, addressing the variability and uncertainty in construction projects. It underscored the tool’s alignment with the study’s objective of providing nuanced, project-specific cost escalation forecasts, moving beyond traditional CCI predictions. In conclusion, this study introduces a tool that caters to specific project resources, timelines, and uncertainties in cost escalations. While current limitations prevent its immediate practical application, this proof-of-concept lays the groundwork for future improvements. Focus of future research should be on refining accuracy and comparing the tool’s forecasts with escalation of historic projects to establish more robust insight into the actual escalation of projects.

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.

Food safety inspections play an important role in the safety of consumers of food products. Even though food safety regulations have disciplinary power over food-related businesses, inspections are needed to ensure that regulations are followed for the well-being of consumers. Traditionally inspections did not have a goal of targeting higher risks. However, since 2004 EU regulations require all EU Member State inspectorates to follow a risk-based approach within their inspection plan. This is an effective way of inspecting and gives high results in catching violations. However, to be able to know what is happening in the overall industry representative results are needed, and performing inspections based on risks does not provide representativeness. Random inspections if performed in a representative way can provide this. Inspectorates are looking for ways to combine random and
risk-based inspections to get representativeness from risk-based approaches. To do this the inspection strategy might need to consist of a combination of random and risk-based approaches. The reason for this is to avoid bias that may result from performing risk-based inspections.
The main research question of this thesis therefore is: How can inspectorates adapt their inspection strategies to mitigate bias that results from risk-based inspections?
The exploration of how risk-based and representative inspections can be combined is aimed to
be supported with an investigation of methods that support this decision. This research aims to develop an assessment framework for evaluating such methods. In order to evaluate these methods, assessment criteria are needed that measure the methods appropriately. Since the reason to combine random and risk-based approaches is to reduce the assumed bias of risk-based inspections, there is a need to understand what bias is in risk-based inspections. Furthermore, to understand this, what risk means needs to be investigated. Therefore, the goal of the research is to find out how risk and bias affect the choice of inspection strategies, by investigating random and risk-based inspections and how these can be combined in order to leverage their advantages.

The modern power grid is becoming more susceptible to cyber-attacks due to an increase in digitalization, leading to a larger attack surface for malicious actors to attack. Such attacks on critical infrastructure could lead to partial power outages, minor societal disruption, or in the worst-case scenario, a rolling black-out in which the entire country has no access to electricity. Electrical utility companies can decrease the likelihood of a successful cyber-attack on the Cyber Physical Power System (CPPS) – consisting of the physical power grid, and vulnerable Information Technology (IT) and Operational Technology (OT)- by implementing cyber security interventions. Investing in these cyber security mechanisms is not cheap, which is why it is expected to have a certain return on investment. However, it is hard to quantify the effects of prospective cyber security investments. The main research question of this study is: “To what extent can cyber security measures decrease the risk of cyber attacks on CPPS substations?” This research question is answered by means of an implicitly mixed research approach that uses computer-assisted attack tree modelling and Monte Carlo simulation. The model is based on the publicly available technical system information of known suppliers of relevant substation components and other documentation acquired by means of multiple literature studies and document analyses. The change in likelihood and subsequent risk has been studied by extensively modelling the possible attack paths of a digital substation. This has been combined with financial analysis in the form of a societal cost-benefit analysis. As a result, potential cyber security investments can be evaluated on their merits in the form of risk reduction and their required costs as expressed in dollars. The contribution of the performed research to science is the elaboration of existing models to more accurately represent reality, and simultaneously provide the cyber security decision-making process with a tool that provides guiding Key Performance Indicators (KPIs). This study has shown that suggested measures from the quantified model are able to increase the TTCavg needed by malicious actors to reach their intended target, and therefore cause a decrease in likelihood and subsequent risk of the studied scenarios. An important finding of this study emphasizes the need for extensive attack path modelling. This finding was the fact that the application of some well-intended countermeasure (such as remote-attestation), might have no significant effect on the likelihood and risk of a certain scenario at all, but only changes the dominant attack path. While the constructed quantified model, as proposed in this study, is able to provide quantified insights into the effects of proposed cyber security investments, it is merely a simplified tool that should be expanded upon to generate more accurate insights. Besides the aforementioned there have been additional findings from this study. Such as a list of weaknesses in the current state of digital substation cyber security. This list has been created by an extensive document analysis of over 40 sources. Also, an overview of 23 different possible cyber security interventions has been compiled by a systemic literature review of over 16 sources. According to the quantified model, a reduction (between 21.8% and 93%) in the total risk of certain attack scenarios against digital substation by malicious actors can be achieved. The costs for these possible risk reductions range between $28 thousand for a honeypot deception system and $413 thousand for a combination of all the simulated countermeasures. These countermeasures could, in comparison to a base case with no protection, potentially reduce the total risk by an amount between $3.7 billion and $15.9 billion. According to the general societal cost-benefit analyses, the best Retun-on- Investment (ROI)/cost-effectiveness of investment is the investment in a honeypot (scenario 5) which has an ROI of 247,390, and the least cost-effective is the investment in remote attestation (scenario 4), which has an ROI of -2,066. Altogether, this study has shown that there is added value in using a simplified quantified model to aid in decision-making for digital substation cyber security investments aimed at risk reduction.

Currently, the Dutch National Police are looking to optimize resource allocation and decision-making through the measurement of the concept: “safety.” However, the measurement of safety does not have a universally defined method in the branch of policing and security. Qualitative data collection methods are being increasingly implemented in police departments around the world to improve safety and security. These data collection methods aim to implement evidence-based policing practices in order to form predictive assessments of future crime. Additionally, qualitative reporting methods such as officer interviews and victimization surveys can complement current quantitative data collection by improving police-community engagement and mitigating
"dark numbers" (unreported crimes).

This research aims to address what the implementation of victimization surveys and officer interviews, in coordination with current data collection methods, can add to an optimized police response and resource allocation to future crimes in theft for the Dutch National Police. The research approach of this thesis takes inspiration from commonalities found in a scoping literature review of policing methods around the world and a former joint interdisciplinary project (JIP) with the Dutch National Police. The perspectives of victims and police, which can be partially measured by surveys and interviews, are considered to significantly affect safety and security within society.

The research method was executed through the use of surveys (historical CBS data and theoretical scenario surveys) and officer interviews. These surveys and officer interviews were designed to determine how significantly certain victim factors, such as amount stolen, income, geography, and past experiences of theft affect a victim's perception on the severity of the theft, reporting threshold, and desired outcome in reporting theft. The method designed was flexible, as the ability to implement surveys to people that measures income and geography was dependent on both the Human Research Ethics Committee (HREC) and the data security concerns of the Dutch National Police.

Based on a victimization survey given to 1547 respondents, these victim factors do correlate with the perceived significance of theft. The magnitude and significance of these correlations are displayed in this report, and comparison with police employee interview results leads to significant insights that may determine why these correlations exist. In brief summary, the victim factors of income, geography, previous victimhood, and the amount stolen in theft generally have positive correlations with a respondent's victimization chance, reporting threshold, desired monetary compensation after theft, general satisfaction in successful reporting outcomes, and the perceived severity of theft.

A concluding recommendation from this research is to consider the further implementation of victimization surveys as a complementary data collection method. Specifically, the correlation of victim factors to the perceived significance of theft can assist in predictive policing through victim profiles that more accurately estimate dark numbers. In addition, the concept of adding qualitative measurement methods on victim factors to a universally-defined equation of safety can serve as a complement to current quantitative crime statistics. Through this implementation, current theft prevention and resource allocation strategies may be improved for the Dutch National Police, leading to a safer society.

Equalized involvement of stakeholders in decision-making processes (DMP) allows them to achieve a balanced outcome, when aiming to create value for each participant. However, as the number of stakeholders increases, traditional methods of interactive engagement become more complex, often leading to unbalanced solutions. Identifying such balanced outcomes also becomes more challenging and time-consuming, considering the wide range of potential outcomes. To address this complexity, this thesis introduces a new approach, Preference-based Goal Attainment (PBGA), and develops a tool for its application in DMPs. The PBGA tool facilitates transparent communication of stakeholders' needs, desires, and values within the DMP, helping to bridge satisfaction gaps among various stakeholders and yield an equitable and optimal outcome.

For the development of this tool, a multi-objective optimization framework is proposed. This framework allows stakeholders to integrate their needs, desires, and values as objective functions, with decision parameters represented as variables, in the tool. Crucially, it enables stakeholders to specify preferred states within their objectives, known as preferences. The framework also considers the varying influence of stakeholders by incorporating a weighted goal attainment method. This method focuses on improving the objective with the largest weighted preference deficit in each iteration of the genetic algorithms used in the optimization process.

For testing and validation, the approach is applied to a DMP in a selected case study. Data, including objectives, variables, and stakeholder preferences, are collected through semi-structured interviews, and transformed into a computational format for MATLAB® implementation. The outcomes are verified using the Kolmogorov-Smirnov and Chi-Square comparative methods. Additionally, the tool and its results are validated by stakeholders, ensuring the tool's practicality and acceptance of the new design.

With the PBGA tool, stakeholders can transparently communicate their needs and desires in the DMP and achieve an outcome that involves equal participation from each stakeholder. The thesis concludes with a comprehensive discussion on the practical applications, limitations, and assumptions of the tool, highlighting its potential to improve stakeholder collaboration and decision-making in project management.

In a rapidly evolving digital landscape, where information is the currency of progress, universities play a vital role in fostering innovation, research, and knowledge dissemination. However, this invaluable role also exposes universities to significant cybersecurity challenges. Cybersecurity is an increasingly important topic for organisations in all sectors, including universities. As repositories of valuable research data and other sensitive information, universities are attractive targets for cyber attacks.
Addressing these challenges is crucial not only to protect intellectual property and sensitive data but also to maintain the trust and integrity of academic institutions. Despite the importance of cybersecurity for universities, there is a lack of research on how to effectively implement cybersecurity policies and practices in this context. The lack of a standardised approach to cybersecurity can leave universities vulnerable to cyber threats and hinder the sharing of best practices. This study is expected to identify key challenges and measures for cybersecurity policy in Dutch universities. It will provide insights into the implementation of effective cybersecurity policies and contribute to the development of an approach to cybersecurity in the higher education sector. The research question is: "How should the cybersecurity policies of Dutch universities be designed to mitigate cyber threats to ensure knowledge security?".
To accomplish this research, a multidimensional approach was adopted. Extensive literature review provided a foundation for understanding cybersecurity standards and cyber ethics, while interviews were conducted with various Dutch universities to gain insights into their experiences and perspectives. Additionally, a comprehensive survey was administered to students and staff members of Dutch universities, enriching the study with diverse viewpoints. This work aligns with the objective of examining the quality of decision-making concerning grand societal challenges within the context of their socio-economic and political environments. It aspires to inform decision-makers in the public (policy) domain or at the intersection of the public and private spheres. By shedding light on the barriers faced by universities in implementing cybersecurity norms, this research aims to contribute to the ongoing discourse on securing knowledge assets in the face of emerging cyber threats. The thesis is structured as follows: it begins with an introduction that provides a comprehensive overview of the topic, delineating the problem at hand and outlining the proposed approach. Subsequently, the literature review section presents the findings of the extensive research conducted, exploring various subjects such as cybersecurity standards and cyber ethics, among others. The expected outcomes of this study are an overview of current cybersecurity challenges for Dutch universities, an understanding of the opinions and experiences of university staff and students, and recommendations for developing and implementing cybersecurity policies for Dutch universities.

The Dutch railway transport system is a system of systems and also a sociotechnical system that will migrate to a radio-based signalling standard ERTMS (European Rail Traffic Management System). ERTMS will influence the train drivers and dispatchers the most, especially due to the introduction of the signalling and control element of ERTMS: the European train control system (ETCS). A reliability requirement for the migration towards ERTMS obligates to demonstrate that the reliability of the system stays the same or improves. Reliability can be quantified if all the possible risks are known, but identifying risks with traditional models is insufficient, because they do not capture the complexities and dynamics of socio-technical systems.

The hazard analysis technique ‘systems theoretic process analysis’ (STPA) is a promising technique to sufficiently identify hazards that models the system in a control structure and searches systematically for hazards. The main research question of this thesis is: ‘To what extent can STPA be applied to identify risks and determine the system reliability of interactions between ETCS, train drivers and dispatchers?’ What are the risks caused by those interactions and how can STPA be applied for an effective risk assessment are the two research objectives.

STPA consists of 4 structured steps. First the analysed system is described and the purpose of the analysis is set. The system is modelled in a control structure in the second step. The third step of STPA is to identify unsafe control actions with guided words. 27 unsafe control actions are identified for the 8 control actions that are present in the control structure. The last step of STPA is to identify loss scenarios that could lead to the unsafe control actions, those were formulated with system experts.

The desk research and this research demonstrates that STPA is completer and more thorough in identifying hazards than the tradition hazard analysis technique ‘failure mode effect and criticality analysis’ (FMECA). In this research, STPA identified 70 loss scenarios in the analysed procedure (compared to 4 issues identified with FMECA), those hazards ranged from missing or inadequate feedback mechanisms to inconsistent process models of the train drivers or dispatchers. STPA identified besides technical failures also design flaws in the procedure and unsafe interaction between the ETCS, train drivers and dispatchers.

Besides the conclusion that STPA turned out to be more complete and thorough in identifying hazards, another advantages of STPA is that performing STPA is very structured and not superficial. An identified disadvantage of STPA is that the method stops immediately after the hazards are defined. Determining the probability of occurrence and the impact expressed in train delay minutes can result in prioritization of the hazards and a better risk assessment.

To conclude, this research recommends applying STPA for complex systems where multiple controllers are involved. An STPA expert, someone who has experience with applying STPA in different projects, is a key to successfully implement STPA in an organisation.

This research explores the effects different cooperation levels between countries have on water resources from a shared river. The water resources that are taken into account are: hydro electricity generated per dam, water that flows to the environment per dam, and irrigation water per station. The Zambezi river basin is selected as study area due to its trans-boundary multi-stakeholder complex character. The cooperation levels that are explored are no cooperation, full cooperation, and section cooperation. No cooperation entails that every hydroelectric dam in the river only tries to maximize its own electricity. With full cooperation the dams releases are adjusted to benefit all the water resources in the whole basin. With section cooperation the dam releases are adjusted to maximize the water resources within the section of the specific dam. A section is a geographical area that is based on country borders. A simulation model is built in order to explore the effects of the different cooperation levels. The release policies that maximize the water resources for full and section cooperation are found with a multi-objective evolutionary algorithm (MOEA). From this research three findings are extracted. First, for the full cooperation level and the section cooperation level policies exist that dominate the no cooperation level policy for every examined water resource. Moreover, for these water resources found policies for either full or section cooperation produce similar results. Therefore, any of the explored cooperation forms could be more beneficial than no cooperation at all. Second, outcomes of this study indicate that owners of water resources located in the upstream of the river have less possible benefits from entering a cooperation agreement in which their dams have to adjust their release policy. These water resources can only improve by a small amount compared to water resources located in the downstream of the river. Third, policies do exist that are able to perform best for certain water resources, but also cause other water resources to decline to lower than their no cooperation value. Therefore, selecting fitting release policies is important since not every found policy leads to a better solution. Last, in this study best performing policies for electricity production and irrigation for both full and section cooperation are compared. It is discovered that adapting the best electricity policy for either one of the cooperation forms would be most beneficial for the involved stakeholders. This is due to the electricity policies having the highest relative resource gains. To conclude, cooperation in a river basin could result in an overall increase in water resources. However, reaching cooperation might prove to be difficult, because of the lower incentives for the stakeholders in the upstream of the river. Furthermore, extra care has to go towards selecting better performing policies, because policies exist that can cause a decline in water resources compared to when there is no cooperation in the river basin. Moreover, stakeholders are expected to select the policy that maximizes the electricity production, since these policies also perform well for other examined water resources.

Ransomware attacks based on social engineering have been increasing since COVID-19. Attackers have commonly used phishing as a social engineering technique to deploy a ransomware attack. Critical infrastructures such as hospitals have been the common target of these attacks due to hospitals’ sudden increase in digitization and interconnectivity, and the richness of data housed by them. 70 percent of the ransomware attacks against organizations were attributed to the shortage of cybersecurity skills amongst employees. The basic knowledge amongst employees in recognizing suspicious signals is scarce even though there are numerous workshops, programs, and online websites to educate users of such threats. To tackle this problem, effective employee awareness methods are necessary and in this research serious games are used to help solve the problem. The Game Design Research Approach was used to design a serious game based on the combination of the PMT and MINDSPACE frameworks. The game design focuses on creating awareness on phishing signals in the players by using the elements of the PMT framework, that is, threat appraisal (making the players aware of the severity of and vulnerability to a threat) and coping appraisal (coping responses available to the player to deal with the threat). The influencers of the MINDSPACE framework are used in the design of the game to act as catalysts to improve threat appraisal and coping appraisal. The designed game, Phish Phishy, is a tabletop card game with four different sets of cards and is played in two rounds. Two gameplay sessions were conducted in two large academic hospitals in The Netherlands. Based on the game design, the results from the game survey suggested an increase in the awareness levels, that is, improved understanding of phishing signals (threat appraisal) and improved response to threat by reporting them (coping appraisal). Therefore, the PMT and MINDSPACE framework combination suggested by Briggs (2017) was explored for the first time through the serious game, Phish Phishy, to make hospital staff aware of phishing signals in the work environment and report them. The imitations of this research are that the gameplay was conducted only in two hospitals so the sample size is too small for generalisability. Future research should focus on conducting more gameplay sessions by replicating the samples to consider the potential of making the PMT and MINDSPACE frameworks as the industry standard for creating cybersecurity awareness interventions.

Within the Netherlands, the building industry is startled every couple of years by an impactful structure collapse. Even though over the years, a large amount of structural failure investigation reports and studies have provided abundant lessons, regulations, and good practices, structural failures still occur occasionally. The question arises why the existing control measures and regulations have not led to safer building industry, with fewer (or none) structural failures.
A Human Reliability Assessment method is applied on a case study to test the effectiveness of control measures to increase the structural safety of temporary falsework. The control measures are self-checking, third-party check, and construction check.
Overall, this research has provided a framework to determine the effectiveness of control measures when dealing with human errors. It is possible to perform sensitivity analysis on project tasks, to find out when control measures are most effective. Therefore, this research brings the practical application of HRA models in the building industry a step further.

Although inland waterways (IWW) are considered one of the most reliable transport modes, it is very vulnerable to climate change, more than other transport modes such as rail and road transport. The inland waterway transport (IWT) performance strongly depends on the available water depth in the waterway, which is related to the river discharge. Due to climate change, it is expected that the number of days with low and extremely low discharges will occur more often during summer and autumn and that the absolute values of these low discharges will reduce. It is essential to maintain the current inland waterway transport capacity and promote a modal shift of transport by road and rail to IWT.

The Dynamic Adaptive Policy Pathway (DAPP) approach is used as a starting point for this research because the structure and steps cover objectives similar to the objective of this thesis. The essence of the DAPP approach is to develop an adaptive planning that can cope with deep uncertainties that a decision-maker has when the concerned time frame consists of many years in the future.

In step I of the DAPP approach, the objective of the inland waterway transport of dry-bulk and liquid-bulk is defined as performing better than railway transport during periods of low flow. This study considers the inland waterway transport of dry-bulk from Rotterdam to Duisburg and liquid-bulk transport from Rotterdam to Wesseling.
To assess if the objective is met, the transportation costs per ton kilometre of IWT are compared with the transportation costs per ton kilometre of railway transport. The condition for which the IWT value exceeds the railway transport value is called a tipping point.
To determine tipping points in steps II and III of the DAPP approach, a simulation model has been set up in the form of a stress test. The model concept chosen for the simulation is based on the OpenCLSim software package, a rule-based planning tool for cyclic activities.

No tipping points were found for the dry-bulk supply chain, indicating it performs better than railway transport during low flow periods based on transportation costs per ton kilometre. This also implies that no adaptation measures are needed to contribute to achieving the objective.
However, the liquid-bulk supply chain experiences tipping points between 35 and 65 consecutive days of low flow. Two adaptation measures have been analysed for the liquid-bulk supply chain to improve its performance during low flow periods. The first is an adjustment of the maintenance criterion of the waterway. This means that skippers know exactly where the bottlenecks are located so that they can sail around them. This results in a larger available water depth. The second adaptation measure is the availability of a diverse fleet, which means that extra vessels are deployed during low flow but idle during high flow. Adjusting the maintenance criterion results in an extension of the tipping points, and implementation of a diverse liquid-bulk fleet results in a situation where already tipping conditions occur after five days, based on transportation costs per ton kilometre. Based on KNMI ’06 climate scenarios covering a time horizon from 2001 to 2100, an estimation can be given on the timing of the tipping points.
As no tipping points were found for dry-bulk, it can be concluded that the supply chain is a more attractive transport modality than railway transport until 2100. The liquid-bulk supply chain is expected to experience its first tipping point around 2030, but this can be extended to 2072 by adjusting the maintenance criterion.

The floods of 1953, 1993, 1995, 2021 and the almost flood in Rivierenland show that floods have major consequences. Before a flood people can evacuate preventively or vertical and during a flood people can flee, being rescued or permanently stay behind. This research focuses on fleeing and being rescued to a safe region. The research question is: ‘How much time does it take to flee to a safe region during a flood, and what are the core factors that determine the success of a rescue operation by a lifeboat in the Netherlands?’. To indicate how much time it takes for people to flee to a safe region, an experiment took place at test facility Flood Proof Holland in Delft. During this experiment 25 persons walked or bicycled over a parcourse divided into five rounds with different water depths, namely 0.2, 0.4 and 0.6 meter. Additionally, there were other variations, such as walking with a floating object, bringing luggage, bringing a domestic animal, fleeing during darkness and the addition of debris in the water. Time measurements took place during the experiment. If people cannot flee, rescue is needed. To gain more insight into rescuing of people during floods, a questionnaire is spread among experts. Relations between time measurements are found with the use of the Pearson moment correlation coefficient, paired t-test and determining the line by using intersept free linear regression. The relations are combined into a flow chart. Further, the time needed during a rescue attempt is combined into a formula. The navigation speed deviates for different water depths and types of lifeboats. Further, it is estimated by the experts that it takes 7½, 30 and 1 minutes from the contact with a person located at respectively a higher floor or attic, collapsed building and out of the water until having this person into a lifeboat. There is no direct singular answer to the research question, because it depends on the area where the flood takes place and the extent of the flood. Larger water depths increase the fleeing time. If a person decides to flee, it is recommended to walk with a bicycle or bring an air mattress. A stick is useful to check where the road is located. Avoid areas with a lot of debris and do not flee during darkness, except if this is inevitable to survive. People can also wait for rescue at a higher (dry) floor. For the rescue crew, number one priority is safety. Try to avoid to navigate through areas with a lot of debris as this may damage the lifeboat. Search the area systematically and make notes of where people are located.

The Dutch government provides a calculation method to determine the risks to society outside the boundaries of institutions handling Dangerous Goods (the risk affecting External Safety). The current method to calculate the risk affecting External Safety for train marshalling yards is deterministic, outdated, and not transparent. This thesis aims to improve the calculation method regarding rear-end collisions in automated marshalling. It provides a reliability-based approach to the study case Kijfhoek and uses a multi-body dynamic model to simulate the collisions. A Monte Carlo simulation is performed implementing over twenty-five parameters to determine the probability of damage in case of a rear-end collision. From the results, a simplified method is designed to assess the probability of damage to the wagon’s structure. This method uses a newly found relation to determine the absorbed energy at the impact interface in case of a collision. Furthermore, the simplified method reduced the calculation to five parameters and an analytically solvable Limit State function. A first-order reliability method shows that the impact velocity is the single most important parameter in determining the probability of damage.
The simplified method also allows for site-specific values and gives insight into the system and the most important parameters. It is recommended that this method, to determine damage to especially tank wagons in rear-end collisions, is used to improve the existing calculation method for determining the risk affecting External Safety.

Crossings are important yet vulnerable parts of a railway network. This warrants that, in the Netherlands (a country with high traffic loads), crossing geometry is measured twice per year by dedicated measurement vehicles. This produces so much (point cloud) data that prioritizing and predicting is not possible by hand. It can be done by automating the assessments, by an automated process of four steps: cleaning the data, generating relevant performance indicators (features) per measurement, drawing conclusions from all features and visualizing/communicating the results. This project encompasses the first three steps for the most common type of crossing (the 1:9 fixed UIC54 common crossing). The results show that the features yield useful information and insights for both prioritizing and predicting.

The homicide rate dropped globally in the last decades, while in the same period the percentage of unsolved homicide cases increased. At the same time technological developments have drastically changed our lives and little is known about how this influenced the process of decision-making within homicide investigations. Therefore, the aim of this research is to develop a methodology that supports homicide investigators in the collection, prioritizing and elimination of persons of interest using pieces of evidence, in the new digital era, in such a way that the methodology is effective, reduces tunnel vision and obeys the laws for privacy.Currently used methodologies that use pieces of evidence to manage persons of interest were applied to three recent real-world homicide cases to evaluate whether these methodologies effectively collect, prioritize and eliminate persons of interest. The potential of a general approach, relying on big data and data science, to limit the number of persons of interest to be incorporated in homicide investigation, was explored. Subsequently, a methodology to incorporate and prioritize persons of interest was developed based on literature and the knowledge and expertise of experts in criminal investigation. This case specific elements library (C-SEL) consists of twenty-four elements and twelve underlying factors that can be used for the incorporation and prioritization of persons of interest. This new developed methodology was evaluated and compared to the currently used methodologies, not only on the performance measures of effectiveness, but also on tunnel vision reducing properties and the obedience to the laws of privacy.C-SEL effectively collects and prioritizes the perpetrator in all three real-world homicide cases and showed better results compared to the currently used methodologies on the tunnel vision reducing character and on the obedience to the laws of privacy. These results provide sufficient leads to further validate the new developed methodology.@en

This research focusses on finding an answer to the question how can safety be improved in a public-private environment. By means of literature and a case study some improvement opportunities are identified, which pose promising opportunities for improving safety management. The research focused on one of the opportunities, a collaborative approach in managing safety. This can be done by setting up a safety organisation, this new organisation was further investigated to identify the required characteristics. The second part of the research focused on finding answers to the questions how the what the preconditions, composition, mandate and tasks of this organisation should be. The final results show that the safety organisation should be an independent advisory group which consists of all long-term project partners. Their main function will consist of coordinating and improving safety practices on a central level.