Cancer is one of the major public health problems of the 21st century, with chronic pain prevalent in a large section of its affected population. Automated Pain Assessments (APAs) from facial expressions, leveraging advancements in the field of Artificial Intelligence(AI) have been proposed as a solution to this ever-growing problem and continue to be an active area of research. However, challenges surround their implementation and validity of the subjective construct of pain that many APAs purport to measure. Furthermore, existing datasets to train AI/ML models cover clinical and experimental pain, with cancer pain remaining largely unaddressed.

This thesis therefore examines how an APA model can be developed and validated to measure specific pain constructs, using which implications for cancer pain applications are derived or hypothesised. Two typical pipelines associated with APAs- Classical Machine Learning and Deep Learning, are evaluated in the study, with construct validity evaluated at each sequential phase. The former involves the extraction of Action Units (AUs) using open-source toolkits, classified by SVMs and the latter employs a CNN-LSTM architecture, capable of self-learning facial representations. The Deep-Learning branch reduced dependencies on inconsistent toolkit outputs and captured the subjective pain experience (measured through patients' self-reported ratings) marginally better than its SVM counterparts. However, both APAs were aligned more closely with observer ratings than with self-reported pain.

Development and validation are therefore inseparable in APA design, with the selection of ground truths and datasets largely determining the accuracy of the models. This matters most for cancer pain, where facial expression is known to be subdued and patient-reported outcomes carry higher clinical weight, as validated on real-world cancer pain data developed as part of the SENSAI dataset. The proposed models and the results serve as preliminary findings for the SENSAI Application/Research Programme. Finally, the empirical results are evaluated from a critical and business-oriented lens, to provide takeaways for cancer pain applications and different stakeholders. ... Read more

Mental health problems among adolescents continue to rise, with growing interest in identifying early signs of psychological distress, including cognitive distortions (CDs). CDs are negatively biased patterns of thinking associated with conditions such as depression and anxiety. As CDs are primarily expressed through language, Natural Language Processing (NLP) methods have increasingly been explored for their automatic detection and classification, potentially enabling earlier intervention. This work provides a dataset of Dutch adolescent forum posts from the Kindertelefoon, annotated by professionals with a background in Cognitive Behavioral Therapy (CBT). The dataset is utilized for sentence-level CD detection and classification, exploring the role of local context (the surrounding post) and longitudinal context (previous posts from the same user) on model performance. Experiments compare zero-shot prompting methods with finetuned transformer-based models, using different approaches to incorporate context. Results show that incorporating context does not consistently improve CD detection or classification performance in naturalistic adolescent forum posts. ... Read more

Soft tissue sarcomas (STS) are rare cancers with a high risk of recurrence, therefore requiring structured and effective follow-up strategies. Current follow-up guidelines, such as those provided by the NCCN and ESMO, use fixed time intervals and do not fully account for individual patient risk factors when scheduling follow-up visits. Additionally, adherence to these guidelines is inconsistent, with some patients receiving unnecessary follow-up visits while others do not receive sufficient monitoring. This inefficiency places a burden on healthcare resources while potentially impacting patient outcomes.
This research aimed to develop a decision-support tool, in the form of a Bayesian network, that helps to personalise follow-up strategies. It does so by combining insights from historical patient data and expert knowledge to estimate recurrence risk, where risk is based on both likelihood of a recurrence and the severity of a potential recurrence and its treatment. The tool shows how frequent similar patients were historically followed-up and what their outcomes were. Rather than giving recommendations, the model supports clinicians by providing insights into recurrence patterns and follow-up intensity among similar cases, helping clinicians reflect on what might be appropriate for a given case.
The study consisted of three phases: identifying key recurrence risk factors and follow-up patterns through data analysis and literature review, developing and validating the Bayesian model in consultation with clinical experts and finally analysing mortality and economic effects for different follow-up frequencies.
Tumour grade and surgical margin were found to be most important risk factors for recurrence, with grade being the strongest. Two main model structures were made, one including both tumour grade and surgical margin to estimate probability of recurrence, while the other included only tumour grade as a reflection of probability of recurrence. Both models contained the variables expected remaining lifespan and fitness of the patient to estimate consequence of recurrence. The models showed considerable variation in risk classification compared to expert opinion and tumour grade alone. Since there is not one ‘correct’ way to classify risk, this reflects the perceptive nature of risk and the different models offer different viewpoints. Additionally, the study found no clear survival benefit from more intensive follow-up in any of the models, while it did substantially increase costs, especially per survivor. This highlights the need for clinicians to reflect on and discuss optimal allocation of follow-up resources.
By developing a structured, evidence-based approach to follow-up, this study contributes to improving STS care. The proposed decision-support tool could help clinicians make more transparent, risk-based follow-up decisions, while reducing unnecessary burdens on healthcare systems.
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Gen Z, born between the mid-1990s and late-2000s, has unique workplace behaviours and expectations due to their upbringing in the digital age. This generation often lacks real-world work experience and necessary communication skills which can often lead to unrealistic job expectations and higher susceptibility to mental health issues. As a result, there is a considerable loss of human resources in the workplace which can be circumvented with better management of the Gen Z cohort. Adopting a growth mindset can help the Gen Z cohort navigate their issues in the modern workplace. Prevailing research on the application of mindset theory in the workplace posits a growth mindset helps sustain strong mental health in a hectic and stressful workplace. Consequently, a culture of growth mindset improves the productivity and leadership qualities of the employees.

This research investigates the extent of influence the growth mindset has on Gen Z employee attitude as well as the potential pitfalls of adopting such a mindset in the workplace. The study aims to investigate how mindset can impact the attitude of Gen Z individuals in the workplace, specifically focusing on their approach to challenges, preferences for work compensation, and levels of satisfaction. The study proposes that Gen Z employees with a growth mindset are more likely to take on challenging tasks and place greater importance on skill development in their compensation preferences. Additionally, the study anticipates that behavioural inhibition system (BIS) and behavioural approach system (BAS) personality traits will moderate the attitude and preference changes due to mindset adoption. To measure the effects of mindset interventions, the research utilizes a PreTest-PostTest experimental design to assess task choices, work compensation preferences, and satisfaction.

The results of the study reveal that participants with a growth mindset were significantly more likely to choose challenging tasks, aligning with mindset theory. However, the lack of significant change in skill development preferences underscores the challenge of fostering a genuine growth mindset. The interaction of mindset and BIS/BAS traits indicated complex underlying dynamics.

In conclusion, the study finds that promoting a growth mindset among Gen Z employees can positively impact their engagement with challenging tasks and overall satisfaction in the workplace. However, achieving a genuine growth mindset requires addressing both internal motivations and external compensation structures. The study acknowledges limitations, such as the small sample size and potential biases, and suggests further research to explore the interaction between mindset and BIS/BAS personality traits in more depth. The findings contribute to a deeper understanding of Gen Z's unique needs and preferences, offering valuable insights for developing effective management strategies tailored to this new generation of employees. ... Read more

This research explores the application of geospatial intelligence and machine learning to map and analyse routes used in illicit supply chains, specifically targeting the networks that transport illegal goods from South America to Europe. With drug trafficking routes becoming more adaptive and complex, particularly in the corridors connecting South America to Europe, traditional methods of monitoring and interception have proven insufficient. Enforcement strategies often rely on seizure data, which is delayed and offers an incomplete view of these evolving networks. To address this, the study proposes a novel methodology that combines satellite data with machine learning to identify and map elements of the supply chain, such as cultivation fields, clandestine laboratories, and transportation routes. This approach aims to create a scalable solution for tracking these illicit networks and supporting law enforcement.

Through the use of supervised machine learning models, potential cultivation fields and clandestine laboratories are detected in satellite imagery. The methodology includes sourcing data, configuring machine learning models based on literature, and evaluating model performance. The challenge in training models is the limited labelled data for illegal activities.

Once these nodes are identified, a connected graph is constructed that integrates both licit and illicit transport routes. This graph serves as the foundation for estimating smuggling routes. The process involves combining graphs of waterways and roads with airstrips, ports, potential cultivation fields and laboratories. To estimate paths between illicit sites and the main infrastructure network, an algorithm is applied that estimates the least steep path based on a slope raster.

Route estimation is the next step, where Dijkstra's algorithm is employed to calculate the most probable paths. Each edge is weighted based on travel time, cost, and risk, with factors adjusted to account for various modes of transport, including trucks, boats, planes, and donkeys, depending on terrain. There is also a factor added to account for transloading. These weighted paths offer a realistic picture of choices traffickers might make to minimize travel time, cost or risk.

To test the model, a case study is performed focusing on the Colombia-Venezuela border region, a region with high trafficking activity. The case study applies the graph-based methodology to identify plausible trafficking routes, connecting cultivation sites to ports. By calculating optimal paths, the study reveals which routes are likely to be used most frequently, providing insights that could potentially aid in targeted enforcement.

There are some limitations and areas for future improvement in this proof of concept, such as challenges with data quality and model accuracy, as well as potential refinements to the methodology to enhance real-world applicability. However, there is potential in combining machine learning with geospatial intelligence to create a tool for tracking and disrupting illicit supply chains. This research offers a proof of concept for enabling scalable monitoring of illegal activities, supporting efforts to disrupt global trafficking networks. ... Read more

The increasing presence of vehicle automation is transforming freeways into environments of mixed traffic, where vehicles of varying autonomy levels interact. Before all vehicles become fully autonomous, a transition will be made that causes a high mix of those autonomy levels. Therefore, this thesis researches the impact of different levels of vehicle automation on traffic performance and safety on a multi-lane freeway with an on-ramp. Microscopic simulation is utilised to explore how varying levels of vehicle automation, while taking human driving factors into account, affect traffic flow, speed, density and dangerous car-following interactions.
Currently, the majority of vehicles are defined as level 0 vehicles. This does not mean that these vehicles have no automated features at all but the Advanced Driver-Assistance Systems (ADAS) only provide temporary support such as an emergency brake. This is different for level 1 vehicles where the car-following driving tasks are automated and for level 2 vehicles both the car-following and lane-changing tasks are automated to support the driver. Level 3 vehicles are conditionally autonomous where all driving tasks are automated. The study aims to fill the knowledge gap in understanding the impact of these mixed traffic conditions on overall traffic dynamics.
To simulate the varying levels of automation, the study utilizes OpenTrafficSim (OTS), a microscopic traffic simulation software that incorporates a mental model to realistically represent human driving behaviour. This allows the simulation to account for human factors such as reaction time, perception, cognitive workload, and distractions, which are crucial in differentiating human drivers from automated vehicles. Four automation levels (0, 1, 2, and 3) defined by the Society of Automotive Engineers are modelled for specific driving characteristics within the freeway environment. Model parameters are adjusted for each level based on literature findings and practical considerations.
Simulation results indicate that the introduction of level 1 and level 2 vehicles, characterised by larger headway values, can negatively impact traffic performance but also result in less dangerous car-following behaviour. The increased headway leads to disruptions in traffic flow and an earlier onset of congestion. However, as the penetration rate of level 3 vehicles increases, traffic conditions significantly improve, with higher mean speeds, reduced travel times, and increased traffic flow observed. These findings highlight the potential benefits of higher levels of automation in enhancing traffic performance and safety.
The study also examines the impact of driver distraction on traffic performance and safety. By simulating both in-vehicle and roadside distractions, the research demonstrates that higher cognitive workloads can lead to more disruptive driving behaviour. As automation levels increase, the negative effects of distraction are mitigated.
Overall, this research provides valuable insights into the complexities of mixed traffic with varying automation levels. It demonstrates that while the transition phase may present challenges, higher levels of vehicle automation can significantly improve both traffic performance and safety on multi-lane freeways. Special emphasis is given to accurately simulating human driver behaviour and suggestions are made for future research, including the need for a dual-perception framework for more accurate modelling of level 1 vehicles and further investigation into the impact of different distraction types.
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Math Anxiety (MA) is some of the most notable mental health issues. The causes and development of MA are complex and inconclusive. This study highlighted that language patterns differ between people with high and low MA. Especially, the word categories positive emotion, insight, articles and linguistic dimensions. This study included two personality differences (self-esteem and curiosity) to better explore the inner world of people with high and low MA through their language patterns. Self-esteem and curiosity moderate the relationship between MA and people’s language patterns.
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Maintaining a healthy eating behavior can be challenging, especially under stressful situations. Self-control plays a pivotal role in this struggle, since it allows one to resist immediate desires in pursuit of higher-level goals. The level of self-control can differ among individuals, with varying degrees of “strength”. This strength can be likened to a “muscle”, which has specific power and endurance and enables the exertion of self-control at a certain time. Power is linked to the individual situation-invariant levels of trait self-control, which enables the initial exercise of self-control at a particular moment. However, the maintenance of self-control over time is attributed to “muscle endurance”, which is represented by individual depletion sensitivity. Like a muscle that can become depleted after exertion, the exercise of self-control can deplete available cognitive resources, leading to ego depletion, decreased self-control capacity, and thus unhealthy food choices. Food recommender systems are considered effective tools for promoting healthier choices and may be seen as nudges that make healthier options more salient. Many studies have demonstrated the effectiveness of recommender systems and digital nudging in influencing users’ food choices in the direction of healthier ones. Attribute frames, including positive and negative types, are popular information nudges in the health field. Nonetheless, their impact on eating behaviors remains a controversial issue. Evidence has shown that dispositional factors, such as self-control levels, may moderate the effectiveness of attribute frames.

This study aimed to examine to what extent attribute frames steer human food choices toward healthy ones, and in what way self-control levels influence that relationship. An online experiment was conducted, utilizing attribute frames as the independent variable, food choices as the dependent variable, and depletion sensitivity and trait self-control as moderating variables. Participants were randomly provided seven different food options, each of which came with an attribute framing message, to evaluate the effects of positive and negative attribute frames on healthy food choices. The Depletion Sensitivity Scale (DSS), and Brief Self-Control Scale (BSCC) were used to assess the individuals’ levels of depletion sensitivity and trait self-control, in respect. The findings revealed that positive attribute frames effectively encouraged healthy food choices, while negative frames had a relatively lower impact. Depletion sensitivity moderated the relationship between attribute frames and healthy food choices, suggesting that individuals with low depletion sensitivity were more likely to select healthy options under positive framing conditions. However, no significant moderating effect of trait self-control was observed. Lastly, the relationship between attribute frames and food preference was found to be significantly moderated by gender differences. In conclusion, this study provided evidence supporting the influence of attribute frames on healthy food choices and the moderating role of depletion sensitivity on this relationship. The significance of considering gender differences in the analysis of the effect of attribute frames on food preferences was also illustrated. ... Read more

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. ... Read more

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 ... Read more

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. ... Read more

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. ... Read more

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. ... Read more

Dietary habits refer to the set of choices or decisions one makes regarding the foods consumed. They include choices and decisions on what to eat when to eat, how much to eat, and where to eat. Taste preferences, food diversity, frequency of meal consumption, portion sizes, snacking behavior, and meal skipping all have an impact on this. According to research, not only physiological but also psychological elements such as emotions, stress, and personality differences influence our dietary choices. Many studies have been conducted to determine how to alter problematic eating choices by regulating emotions. Understanding one’s personality type, on the other hand, can help someone to become more aware of the foods liked and disliked. This is the first study to look at the combined effect of emotion regulation strategies and, in particular, Type-D personality type on eating choices. This thesis focused on women from various ethnic origins. The study looked at which emotion control strategies, such as cognitive reappraisal and expressive suppression, were employed. Individuals were also assessed on the extent to which they were in the possession of a Type-D personality type by scoring on the two variables in this model, which are negative affectivity and social inhibition. This study hypothesized that people with Type-D personality, or those who score high in Negative Affectivity and Social Inhibition, employ less emotion regulation strategies and make poor dietary choices. The main findings confirm the hypothesis. As per the findings, reappraisal strategies outperform suppression methods. It was discovered that when reappraisal methods were used, women preferred more healthy food items like vegetables. However, when suppression strategies were used, unhealthy food items such as snacks were favored. Furthermore, the study looked at how age, a control variable, affected the use of emotion regulation strategies in food choices. Younger women were found to apply more reappraisal strategies for healthier foods, whereas older women used suppressing methods. In conclusion, this study confirmed previous research on the independent influence of emotion regulation and personality type on diet, and it also added new insights to the research by examining the combined effect of these factors on diet choices. ... Read more

In the last decades, the environmental impact of aviation has been an emerging topic of discussion worldwide. With an expected CO2 emission increase of 21% by 2040 for air traffic, there is an urgency to reduce the negative impact on the environment. On top of that, the number of passengers is expected to double within the next 20 years. This challenge requires intervention from governments, airlines and passengers. Creating a more reliable flight planning would reduce the fuel consumption and costs for airlines. However, for airlines with a hub-and-spoke network, the schedule design relies on maximizing the connecting passengers. A more reliable planning with embedded buffers could lead to a lower offering of connecting flights. The goal of this research is to design a novel optimization model for balancing reliability and connectivity by buffer scheduling in the flight schedule, and to evaluate the impact on environmental sustainability. This research provides a Proof of Concept with a case of KLM Royal Dutch Airlines. The optimization model presents a Pareto optimal front that makes the trade-off between reliability and connectivity explicit. Main findings of this research are that (i) the model is suitable for supporting decision-making on a basic level, (ii) the stochastic variance of the model should be limited and (iii) the flight schedule presented by the model reduces the CO2 emissions significantly. Further research is required on how to optimize schedules with a higher level of complexity for the methodological elements and on the business side. ... Read more

Urban areas are the biggest consumers of electricity and energy consumption is only likely to increase with rapid urbanization. Out of the urban building stock residential buildings require continuous supply of energy for space heating and appliances. To answer to this demand in a sustainable way policy maker need to design energy efficiency strategies that must rely on accurate and traceable models. These models estimate energy demand based on a series of building features, out of which building age is of prime importance because it predicts the insulation properties of the building. To support the energy modelling process, we propose a method of automatically identifying building age from spatial data at a large scale. We identify features of buildings that are significant for age prediction and determine which set of features has best prediction power at national scale, in Germany. It is expected that the accuracy of classification will be strongly related to sampling design and data availability. The final results will be used to identify the impact of misclassification errors on estimating energy use in urban energy models, providing in this manner a measure of the reliability of such models. ... Read more

Global containerised cargo volumes are increasing at a high rate, requiring ports to increase their throughput. Containers are transported to the hinterland by truck, train, or barge. In the ports of Rotterdam and Antwerp, container barges have high in-port times, of which they spend a lot of time ineffectively. This affects these ports’ connectivity with the hinterland. The goal of this research, therefore, is to identify the main causes for, and most promising measures against, high barge in-port times. These causes and measures were gathered by desk research, using scientific literature, technical reports, and interviews with stakeholders.
To assess the importance of different causes and compare different measures, a simulation model was built, based on Rotterdam. Because there are many deep-sea terminals and barges that each make their own planning, and many aspects of the in-port operations are subject to stochastics, Simio was used to make an agent-based simulation model. Although many factors contributing to high in-port times were found in the literature, the majority of them contribute to two overarching causes: there is insufficient barge handling capacity at deep-sea terminals, and coordination problems result in inefficient utilisation of the available capacity. Therefore, many measures that are suggested in the literature aim to mitigate the negative effects of the two main causes.
The current state of the system was replicated in the model, in order to understand the response of in-port times to various system changes. Experiments evaluated changes to terminals’ barge handling capacity by increasing the capacity, or by changing how current capacity is distributed over time. The number of calls barges make on average was changed. Additionally, the effects of market mechanics on the in-port times of barges with different levels of willingness-to-pay were assessed, by auctioning timeslots or allowing barges to submit priority requests. Finally, increased information sharing and instantaneous reservations were implemented as an experiment.
The findings indicate that increasing handling capacity at terminals can strongly improve barge in-port times. Without increasing capacity the system can also be improved by increasing the information that is exchanged between agents, thereby influencing the way agents interact. Finally, implementing a priority system can reliably reduce in-port time for barges willing to pay for that service, without negatively impacting the average in-port times of the whole system. ... Read more