Car use in the sprawled urban region of Noord‐Brabant is above the Dutch average. Does this reflect car dependency due to the lack of competitive alternative modes? Or are there other factors at play, such as differences in preferences? This article aims to determine the nature of car use in the region and explore to what extent this reflects car dependency. The data, comprising 3,244 respondents was derived from two online questionnaires among employees from the High‐Tech Campus (2018) and the TU/e‐campus (2019) in Eindhoven. Travel times to work by car, public transport, cycling, and walking were calculated based on the respondents’ residential location. Indicators for car dependency were developed using thresholds for maximum commuting times by bicycle and maximum travel time ratios between public transport and car. Based on these thresholds, approximately 40% of the respondents were categorised as car‐dependent. Of the non‐car‐dependent respondents, 31% use the car for commuting. A binomial logit model revealed that higher residential densities and closer proximity to a railway station reduce the odds of car commuting. Travel time ratios also have a significant influence on the expected directions. Mode choice preferences (e.g., comfort, flexibility, etc.) also have a significant, and strong, impact. These results highlight the importance of combining hard (e.g., improvements in infrastructure or public transport provi-sion) and soft (information and persuasion) measures to reduce car use and car dependency in commuting trips.
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This research explores the attitudes of children from different socio-economic backgrounds towards cars. This paper explores their projected choices and motivations in the context of (1) post-materialist values; (2) economic constraints; and (3) social status theories; and draws upon survey research among 140 upper elementary school children in the Netherlands between September 2010 and January 2011. Comparative analysis shows that there are significant differences in attitudes of children from different socio-economic backgrounds. Pupils from the affluent predominantly ethnically Dutch schools showed greater awareness of and concern about their parents’ and general use of cars, and less desire to own a car in the future, children from less economically advantaged schools demonstrated lower environmental awareness and concern and more desire to own a car in the future. This study is based on a small sample and indicates a need for large-scale follow-up study of children's attitudes towards cars. https://doi.org/10.1016/j.tranpol.2012.07.010 https://www.linkedin.com/in/helenkopnina/
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Two key air pollutants that affect asthma are ozone and particle pollution. Studies show a direct relationship between the number of deaths and hospitalizations for asthma and increases of particulate matter in the air, including dust, soot, fly ash, diesel exhaust particles, smoke, and sulfate aerosols. Cars are found to be a primary contributor to this problem. However, patient awareness of the link is limited. This chapter begins with a general discussion of vehicular dependency or ‘car culture’, and then focuses on the discussion of the effects of air pollution on asthma in the Netherlands. I argue that international organizations and patient organizations have not tended to put pressure on air-control, pollution-control or environmental standards agencies, or the actual polluters. While changes in air quality and the release of greenhouse gases are tied to practices like the massive corporate support for the ongoing use of motor vehicles and the increased prominence of ‘car culture’ globally, patient organizations seem more focused on treating the symptoms rather than addressing the ultimate causes of the disease. Consequently, I argue that to fully address the issue of asthma the international health organizations as well as national health ministries, patient organizations, and the general public must recognize the direct link between vehicular dependency and asthma. The chapter concludes with a recommendation for raising environmental health awareness by explicitly linking the vehicular dependency to the state of poor respiratory health. Strategic policy in the Netherlands then should explicitly link the present pattern of auto mobility to public health. https://onlinelibrary.wiley.com/doi/book/10.1002/9781118786949 LinkedIn: https://www.linkedin.com/in/helenkopnina/
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Huntington’s disease (HD) and various spinocerebellar ataxias (SCA) are autosomal dominantly inherited neurodegenerative disorders caused by a CAG repeat expansion in the disease-related gene1. The impact of HD and SCA on families and individuals is enormous and far reaching, as patients typically display first symptoms during midlife. HD is characterized by unwanted choreatic movements, behavioral and psychiatric disturbances and dementia. SCAs are mainly characterized by ataxia but also other symptoms including cognitive deficits, similarly affecting quality of life and leading to disability. These problems worsen as the disease progresses and affected individuals are no longer able to work, drive, or care for themselves. It places an enormous burden on their family and caregivers, and patients will require intensive nursing home care when disease progresses, and lifespan is reduced. Although the clinical and pathological phenotypes are distinct for each CAG repeat expansion disorder, it is thought that similar molecular mechanisms underlie the effect of expanded CAG repeats in different genes. The predicted Age of Onset (AO) for both HD, SCA1 and SCA3 (and 5 other CAG-repeat diseases) is based on the polyQ expansion, but the CAG/polyQ determines the AO only for 50% (see figure below). A large variety on AO is observed, especially for the most common range between 40 and 50 repeats11,12. Large differences in onset, especially in the range 40-50 CAGs not only imply that current individual predictions for AO are imprecise (affecting important life decisions that patients need to make and also hampering assessment of potential onset-delaying intervention) but also do offer optimism that (patient-related) factors exist that can delay the onset of disease.To address both items, we need to generate a better model, based on patient-derived cells that generates parameters that not only mirror the CAG-repeat length dependency of these diseases, but that also better predicts inter-patient variations in disease susceptibility and effectiveness of interventions. Hereto, we will use a staggered project design as explained in 5.1, in which we first will determine which cellular and molecular determinants (referred to as landscapes) in isogenic iPSC models are associated with increased CAG repeat lengths using deep-learning algorithms (DLA) (WP1). Hereto, we will use a well characterized control cell line in which we modify the CAG repeat length in the endogenous ataxin-1, Ataxin-3 and Huntingtin gene from wildtype Q repeats to intermediate to adult onset and juvenile polyQ repeats. We will next expand the model with cells from the 3 (SCA1, SCA3, and HD) existing and new cohorts of early-onset, adult-onset and late-onset/intermediate repeat patients for which, besides accurate AO information, also clinical parameters (MRI scans, liquor markers etc) will be (made) available. This will be used for validation and to fine-tune the molecular landscapes (again using DLA) towards the best prediction of individual patient related clinical markers and AO (WP3). The same models and (most relevant) landscapes will also be used for evaluations of novel mutant protein lowering strategies as will emerge from WP4.This overall development process of landscape prediction is an iterative process that involves (a) data processing (WP5) (b) unsupervised data exploration and dimensionality reduction to find patterns in data and create “labels” for similarity and (c) development of data supervised Deep Learning (DL) models for landscape prediction based on the labels from previous step. Each iteration starts with data that is generated and deployed according to FAIR principles, and the developed deep learning system will be instrumental to connect these WPs. Insights in algorithm sensitivity from the predictive models will form the basis for discussion with field experts on the distinction and phenotypic consequences. While full development of accurate diagnostics might go beyond the timespan of the 5 year project, ideally our final landscapes can be used for new genetic counselling: when somebody is positive for the gene, can we use his/her cells, feed it into the generated cell-based model and better predict the AO and severity? While this will answer questions from clinicians and patient communities, it will also generate new ones, which is why we will study the ethical implications of such improved diagnostics in advance (WP6).
The consistent demand for improving products working in a real-time environment is increasing, given the rise in system complexity and urge to constantly optimize the system. One such problem faced by the component supplier is to ensure their product viability under various conditions. Suppliers are at times dependent on the client’s hardware to perform full system level testing and verify own product behaviour under real circumstances. This slows down the development cycle due to dependency on client’s hardware, complexity and safety risks involved with real hardware. Moreover, in the expanding market serving multiple clients with different requirements can be challenging. This is also one of the challenges faced by HyMove, who are the manufacturer of Hydrogen fuel cells module (https://www.hymove.nl/). To match this expectation, it starts with understanding the component behaviour. Hardware in the loop (HIL) is a technique used in development and testing of the real-time systems across various engineering domain. It is a virtual simulation testing method, where a virtual simulation environment, that mimics real-world scenarios, around the physical hardware component is created, allowing for a detailed evaluation of the system’s behaviour. These methods play a vital role in assessing the functionality, robustness and reliability of systems before their deployment. Testing in a controlled environment helps understand system’s behaviour, identify potential issues, reduce risk, refine controls and accelerate the development cycle. The goal is to incorporate the fuel cell system in HIL environment to understand it’s potential in various real-time scenarios for hybrid drivelines and suggest secondary power source sizing, to consolidate appropriate hybridization ratio, along with optimizing the driveline controls. As this is a concept with wider application, this proposal is seen as the starting point for more follow-up research. To this end, a student project is already carried out on steering column as HIL
Possibly, the aviation sector’s decarbonization challenge (see Dutch knowledge key in international climate study for tourism | CELTH) has profound implications for the ability of aviation-de-pendent outbound tour operators to attract capital and with that their ability to maintain or trans-form their current business portfolio (understood here as the current product offers and approximate carbon footprints, business models, and ownership structures present in this economic do-main). Knowledge about these (possible) investment risks and their business and policy implications is lacking. This project therefore addresses this knowledge gap by means of the following research questions.1. What is the current business portfolio of Dutch outbound tour operators?a. To what extend do Dutch outbound tour operators depend on aviation in terms of product offer and turnover?b. What is the relative carbon footprint share of aviation-based products compared to the total outbound product offer and turnover of Dutch outbound tour operators?2. What are investment risks of this business portfolio as indicated by investors?a. How do investors evaluate investment risks in relation to climate change mitigation and de-carbonisation?b. What are investment risks of the business portfolio of Dutch outbound tour operators?c. What are the reflections on and implications of these investment risks from the perspective of policymakers and tour operators?
