In this case study, we want to gain insights into how residents of three municipalities communicate about the new murder scenario of the cold case of Marianne Vaatstra and the possibility of a large-scale DNA familial searching. We investigate how stakeholders shape their arguments in conversation with each other and with the police. We investigate the repertoires that participants use to achieve certain effects in their interactions with others in three focus groups. The results show that the analyzed repertoires are strong normative orientated. We see two aspects emerge that affect the support for large-scale DNA familial searching. These are: 1. Cautious formulations: respondents showed restraint in making personal judgments and often formulated these on behalf of others. Participants would not fully express themselves, but adjusted to what seemed the socially desirable course. 2. Collective identity: respondents focused on the similarities between themselves and the needs, interests, and goals of other participants. Participants also tried in a discursive way to convince each other to participate in the large-scale familial searching. These two major discursive activities offered the communication discipline guidance for interventions into the subsequent communication strategy.
MULTIFILE
On the eve of the large-scale introduction of electric vehicles, policy makers have to decide on how to organise a significant growth in charging infrastructure to meet demand. There is uncertainty about which charging deployment tactic to follow. The main issue is how many of charging stations, of which type, should be installed and where. Early roll-out has been successful in many places, but knowledge on how to plan a large-scale charging network in urban areas is missing. Little is known about return to scale effects, reciprocal effects of charger availability on sales, and the impact of fast charging or more clustered charging hubs on charging preferences of EV owners. This paper explores the effects of various roll-out strategies for charging infrastructure that facilitate the large-scale introduction of EVs, using agent-based simulation. In contrast to previously proposed models, our model is rooted in empirically observed charging patterns from EVs instead of travel patterns of fossil fuelled cars. In addition, the simulation incorporates different user types (inhabitants, visitors, taxis and shared vehicles) to model the diversity of charging behaviours in an urban environment. Different scenarios are explored along the lines of the type of charging infrastructure (level 2, clustered level 2, fast charging) and the intensity of rollout (EV to charging point ratio). The simulation predicts both the success rate of charging attempts and the additional discomfort when searching for a charging station. Results suggest that return to scale and reciprocal effects in charging infrastructure are considerable, resulting in a lower EV to charging station ratio on the longer term.
This paper describes a comparative case study that aims to uncover the quantifiable differences between non-interactive and interactive public displays in the urbanenvironment. The study involved a large temporaryinteractive public display on a central city square showing a selection of custom-made content. We have evaluated the effect on passers-by and spectators in two conditions: 1) non-interactive (2102 passers-by, 228 viewers), by showing a content loop, and 2) interactive (1676 passers-by, 257 viewers), by adding physical pushbuttons for content selection and gaming. We discuss the influence of noninteractive and interactive public displays on: 1) attracting attention, 2) engaging people, 3) improving social dynamics within and among groups of viewers, and 4) catering for the suitable time of day. Based on our observations, we provide quantitative support for the hypothesis that interactive displays are more successful than non-interactive displays to engage viewers, and to make city centers more lively and attractive.
Creating and testing the first Brand Segmentation Model in Augmented Reality using Microsoft Hololens. Sanoma together with SAMR launched an online brand segmentation tool based on large scale research, The brand model uses several brand values divided over three axes. However they cannot be displayed clearly in a 2D model. The space of BSR Quality Planner can be seen as a 3-dimensional meaningful space that is defined by the terms used to typify the brands. The third axis concerns a behaviour-based dimension: from ‘quirky behaviour’ to ‘standardadjusted behaviour’ (respectful, tolerant, solidarity). ‘Virtual/augmented reality’ does make it possible to clearly display (and experience) 3D. The Academy for Digital Entertainment (ADE) of Breda University of Applied Sciences has created the BSR Quality Planner in Virtual Reality – as a hologram. It’s the world’s first segmentation model in AR. Breda University of Applied Sciences (professorship Digital Media Concepts) has deployed hologram technology in order to use and demonstrate the planning tool in 3D. The Microsoft HoloLens can be used to experience the model in 3D while the user still sees the actual surroundings (unlike VR, with AR the space in which the user is active remains visible). The HoloLens is wireless, so the user can easily walk around the hologram. The device is operated using finger gestures, eye movements or voice commands. On a computer screen, other people who are present can watch along with the user. Research showed the added value of the AR model.Partners:Sanoma MediaMarketResponse (SAMR)
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 reclaiming of street spaces for pedestrians during the COVID-19 pandemic, such as on Witte de Withstraat in Rotterdam, appears to have multiple benefits: It allows people to escape the potentially infected indoor air, limits accessibility for cars and reduces emissions. Before ordering their coffee or food, people may want to check one of the many wind and weather apps, such as windy.com: These apps display the air quality at any given time, including, for example, the amount of nitrogen dioxide (NO2), a gas responsible for an increasing number of health issues, particularly respiratory and cardiovascular diseases. Ships and heavy industry in the nearby Port of Rotterdam, Europe’s largest seaport, exacerbate air pollution in the region. Not surprisingly, in 2020 Rotterdam was ranked as one of the unhealthiest cities in the Netherlands, according to research on the health of cities conducted by Arcadis. Reducing air pollution is a key target for the Port Authority and the City of Rotterdam. Missing, however, is widespread awareness among citizens about how air pollution links to socio-spatial development, and thus to the future of the port city cluster of Rotterdam. To encourage awareness and counter the problem of "out of sight - out of mind," filmmaker Entrop&DeZwartFIlms together with ONSTV/NostalgieNet, and Rotterdam Veldakademie, are collaborating with historians of the built environment and computer science and public health from TU Delft and Erasmus University working on a spatial data platform to visualize air pollution dynamics and socio-economic datasets in the Rotterdam region. Following discussion of findings with key stakeholders, we will make a pilot TV-documentary. The documentary, discussed first with Rotterdam citizens, will set the stage for more documentaries on European and international cities, focusing on the health effects—positive and negative—of living and working near ports in the past, present, and future.
