Imagery Rehearsal Therapy (IRT) is effective for trauma-related nightmares and is also a challenge to patients in finding access to their traumatic memories, because these are saved in non-verbal, visual, or audiovisual language. Art therapy (AT) is an experiential treatment that addresses images rather than words. This study investigates the possibility of an IRT-AT combination. Systematic literature review and field research was conducted, and the integration of theoretical and practice-based knowledge resulted in a framework for Imagery Rehearsal-based Art Therapy (IR-AT). The added value of AT in IRT appears to be more readily gaining access to traumatic experiences, living through feelings, and breaking through avoidance. Exposure and re-scripting take place more indirectly, experientially and sometimes in a playlike manner using art assignments and materials. In the artwork, imagination, play and fantasy offer creative space to stop the vicious circle of nightmares by changing theme, story line, ending, or any part of the dream into a more positive and acceptable one. IR-AT emerges as a promising method for treatment, and could be especially useful for patients who benefit least from verbal exposure techniques. This description of IR-AT offers a base for further research.
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Transcript of a lecture during the conference 'Is contemporary art history', Institute of Fine Arts, New York, 28th february 2014.
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In this paper, we report on the initial results of an explorative study that aims to investigate the occurrence of cognitive biases when designers use generative AI in the ideation phase of a creative design process. When observing current AI models utilised as creative design tools, potential negative impacts on creativity can be identified, namely deepening already existing cognitive biases but also introducing new ones that might not have been present before. Within our study, we analysed the emergence of several cognitive biases and the possible appearance of a negative synergy when designers use generative AI tools in a creative ideation process. Additionally, we identified a new potential bias that emerges from interacting with AI tools, namely prompt bias.
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Production processes can be made ‘smarter’ by exploiting the data streams that are generated by the machines that are used in production. In particular these data streams can be mined to build a model of the production process as it was really executed – as opposed to how it was envisioned. This model can subsequently be analyzed and stress-tested to explore possible causes of production prob-lems and to analyze what-if scenarios, without disrupting the production process itself. It has been shown that such models can successfully be used to diagnose possible causes of production problems, including scrap products and machine defects. Ideally, they can even be used to model and analyze production processes that have not been implemented yet, based on data from existing production pro-cesses and techniques from artificial intelligence that can predict how the new process is likely to be-have in practice in terms of data that its machines generate. This is especially important in mass cus-tomization processes, where the process to create each product may be unique, and can only feasibly be tested using model- and data-driven techniques like the one proposed in this project. Against this background, the goal of this project is to develop a method and toolkit for mining, mod-elling and analyzing production processes, using the time series data that is generated by machines, to: (i) analyze the performance of an existing production process; (ii) diagnose causes of production prob-lems; and (iii) certify that a new – not yet implemented – production process leads to high-quality products. The method is developed by researching and combining techniques from the area of Artificial Intelli-gence with techniques from Operations Research. In particular, it uses: process mining to relate time series data to production processes; queueing networks to determine likely paths through the produc-tion processes and detect anomalies that may be the cause of production problems; and generative adversarial networks to generate likely future production scenarios and sample scenarios of production problems for diagnostic purposes. The techniques will be evaluated and adapted in implementations at the partners from industry, using a design science approach. In particular, implementations of the method are made for: explaining production problems; explaining machine defects; and certifying the correct operation of new production processes.
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).
A major challenge in the textile sector is achieving true circularity while preventing fraud, including false sustainability claims and material mislabelling. The complexity of supply chains and outdated certification systems have resulted in a lack of accountability and transparency. This project addresses these issues by developing and implementing Digital Product Passports, integrated with digital trust mechanisms as verifiable credentials, to create a transparent, responsible, and accountable textile supply chain. The project traces the journey of a corporate fashion t-shirt from cotton sourcing in India to production and distribution in the Netherlands, ensuring full transparency and traceability. Its goal is to drive a shift towards a circular economy by fostering collaboration across the supply chain and empowering stakeholders, particularly Tiers 3 and 4 in the Global South. Schijvens Corporate Fashion leads the effort with regenerative cotton sourcing through Raddis®Cotton, utilising Aware™’s technology solution. Adopting a ‘Fibre-Forward’ approach, the consortium ensures traceability by integrating data from raw material sourcing to end-user. This approach benefits all stakeholders, from farmers to garment producers, by providing verifiable information on fibre origins, social conditions, and ecological impacts. By tracking each fibre and collecting direct data, the project removes the opacity that can undermine sustainability claims. The project enhances accountability and sustainability compliance by utilising decentralised technologies for product verification. Integrating digital identity wallets for individuals and organisations, secured with verifiable credentials, enhances trust and accountability, fostering circular economy practices. Rather than seeing DPPs as the end goal, the project views them as catalysts for systemic change. It prioritises continuous improvement, collaboration, and shared benefits, aiming to establish a regenerative circular economy. Through a practical toolkit, the project will help organisations and policymakers navigate DPP adoption, strengthening transparency and creating a scalable, inclusive system for supply chains across the Global South and -North.
Lectorate, part of HAS green academy
