Aim. Cognitive rehabilitation is of interest after paediatric acquired brain injury (ABI). The present systematic review examined studies investigating cognitive rehabilitation interventions for children with ABI, while focusing on identifying effective components. Components were categorized as (1) metacognition and/or strategy use, (2) (computerized) drill-based exercises, and (3) external aids. Methods. The databases PubMed (including MEDLINE), Psyclnfo, and CINAHL were searched until 22nd June 2017. Additionally, studies were identified through cross-referencing and by consulting experts in the field. Results. A total of 20 articles describing 19 studies were included. Metacognition/strategy use trainings (five studies) mainly improved psychosocial functioning. Drill-based interventions (six studies) improved performance on tasks similar to training tasks. Interventions combining these two components (six studies) benefited cognitive and psychosocial functioning. External aids (two studies) improved everyday memory. No studies combined external aids with drill-based interventions or all three components. Conclusion. Available evidence suggests that multi-component rehabilitation, e.g. combining metacognition/strategy use and drill-based training is most promising, as it can lead to improvements in both cognitive and psychosocial functioning of children with ABI. Intervention setting and duration may play a role. Conclusions remain tentative due to small sample sizes of included studies heterogeneity regarding outcome measures, intervention and therapist variables, and patient characteristics. https://doi.org/10.1080/02699052.2018.1458335
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In this study, we address the function of role models for entrepreneurship students. By using entrepreneurs as role models, students can get a better and realistic picture of the complexity of the entrepreneurial path. Choosing whom to interview as role model can be diverse, but it can be problematic if, as a result of that choice, the learning effect in the same group of students is different.
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Designing cities that are socially sustainable has been a significant challenge until today. Lately, European Commission’s research agenda of Industy 5.0 has prioritised a sustainable, human-centric and resilient development over merely pursuing efficiency and productivity in societal transitions. The focus has been on searching for sustainable solutions to societal challenges, engaging part of the design industry. In architecture and urban design, whose common goal is to create a condition for human life, much effort was put into elevating the engineering process of physical space, making it more efficient. However, the natural process of social evolution has not been given priority in urban and architectural research on sustainable design. STEPS stems from the common interest of the project partners in accessible, diverse, and progressive public spaces, which is vital to socially sustainable urban development. The primary challenge lies in how to synthesise the standardised sustainable design techniques with unique social values of public space, propelling a transition from technical sustainability to social sustainability. Although a large number of social-oriented studies in urban design have been published in the academic domain, principles and guidelines that can be applied to practice are large missing. How can we generate operative principles guiding public space analysis and design to explore and achieve the social condition of sustainability, developing transferable ways of utilising research knowledge in design? STEPS will develop a design catalogue with operative principles guiding public space analysis and design. This will help designers apply cross-domain knowledge of social sustainability in practice.
The textile and clothing sector belongs to the world’s biggest economic activities. Producing textiles is highly energy-, water- and chemical-intensive and consequently the textile industry has a strong impact on environment and is regarded as the second greatest polluter of clean water. The European textile industry has taken significant steps taken in developing sustainable manufacturing processes and materials for example in water treatment and the development of biobased and recycled fibres. However, the large amount of harmful and toxic chemicals necessary, especially the synthetic colourants, i.e. the pigments and dyes used to colour the textile fibres and fabrics remains a serious concern. The limited range of alternative natural colourants that is available often fail the desired intensity and light stability and also are not provided at the affordable cost . The industrial partners and the branch organisations Modint and Contactgroep Textiel are actively searching for sustainable alternatives and have approached Avans to assist in the development of the colourants which led to the project Beauti-Fully Biobased Fibres project proposal. The objective of the Beauti-Fully Biobased Fibres project is to develop sustainable, renewable colourants with improved light fastness and colour intensity for colouration of (biobased) man-made textile fibres Avans University of Applied Science, Zuyd University of Applied Sciences, Wageningen University & Research, Maastricht University and representatives from the textile industry will actively collaborate in the project. Specific approaches have been identified which build on knowledge developed by the knowledge partners in earlier projects. These will now be used for designing sustainable, renewable colourants with the improved quality aspects of light fastness and intensity as required in the textile industry. The selected approaches include refining natural extracts, encapsulation and novel chemical modification of nano-particle surfaces with chromophores.
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.
