Deze publicatie richt zich vooral op het concept Design Based Research,gezien vanuit het perspectief van de bijna 40 lectoren die de hogeschool rijk is. Dit lectoratenoverzicht kan worden beschouwd als een atlas of reisgids waarmee de lezer een route kan afleggen langs de verschillende lectoraten. De lectoraten die actief zijn op het gebied van de Service Economy worden beschreven in hoofdstuk 2. De lectoraten die actief zijn op het gebied van Vitale Regio worden beschreven in hoofdstuk 3. De lectoraten die actief zijn op het gebied van Smart Sustainable Industries worden beschreven in hoofdstuk 4. De lectoraten die actief zijn op het gebied van de hogeschoolbrede thema’s Design Based Education en Research worden beschreven in hoofdstuk 5. Tenslotte wordt er in hoofdstuk 6 een eerste aanzet gedaan om één of meer verbindende thema’s of werkwijzen te ontdekken in de aanpak van de verschillende lectoraten. Het is niet de bedoeling van deze publicatie om een definitief antwoord te geven op de vraag wat NHL Stenden precies bedoelt met het concept Design Based Research. Het doel van deze publicatie is wel om een indruk te krijgen van wat er allemaal gebeurt binnnen de lectoraten van NHL Stenden, en om nieuwsgierig te worden naar meer.
Deze casestudie geeft inzicht in verschillende soorten kennis die kenmerkend zijn voor applied design research. Er wordt onderscheid gemaakt tussen kennis over de huidige situatie, over wenselijke alternatieven en over effectieve oplossingen om daar te komen. Ofwel, kennis hoe het is, kennis over hoe het kan zijn en kennis over hoe het zal zijn als we effectieve oplossingen toepassen. Elk van deze soorten kennis heeft andere kwaliteitscriteria.
The current set of research methods on ictresearchmethods.nl contains only one research method that refers to machine learning: the “Data analytics” method in the “Lab” strategy. This does not reflect the way of working in ML projects, where Data Analytics is not a method to answer one question but the main goal of the project. For ML projects, the Data Analytics method should be divided in several smaller steps, each becoming a method of its own. In other words, we should treat the Data Analytics (or more appropriate ML engineering) process in the same way the software engineering process is treated in the framework. In the remainder of this post I will briefly discuss each of the existing research methods and how they apply to ML projects. The methods are organized by strategy. In the discussion I will give pointers to relevant tools or literature for ML projects.
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Due to societal developments, like the introduction of the ‘civil society’, policy stimulating longer living at home and the separation of housing and care, the housing situation of older citizens is a relevant and pressing issue for housing-, governance- and care organizations. The current situation of living with care already benefits from technological advancement. The wide application of technology especially in care homes brings the emergence of a new source of information that becomes invaluable in order to understand how the smart urban environment affects the health of older people. The goal of this proposal is to develop an approach for designing smart neighborhoods, in order to assist and engage older adults living there. This approach will be applied to a neighborhood in Aalst-Waalre which will be developed into a living lab. The research will involve: (1) Insight into social-spatial factors underlying a smart neighborhood; (2) Identifying governance and organizational context; (3) Identifying needs and preferences of the (future) inhabitant; (4) Matching needs & preferences to potential socio-techno-spatial solutions. A mixed methods approach fusing quantitative and qualitative methods towards understanding the impacts of smart environment will be investigated. After 12 months, employing several concepts of urban computing, such as pattern recognition and predictive modelling , using the focus groups from the different organizations as well as primary end-users, and exploring how physiological data can be embedded in data-driven strategies for the enhancement of active ageing in this neighborhood will result in design solutions and strategies for a more care-friendly neighborhood.
Human kind has a major impact on the state of life on Earth, mainly caused by habitat destruction, fragmentation and pollution related to agricultural land use and industrialization. Biodiversity is dominated by insects (~50%). Insects are vital for ecosystems through ecosystem engineering and controlling properties, such as soil formation and nutrient cycling, pollination, and in food webs as prey or controlling predator or parasite. Reducing insect diversity reduces resilience of ecosystems and increases risks of non-performance in soil fertility, pollination and pest suppression. Insects are under threat. Worldwide 41 % of insect species are in decline, 33% species threatened with extinction, and a co-occurring insect biomass loss of 2.5% per year. In Germany, insect biomass in natural areas surrounded by agriculture was reduced by 76% in 27 years. Nature inclusive agriculture and agri-environmental schemes aim to mitigate these kinds of effects. Protection measures need success indicators. Insects are excellent for biodiversity assessments, even with small landscape adaptations. Measuring insect biodiversity however is not easy. We aim to use new automated recognition techniques by machine learning with neural networks, to produce algorithms for fast and insightful insect diversity indexes. Biodiversity can be measured by indicative species (groups). We use three groups: 1) Carabid beetles (are top predators); 2) Moths (relation with host plants); 3) Flying insects (multiple functions in ecosystems, e.g. parasitism). The project wants to design user-friendly farmer/citizen science biodiversity measurements with machine learning, and use these in comparative research in 3 real life cases as proof of concept: 1) effects of agriculture on insects in hedgerows, 2) effects of different commercial crop production systems on insects, 3) effects of flower richness in crops and grassland on insects, all measured with natural reference situations
What if living organisms communicated signals from the environment to us and thereby offered a sustainable alternative to electronic sensors? Within the field of biodesign, designers and scientists are collaborating with living organisms to produce new materials with ecological benefits. The company Hoekmine, in collaboration with designers, has been researching the potential of flavobacteria for producing sustainable colorants to be applied on everyday products. These non-harmful bacteria can change their form, texture and iridescent color in response to diverse environmental factors, such as humidity and temperature. Here, billions of cells are sensing and integrating the results as color. Therefore, Hoekmine envisions biosensors, which would minimize the use of increasingly demanded electronic sensors, and thus, the implementation of scarce and toxic materials. Developing a living sensor by hosting flavobacteria in a biobased and biodegradable flexible material offers opportunities for sustainable alternatives to electronic sensors. Aiming to take this concept to the next level, we propose a research collaboration between Avans, Hoekmine and a company specialized in biobased and biodegradable labels, Bio4Life. Together with this interdisciplinary team, we aim to bridge microbiology and embodiment design, and contribute to the development of a circular economy where digital technology and organic systems merge in the design of Living Circular Labels (LCLs). Throughout the project we will use an iterative approach between designing and testing LCLs that host living flavobacteria and additionally, methods for the end user to activate the bacteria’s growth at a given time.