We describe here the design and implementation of the Fashion Tech Farm (FTF), which aims to drive sustainable innovation in garments and fashion. We describe our goals, design principles, and the implementation. The design principles are rooted in an understanding of the fashion system, open networks, and entrepreneurial thinking. After four years of work on the FTF, we review three projects to evaluate how far the work has achieved the main goals and how our design principles are developing.
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Loneliness among young adults is a growing concern worldwide, posing serious health risks. While the human ecological framework explains how various factors such as socio-demographic, social, and built environment characteristics can affect this feeling, still, relatively little is known about the effect of built environment characteristics on the feelings of loneliness that young people experience in their daily life activities. This research investigates the relationship between built environment characteristics and emotional state loneliness in young adults (aged 18–25) during their daily activities. Leveraging the Experience Sampling Method, we collected data from 43 participants for 393 personal experiences during daily activities across different environmental settings. The findings of a mixed-effects regression model reveal that built environment features significantly impact emotional state loneliness. Notably, activity location accessibility, social company during activities, and walking activities all contribute to reducing loneliness. These findings can inform urban planners and municipalities to implement interventions that support youngsters’ activities and positive experiences to enhance well-being and alleviate feelings of loneliness in young adults. Specific recommendations regarding the built environment are (1) to create spaces that are accessible, (2) create spaces that are especially accessible by foot, and (3) provide housing with shared facilities for young adults rather than apartments/studios.
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Masonry structures represent the highest proportion of building stock worldwide. Currently, the structural condition of such structures is predominantly manually inspected which is a laborious, costly and subjective process. With developments in computer vision, there is an opportunity to use digital images to automate the visual inspection process. The aim of this study is to examine deep learning techniques for crack detection on images from masonry walls. A dataset with photos from masonry structures is produced containing complex backgrounds and various crack types and sizes. Different deep learning networks are considered and by leveraging the effect of transfer learning crack detection on masonry surfaces is performed on patch level with 95.3% accuracy and on pixel level with 79.6% F1 score. This is the first implementation of deep learning for pixel-level crack segmentation on masonry surfaces. Codes, data and networks relevant to the herein study are available in: github.com/dimitrisdais/crack_detection_CNN_masonry.
Post-earthquake structural damage shows that wall collapse is one of the most common failure mechanisms in unreinforced masonry buildings. It is expected to be a critical issue also in Groningen, located in the northern part of the Netherlands, where human-induced seismicity has become an uprising problem in recent years. The majority of the existing buildings in that area are composed of unreinforced masonry; they were not designed to withstand earthquakes since the area has never been affected by tectonic earthquakes. They are characterised by vulnerable structural elements such as slender walls, large openings and cavity walls. Hence, the assessment of unreinforced masonry buildings in the Groningen province has become of high relevance. The abovementioned issue motivates engineering companies in the region to research seismic assessments of the existing structures. One of the biggest challenges is to be able to monitor structures during events in order to provide a quick post-earthquake assessment hence to obtain progressive damage on structures. The research published in the literature shows that crack detection can be a very powerful tool as an assessment technique. In order to ensure an adequate measurement, state-of-art technologies can be used for crack detection, such as special sensors or deep learning techniques for pixel-level crack segmentation on masonry surfaces. In this project, a new experiment will be run on an in-plane test setup to systematically propagate cracks to be able to detect cracks by new crack detection tools, namely digital crack sensor and vision-based crack detection. The validated product of the experiment will be tested on the monument of Fraeylemaborg.
This project is devised for establishing pilot case studies in the Groningen gas field area for i) developing methodologies of proper evaluation of the monitoring data, ii) for establishing standards of structural monitoring in case of induced earthquakes, and for iii) increasing awareness among professionals on “why” and “how” to do structural monitoring in historical buildings in the region. The main focus of the project is both monitoring and also interpretation of results from the monitoring activities, which are the effects of maintenance and/or structural operations as well as the added value of monitoring in protecting historical buildings.
The structure will be monitored real-time and reasons behind the damages will be found. Proposals for protecting the structure against earthquakes will be made. - Damage scenario of the building, in relation to the induced seismicity effects on structures in the region- Establishment of a real-time structural monitoring toolThe building will be instrumented with accelerometers and displacement crack sensors. Additionally to the monitoring efforts, the structure will also be modelled in FE computer simulations in an effort trying to find out possible future response of the monument to strong earthquakes. The monitoring data will be combined with FE simulations in concluding the response of the structure to recursive induced seismic events.
