This paper describes some explorations on the concept of disassemblability as an important circularity indicator for products because of its severe impact on reuse value. Although usefulness of the concept for determining disassembly strategies and for improving circular product design clearly shows in earlier studies, the link with Industry 4.0 (I4.0)-related process innovation is still underexposed. For further technical development of the field of remanufacturing, research is needed on tools & training for operators, diagnostics, disassembly/repair instructions and forms of operator support. This includes the use of IoT and cobots in remanufacturing lines for automatic disassembly, sorting and recognition methods; providing guidance for operators and reduction of change-over times. A prototype for a disassembly work cell for a mobile phone has been developed together with researchers and students. This includes the removal of screws by means of a cobot using both vision & the available info in the product’s Bill-Of-Materials, the removal of covers, opening of snap fits and replacement of modules. This prototyping demonstrates that it is relatively easy to automate disassembly operations for an undamaged product, that has been designed with repairability in mind and for which product data and models are available. Process innovations like robotisation influence the disassemblability in a positive way, but current indicators like a Disassembly Index (DI) can’t reflect this properly. This study therefore concludes with suggestions for an evaluation of disassemblability by looking at the interaction between product, process and resources in a coherent way.
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Background: Although physical activity (PA) has positive effects on health and well-being, physical inactivity is a worldwide problem. Mobile health interventions have been shown to be effective in promoting PA. Personalizing persuasive strategies improves intervention success and can be conducted using machine learning (ML). For PA, several studies have addressed personalized persuasive strategies without ML, whereas others have included personalization using ML without focusing on persuasive strategies. An overview of studies discussing ML to personalize persuasive strategies in PA-promoting interventions and corresponding categorizations could be helpful for such interventions to be designed in the future but is still missing. Objective: First, we aimed to provide an overview of implemented ML techniques to personalize persuasive strategies in mobile health interventions promoting PA. Moreover, we aimed to present a categorization overview as a starting point for applying ML techniques in this field. Methods: A scoping review was conducted based on the framework by Arksey and O’Malley and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) criteria. Scopus, Web of Science, and PubMed were searched for studies that included ML to personalize persuasive strategies in interventions promoting PA. Papers were screened using the ASReview software. From the included papers, categorized by the research project they belonged to, we extracted data regarding general study information, target group, PA intervention, implemented technology, and study details. On the basis of the analysis of these data, a categorization overview was given. Results: In total, 40 papers belonging to 27 different projects were included. These papers could be categorized in 4 groups based on their dimension of personalization. Then, for each dimension, 1 or 2 persuasive strategy categories were found together with a type of ML. The overview resulted in a categorization consisting of 3 levels: dimension of personalization, persuasive strategy, and type of ML. When personalizing the timing of the messages, most projects implemented reinforcement learning to personalize the timing of reminders and supervised learning (SL) to personalize the timing of feedback, monitoring, and goal-setting messages. Regarding the content of the messages, most projects implemented SL to personalize PA suggestions and feedback or educational messages. For personalizing PA suggestions, SL can be implemented either alone or combined with a recommender system. Finally, reinforcement learning was mostly used to personalize the type of feedback messages. Conclusions: The overview of all implemented persuasive strategies and their corresponding ML methods is insightful for this interdisciplinary field. Moreover, it led to a categorization overview that provides insights into the design and development of personalized persuasive strategies to promote PA. In future papers, the categorization overview might be expanded with additional layers to specify ML methods or additional dimensions of personalization and persuasive strategies.
Objectives: To investigate immediate changes in walking performance associated with three implicit motor learning strategies and to explore patient experiences of each strategy. Design: Participants were randomly allocated to one of three implicit motor learning strategies. Within-group comparisons of spatiotemporal parameters at baseline and post strategy were performed. Setting: Laboratory setting. Subjects: A total of 56 community-dwelling post-stroke individuals. Interventions: Implicit learning strategies were analogy instructions, environmental constraints and action observation. Different analogy instructions and environmental constraints were used to facilitate specific gait parameters. Within action observation, only videotaped gait was shown. Main measures: Spatiotemporal measures (speed, step length, step width, step height) were recorded using Vicon 3D motion analysis. Patient experiences were assessed by questionnaire. Results: At a group level, three of the four analogy instructions (n=19) led to small but significant changes in speed (d=0.088m/s), step height (affected side d=0.006m) and step width (d=–0.019m), and one environmental constraint (n=17) led to significant changes in step width (d=–0.040m). At an individual level, results showed wide variation in the magnitude of changes. Within action observation (n=20), no significant changes were found. Overall, participants found it easy to use the different strategies and experienced some changes in their walking performance. Conclusion: Analogy instructions and environmental constraints can lead to specific, immediate changes in the walking performance and were in general experienced as feasible by the participants. However, the response of an individual patient may vary quite considerably.
Circular Economy is a novel disruptive paradigm redefining sustainability in the hospitality industry and addressing the environmental challenges set by this fast-growing impactful industry. To address these challenges, the creation of further knowledge on circular economy and its applications in the hospitality sector is fundamental, together with providing hoteliers and restaurateurs with proper skills and knowhow to tackle such challenges. Drawing on a on going pilot project on Circular Economy in Hotels in Amsterdam, the Friesland hospitality sector and the Professorship of Sustainability in Hospitality and Tourism at NHL Stenden University of Applied Sciences have set out to develop an innovative learning experimental environment in which Friesland hoteliers and restaurateurs can develop further knowledge and identify - together with students, researchers, and experts – possible key actions and strategies to implement regenerative circular processes of material up-cycling. To which extent this learning community of the Northern Netherlands contributes to develop wider knowledge on circular economy in hospitality and to identify, implement, and test innovative regenerative circular actions will be evaluated.
This project addresses the critical issue of staff shortages and training inefficiencies in the hospitality industry, particularly focusing on the hotel sector. It connects with the urgent need for innovative, and effective training solutions to equip (inexperienced) staff with hospitality skills, thereby improving service quality and sustainable career prospects in the hotel industry. The project develops and tests immersive technologies (augmented and virtual reality, AR/VR) tailored to meet specific training needs of hotels. Traditional training methods such as personal trainings, seminars, and written manuals are proving inadequate in terms of learning effectiveness and job readiness, leading to high working pressure and poor staff well-being. This project aims to break this cycle by co-creating immersive training methods that promise to be more engaging and effective. Hotelschool The Hague has initiated steps in this direction by exploring AR and VR technologies for hotel staff training. This project builds on these efforts, aiming to develop accessible, immersive training tools specifically designed for the hotel sector. Specifically, this project aims to explore the effectiveness of these immersive trainings, an aspect largely overlooked in the rapid development of immersive technology solutions. The central research question is: How do immersive AR and VR training methods impact job readiness and learning effectiveness in the hotel sector? The one-year KIEM project period involves co-creating, implementing, and evaluating immersive training in collaboration with Hotelschool The Hague and Hyatt Andaz Amsterdam Prinsengracht Hotel in real-life settings. The partnership with Warp Industries, a leader in immersive technology, is crucial for the project’s success. Our findings will be co-created and multiplied through relevant sector associations such as House of Hospitality. This project aligns with the MV’s Impact Level 1: Transitions by promoting innovative training strategies that can lead to a fundamental shift in the hospitality industry, thereby enhancing social earning capacities.
Om de ambities te behalen zoals geformuleerd in de Sustainable Development Goals, is transdisciplinaire samenwerking nodig tussen overheden, bedrijfsleven, burgers en wetenschap. Dit vraagt om multi-stakeholderbenaderingen waarin leren van en met elkaar centraal staat. Dit onderzoeksvoorstel is een vertaalslag van bovenstaande ambitie zoals geformuleerd in samenwerking met hoger onderwijs partners in Bandung, Indonesië. Het Living Lab Upper Citarum biedt een context om onderzoek te doen binnen een bestaand Living Lab gekenmerkt door de multi-stakeholder setting en de complexiteit van duurzaam beheer van natuurlijke hulpbronnen. Het onderzoek beoogt inzicht te verkrijgen in essentiële ‘21st century skills’ voor deelnemers met faciliterende rollen in een Living Lab. De onderzoeksstrategie wil een bijdrage leveren aan de duurzaamheidsagenda van Living Labs met het ontwerpen, het ervaren, reflecteren en documenteren van praktische interventies in de lokale context. De keuze voor werken op locatie is een eerste benadering in het creëren van een realistische leerervaring voor Living Lab facilitators met diverse achtergronden en valt daarmee te beschrijven als ‘learning by doing’. De algemene onderzoekvraag is als volgt geformuleerd: Which contemporary skills and capabilities are present and which need to be developed to establish a widely shared mind set for trans- or interdisciplinary strategies so that communities and institutions in a Living Lab configuration increase their performance? De onderzoeksstrategie krijgt vorm in een ‘21st century skills exploration’ die een experimentele leerruimte biedt aan medewerkers van diverse instituten en (overheids-)organisaties die actief zijn in het Living Lab om kennis te maken met de creatieve methodes voor publieke participatie. Dit vindt vooral plaats in interactie met lokale gemeenschappen met nadruk op creatieve methodes zoals een poetry route, participatory mapping en film. Na presentatie op locatie worden de resultaten gepresenteerd tijdens een mini-symposium in Bandung.
