Smart glasses were perceived to be potentially revolutionary for healthcare, however, there is only limited research on the acceptance and social implications of smart glasses in healthcare. This study aims to get a better insight into the theoretical foundations and the purpose was to identify themes regarding adoption, mediation, and the use of smart glasses from the perspective of healthcare professionals. A qualitative research design with focus groups was used to collect data. Three focus groups with 22 participants were conducted. Data were analyzed using content analysis. Our analysis revealed six overarching themes related to the anticipated adoption of smart glasses: knowledge, innovativeness, use cases, ethical issues, persuasion, and attitude. Nine themes were found related to anticipated mediation and use of smart glasses: attention, emotions, social influences, design, context, camera use, risks, comparisons to known products, and expected reaction and might influence the acceptance of smart glasses.
MULTIFILE
Smart glasses were perceived to be potentially revolutionary for healthcare, however, there is only limited research on the acceptance and social implications of smart glasses in healthcare. This study aims to get a better insight into the theoretical foundations and the purpose was to identify themes regarding adoption, mediation, and the use of smart glasses from the perspective of healthcare professionals. A qualitative research design with focus groups was used to collect data. Three focus groups with 22 participants were conducted. Data were analyzed using content analysis. Our analysis revealed six overarching themes related to the anticipated adoption of smart glasses: knowledge, innovativeness, use cases, ethical issues, persuasion, and attitude. Nine themes were found related to anticipated mediation and use of smart glasses: attention, emotions, social influences, design, context, camera use, risks, comparisons to known products, and expected reaction and might influence the acceptance of smart glasses.
MULTIFILE
In this proposal, a consortium of knowledge institutes (wo, hbo) and industry aims to carry out the chemical re/upcycling of polyamides and polyurethanes by means of an ammonolysis, a depolymerisation reaction using ammonia (NH3). The products obtained are then purified from impurities and by-products, and in the case of polyurethanes, the amines obtained are reused for resynthesis of the polymer. In the depolymerisation of polyamides, the purified amides are converted to the corresponding amines by (in situ) hydrogenation or a Hofmann rearrangement, thereby forming new sources of amine. Alternatively, the amides are hydrolysed toward the corresponding carboxylic acids and reused in the repolymerisation towards polyamides. The above cycles are particularly suitable for end-of-life plastic streams from sorting installations that are not suitable for mechanical/chemical recycling. Any loss of material is compensated for by synthesis of amines from (mixtures of) end-of-life plastics and biomass (organic waste streams) and from end-of-life polyesters (ammonolysis). The ammonia required for depolymerisation can be synthesised from green hydrogen (Haber-Bosch process).By closing carbon cycles (high carbon efficiency) and supplementing the amines needed for the chain from biomass and end-of-life plastics, a significant CO2 saving is achieved as well as reduction in material input and waste. The research will focus on a number of specific industrially relevant cases/chains and will result in economically, ecologically (including safety) and socially acceptable routes for recycling polyamides and polyurethanes. Commercialisation of the results obtained are foreseen by the companies involved (a.o. Teijin and Covestro). Furthermore, as our project will result in a wide variety of new and drop-in (di)amines from sustainable sources, it will increase the attractiveness to use these sustainable monomers for currently prepared and new polyamides and polyurethanes. Also other market applications (pharma, fine chemicals, coatings, electronics, etc.) are foreseen for the sustainable amines synthesized within our proposition.
The increasing amount of electronic waste (e-waste) urgently requires the use of innovative solutions within the circular economy models in this industry. Sorting of e-waste in a proper manner are essential for the recovery of valuable materials and minimizing environmental problems. The conventional e-waste sorting models are time-consuming processes, which involve laborious manual classification of complex and diverse electronic components. Moreover, the sector is lacking in skilled labor, thus making automation in sorting procedures is an urgent necessity. The project “AdapSort: Adaptive AI for Sorting E-Waste” aims to develop an adaptable AI-based system for optimal and efficient e-waste sorting. The project combines deep learning object detection algorithms with open-world vision-language models to enable adaptive AI models that incorporate operator feedback as part of a continuous learning process. The project initiates with problem analysis, including use case definition, requirement specification, and collection of labeled image data. AI models will be trained and deployed on edge devices for real-time sorting and scalability. Then, the feasibility of developing adaptive AI models that capture the state-of-the-art open-world vision-language models will be investigated. The human-in-the-loop learning is an important feature of this phase, wherein the user is enabled to provide ongoing feedback about how to refine the model further. An interface will be constructed to enable human intervention to facilitate real-time improvement of classification accuracy and sorting of different items. Finally, the project will deliver a proof of concept for the AI-based sorter, validated through selected use cases in collaboration with industrial partners. By integrating AI with human feedback, this project aims to facilitate e-waste management and serve as a foundation for larger projects.
De installatiebranche staat voor een aantal grote uitdagingen. Het personeel vergrijst en minder jongeren kiezen voor een baan in de installatiebranche. Tegelijkertijd vindt er een inhoudelijke transitie plaats, mede gedreven door technologische innovaties van prestatiegericht installeren naar mensgericht installeren. Het betekent dat installaties in gebouwen niet alleen energiezuinig behoren te zijn maar ook behoren zij bij te dragen aan het welzijn en de gezondheid van de gebruikers. Ook het huidige personeel zal op een andere manier moeten gaan werken dan gewend te zijn. Grotere bedrijven zetten meer en meer opkomende technologieën in, maar hoe snel kan het MKB hierin meebewegen? En zullen deze ontwikkelingen meer jongeren naar de branche trekken? Doel Het OMTECH_IDGB project onderzoekt in hoeverre het MKB in de installatiebranche gereed is om te kunnen werken met opkomende technologieën, zoals bijvoorbeeld AI en AR. Vragen zijn: Hoe opereert het MKB in de installatiebranche bij het gebruik van AI en AR? Wat zijn aantrekkelijke use cases voor het gebruik van opkomende technologieën? Hoe krijgen wij onze mensen, maar ook jongeren gereed om te werken in een digitale werkomgeving? Resultaten Overzicht van use cases, animatie over werken met AI en AR in de installatiebranche en een RAAKpro vooraanmelding over inzet van AI en AR in de installatiebranche. Looptijd 01 november 2020 - 31 mei 2021 Aanpak Literatuurstudie/deskresearch naar opkomende technologieën, AI en AR, zowel binnen als buiten de installatiebranche. Inventariseren van het gebruik en inzet van opkomende technologieën in de installatiebranche. Ophalen van ‘use cases’ in de praktijk d.m.v. interviews. Bijeenkomsten met de praktijk. In een samenstelling van professionals, ontwikkelaars, branche organisaties, MKB partijen en groot bedrijven op dit thema. Met als doel om het verder uitwerken en scherp stellen van de vraag te bewerkstelligen. In dit project wordt tevens samengewerkt met het Centre of Expertise Smart Sustainable Cities.
