Using a Dietetic Care Process (DCP) can lead to improved application of evidence-based guidelines and critical thinking in dietetics. One aim of the project Improvement of Education and Competences in Dietetics (IMPECD) is to develop a unified DCP for international educational purposes. Therefore, a comparison of European DCPs was needed.A concise literature search and semi-structured interviews with experts representing the full EFAD (European Federation of the Associations of Dietitians) member states were conducted from June to October 2017.16 out of 23 EFAD member states responded (70%) from which 13 indicated to use a DCP. Eight different DCPs were found, with four to six core steps and three graphical representations. In one country the use of a dietetic process is indicated by law. The DCPs have more similarities than differences as they follow the same principles. Differences in language or form may not limit the improvement in collaboration and international exchange in dietetic practice. These results provide a good basis for the development of a unified DCP for educational purposes.
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Introduction: Different models of frameworks for dietetic care are used in Europe. There is a substantial need for a consistent framework to compare research results and to cooperate on an international level. Therefore, one of the goals of the EU-funded project IMPECD was the development of a unified framework Dietetic Care Process (DCP) in order to foster a shared understanding of process-driven dietetic counselling. Materials and Methods:: Based on a literature review and in-depth analysis of different frameworks an iterative and incremental development process of finding solutions for decision-making within the consortium consisting of dietetic experts from 5 European HEI was passed. The developed DCP model was integrated in an online training course including 9 clinical cases (MOOC) to train students. The draft versions and the concluding final version DCP model were evaluated and re-evaluated by teachers and 25 students at two Intensive Study Programmes. Results:: The DCP model consists of five distinct, interrelated steps which the consortium agreed on: Dietetic Assessment, Dietetic Diagnosis, Planning Dietetic Intervention, Implementing Dietetic Intervention, Dietetic Outcome Evaluation. A standardized scheme was developed to define the process steps: dedication, central statement, aim and principles, and operationalization. Discussion:: Existing different process models were analyzed to create a new and consistent concept of a unified framework DCP. The variety within the European countries represented by the consortium proved to be both a challenge in decision-making and an opportunity to integrate multinational perspectives and intensify the scientific discourse. The development of a standardized scheme with precise definitions is a prerequisite for planning study designs in health services research. Besides, clarification is essential for establishing process-guided work in practice. The evaluated MOOC is now implemented in study programmes used by 5 European HEI in order to keep approaches and process-driven action comparable. The MOOC promotes the exchange of ideas between future professionals on an international level.
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High-tech horticulture production methods (such as vertical farming, hydroponics and other related technology possibilities), combined with evolving market side possibilities (consumer’s willingness to pay for variety, food safety and security), are opening new ways to create and deliver value. In this paper we present four emerging business models and attempt to understand the conditions under which each business model is able to create positive market value and sustained business advantage. The first of these four models is the case of a vertically integrated production to retail operation. The second model is the case of a production model with assured retail/distribution side commitment. The third model deals with a marketing/branding driven production model with differentiated market positioning. Finally, the forth is a production model with direct delivery to the end-consumer based upon the leveraging of wide spread digital technology in the consumer market. To demonstrate these four business models, we analyze practical case studies and analyze their market approach and impact. Using this analysis, we create a framework that enables entrepreneurs and businesses to adopt a business model that matches their capabilities with market opportunities.
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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.
Developing a framework that integrates Advanced Language Models into the qualitative research process.Qualitative research, vital for understanding complex phenomena, is often limited by labour-intensive data collection, transcription, and analysis processes. This hinders scalability, accessibility, and efficiency in both academic and industry contexts. As a result, insights are often delayed or incomplete, impacting decision-making, policy development, and innovation. The lack of tools to enhance accuracy and reduce human error exacerbates these challenges, particularly for projects requiring large datasets or quick iterations. Addressing these inefficiencies through AI-driven solutions like AIDA can empower researchers, enhance outcomes, and make qualitative research more inclusive, impactful, and efficient.The AIDA project enhances qualitative research by integrating AI technologies to streamline transcription, coding, and analysis processes. This innovation enables researchers to analyse larger datasets with greater efficiency and accuracy, providing faster and more comprehensive insights. By reducing manual effort and human error, AIDA empowers organisations to make informed decisions and implement evidence-based policies more effectively. Its scalability supports diverse societal and industry applications, from healthcare to market research, fostering innovation and addressing complex challenges. Ultimately, AIDA contributes to improving research quality, accessibility, and societal relevance, driving advancements across multiple sectors.
The maximum capacity of the road infrastructure is being reached due to the number of vehicles that are being introduced on Dutch roads each day. One of the plausible solutions to tackle congestion could be efficient and effective use of road infrastructure using modern technologies such as cooperative mobility. Cooperative mobility relies majorly on big data that is generated potentially by millions of vehicles that are travelling on the road. But how can this data be generated? Modern vehicles already contain a host of sensors that are required for its operation. This data is typically circulated within an automobile via the CAN bus and can in-principle be shared with the outside world considering the privacy aspects of data sharing. The main problem is, however, the difficulty in interpreting this data. This is mainly because the configuration of this data varies between manufacturers and vehicle models and have not been standardized by the manufacturers. Signals from the CAN bus could be manually reverse engineered, but this process is extremely labour-intensive and time-consuming. In this project we investigate if an intelligent tool or specific test procedures could be developed to extract CAN messages and their composition efficiently irrespective of vehicle brand and type. This would lay the foundations that are required to generate big data-sets from in-vehicle data efficiently.
