Buildings need to be carefully operated and maintained for optimum health, comfort, energy performance, and utility costs. The increasing use of Machine Learning combined with Big Data in the building services sector has shown the potential to bring energy efficiency and cost-effectiveness. Therefore, upskilling and reskilling the current workforce is required to realize new possibilities. In addition, sharing and preserving knowledge are also required for the sustainable growth of professionals and companies. This formed the basis for the Dutch Research Council funded TransAct project. To increase access to education on the job, online learning is experiencing phenomenal growth. A study was conducted with two focus groups - professionals of a building service company and university researchers - to understand the existing challenges and the ways to improve knowledge sharing and upskilling through learning on the job. This study introduced an Enterprise Social Network platform that connects members and may facilitate knowledge sharing. As a community forum, Yammer from office 365 was used. For hosting project files, a SharePoint page was created. For online courses, the company’s online learning site was utilized. The log data from the online tools were analysed, semi-structured interviews and webinars were conducted and feedback was collected with google forms. Incentive models like social recognition and innovative project results were used to motivate the professionals for online activities. This paper distinguishes the impacts of initiatives on the behaviour of university researchers vs company employees.
Hoofdstuk 2 uit Position paper Learning Communities van Netwerk learning Communities Grote maatschappelijke uitdagingen op het gebied van vergrijzing, duurzaamheid, digitalisering, segregatie en onderwijskwaliteit vragen om nieuwe manieren van werken, leren en innoveren. In toenemende mate wordt daarom ingezet op het bundelen van kennis en expertise van zowel publieke als private organisaties, die elkaar nodig hebben om te innoveren en complexe vraagstukken aan te pakken. Het concept ‘learning communities’ wordt gezien als dé oplossing om leren, werken en innoveren anders met elkaar te verbinden: collaboratief, co-creërend en contextrijk. Vanuit het Netwerk Learning Communities is een groep onafhankelijk onderzoekers van een groot aantal Nederlandse kennisinstellingen aan de slag gegaan met een kennissynthese rondom het concept ‘Learning Community’. Het Position paper is een eerste aanzet tot kennisbundeling. Een ‘levend document’ dat in de komende tijd verder aangevuld en verrijkt kan worden door onderzoekers, praktijkprofessionals en beleidsmakers.
Manual crack inspection is labor-intensive and impractical at scale, prompting a shift toward AI-based segmentation methods. We present a novel crack segmentation model that leverages the Segment Anything Model 2 (SAM 2) through transfer learning to detect cracks on masonry surfaces. Unlike prior approaches that rely on encoders pretrained for image classification, we fine-tune SAM 2, originally trained for segmentation tasks, by freezing its Hiera encoder and FPN neck, while adapting its prompt encoder, LoRA matrices, and mask decoder for the crack segmentation task. No prompt input is used during training to avoid detection overhead. Our aim is to increase robustness to noise and enhance generalizability across different surface types. This work demonstrates the potential of foundational segmentation models in enabling more reliable and field-ready AI-based crack detection tools.
Teachers have a crucial role in bringing about the extensive social changes that are needed in the building of a sustainable future. In the EduSTA project, we focus on sustainability competences of teachers. We strengthen the European dimension of teacher education via Digital Open Badges as means of performing, acknowledging, documenting, and transferring the competencies as micro-credentials. EduSTA starts by mapping the contextual possibilities and restrictions for transformative learning on sustainability and by operationalising skills. The development of competence-based learning modules and open digital badge-driven pathways will proceed hand in hand and will be realised as learning modules in the partnering Higher Education Institutes and badge applications open for all teachers in Europe.Societal Issue: Teachers’ capabilities to act as active facilitators of change in the ecological transition and to educate citizens and workforce to meet the future challenges is key to a profound transformation in the green transition.Teachers’ sustainability competences have been researched widely, but a gap remains between research and the teachers’ practise. There is a need to operationalise sustainability competences: to describe direct links with everyday tasks, such as curriculum development, pedagogical design, and assessment. This need calls for an urgent operationalisation of educators’ sustainability competences – to support the goals with sustainability actions and to transfer this understanding to their students.Benefit to society: EduSTA builds a community, “Academy of Educators for Sustainable Future”, and creates open digital badge-driven learning pathways for teachers’ sustainability competences supported by multimodal learning modules. The aim is to achieve close cooperation with training schools to actively engage in-service teachers.Our consortium is a catalyst for leading and empowering profound change in the present and for the future to educate teachers ready to meet the challenges and act as active change agents for sustainable future. Emphasizing teachers’ essential role as a part of the green transition also adds to the attractiveness of teachers’ work.
The pressure on the European health care system is increasing considerably: more elderly people and patients with chronic diseases in need of (rehabilitation) care, a diminishing work force and health care costs continuing to rise. Several measures to counteract this are proposed, such as reduction of the length of stay in hospitals or rehabilitation centres by improving interprofessional and person-centred collaboration between health and social care professionals. Although there is a lot of attention for interprofessional education and collaborative practice (IPECP), the consortium senses a gap between competence levels of future professionals and the levels needed in rehabilitation practice. Therefore, the transfer from tertiary education to practice concerning IPECP in rehabilitation is the central theme of the project. Regional bonds between higher education institutions and rehabilitation centres will be strengthened in order to align IPECP. On the one hand we deliver a set of basic and advanced modules on functioning according to the WHO’s International Classification of Functioning, Disability and Health and a set of (assessment) tools on interprofessional skills training. Also, applications of this theory in promising approaches, both in education and in rehabilitation practice, are regionally being piloted and adapted for use in other regions. Field visits by professionals from practice to exchange experiences is included in this work package. We aim to deliver a range of learning materials, from modules on theory to guidelines on how to set up and run a student-run interprofessional learning ward in a rehabilitation centre. All tested outputs will be published on the INPRO-website and made available to be implemented in the core curricula in tertiary education and for lifelong learning in health care practice. This will ultimately contribute to improve functioning and health outcomes and quality of life of patients in rehabilitation centres and beyond.
Multiple sclerosis (MS) is a severe inflammatory condition of the central nervous system (CNS) affecting about 2.5 million people globally. It is more common in females, usually diagnosed in their 30s and 40s, and can shorten life expectancy by 5 to 10 years. While MS is rarely fatal; its effects on a person's life can be profound, which signifies comprehensive management and support. Most studies regarding MS focus on how lymphocytes and other immune cells are involved in the disease. However, little attention has been given to red blood cells (erythrocytes), which might also be important in developing MS. Artificial intelligence (AI) has shown significant potential in medical imaging for analyzing blood cells, enabling accurate and efficient diagnosis of various conditions through automated image analysis. The project aims to implement an AI pipeline based on Deep Learning (DL) algorithms (e.g., Transfer Learning approach) to classify MS and Healthy Blood cells.
