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The future of education in sleep science and medicine in Europe : Report of an ESRS workshop
(2025-10) McNicholas, W. T.; Arnardottir, E. S.; Vyazovskiy, V. V.; Pevernagie, D.; Penzel, T.; Peigneux, P.; Grote, L.; Van Der Werf, Y.; de Jongh, F.; Schmidt, M.; Adamantidis, M.; Sekaran, S.; Biller, A. M.; Knobl, B.; Luppi, P. H.; Bassetti, C. L.; Paunio, T.; Department of Engineering
The European Sleep Research Society (ESRS) is the leading voice for sleep medicine and research in Europe and includes in its mission the objective to promote and develop sleep education. As part of this objective, the Society established a Multidisciplinary Task Force on sleep education, which proposed a workshop on this topic to define the role of ESRS in the future of sleep education in Europe. This includes defining the domains of operation by the ESRS, its most important links and networks, and to recognise areas for development. A concrete goal was to make an action plan with suggestions for eventual Task Forces. Recommendations from the workshop include active collaboration with other Societies and University degree courses in sleep with the objective to position the ESRS as the central focus for sleep education in Europe. Predefined learning objectives and Entrustable Professional Activities were identified as key aspects of future direction. The ESRS Textbook in Sleep Medicine provides a valuable educational resource, and the next edition should expand on the traditional format to include digital material and an integrative platform. The workshop also identified the need to develop a more compact publication to inform non-sleep specialists, university students, and interested members of the public with basic information on sleep and sleep disorders.
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Terahertz imaging for breast cancer detection
(2021-10-01) Wang, Lulu; Department of Engineering
Terahertz (THz) imaging has the potential to detect breast tumors during breast-conserving surgery accurately. Over the past decade, many research groups have extensively studied THz imaging and spectroscopy techniques for identifying breast tumors. This manuscript presents the recent development of THz imaging techniques for breast cancer detection. The dielectric properties of breast tissues in the THz range, THz imaging and spectroscopy systems, THz radiation sources, and THz breast imaging studies are discussed. In addition, numerous chemometrics methods applied to improve THz image resolution and data collection processing are summarized. Finally, challenges and future research directions of THz breast imaging are presented.
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Aligning Perceptions and Action : The Role of Organisational Coherence in Driving Employee Engagement for SDG 13 Implementation
(2026-03-05) Rosa, Angelo; Romeo, Emilia; Durst, Susanne; Department of Business and Economics
Climate change poses urgent challenges for human well-being and economic systems. As key social actors, organisations play a central role in integrating climate policies into daily operations. This study explores how employees' perceptions of organisational coherence, defined as the alignment between climate discourse and actual practices, influence sustainable behaviours and engagement with climate initiatives. Using a mixed-methods approach, the study combines a cross-sectional survey (n = 171) with qualitative analysis of open-ended responses. Results show that perceived coherence strongly predicts both sustainable behaviours and climate engagement (R2 = 0.53 and 0.66). Qualitative findings deepen this insight, highlighting ethical motivations, psychological mechanisms and structural barriers. The study advances the literature by identifying value alignment and internal trust as key enablers of effective climate action. Practically, it underscores the importance of transparent communication, participatory processes and leadership coherence in mobilising employees and aligning internal strategies with Sustainable Development Goal 13.
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Sleep Medicine—What's in a Name?
(2025-10) Pevernagie, Dirk A.; Arnardottir, Erna Sif; Bruni, Oliviero; Hartley, Sarah; Lammers, Gert Jan; Paunio, Tiina; Riemann, Dieter; Riha, Renata L.; Department of Engineering
Sleep medicine has matured into a recognised medical discipline, characterised by defined diagnostic concepts, evidence-based treatments, and significant progress in understanding sleep physiology and disorders. Sleep and its disturbances impact virtually every aspect of health and well-being. The major categories of sleep disorders include insomnia, neurological and psychiatric sleep disorders, sleep-disordered breathing, and paediatric sleep disorders. Breakthroughs in biomedical research have deepened clinical expertise across each of these domains. Although sleep medicine has historically developed from various specialties, the current approach emphasises interdisciplinary collaboration. Today, diagnostic and therapeutic pathways are well established, supported by professional standards outlined in nosological classifications, clinical guidelines, and structured frameworks of competencies and skills. Despite the universal importance of sleep and the high prevalence of sleep disorders, the field continues to face systemic challenges—most notably limited access to care, inadequate funding for clinical services, and insufficient investment in research. The central challenge is to balance the integration of new opportunities with the resolution of persistent uncertainties. However, advances in technology and the emergence of precision medicine offer promising prospects for progress. Sleep medicine stands at a crossroads. Its future will depend on rearticulating its mission and vision, addressing structural shortcomings, embracing innovation, and affirming its essential role in promoting public health.
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Self-supervised learning and transformer-based technologies in breast cancer imaging
(2025) Wang, Lulu; Department of Engineering
Breast cancer is the most common malignancy among women worldwide, and imaging remains critical for early detection, diagnosis, and treatment planning. Recent advances in artificial intelligence (AI), particularly self-supervised learning (SSL) and transformer-based architectures, have opened new opportunities for breast image analysis. SSL offers a label-efficient strategy that reduces reliance on large annotated datasets, with evidence suggesting that it can achieve strong performance. Transformer-based architectures, such as Vision Transformers, capture long-range dependencies and global contextual information, complementing the local feature sensitivity of convolutional neural networks. This study provides a comprehensive overview of recent developments in SSL and transformer models for breast lesion segmentation, detection, and classification, highlighting representative studies in each domain. It also discusses the advantages and current limitations of these approaches and outlines future research priorities, emphasizing that successful clinical translation depends on access to multi-institutional datasets to ensure generalizability, rigorous external validation to confirm real-world performance, and interpretable model designs to foster clinician trust and enable safe, effective deployment in clinical practice.