The transition to a circular economy is an essential prerequisite for tackling sustainability challenges in the construction industry. However, markets for reused products lack demand. To address this issue, we examine the conditions under which consumers prefer reused products, such as building components and furnishings, over new products. Drawing on the literature on consumer value theory, we examine how consumers’ functional and symbolic value perceptions of reused furnishings influence behavior. In Study 1 (qualitative), we find that both perceptions of functional and symbolic value positively influence the decision to reuse, while the absence of functional value is a significant barrier. Study 2 (quantitative) with a representative sample in Switzerland shows that the perception of functional value generally has a greater influence on consumer preferences than the perception of symbolic value. Finally, Study 3 (experimental) shows that emphasizing symbolic value (as opposed to functional value) increases consumers' preference for reused furnishings over new ones. We contribute to the literature by providing a nuanced understanding of the interplay between functional and symbolic value in product reuse. Companies offering reused building components and furnishings must address the functional value deficiencies of reused products while leveraging their symbolic value to increase market demand.
The current study investigated sleep characteristics and determinants of sleep quality in elite male youth ice hockey players from a Swiss major league club, with a focus on circadian chronotype, medical sleep disorders, competition-related sleep disturbances, and psychological symptoms. A cross-sectional online questionnaire was administered to U15, U17, and U20 elite players of a Swiss ice hockey club (N = 74). The survey assessed sleep duration, sleep latency, bedtime regularity, chronotype, symptoms suggestive of sleep disorders, evening behaviors, and pre-competition sleep disturbances as well as mental health using the Patient Health Questionnaire‑2 (PHQ-2) and Generalized Anxiety Disorder‑2 (GAD-2) screening tools. Descriptive statistics and Spearman rank correlations were applied. Mean self-reported sleep duration was 8.1 h per night. In total, 20
The European Universities Initiative (EUI) seeks to drive the transformation of participating institutions by promoting shared governance, challenge-based learning, socially impactful research, and stakeholder engagement. These aspects reflect broader trends in the transformation of higher education institutions, as discussed in the literature on organisational actorhood. From this perspective, we theoretically identify five key transformations implied by the EUI. Using an exploratory qualitative approach, we conducted expert interviews at four traditional research universities and four universities of applied sciences (UAS) to examine how alliance members navigate these key transformations. Our findings indicate that the EUI, through its impact agenda, is not the primary driver of organisational change in higher education institutions but rather acts as a facilitator of institutional reorganisation processes already underway—particularly in the case of UAS. Furthermore, we find that the EUI exerts its most transformative influence in two core areas: the emphasis on the European dimension of university alliances and the implementation of shared governance structures. These aspects are not captured by general transformational trends in higher education, thereby highlighting the EUI’s distinctive contribution to institutional development.
Wildfires, insect outbreaks, and storms cause large pulses of tree mortality. Climate change amplifies these forest disturbances, yet their future magnitude and extent remain uncertain. Here, we simulated future forest disturbance regimes at 100-meter resolution across Europe using a deep learning-based simulation framework. Our results show that forest disturbances will continue to increase throughout the 21st century, with disturbed areas more than doubling relative to the recent past under an unabated continuation of climate change. Wildfires are the main agent driving future disturbance change. Changing disturbances result in an increase in young forests, substantially altering Europe's forest demography. Because of their profound implications for forest carbon storage and the habitat value of forest ecosystems, disturbances should be a priority of forest policy and management.
Classifying subtypes of primary progressive aphasia (PPA) from connected speech presents significant diagnostic challenges due to overlapping linguistic markers. This study benchmarks the performance of traditional machine learning models with various feature extraction techniques, transformer-based models, and large language models (LLMs) for PPA classification. Our results indicate that while transformer-based models and LLMs exceed chance-level performance in terms of balanced accuracy, traditional classifiers combined with contextual embeddings remain highly competitive. Notably, SVM using RoBERTa's embeddings achieves the highest classification accuracy. These findings underscore the potential of machine learning in enhancing the automatic classification of PPA subtypes. ### Competing Interest Statement The authors have declared no competing interest.
