1. Positive body image is a pathway between nature contact and life satisfaction across 58 nationsViren Swami, Martin Voracek, Stefan Stieger, Toivo Aavik, Hamed Abdollahpour Ranjbar, Sulaiman Olanrewaju Adebayo, Reza Afhami, Oli Ahmed, Annie Aimé, Marwan Akel, Mirjam Koprivnik, Vita Poštuvan, 2026, original scientific article Abstract: Time spent in nature benefits human mental and physical well-being. However, much of the variance in out- comes of nature contact remains unexplained, suggesting that new mechanistic pathways need to be considered. Here, we tested a novel conceptual model linking nature contact with life satisfaction via pathways involving positive experiences of living in and experiencing the world through the physical self. Using data from the Body Image in Nature Survey (BINS; N = 50,363), representing respondents from 58 nations and speaking 36 different languages, we find that nature contact is associated with greater self-compassion and greater perceived resto- ration in nature, which in turn are associated with more positive body image. In addition, more positive body image is associated with greater life satisfaction. These associations were robust to sensitivity tests, generalised to all gender identities and age groups, and held individually in almost all national groups and languages. Although replications are needed, we propose that the materialities of natural environments help to link bodily experiences to the production and experience of well-being, a process that is largely stable across national groups.
Keywords: body image, connectedness to nature, perceived restoration, nature contact, life satisfaction, self-compassion
Published in RUP: 10.07.2026; Views: 15; Downloads: 2
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2. Co-Creation and Destination Image : A Bibliometric and Thematic Analysis Using CiteSpaceTuyen Tran, original scientific article Abstract: Co-creation has gained increasing scholarly attention as an approach for involving tourists, residents, and stakeholders in shaping destination image. However, existing research is fragmented, lacking an integrated review of its conceptual foundations, thematic evolution, and implications. This study aims to address this gap by mapping the intellectual structure and emerging trends of co-creation and destination images. A bibliometric and thematic analysis was conducted on 72 peer-reviewed publications indexed in Scopus, using CiteSpace for co-citation and keyword co-occurrence mapping, complemented by Scopus AI for trend identification. The analysis revealed four thematic clusters: destination branding, stakeholder engagement, emotional experience, and digital interaction. Results demonstrate that co-creation operates as a multidimensional, layered process, enhancing emotional attachment, destination loyalty, and brand value, rather than a single experiential design activity. The study concludes that co-creation plays a structurally significant role in destination branding and stakeholder engagement, as demonstrated by the dominant clusters in the bibliometric and thematic analyses. By synthesising existing knowledge and identifying research gaps, this paper offers a comprehensive reference for academics and practitioners, reinforcing the relevance of co-creation in destination management.
Keywords: co-creation, destination image, bibliometric analysis, destination branding, CiteSpace
Published in RUP: 06.03.2026; Views: 399; Downloads: 25
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3. Application of the New Visit Intention Model for Gastronomy Tourism : An Empirical Study in VietnamTrong Tien Bao Bui, Minh Chanh Trinh, Thi Thuy Ngan Dang, original scientific article Abstract: The study aims to address research gaps in Vietnam’s gastronomy tourism by developing and validating a new visit intention model based on the Theory of Planned Behaviour (TPB). The model incorporates the concepts of gastronomic destination image and novelty-seeking to gain a comprehensive understanding of domestic tourists’ intention to visit Ho Chi Minh City, a leading gastronomic destination in Vietnam. Primary data were collected from 417 domestic tourists through a questionnaire survey conducted in Ho Chi Minh City from April to August 2023. Partial Least Squares Structural Equation Modelling (PLS-SEM) was employed to test the proposed model and examine the relationships among novelty seeking, gastronomic image, attitudes, subjective norms, perceived behavioural control, and visit intention. The findings indicate that novelty-seeking, gastronomic image, and the core TPB constructs play a crucial role in explaining the intention to visit a gastronomic destination. The model explains 71.2% of the variance in visit intention, with domestic tourists’ visit intention being strongly influenced by the desire for novel culinary experiences and the attractiveness of Ho Chi Minh City’s gastronomic image. This study reaffirms the applicability of the extended TPB framework in explaining visit intention toward gastronomy destinations. The significant findings provide empirical evidence from Vietnam, clarifying how novelty-seeking and gastronomic image strengthen visit intention. The study also provides practical guidance for local authorities and tourism marketers to enhance Ho Chi Minh City’s competitiveness and support sustainable tourism development goals (SDG 11).
Keywords: gastronomic image, novelty seeking, TPB model, visit intention, Sustainable Development Goals (SDG 11), Ho Chi Minh City
Published in RUP: 06.03.2026; Views: 480; Downloads: 19
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4. Image-based analysis of tourist destination perceptions : a deep learning and spatial–temporal study in SloveniaDejan Paliska, Aleksandra Brezovec, Gorazd Sedmak, 2026, original scientific article Abstract: In the context of fierce competition among tourist destinations and increasing difficulty of differentiation, developing a strong destination image is particularly important. A comprehensive understanding of how tourists perceive destinations through user-generated images can help destination management organizations (DMOs) design more effective marketing strategies. This is especially relevant for destinations with spatially and temporally dispersed tourism resources and strong seasonal dynamics. This paper analyses inbound tourist photographs by combining deep learning techniques with spatial analysis to examine the spatial and temporal distribution of photo scenes and shifts in scene preferences among tourists. The study focuses on three distinct types of destinations in Slovenia—urban (Ljubljana), nature-based/alpine (Bled), and coastal (Piran, Izola, Koper)—providing insights into how image-based spatial scene analysis can inform destination marketing strategies. The results reveal significant spatial and temporal heterogeneity of scenes across micro destinations. Nature-based destinations exhibit lower topic entropy and fewer topic changes per user, whereas urban destinations show higher variability, with users changing topics on average five times per day. Seasonal effects are moderate: nature-based destinations display lower topic entropy in winter and higher in autumn and spring, coastal destinations show less pronounced seasonal variation, and urban destinations show almost none. These findings provide valuable insights into the spatial and temporal distribution of tourist interests and offer practical guidance for DMOs in strategic marketing planning.
Keywords: tourist destination image, user-generated content, deep learning, spatial-temporal analysis, destination marketing strategy
Published in RUP: 18.02.2026; Views: 647; Downloads: 11
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5. Deep learning for brain MRI tissue and structure segmentation : a comprehensive reviewNedim Šišić, Peter Rogelj, 2025, review article Abstract: Brain MRI segmentation plays a crucial role in neuroimaging studies and clinical trials by enabling the precise localization and quantification of brain tissues and structures. The advent of deep learning has transformed the field, offering accurate and fast tools for MRI segmentation. Nevertheless, several challenges limit the widespread applicability of these methods in practice. In this systematic review, we provide a comprehensive analysis of developments in deep learning-based segmentation of brain MRI in adults, segmenting the brain into tissues, structures, and regions of interest. We explore the key model factors influencing segmentation performance, including architectural design, choice of input size and model dimensionality, and generalization strategies. Furthermore, we address validation practices, which are particularly important given the scarcity of manual annotations, and identify the limitations of current methodologies. We present an extensive compilation of existing segmentation works and highlight the emerging trends and key results. Finally, we discuss the challenges and potential future directions in the field.
Keywords: magnetic resonance imaging, brain, image segmentation, deep learning
Published in RUP: 10.10.2025; Views: 1233; Downloads: 21
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8. Kidney segmentation in renal magnetic resonance imaging - current status and prospectsFrank G. Zöllner, Marek Kociński, Laura L. Hansen, Alena-Kathrin Golla, Amira Šerifović-Trbalić, A. Lundervold, Andrzej Materka, Peter Rogelj, 2021, review article Keywords: renal MRI, image segmentation, deep learning
Published in RUP: 04.06.2021; Views: 3887; Downloads: 51
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10. Image registration in dynamic renal MRI-current status and prospectsFrank G. Zöllner, Amira Šerifović-Trbalić, Gordian Kabelitz, Marek Kociński, Andrzej Materka, Peter Rogelj, 2020, review article Keywords: kidney disease, image registration, dynamic MRI, DCE-MRI, ASL
Published in RUP: 18.03.2020; Views: 2902; Downloads: 96
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