1. Time-money segment differences in ideation and collaboration readiness in sustainable tourism educationDejan Križaj, 2026, izvirni znanstveni članek Opis: This study examines whether tourism students’ self-reported time–money use patterns are related to their readiness to collaborate on idea development, and whether sustain- ability emerges spontaneously in their tourism innovation ideas. Using an anonymised dataset of open-ended questionnaire responses from Slovenian higher education tourism students (N = 597; 2019–2025), we applied deterministic rule-based coding to classify the presence of actionable ideas and sustainability framing, as well as collaboration readiness and conditions. Actionable ideas were common (53.4%), but sustainability framing was uncommon (7.5%). Most respondents were unconditionally willing to collaborate (69.3%), while 30.7% expressed conditional willingness or unwillingness. Time–money behavioural segments were significantly associated with collaboration reservations, whereas segment differences in ideation and sustainability framing were not significant. Among students expressing reservations, topic match and perceived team quality were the most frequently stated conditions. These findings indicate that sustainability-oriented tourism education should support both sustainability integration and low-risk collaboration through clear project briefs, topic-based matching, and team-process supports. The conclusions should be interpreted with reasonable caution as they are context-specific evidence based on self- reported, rule-coded responses, particularly for sustainability framing, where positive cases were rare. In this context, segmentation should be regarded as a diagnostic tool for course design rather than as a basis for labelling students.
Ključne besede: tourism education, sustainability, collaboration readiness, behavioural segmentation, time–money trade-offs, project-based learning, open-ended survey, clustering
Objavljeno v RUP: 04.05.2026; Ogledov: 178; Prenosov: 13
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2. Machine learning identifies distinct movement control impairment clusters in patients with chronic neck painŽiva Majcen Rošker, Jernej Rošker, 2026, izvirni znanstveni članek Opis: atients with chronic neck pain experience various impairments, with reduced movement control suggested as a significant contributing factor. The heterogeneity of this patient population and suboptimal rehabilitation outcomes suggests the existence of latent subgroup characteristics. The aim of this study was to identify distinct groups among patients with neck pain based on the movement control test and pain intensity and to provide information on cluster-specific impairments. 135 patients with idiopathic neck pain performed a movement control test (the Butterfly test) at three difficulty levels and were assessed for pain intensity, providing 13 different parameters (classifiers). Louvain, hierarchical and k-means clustering methods were applied and the number of clusters determined by observing the symmetry and size of silhouette scores. Further, different machine learning algorithms were applied to develop and evaluate a classification framework (based on AUC, classification accuracy, sensitivity, and specificity) and to provide information on individual cluster characteristics using the Shapley Additive Explanations. The k-means and deep learning neural network methods provided the most efficient clustering and classification performance extracting 4 meaningful clusters. Patients between groups differed in the amount of impairment, with cluster 2 and 1 representing the most severe impairments and with clusters 3 and 4 the least severe impairments. Additionally, specific motor control impairments were observed in individual clusters suggesting distinct neck movement control adaptations. Identifying subgroups of patients with neck pain and their specific characteristics based on the results of the Butterfly test may inform future development of targeted rehabilitation strategies.
Ključne besede: neck pain, kinesthesia, proprioception clustering, machine learning
Objavljeno v RUP: 16.03.2026; Ogledov: 455; Prenosov: 6
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3. Transit functions and clustering systemsManoj Changat, Ameera Vaheeda Shanavas, Peter F. Stadler, 2025, izvirni znanstveni članek Opis: Transit functions serve not only as abstractions of betweenness and convexity but are also closely connected with clustering systems. Here, we investigate the canonical transit functions of binary clustering systems inspired by pyramids, i.e., interval hypergraphs. We provide alternative characterizations of weak hierarchies, and introduce union-closed binary clustering systems as a subclass of pyramids and weakly pyramidal clustering systems as an interesting generalization.
Ključne besede: transit function, convexity, binary clustering, weak hierarchy, pyramid
Objavljeno v RUP: 03.11.2025; Ogledov: 549; Prenosov: 4
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4. Obesity measures and dietary parameters as predictors of gut microbiota phyla in healthy individualsKatja Kranjc, Ana Petelin, Jure Pražnikar, Esther Nova, Noemi Redondo, Marcos Ascensión, Zala Jenko Pražnikar, 2020, izvirni znanstveni članek Opis: : The dynamics and diversity of human gut microbiota that can remarkably influence the wellbeing and health of the host are constantly changing through the host%s lifetime in response to various factors. The aim of the present study was to determine a set of parameters that could have a major impact on classifying subjects into a single cluster regarding gut bacteria composition. Therefore, a set of demographical, environmental, and clinical data of healthy adults aged 25%50 years (117 female and 83 men) was collected. Fecal microbiota composition was characterized using Illumina MiSeq 16S rRNA gene amplicon sequencing. Hierarchical clustering was performed to analyze the microbiota data set, and a supervised machine learning model (SVM; Support Vector Machines) was applied for classification. Seventy variables from collected data were included in machine learning analysis. The agglomerative clustering algorithm suggested the presence of four distinct community types of most abundant bacterial phyla. Each cluster harbored a statistically significant different proportion of bacterial phyla. Regarding prediction, the most important features classifying subjects into clusters were measures of obesity (waist to hip ratio, BMI, and visceral fat index), total body water, blood pressure, energy intake, total fat, olive oil intake, total fiber intake, and water intake. In conclusion, the SVM model was shown as a valuable tool to classify healthy individuals based on their gut microbiota composition.
Ključne besede: gut microbiota, nutrition, obesity, lifestyle parameters, clustering
Objavljeno v RUP: 10.09.2020; Ogledov: 3217; Prenosov: 84
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