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5. Vloga generativne umetne inteligence pri razvoju spretnosti samoregulacije učenja : the case of Slovenia’s DC pensionsAlja Polanec, Sonja Čotar Konrad, 2026, samostojni znanstveni sestavek ali poglavje v monografski publikaciji Opis: V prispevku naslavljamo vlogo generativne umetne inteligence (Gen-UI) pri učenju in poučevanju, s katero lahko podpremo individualizirano in diferencirano učenje in vklju čimo analizo napredka pri učenju ter prilagajamo učne strategije posamezniku. Osrednji proces učenja, ki ga z Gen-UI lahko podpremo, je samoregulacija učenja (SRU). V pri spevku obravnavamo najpogosteje navedena modela SRU, ki poudarjata medsebojno povezanost in cikličnost faz načrtovanja, izvedbe in samorefleksije učenja, pri čemer v vsaki fazi nakažemo, kako lahko vključevanje Gen-UI podpre vsako izmed njih. Nada lje izpostavljamo, kako lahko orodja Gen-UI vključujemo v različne komponente SRU: na kognitivni ravni s spodbujanjem strategij ponavljanja, elaboracije in organizacije z omogočanjem sinteze znanja, iskanja analogij ter oblikovanja konceptualnih povezav; na metakognitivni ravni podpira načrtovanje (npr. določanje SMART ciljev), spremljanje razumevanja in refleksirano vrednotenje; na motivacijsko-čustveni ravni prispeva k večji samoučinkovitosti, notranji motivaciji ter zmanjševanju testne anksioznosti. V prispev ku izpostavljamo tudi nove modele vključevanja Gen-UI v učenje in poučevanje, kot sta HHAIR (Hybrid Human-AI Regulation) in ISAR (Inversion, Substitution, Augmentation, Redefinition), ki poudarjata postopno prenosljivost samoregulacijskih spretnosti z UI na učenca ter redefinicijo učnih procesov kot najvišje ravni podpore učenju in poučevanju z vključevanjem Gen-UI. Kljub številnim prednostim rabe Gen-UI opozarjamo tudi na tve ganja, kot so čezmerno zanašanje na tehnologijo, zmanjšanje kritičnega mišljenja, meta kognitivna pasivnost, kognitivna lenoba in pristranost. Zato je učinkovitost rabe Gen-UI za spodbujanje SRU odvisna od smiselne, ozaveščene in reflektirane uporabe, ki lahko podpre poglobljeno in odgovorno samoregulacijo učenja.
Ključne besede: generativna umetna inteligenca v izobraževanju, samoregulacija učenja, kritično mišljenje, model HHRI, model ISAR
Objavljeno v RUP: 22.04.2026; Ogledov: 481; Prenosov: 13
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7. Fast prediction of protein flexibilityJure Pražnikar, 2026, izvirni znanstveni članek Opis: Motivation Advances in hardware have made molecular dynamics (MD) simulations of protein structures faster and more accessible to the scientific community. However, accurately estimating protein flexibility using MD remains computationally demanding, especially for large systems and long time scales. Several MD-based resources—including MdMD, the DynamD database, and more recently ATLAS and mdCATH—now provide MD trajectories for thousands of proteins, enabling the development of predictive models. Results Here, the Graphlet Degree Vector (GDV) is introduced as a lightweight, fast, and easy-to-implement linear model for predicting protein flexibility directly from atom coordinates. GDV is a 15-dimensional feature vector that captures local packing and the spatial connectivity of each atom with its nearby neighbors. Trained on a subset of globular-like proteins from the ATLAS database, the GDV model achieves a Spearman correlation of 0.828 compared to MD data. The model trained on ATLAS dataset was further evaluated on independent Nuclear Magnetic Resonance and cryo-electron microscopy datasets, demonstrating the robustness and generalizability of the GDV-based approach. A key advantage of the GDV model is that it requires no additional external or experimental data and can be applied in near real time (on the order of 10 seconds) even for large proteins with 20,000 atoms on a standard desktop or laptop. Overall, the results show that a lightweight, fast, and purely coordinate-based model can provide accurate and generalizable predictions of protein flexibility across diverse folds and sizes.
Ključne besede: protein flexibility, graphlets, predictive model
Objavljeno v RUP: 16.04.2026; Ogledov: 463; Prenosov: 12
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8. Random walks and the electronic structure of grapheneNino Bašić, Patrick W. Fowler, Barry T. Pickup, Primož Potočnik, 2026, izvirni znanstveni članek Opis: Results from the mathematical literature on random walks reveal a closed-form analytical expression for the ▫$\pi$▫-energy and bond number of graphene in the simplest tight-binding model and its Hartree-Fock Hubbard extension. Closed-form expressions follow for all ▫$\pi$▫ spectral moments of graphene. Bond numbers of carbon and boron nitride (BN) zigzag nanotubes are found as finite sums, with graphene and hexagonal boron nitride sheets as asymptotes.
Ključne besede: graph theory, random walks, graphene, bond number, tight-binding model, spectral moments, Hubbard model, zigzag nanotubes, hexagonal boron nitride, gamma function
Objavljeno v RUP: 10.03.2026; Ogledov: 574; Prenosov: 6
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9. 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, izvirni znanstveni članek Opis: 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).
Ključne besede: gastronomic image, novelty seeking, TPB model, visit intention, Sustainable Development Goals (SDG 11), Ho Chi Minh City
Objavljeno v RUP: 06.03.2026; Ogledov: 493; Prenosov: 20
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10. Mapping the Perceived Usefulness and Intentions of Small Farmers in the Czech Republic to Engage in Short Food Supply ChainsLibor Grega, Kristina Somerlíková, Eliska Svobodova, 2015, objavljeni znanstveni prispevek na konferenci Opis: Short food supply chains (SFSCs) represent an innovative approach to the distribution of agricultural products, characterized by minimizing the distance between the producer and the consumer. This approach is increasingly perceived as a key factor in the resilience and sustainability of small farm development. The aim of this paper is to analyze the current use of SFSCs as a tool for strengthening the economic resilience and sustainability of small agricultural farms in the Czech Republic. It focuses on mapping factors influencing the perceived usefulness and ease of engagement and determinants of farmers’ behavioral intention to engage in SFSCs. The theoretical framework of the presented paper is based on the Technology Acceptance Model, which is applied to the agricultural sector. This framework provides a suitable structure for understanding the factors influencing the behavioural intention to adopt new technologies and innovations, including perceived usefulness and perceived ease of use, which are key to understanding how farmers perceive their involvement in SFSCs and what their motivations and barriers are. The necessary data were obtained using semi-structured interviews with 156 small farmers in the Czech Republic. Categorical data were subjected to statistical hypothesis testing using contingency tables and the calculation of the χ² (chi-square) test to determine whether there was a statistically significant relationship between the variables. The use of contingency tables allowed for effective summarization and visualization of relationships between individual variables and the identification of patterns and trends in the data, which facilitated the interpretation of factors influencing the involvement of small farmers in the Czech Republic in SFSCs. The paper concludes by summarizing the importance of SFSCs as a tool for strengthening the resilience and sustainability of small agricultural farms. It also contributes to a better understanding of the factors influencing the involvement of small farmers in short food supply chains and provides recommendations for the development of agricultural policies and practices that can support the development of sustainable agricultural systems.
Ključne besede: short food supply chains, agriculture, economic resilience, Technology Acceptance Model
Objavljeno v RUP: 04.03.2026; Ogledov: 390; Prenosov: 6
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