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An efficient iterative approach to explainable feature learning
Dino Vlahek, Domen Mongus, 2023, original scientific article

Keywords: data classification, explainable artificial intelligence, feature learning, knowledge discovery
Published in DKUM: 13.06.2024; Views: 48; Downloads: 4
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Research trends in motivation and weight loss : a bibliometric-based review
Uroš Železnik, Peter Kokol, Jasmina Starc, Danica Železnik, Jernej Završnik, Helena Blažun Vošner, 2023, review article

Abstract: Obesity is a complex disease that, like COVID-19, has reached pandemic proportions. Consequently, it has become a rapidly growing scientific field, represented by an extensive body of research publications. Therefore, the aim of this study was to present the research trends in the scientific literature on motivation and weight loss. Because traditional knowledge synthesis approaches are not appropriate for analyzing large corpora of research evidence, we utilized a novel knowledge synthesis approach called synthetic knowledge synthesis (SKS) to generate new holistic insights into obesity research focusing on motivation. SKS is a triangulation of bibliometric analysis, bibliometric mapping, and content analysis. Using it, we analyzed the corpus of publications retrieved from the Scopus database, using the search string TITLE-ABS-KEY((obesity or overweight) and “weight loss” and motiv*) in titles, keywords, and abstracts, without any additional inclusion or exclusion criteria. The search resulted in a corpus of 2301 publications. The United States of America, the United Kingdom, and Australia were the most productive countries. Four themes emerged, namely, weight loss and weight-loss maintenance through motivational interventions, lifestyle changes supported by smart ICT, maintaining sustainable weight with a healthier lifestyle, and weight management on the level of primary healthcare and bariatric surgery. Further, we established that the volume of research literature is growing, as is the scope of the research. However, we observed a regional concentration of research and its funding in developed countries and almost nonexistent research cooperation between developed and less-developed countries.
Keywords: obesity, weight loss, motivation, synthetic knowledge synthesis, bibliometrics, content analysis
Published in DKUM: 05.06.2024; Views: 66; Downloads: 4
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Public opinions and knowledge about microorganisms
Andreja Špernjak, Anja Puhmeister Jug, Andrej Šorgo, 2023, original scientific article

Keywords: opinions about microorganisms, knowledge about microorganisms
Published in DKUM: 23.05.2024; Views: 110; Downloads: 3
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In the search for the ideal mentor by applying the "Mentoring for effective teaching practice instrument"
Mateja Ploj Virtič, Andre Du Plessis, Andrej Šorgo, 2023, original scientific article

Abstract: The study aims to explore and analyse the perceived experiences of Slovenian pre-service teachers with their mentors during their teaching practicum, by using the "Mentoring for effective teaching practice instrument (METPI)". Conclusions were based on 105 responses using paper and pencil, collected from Slovenian preservice teachers in their 4th year of study. The initial set of 62 items was shortened to 36 items for inclusion in the models. From two concurrent SEM models, it was concluded that students found their experiences related to Hudson's five-factor model, consisting of personal attributes of their mentor, modelling, pedagogical knowledge, system requirements and feedback, to be satisfactory, while their experience pertaining to Information and Communication Technology as a new, additional factor with reference to their mentor, was unsatisfactory. Several recommendations are made related to the dimensions, especially pertaining to Information and Communication Technology (ICT) tools.
Keywords: ICT, mentoring pre-service teachers, METPI, teacher professional knowledge, teaching practicum
Published in DKUM: 15.04.2024; Views: 146; Downloads: 4
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Understanding, trusting, and applying scientific insights to improve your health : a latent profile analysis approach
Nejc Plohl, Bojan Musil, 2022, original scientific article

Abstract: Various leading causes of death can be prevented or delayed through informed decision-making and lifestyle changes. Previous work has, to some extent, linked such health-promoting behavior (HPB) with variables capturing individuals’ understanding of science, trust in science, and capacity to apply evidence-based information in the health context. However, empirical research on the relationship between scientific knowledge, trust in science, health literacy, and HPB is scarce. Additionally, no study has investigated whether these characteristics interact to form homogeneous, high-risk subgroups of the population. The present online study (N = 705) revealed that trust in science and health literacy were positively related to a wide array of HPBs (e. g., healthy nutrition, physical activity, stress management), while scientific knowledge was only positively associated with COVID-19 vaccination intention. Furthermore, the results of latent profile analyses yielded four subgroups (i. e., low, moderate, and high levels of all three variables and a varied profile exhibiting very low trust in science, low health literacy, and moderate scientific knowledge). The identified subgroups differ significantly in HPB and variables determining profile membership (e. g., political conservatism). Hence, the present study offers some guidance on which groups may be targeted with public health campaigns and how they may be designed.
Keywords: scientific knowledge, trust in science, health literacy, health behaviour, COVID-19
Published in DKUM: 12.04.2024; Views: 193; Downloads: 9
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43rd International Conference on Organizational Science Development: Green and Digital Transition – Challenge or Opportunity : Conference Proceedings
2024, proceedings of peer-reviewed scientific conference contributions (international and foreign conferences)

Abstract: The 43rd International Scientific Conference on the Development of Organisational Science was held in Portorož from the 20th to the 22nd of March 2024. The main aim of the conference was to promote the importance of knowledge in the context of green and digital transtition of organizations, through academic and scientific debates and professional developments from the economic and non-economic worlds. The conference, which was held under the umbrella title " Green and Digital Transition - Challenge or Opportunity", aimed to highlight the importance of the green and digital transition for the successful achievement of organisational objectives and the appropriate overcoming of societal challenges. The objectives pursued in this context are related to the natural dimensions on the one hand, and to stimulating the economy through green technology, sustainable development and pollution reduction on the other. The green and digital transition is also an opportunity for organisations to make changes towards overarching environmental challenges. It is undoubtedly more important than ever to take decisions that generate synergies between innovation, different sciences, and applied approaches, and to promote changes in business models that will lead to a sustainable and socially responsible development environment. In Portorož, we have reconnected opinions, views, and scientific and professional debates that will stimulate all of us to act in the future, both in organizations and in society at large.
Keywords: organization, knowledge, interdisciplinarity, science, economy
Published in DKUM: 22.03.2024; Views: 315; Downloads: 61
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CNN-Based Vessel Meeting Knowledge Discovery From AIS Vessel Trajectories
Peng Chen, Shuang Liu, Niko Lukač, 2023, original scientific article

Abstract: How to extract a collection of trajectories for different vessels from the raw AIS data to discover vessel meeting knowledge is a heavily studied focus. Here, the AIS database is created based on the raw AIS data after parsing, noise reduction and dynamic Ramer-Douglas-Peucker compression. Potential encountering trajectory pairs will be recorded based on the candidate meeting vessel searching algorithm. To ensure consistent features extracted from the trajectories in the same time period, time alignment is also adopted. With statistical analysis of vessel trajectories, sailing segment labels will be added to the input feature. All motion features and sailing segment labels are combined as input to one trajectory similarity matching method based on convolutional neural network to recognize crossing, overtaking or head-on situations for each potential encountering vessel pair, which may lead to collision if false actions are adopted. Experiments on AIS data show that our method is effective in classifying vessel encounter situations to provide decision support for collision avoidance.
Keywords: AIS Data, CNN, Dynamic Rammer-Douglas-Peucker, knowledge discovery, maneuvering pattern, traffic pattern, trajectory
Published in DKUM: 19.03.2024; Views: 466; Downloads: 416
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Assessment of environmental sustainability integration into higher education for future experts and leaders
Matevž Obrecht, Zane Feodorova, Maja Rosi, 2022, original scientific article

Abstract: The environmental and climate crisis is gaining priority among critical human concerns; therefore, environmental knowledge and sustainable solutions play a significant role and gaining global popularity as an academic discipline. Additionally, United Nations Sustainable Development Goal no. 4 acknowledges education's role in promoting sustainable development, sustainable lifestyles, human rights, social responsibility, circular economy, and greening our economy and society. In this paper new conceptual framework that focuses on systematically analyzing environmental sustainability integration in study programs and subjects is developed. The main aim is to reflect the conceptual basis of education related to environmental sustainability topics such as sustainable development, social responsibility, circular economy, ecology, environmental protection, and greening to boost future environmental management practices in industry and services. Defining the level of integration of environmental and sustainability-related topics is divided by research field classification and study cycles (BSc, MSc, PhD). The specific added value of the developed conceptual framework is reflected by defining cross-sections and inter-and trans-disciplinary of sustainability topics integrated within specific programs and subjects. The conceptual framework was tested by assessing 1051 programs in progress in Slovenia. It was revealed that the highest share of study programs has an intermediate level of environmental sustainability with 2–3 subjects (392 study programs). These are represented mainly by integrating environmental protection, ecology, and “greening” but less with circular economy and social responsibility. Significant differences among specific study programs and study fields reflect that young intellectuals will not be equally educated and qualified for future managerial challenges. Master students gain the best insight into core sustainability and environmental topics needed for future environmental managers.
Keywords: higher education institution, environmental sustainability, educating environmental expert, managing knowledge supply, environmental competence
Published in DKUM: 19.02.2024; Views: 208; Downloads: 13
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Knowledge Graph Completion with Triple Structure and Text Representation
Shuang Liu, Yufeng Qin, Man Xu, Simon Kolmanič, 2023, original scientific article

Abstract: Knowledge Graphs (KGs) describe objective facts in the form of RDF triples, each triple contains sufficient semantic information and triple structure information. Knowledge Graph Completion (KGC) is to acquire new knowledge by predicting hidden relationships between entities and adding the new knowledge to the KG. At present, the mainstream KGC approaches only applied the triple structure information or only utilized the semantic information of the text. This paper proposes an approach (TSTR) using BERT and deep neural networks to fully extract the semantic information of knowledge, and designs an aggregated re-ranking scheme that incorporates existing graph embedding approach to learn the structural information of triples. In experiments, the approach achieves state-of-the-art performance on three benchmark datasets, and outperforms recent KGC approaches on sparsely connected datasets.
Keywords: knowledge graph completion, BERT, deep convolutional architecture, re-ranking
Published in DKUM: 19.02.2024; Views: 163; Downloads: 17
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How much sustainable knowledge will soon-to-be experts in Slovenia have? : findings of higher education study programs’ analysis
Matevž Obrecht, Lazar Pavić, 2023, published scientific conference contribution

Abstract: Environmental topics are gaining more and more important both in the European Union and in Slovenia in the last decade. Climate changes (floods, droughts, and heat waves), pollution (soil, water, and air), management of invasive species, noise pollution, food self-sufficiency, sustainable development in the field of tourism, etc. are particularly present both in Slovenian Eastern and Western cohesion region. However, the level of knowledge about environmental challenges and competences of current experts and leaders to effectively manage and deal with them is perceived to be too low. The paper examines the inclusion of sustainable competences, knowledge, and skills in higher education programs in Slovenia to predict the level of sustainable knowledge. Slovenian soon-to-be professionals will be empowered by the Slovenian higher education system (Daneshjoo et al., 2020). Analysis included the content of 956 higher education study programs on the individual course level. In the next phase, a comprehensive comparative analysis of the situation in the areas of higher education and environmental education was performed. The factors based on which comparison was made are field of study, type of institution, level of study and number of subjects in the program that include some sustainable content. Non-parametric tests were used to determine statistically significant differences are the Mann-Whitney U test and the Kruskal-Wallis H test.
Keywords: sustainable development, sustainable knowledge, higher education, sustainable competences, higher education study programs
Published in DKUM: 12.01.2024; Views: 258; Downloads: 12
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