1. Protocol for the fabrication of self-standing (nano)cellulose-based 3D scaffolds for tissue engineeringTamilselvan Mohan, Matej Bračič, Doris Bračič, Florian Lackner, Chandran Nagaraj, Andreja Dobaj-Štiglic, Rupert Kargl, Karin Stana-Kleinschek, 2025, original scientific article Abstract: Three-dimensional (3D) and porous scaffolds made from nanocellulosic materials hold significant potential in tissue engineering (TE). Here, we present a protocol for fabricating self-standing (nano)cellulose-based 3D scaffolds designed for in vitro testing of cells from skin and cartilage tissues. We describe steps for preparation of nanocellulose ink, scaffold formation using 3D printing, and freeze-drying. We then detail post-processing procedures to enhance mechanical properties, stability, and biocompatibility. This protocol offers researchers a framework for developing versatile and sustainable biomaterials for regenerative medicine.
Keywords: tissue engineering, 3D scaffolds, nanocellulosic materials, biocompatibility
Published in DKUM: 03.11.2025; Views: 0; Downloads: 4
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2. Engineering Materials and Process Technologies : 2nd Conference of Programme Groups of the Faculty of Mechanical Engineering University of Maribor, Book of Abstracts2025 Abstract: The publication presents an overview of the research achievements of Programm Groups of the Faculty of Mechanical Engineering: P2-0424 Design of Novel Properties of (Nano)Materials & Applications, P2-0063 Design of Cellular Structures, P2-0120 Technologies of Metastable Materials, P2-0118 Textile Chemistry and Advanced Textile Materials, and P2-0196 Research in Power, Process, and Environmental Engineering from 2024 to 2025. Members of the program group present their research innovations and progress achieved within the framework of the program group's activities.
Keywords: advanced textile materials, composites, nanomaterials, biotechnology, surface functionalization and modification, cellular structures, metastable materials, power, process and environmental engineering
Published in DKUM: 03.11.2025; Views: 0; Downloads: 2
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3. Teacher opinion about using problem-based learning in technology and engineering educationLara Voler, Andrej Flogie, 2024, original scientific article Abstract: A school for the 21st century requires innovative approaches to learning and teaching. The presented research investigates the current state of innovative teaching methods, particularly problem-based learning (PBL), among teachers of the subject Technology and Engineering (TE) in Slovenian lower secondary schools. The main aim of the research was to determine the attitude towards such innovative teaching approaches, how often innovative teaching methods are used, how teachers perceive their importance, and finally, according to the teachers, how they are qualified for this. In the research, an appropriate questionnaire for teachers is developed. Qualitative and quantitative research was conducted using an online survey and statistical data processing. The online survey results showed that teachers positively assess the importance of using PBL, which is perceived as a more challenging method than traditional teaching methods. Despite numerous challenges, teachers express high motivation to improve their teaching practices. Based on the data obtained, it is found that teachers' knowledge is assessed less positively than their competence assessment, which indicates the need for more accurate methods of evaluating knowledge and emphasizes the importance of continuous professional development. The research findings could be important for improving teaching practices and promoting innovations in the educational process. High motivation and readiness of teachers to accept innovative pedagogical approaches can contribute to a better learning environment and the development of a more competent teacher in the future.
Keywords: continuous professional development, innovative teaching methods, problem-based learning, student motivation, teacher competence, technology and engineering
Published in DKUM: 15.09.2025; Views: 0; Downloads: 4
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4. Urinary catheters : state of the art and future perspectives – a narrative reviewKristijan Skok, Uroš Bele, Špela Pintar, Zdenka Peršin Fratnik, Katja Kuzmič, Matej Bračič, Lidija Fras Zemljič, Uroš Maver, 2025, review article Abstract: Catheter associated urinary tract infection (CAUTI) is the most frequent healthcare associated infection, arising from microbial adhesion to catheter surfaces, biofilm development, and the growing problem of antimicrobial resistance. Many publications have addressed CAUTI epidemiology, biofilm biology, or biomaterials for catheters in isolation, yet there is little literature that connects these areas into a coherent translational perspective. This review seeks to fill that gap by combining an overview of biofilm pathophysiology with recent advances in material based innovations for catheter design, including nanostructured and responsive coatings, sensor enabled systems, additive manufacturing, and three dimensional printing. Established approaches such as hydrophilic or antimicrobial impregnated catheters are considered alongside bio inspired surface textures, zwitterionic polymers, and multifunctional hydrogels. Each strategy is evaluated in terms of maturity, clinical applicability, and barriers to translation, with a focus on shifting from antibiotic dependent treatment toward prevention of biofilm formation. By bringing together knowledge from microbiology, engineering, and clinical urology, the review outlines pathways for developing the next generation of catheters that improve outcomes and reduce infection rates.
Keywords: urinary catheters, urinary tract infection, antibiotic resistance, nanotechnology, biomedical engineering
Published in DKUM: 15.09.2025; Views: 0; Downloads: 1
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5. The use of AI in software engineering : a synthetic knowledge synthesis of the recent research literaturePeter Kokol, 2024, review article Abstract: Artificial intelligence (AI) has witnessed an exponential increase in use in various applications. Recently, the academic community started to research and inject new AI-based approaches
to provide solutions to traditional software-engineering problems. However, a comprehensive and
holistic understanding of the current status needs to be included. To close the above gap, synthetic
knowledge synthesis was used to induce the research landscape of the contemporary research literature on the use of AI in software engineering. The synthesis resulted in 15 research categories and
5 themes—namely, natural language processing in software engineering, use of artificial intelligence
in the management of the software development life cycle, use of machine learning in fault/defect
prediction and effort estimation, employment of deep learning in intelligent software engineering
and code management, and mining software repositories to improve software quality. The most
productive country was China (n = 2042), followed by the United States (n = 1193), India (n = 934),
Germany (n = 445), and Canada (n = 381). A high percentage (n = 47.4%) of papers were funded,
showing the strong interest in this research topic. The convergence of AI and software engineering
can significantly reduce the required resources, improve the quality, enhance the user experience,
and improve the well-being of software developers.
Keywords: software engineering, artificial intelligence, machine learning, synthetic knowledge synthesis, AI
Published in DKUM: 30.07.2025; Views: 0; Downloads: 12
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6. Evaluating Proprietary and Open-Weight Large Language Models as Universal Decimal Classification Recommender SystemsMladen Borovič, Eftimije Tomovski, Tom Li Dobnik, Sandi Majninger, 2025, original scientific article Abstract: Manual assignment of Universal Decimal Classification (UDC) codes is time-consuming and inconsistent as digital library collections expand. This study evaluates 17 large language models (LLMs) as UDC classification recommender systems, including ChatGPT variants (GPT-3.5, GPT-4o, and o1-mini), Claude models (3-Haiku and 3.5-Haiku), Gemini series (1.0-Pro, 1.5-Flash, and 2.0-Flash), and Llama, Gemma, Mixtral, and DeepSeek architectures. Models were evaluated zero-shot on 900 English and Slovenian academic theses manually classified by professional librarians. Classification prompts utilized the RISEN framework, with evaluation using Levenshtein and Jaro–Winkler similarity, and a novel adjusted hierarchical similarity metric capturing UDC’s faceted structure. Proprietary systems consistently outperformed open-weight alternatives by 5–10% across metrics. GPT-4o achieved the highest hierarchical alignment, while open-weight models showed progressive improvements but remained behind commercial systems. Performance was comparable between languages, demonstrating robust multilingual capabilities. The results indicate that LLM-powered recommender systems can enhance library classification workflows. Future research incorporating fine-tuning and retrieval-augmented approaches may enable fully automated, high-precision UDC assignment systems.
Keywords: universal decimal classification, large language models, conversational systems, recommender systems, prompt engineering, zero-shot classification, hierarchical similarity
Published in DKUM: 21.07.2025; Views: 0; Downloads: 7
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10. Impact of developer queries on the effectiveness of conversational large language models in programmingViktor Taneski, Sašo Karakatič, Patrik Rek, Gregor Jošt, 2025, original scientific article Abstract: This study investigates the effects of LLM-based coding assistance on web application development by students using a frontend framework. Rather than comparing different models, it focuses on how students interact with LLM tools to isolate the impact of query type on coding success. To this end, participants were instructed to rely exclusively on LLMs for writing code, based on a given set of specifications, and their queries were categorized into seven types: Error Fixing (EF), Feature Implementation (FI), Code Optimization (CO), Code Understanding (CU), Best Practices (BP), Documentation (DOC), and Concept Clarification (CC). The results reveal that students who queried LLMs for error fixing (EF) were statistically more likely to have runnable code, regardless of prior knowledge. Additionally, students seeking code understanding (CU) and error fixing performed better, even when normalizing for previous coding ability. These findings suggest that the nature of the queries made to LLMs influences the success of programming tasks and provides insights into how AI tools can assist learning in software development.
Keywords: large language models, LLMs, prompt engineering, query type analysis, AI-assisted programming, educational software development
Published in DKUM: 23.06.2025; Views: 0; Downloads: 4
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