1. Design of in vitro hair follicles for different applications in the treatment of alopecia : a reviewMatej Žnidarič, Žan Michel Žurga, Uroš Maver, 2021, review article Abstract: The hair research field has seen great improvement in recent decades, with in vitro hair follicle (HF) models being extensively developed. However, due to the cellular complexity and number of various molecular interactions that must be coordinated, a fully functional in vitro model of HFs remains elusive. The most common bioengineering approach to grow HFs in vitro is to manipulate their features on cellular and molecular levels, with dermal papilla cells being the main focus. In this study, we focus on providing a better understanding of HFs in general and how they behave in vitro. The first part of the review presents skin morphology with an emphasis on HFs and hair loss. The remainder of the paper evaluates cells, materials, and methods of in vitro growth of HFs. Lastly, in vitro models and assays for evaluating the effects of active compounds on alopecia and hair growth are presented, with the final emphasis on applications of in vitro HFs in hair transplantation. Since the growth of in vitro HFs is a complicated procedure, there is still a great number of unanswered questions aimed at understanding the long-term cycling of HFs without losing inductivity. Incorporating other regions of HFs that lead to the successful formation of different hair classes remains a difficult challenge.
Keywords: hair follicle, alopecia, dermal papilla cells, hair transplantation, in vitro hair follicle models
Published in DKUM: 11.03.2025; Views: 0; Downloads: 3
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2. 7th International Conference on Technologies & Business Models for Circular Economy : Conference Proceedings2025, proceedings Abstract: The 7th International Conference on Technologies & Business Models for Circular Economy (TBMCE) was organized by the Faculty of Chemistry and Chemical Engineering of the University of Maribor in collaboration with the Strategic Research and Innovation Partnership – Networks for the transition into circular economy (SRIP – Circular economy), managed by the Chamber of Commerce and Industry of the Štajerska. The event took place from September 4 to 6, 2024 in Portorož, Slovenia, at the Grand Hotel Bernardin. The conference focused on the current challenges and opportunities related to technological development and society's responsibility in the transition to a more sustainable and circular management of resources. The conference program included a round table on "Circular Economy Transition in the South-East Europe", 5 panel discussions, plenary and 2 keynote speeches as well as oral and poster presentations. The conference was held under the patronage of the Ministry of the Economy, Tourism and Sport and the Ministry of Cohesion and Regional Development. EIT RawMaterials RIS Hub Adria, SPIRIT Slovenia Business Development Agency, and Pomurje Technology Park (as part of the GREENE 4.0 and CI-Hub projects) joined us as co-organizers.
Keywords: circular economy, sustainable development, processes and technologies, circular business models, research and development
Published in DKUM: 03.03.2025; Views: 0; Downloads: 5
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3. New approach for automated explanation of material phenomena (AA6082) using artificial neural networks and ChatGPTTomaž Goričan, Milan Terčelj, Iztok Peruš, 2024, original scientific article Abstract: Artificial intelligence methods, especially artificial neural networks (ANNs), have increasingly been utilized for the mathematical description of physical phenomena in (metallic) material
processing. Traditional methods often fall short in explaining the complex, real-world data observed
in production. While ANN models, typically functioning as “black boxes”, improve production
efficiency, a deeper understanding of the phenomena, akin to that provided by explicit mathematical
formulas, could enhance this efficiency further. This article proposes a general framework that
leverages ANNs (i.e., Conditional Average Estimator—CAE) to explain predicted results alongside
their graphical presentation, marking a significant improvement over previous approaches and those
relying on expert assessments. Unlike existing Explainable AI (XAI) methods, the proposed framework mimics the standard scientific methodology, utilizing minimal parameters for the mathematical
representation of physical phenomena and their derivatives. Additionally, it analyzes the reliability
and accuracy of the predictions using well-known statistical metrics, transitioning from deterministic
to probabilistic descriptions for better handling of real-world phenomena. The proposed approach
addresses both aleatory and epistemic uncertainties inherent in the data. The concept is demonstrated through the hot extrusion of aluminum alloy 6082, where CAE ANN models and predicts
key parameters, and ChatGPT explains the results, enabling researchers and/or engineers to better
understand the phenomena and outcomes obtained by ANNs.
Keywords: artificial neural networks, automatic explanation, hot extrusion, aluminum alloy, large language models, ChatGPT
Published in DKUM: 27.02.2025; Views: 0; Downloads: 5
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4. Exploring the feasibility of generative AI in persona research : a omparative analysis of large language model-generated and human-crafted personas in obesity researchUrška Smrke, Ana Rehberger, Nejc Plohl, Izidor Mlakar, 2025, original scientific article Abstract: This study investigates the perceptions of Persona descriptions generated using three different large language models (LLMs) and qualitatively developed Personas by an expert panel involved in obesity research. Six different Personas were defined, three from the clinical domain and three from the educational domain. The descriptions of Personas were generated using qualitative methods and the LLMs (i.e., Bard, Llama, and ChatGPT). The perception of the developed Personas was evaluated by experts in the respective fields. The results show that, in general, the perception of Personas did not significantly differ between those generated using LLMs and those qualitatively developed by human experts. This indicates that LLMs have the potential to generate a consistent and valid representation of human stakeholders. The LLM-generated Personas were perceived as believable, relatable, and informative. However, post-hoc comparisons revealed some differences, with descriptions generated using the Bard model being in several Persona descriptions that were evaluated most favorably in terms of empathy, likability, and clarity. This study contributes to the understanding of the potential and challenges of LLM-generated Personas. Although the study focuses on obesity research, it highlights the importance of considering the specific context and the potential issues that researchers should be aware of when using generative AI for generating Personas.
Keywords: user personas, obesity, large language models, value sensitive design, digital health interventions
Published in DKUM: 14.02.2025; Views: 0; Downloads: 4
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5. Modeling and optimization of anaerobic digestion technology : current status and future outlookTina Kegl, Eloisa Torres Jiménez, Breda Kegl, Anita Kovač Kralj, Marko Kegl, 2025, review article Keywords: renewable energy, anaerobic digestion, biogas plant, mathematical models, optimization algorithms, products utilization
Published in DKUM: 31.01.2025; Views: 0; Downloads: 13
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6. Long-term temperature prediction with hybrid autoencoder algorithmsJorge Pérez-Aracil, Dušan Fister, C. M. Marina, César Peláez-Rodriguez, L. Cornejo-Bueno, P. A. Gutiérrez, Matteo Giuliani, A. Castelleti, Sancho Salcedo-Sanz, 2024, original scientific article Abstract: This paper proposes two hybrid approaches based on Autoencoders (AEs) for long-term temperature prediction. The first algorithm comprises an AE trained to learn temperature patterns, which is then linked to a second AE, used to detect possible anomalies and provide a final temperature prediction. The second proposed approach involves training an AE and then using the resulting latent space as input of a neural network, which will provide the final prediction output. Both approaches are tested in long-term air temperature prediction in European cities: seven European locations where major heat waves occurred have been considered. The longterm temperature prediction for the entire year of the heatwave events has been analysed. Results show that the proposed approaches can obtain accurate long-term (up to 4 weeks) temperature prediction, improving Persistence and Climatology in the benchmark models compared. In heatwave periods, where the persistence of the temperature is extremely high, our approach beat the persistence operator in three locations and works similarly in the rest of the cases, showing the potential of this AE-based method for long-term temperature prediction.
Keywords: autoencoder, temperature prediction, hybrid models, heatwave
Published in DKUM: 29.01.2025; Views: 0; Downloads: 2
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7. Efficient encoding and decoding of voxelized models for machine learning-based applicationsDamjan Strnad, Štefan Kohek, Borut Žalik, Libor Váša, Andrej Nerat, 2025, original scientific article Abstract: Point clouds have become a popular training data for many practical applications of machine learning in the fields of environmental modeling and precision agriculture. In order to reduce high space requirements and the effect of noise in the data, point clouds are often transformed to a structured representation such as a voxel grid. Storing, transmitting and consuming voxelized geometry, however, remains a challenging problem for machine learning pipelines running on devices with limited amount of on-chip memory with low access latency. A viable solution is to store the data in a compact encoded format, and perform on-the-fly decoding when it is needed for processing. Such on-demand expansion must be fast in order to avoid introducing substantial additional delay to the pipeline. This can be achieved by parallel decoding, which is particularly suitable for massively parallel architecture of GPUs on which the majority of machine learning is currently executed. In this paper, we present such method for efficient and parallelizable encoding/decoding of voxelized geometry. The method employs multi-level context-aware prediction of voxel occupancy based on the extracted binary feature prediction table, and encodes the residual grid with a pointerless sparse voxel octree (PSVO). We particularly focused on encoding the datasets of voxelized trees, obtained from both synthetic tree models and LiDAR point clouds of real trees. The method achieved 15.6% and 12.8% reduction of storage size with respect to plain PSVO on synthetic and real dataset, respectively. We also tested the method on a general set of diverse voxelized objects, where an average 11% improvement of storage space was achieved.
Keywords: voxel grid, feature prediction, tree models, prediction-based encoding, key voxels, residuals, sparse voxel octree
Published in DKUM: 09.01.2025; Views: 0; Downloads: 5
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8. Kratek pregled platform za ADME testiranjeTina Maver, Boštjan Vihar, Uroš Maver, 2024, original scientific article Abstract: V zadnjem času je bil dosežen pomemben napredek pri razvoju ADME (absorpcija, distribucija, metabolizem, ekskrecija) modelov, vendar izziv ostaja vzpostaviti platforme, ki bi zmanjšale testiranje na živalih in stroške raziskav. Naraščajoča pomembnost farmakokinetičnih interakcij poudarja potrebo po zanesljivih in
ponovljivih ADME modelih, ki so vse bolj ključni za razvoj zdravil in zagotavljanje varnosti z željo po preprečevanju
resnih kliničnih zapletov in hospitalizacije.
Keywords: ADME, drug interaction studies, in vitro models, pharmacokinetics, multi-organ models
Published in DKUM: 07.01.2025; Views: 0; Downloads: 21
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9. In vitro disease models of the endocrine pancreasMarko Milojević, Jan Rožanc, Jernej Vajda, Laura Činč Ćurić, Eva Paradiž, Andraž Stožer, Uroš Maver, Boštjan Vihar, 2021, review article Abstract: The ethical constraints and shortcomings of animal models, combined with the demand to study disease pathogenesis under controlled conditions, are giving rise to a new field at the interface of tissue engineering and pathophysiology, which focuses on the development of in vitro models of disease. In vitro models are defined as synthetic experimental systems that contain living human cells and mimic tissue- and organ-level physiology in vitro by taking advantage of recent advances in tissue engineering and microfabrication. This review provides an overview of in vitro models and focuses specifically on in vitro disease models of the endocrine pancreas and diabetes. First, we briefly review the anatomy, physiology, and pathophysiology of the human pancreas, with an emphasis on islets of Langerhans and beta cell dysfunction. We then discuss different types of in vitro models and fundamental elements that should be considered when developing an in vitro disease model. Finally, we review the current state and breakthroughs in the field of pancreatic in vitro models and conclude with some challenges that need to be addressed in the future development of in vitro models.
Keywords: in vitro disease models, pancreas, islet of Langerhans, 3D cell culture, scaffolds, acute tissue slices
Published in DKUM: 01.10.2024; Views: 0; Downloads: 400
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10. Understanding cognitive transport mode choice structures : means-ends chains as a type of second-order cyberneticsTomaž Kolar, Iztok Kolar, 2022, original scientific article Abstract: Purpose: This paper aims to inform the promotion of sustainable modes of transport. For this purpose, it deploys a means-ends framework as a type of second-order cybernetics and uses it to explore cognitive transport mode choice structures.
Design/methodology/approach: The empirical study relies on a purposive sample and a qualitative research methodology known as laddering. It is aimed at the identification and comparative analysis of the cognitive means-ends structures of transport users.
Findings: The results reveal more positive and complex associations for the car than for public transport. Two main positive means-ends structures are identified for public transport, one related with the relaxation and the other with doing useful things while travelling. Dominant positive structures for the car are related with self-confidence, satisfaction and personal freedom. Negative means-ends structures in addition reveal important justifications and rationalizations for car use.
Practical implications: Based on the identified distinct means-ends elements and structures, this study holds important implications for developing a communications strategy and policy interventions seeking to promote public transport.
Originality/value: Means-ends theory is proposed as an integrative cybernetic framework for the study of stakeholders' (customers') mental models. The empirical study is the first to concurrently and comparatively examine positive and negative means-ends chains for the car and for the public transport modes.
Keywords: public transport, second-order cybernetics, laddering methodology, means-ends theory, private car, mental models, personal values, marketing, consumer
Published in DKUM: 27.08.2024; Views: 93; Downloads: 15
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