21. Age-related changes in lipid and glucose levels associated with drug use and mortality : an observational studyRene Markovič, Vladimir Grubelnik, Helena Blažun Vošner, Peter Kokol, Matej Završnik, Karmen Janša, Marjeta Zupet, Jernej Završnik, Marko Marhl, 2022, izvirni znanstveni članek Opis: Background: The pathogenesis of type 2 diabetes mellitus is complex and still unclear in some details. The main feature of diabetes mellitus is high serum glucose, and the question arises of whether there are other statistically observable dysregulations in laboratory measurements before the state of hyperglycemia becomes severe. In the present study, we aim to examine glucose and lipid profiles in the context of age, sex, medication use, and mortality. Methods: We conducted an observational study by analyzing laboratory data from 506,083 anonymized laboratory tests from 63,606 different patients performed by a regional laboratory in Slovenia between 2008 and 2019. Laboratory data-based results were evaluated in the context of medication use and mortality. The medication use database contains anonymized records of 1,632,441 patients from 2013 to 2018, and mortality data were obtained for the entire Slovenian population. Results: We show that the highest percentage of the population with elevated glucose levels occurs approximately 20 years later than the highest percentage with lipid dysregulation. Remarkably, two distinct inflection points were observed in these laboratory results. The first inflection point occurs at ages 55 to 59 years, corresponding to the greatest increase in medication use, and the second coincides with the sharp increase in mortality at ages 75 to 79 years. Conclusions: Our results suggest that medications and mortality are important factors affecting population statistics and must be considered when studying metabolic disorders such as dyslipidemia and hyperglycemia using laboratory data.
Ključne besede: diabetes, metabolic syndrome, hematological data, aging
Objavljeno v DKUM: 21.05.2024; Ogledov: 204; Prenosov: 11
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22. Webinar: Data Management and EthicsMilan Ojsteršek, Matjaž Divjak, 2024, znanstveni film, znanstvena zvočna ali video publikacija Opis: Video recording and presentation slides of a lecture by Milan Ojsteršek and Matjaž Divjak for the HybridNeuro webinar "Data Management and Ethics", which was held online on April 8th 2024 at Faculty of Electrical Engineering and Computer Science, University of Maribor, Slovenia.
More info including additional materials (slides, example dataset): https://www.hybridneuro.feri.um.si/webinars/data_management_and_ethics.html
The HybridNeuro project combines the expertise of leading European partners in the field of Neural Interfaces to set up new pathways of analyzing human motor system and human movements and transfer the academic research into clinical and industrial practice. Link: https://www.hybridneuro.feri.um.si/
This project has received funding from the Horizon Europe Research and Innovation Programme under GA No. 101079392, as well as UK Research and Innovation organisation (GA No. 10052152).
This video is available under the Creative Commons Attribution 4.0 International licence (CC BY 4.0, https://creativecommons.org/licenses/by/4.0/).
Ključne besede: HybridNeuro project, webinar, presentation, data management, data annotation, open science, open access, data repository, FAIR data, metadata
Objavljeno v DKUM: 09.05.2024; Ogledov: 201; Prenosov: 6
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23. Profiling of patients with type 2 diabetes based on medication adherence dataRene Markovič, Vladimir Grubelnik, Tadej Završnik, Helena Blažun Vošner, Peter Kokol, Matjaž Perc, Marko Marhl, Matej Završnik, Jernej Završnik, 2023, izvirni znanstveni članek Ključne besede: diabetes, data analysis, public health, statistics
Objavljeno v DKUM: 09.05.2024; Ogledov: 196; Prenosov: 8
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24. Self-organization of enzyme-catalyzed reactions studied by the maximum entropy production principleAndrej Dobovišek, Marko Vitas, Tina Blaževič, Rene Markovič, Marko Marhl, Aleš Fajmut, 2023, izvirni znanstveni članek Opis: The self-organization of open reaction systems is closely related to specific mechanisms that allow the export of internally generated entropy from systems to their environment. According to the second law of thermodynamics, systems with effective entropy export to the environment are better internally organized. Therefore, they are in thermodynamic states with low entropy. In this context, we study how self-organization in enzymatic reactions depends on their kinetic reaction mechanisms. Enzymatic reactions in an open system are considered to operate in a non-equilibrium steady state, which is achieved by satisfying the principle of maximum entropy production (MEPP). The latter is a general theoretical framework for our theoretical analysis. Detailed theoretical studies and comparisons of the linear irreversible kinetic schemes of an enzyme reaction in two and three states are performed. In both cases, in the optimal and statistically most probable thermodynamic steady state, a diffusion-limited flux is predicted by MEPP. Several thermodynamic quantities and enzymatic kinetic parameters, such as the entropy production rate, the Shannon information entropy, reaction stability, sensitivity, and specificity constants, are predicted. Our results show that the optimal enzyme performance may strongly depend on the number of reaction steps when linear reaction mechanisms are considered. Simple reaction mechanisms with a smaller number of intermediate reaction steps could be better organized internally and could allow fast and stable catalysis. These could be features of the evolutionary mechanisms of highly specialized enzymes.
Ključne besede: enzymes, kinetic data analysis, steady state, self-organization, maximum entropy production
Objavljeno v DKUM: 08.05.2024; Ogledov: 234; Prenosov: 9
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25. Elderly pedestrians and road safety: Findings from the Slovenian accident database and measures for improving their safetyStanko Laković, Tomaž Tollazzi, Chiara Gruden, 2023, izvirni znanstveni članek Ključne besede: elderly pedestrians, road safety, regression model, human abilities, crash data, countermeasures, sustainable transportation
Objavljeno v DKUM: 15.04.2024; Ogledov: 258; Prenosov: 35
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26. The essential facilities doctrine, intellectual property rights, and access to big dataRok Dacar, 2023, izvirni znanstveni članek Opis: This paper analyzes the criteria for applying the essential facilities doctrine to intellectual property rights and the possibility of applying it in cases where Big Data is the alleged essential facility. It aims to answer the research question: ‘‘What are the specifics of the intellectual property criteria in essential facilities cases and are these criteria applicable to Big Data?’’ It points to the semantic openness of the ‘‘new product’’ and ‘‘technical progress’’ conditions that have been developed for assessing whether an intellectual property right constitutes an essential facility. The paper argues that the intellectual property criteria are not applicable in all access to Big Data cases because Big Data is not necessarily protected by copyright. While a set of Big Data could be protected by copyright if certain conditions are met, even in such cases the lack of intrinsic value of Big Data significantly limits the applicability of the intellectual property criteria.
Ključne besede: essential facilities doctrine, intellectual property rights, big data, new product condition, technical progress condition
Objavljeno v DKUM: 11.04.2024; Ogledov: 224; Prenosov: 23
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27. Applying integrated data envelopment analysis and analytic hierarchy process to measuring the efficiency of tourist farms : The Case of SloveniaBoris Prevolšek, Maja Borlinič Gačnik, Črtomir Rozman, 2023, izvirni znanstveni članek Opis: This paper examines the efficiency of tourist farms in Slovenia by adopting an approach using a framework of non-parametric programming—Data Envelopment Analysis (DEA) and Analytic Hierarchy Process (AHP), combining the two because the DEA analysis by itself does not take into account all attributes, especially qualitative ones. The beforementioned two methods rank the farm tourism units with respect to their efficiency. By using the DEA method, an input- and output-oriented BCC and CCR model were introduced to upgrade the criteria by including the additional non-numerical criteria of the AHP. The results of the models showed that there are possible improvements on all levels of efficiency, as well as on the criteria of the additional offer of tourist farms, which were analyzed in the AHP model with additional criteria. According to the estimated efficiency, the ranking of tourist farms differed according to the two methods. Within the group of farms assessed as efficient by DEA, the AHP model allowed a more accurate ranking.
Ključne besede: farm tourism, tourist farms, efficiency, data envelopment analysis (DEA), analytic hierarchy process (AHP), Slovenia
Objavljeno v DKUM: 09.04.2024; Ogledov: 281; Prenosov: 19
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28. CNN-Based Vessel Meeting Knowledge Discovery From AIS Vessel TrajectoriesPeng Chen, Shuang Liu, Niko Lukač, 2023, izvirni znanstveni članek Opis: 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.
Ključne besede: AIS Data, CNN, Dynamic Rammer-Douglas-Peucker, knowledge discovery, maneuvering pattern, traffic pattern, trajectory
Objavljeno v DKUM: 19.03.2024; Ogledov: 538; Prenosov: 417
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29. Agile Machine Learning Model Development Using Data Canyons in Medicine : A Step towards Explainable Artificial Intelligence and Flexible Expert-Based Model ImprovementBojan Žlahtič, Jernej Završnik, Helena Blažun Vošner, Peter Kokol, David Šuran, Tadej Završnik, 2023, izvirni znanstveni članek Opis: Over the past few decades, machine learning has emerged as a valuable tool in the field of medicine, driven by the accumulation of vast amounts of medical data and the imperative to harness this data for the betterment of humanity. However, many of the prevailing machine learning algorithms in use today are characterized as black-box models, lacking transparency in their decision-making processes and are often devoid of clear visualization capabilities. The transparency of these machine learning models impedes medical experts from effectively leveraging them due to the high-stakes nature of their decisions. Consequently, the need for explainable artificial intelligence (XAI) that aims to address the demand for transparency in the decision-making mechanisms of black-box algorithms has arisen. Alternatively, employing white-box algorithms can empower medical experts by allowing them to contribute their knowledge to the decision-making process and obtain a clear and transparent output. This approach offers an opportunity to personalize machine learning models through an agile process. A novel white-box machine learning algorithm known as Data canyons was employed as a transparent and robust foundation for the proposed solution. By providing medical experts with a web framework where their expertise is transferred to a machine learning model and enabling the utilization of this process in an agile manner, a symbiotic relationship is fostered between the domains of medical expertise and machine learning. The flexibility to manipulate the output machine learning model and visually validate it, even without expertise in machine learning, establishes a crucial link between these two expert domains.
Ključne besede: XAI, explainable artificial intelligence, data canyons, machine learning, transparency, agile development, white-box model
Objavljeno v DKUM: 14.03.2024; Ogledov: 299; Prenosov: 30
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30. Implementation of acoustic data link technology in industries : diplomsko deloSimon Srebot, 2023, diplomsko delo Opis: The main topic of this bachelor's thesis is the implementation of Acoustic Data Link (ADL) in the industry, where the aim is to find suitable industrial applications where the implementation of ADL would be feasible and expedient. The thesis is based on a six-month Product Innovation Project hosted by the Institute of Industrial Management at the Graz University of Technology and in cooperation with TDK Corporation, where our international team of students were given the challenge of exploring possible industrial applications of ADL.
The bachelor's thesis includes the description, composition and explained operation of ADL technologies as well as other similar technologies. It also includes the comparison between existing technologies and ADL, idea generation methods for possible applications and research results for each use case of the technology. The thesis concludes with a comparison of possible ADL implementations and their market research.
Ključne besede: Data Transfer, Acoustic Data Link, Piezo Elements, Radio Frequency Identification
Objavljeno v DKUM: 28.02.2024; Ogledov: 281; Prenosov: 22
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