1. Multicriteria risk evaluation model : utilizing fuzzy logic for improved transparency and quality of risk evaluation in healthcareRok Drnovšek, Marija Milavec Kapun, Simona Šteblaj, Uroš Rajkovič, 2025, original scientific article Abstract: Introduction: Risk management is essential for quality assurance in modern healthcare organizations. Risk matrices are widely used
to evaluate risks in healthcare settings; however, this approach has noteworthy weaknesses and limitations. This paper introduces
a novel risk evaluation model that utilizes multicriteria decision-making and fuzzy logic, to enhance the transparency and quality of the
risk evaluation process in healthcare.
Methods: The Multicriteria Evaluation Model was developed using the Decision Expert method and expert knowledge integration.
Fuzzy logic was integrated within the model, using partial degrees of membership and probabilistic analysis, to address uncertainties
inherent to healthcare risk evaluation. The evaluation model was tested with healthcare professionals active in the field of risk
management in clinical practice and compared with the risk matrix.
Results: The designed evaluation model utilizes multicriteria decision-making while encompassing the risk matrix framework to
boost user understanding and enable meaningful comparison of results. Compared with the risk matrix, the model provided similar or
marginally higher risk-level evaluations. The use of degrees of membership enables evaluators to articulate a wide range of plausible
risk consequences, which are often overlooked or ambiguously addressed in the traditional risk matrix approach.
Discussion and Conclusions: The evaluation model demonstrates increased transparency of the decision-making process and
facilitates in-depth analysis of the evaluation results. The utilization of degrees of membership revealed distinct strategies for handling
uncertainty among participants, highlighting the weaknesses of using single value evaluation approach for the presented and similar
decision problems. The presented approach is not limited to healthcare-related risk evaluation, but has the capacity to improve risk
evaluation practices in diverse settings
Keywords: multi-criteria decision-making, risk management, fuzzy logic, decision support, patient care, DEX
Published in DKUM: 01.12.2025; Views: 0; Downloads: 0
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2. Measuring students’ use of digital technology to support their studiesVesna Čančer, Polona Tominc, Maja Rožman, 2025, original scientific article Abstract: To provide a more holistic understanding of how digital tools shape the educational environment, this paper includes a comprehensive analysis that explores several dimensions of technology use in higher education: use of artificial intelligence in education, online collaboration, use of an E-Board for learning, and excessive use of technology. With the aim of measuring students’ use of digital technology to support their studies, this research meets the goals of developing the measurement process, building a multi-criteria model, and applying it to a real-life example of determining the degree of students’ use of digital technology in relation to the demonstrated quality of academic performance. The analysis is based on a survey conducted among students at the University of Maribor’s Faculty of Economics and Business. Using factor analysis and multi-criteria evaluation, the findings reveal that students who demonstrate very-high-quality achievements also report the highest level of technology use to support their studies. They are followed by students with outstanding achievements, who excel in using an E-Board for learning and in demonstrating responsibility regarding excessive technology use. Students who achieve acceptable-quality results with certain defects stand out in online collaboration and the use of AI in the study process. The lowest level of technology use was reported by students demonstrating moderate-quality achievements. Theoretically, this research contributes to a better understanding of the multidimensional use of digital technology in higher education, while, practically, it provides useful guidelines for optimizing digital learning tools and enhancing the overall quality of the academic process
Keywords: digital technology in education, artificial intelligence in education, students, academic performance, multi-criteria analysis
Published in DKUM: 20.08.2025; Views: 0; Downloads: 10
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3. Controllability-oriented method to improve small-signal response of virtual synchronous generatorsAntonija Šumiga, Boštjan Polajžer, Jožef Ritonja, Peter Kitak, 2025, original scientific article Abstract: This paper presents a method for optimizing the inertia constants and damping coefficients of interconnected virtual synchronous generators (VSGs) using a genetic algorithm. The goal of optimization is to find a balance between minimizing the rate of change of frequency (RoCoF) and enhancing controllability. Five controllability-based metrics are tested: the minimum eigenvalue, the sum of the two smallest eigenvalues, the maximum eigenvalue, the trace, and the determinant of the controllability Gramian matrix. The approach includes the oscillatory modes’ damping ratio constraints to ensure the small-signal stability of the entire system. The results of optimization on the IEEE 9-bus system with three VSGs show that the proposed method improves controllability, reduces RoCoF, and maintains the desired oscillation damping. The proposed approach was tested through time-domain simulations.
Keywords: virtual synchronous generator, inertia constant, damping coefficient, small-signal stability, multi-objective optimization, genetic algorithm
Published in DKUM: 12.08.2025; Views: 0; Downloads: 11
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4. Computationally efficient multi-objective optimization of an interior permanent magnet synchronous machine using neural networksMitja Garmut, Simon Steentjes, Martin Petrun, 2025, original scientific article Abstract: Improving the power density of an interior permanent magnet synchronous machine requires a complex and comprehensive approach that includes electromagnetic and thermal aspects. To achieve that, a multi-objective optimization of the machine’s geometry was performed according to selected key performance indicators by using numerical and analytical models. The primary objective of this research was to create a computationally efficient and accurate alternative to a direct finite element method-based optimization. By integrating artificial neural networks as meta-models, we aimed to demonstrate their performance in comparison to existing State-of-the-Art approaches. The artificial neural network approach achieved a nearly 20-fold reduction compared with the finite element method-based approach in computation time while maintaining accuracy, demonstrating its effectiveness as a computationally efficient alternative. The obtained artificial neural network can also be reused for different optimization scenarios and for iterative fine-tuning, further reducing the computation time. To highlight the advantages and limitations of the proposed approach, a multi-objective optimization scenario was performed, which increased the power-to-mass ratio by 16.5%.
Keywords: interior permanent magnet synchronous machine, artificial neural network, metamodel, multi-objective optimization, finite element method
Published in DKUM: 08.08.2025; Views: 0; Downloads: 19
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5. A hybrid multi-criteria and creative, problemsolving approach, for measuring local values of information technology productsVesna Čančer, 2023, original scientific article Abstract: This study examines the relationship between digital transformation and sustainable
practices within enterprises against the backdrop of global transformative forces framed within
the holistic paradigm of systems thinking. It examines the extent to which digital advances either
facilitate or impede the sustainable development of companies, while also considering the systemic
impact of demographic variables (such as gender, age, education), national income levels, and
geographical regions on business sustainability. Using data from the Global Entrepreneurship Monitor
(GEM), which encompasses 26,790 entrepreneurs in 47 countries, this research uses multinomial
regression to assess how these factors influence companies’ commitment to social and environmental
goals. A key finding is that the strategic use of digital technologies in sales processes significantly
increases the likelihood that entrepreneurs will integrate social and environmental considerations
into their decision-making. Notably, this conscientious approach to business is most prevalent among
entrepreneurs in Latin America and the Caribbean. Our findings underscore the central role of digital
technologies in driving sustainable business transformation while also highlighting the significant
influence of regional socio-environmental contexts on business sustainability orientations.
Keywords: creative problem solving, information technology, multi-criteria decision making, piecewise linear value function, prescriptive approach
Published in DKUM: 10.07.2025; Views: 0; Downloads: 3
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6. Constrained multi-objective optimization of simulated tree pruning with heterogeneous criteriaDamjan Strnad, Štefan Kohek, 2021, original scientific article Abstract: Virtual pruning of simulated fruit tree models is a useful functionality provided by software tools for computer-aided horticultural education and research. It also enables algorithmic
pruning optimization with respect to a set of quantitative objectives, which is important for analytical
purposes and potential applications in automated pruning. However, the existing studies in pruning
optimization focus on a single type of objective, such as light distribution within the crown. In this
paper, we propose the use of heterogeneous objectives for discrete multi-objective optimization of
simulated tree pruning. In particular, the average light intake, crown shape, and tree balance are
used to observe the emergence of different pruning patterns in the non-dominated solution sets. We
also propose the use of independent constraint objectives as a new mechanism to confine overfitting
of solutions to individual pruning criteria. Finally, we perform the comparison of NSGA-II, SPEA2,
and MOEA/D-EAM on this task. The results demonstrate that SPEA2 and MOEA/D-EAM, which
use external solution archives, can produce better sets of non-dominated solutions than NSGA-II.
Keywords: multi-objective optimization, virtual tree pruning, heterogeneous objectives, constraint objectives, NSGA-II, SPEA2, EuMOEA/D-EAM
Published in DKUM: 19.06.2025; Views: 0; Downloads: 8
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7. A coupled multilevel vial lyophilization model for the pressure coupling in a freeze dryerBlaž Kamenik, Jure Ravnik, Timi Gomboc, Matej Zadravec, Matjaž Hriberšek, 2025, original scientific article Abstract: With computational modeling of lyophilization in vials, the pressure coupling between the sublimation front and the drying chamber has traditionally been calculated using a simplified mass transfer resistance model in the form of a model, which takes into account the headspace and the stopper in a simplified way. In developing a 3D CFD-based digital twin of lyophilization in vials, a need arises for a mass flow rate-dependent vial headspace/stopper model, as it enables a more accurate calculation of the pressure conditions above the shelf as well as pressure conditions directly at the sublimation front, the latter directly affecting the sublimation mass transfer rate as well as the temperature inside the product, which is crucial for determining the risk of product collapse. The local pressure variations at a shelf level affect the heat transfer conditions due to heat conduction in the low pressure environment of the drying chamber. In the present work the development of a coupled multilevel vial lyophilization model for the freeze-drying of vials is reported, with the time-dependent 1D heat and mass transfer model at the vial level coupled with the time-dependent 3D low-pressure CFD model of the flow of the water vapor–air mixture in the drying chamber heated by the shelves. A direct pressure coupling between the sublimation front and the drying chamber space in form of vial type specific headspace/stopper resistance model is implemented. The developed multilevel lyophilization model is used to study the pressure build-up above the shelf and the headspace of the vial and its influence on the product temperature at the bottom of the vial using simulations carried out for different chamber pressures (6 Pa and 22 Pa), shelf temperatures (−20 oC and +10 oC) and vial types (10R and 15R). By implementing previously developed vial headspace/stopper pressure resistance models, the computational results show that the pressure build-up above the shelf and vial headspace significantly affect the product temperature at the bottom of the vial, especially at low chamber pressures ( Pa) and small gap sizes between the rubber stopper and the shelf above it. The increased pressure outside the vial leads also to higher heat transfer by conduction, which is particularly pronounced at the central shelf positions and within smaller shelf gaps. These results underline the importance of using a coupled multilevel model when analyzing the relationship between the local pressure variations above the shelf and their direct influence on product drying conditions, further improving the predictive capabilities of CFD based multilevel lyophilization models, especially with respect to detecting the product collapse temperature.
Keywords: freeze-drying, conjugate heat and mass transfer, computational fluid dynamics, multi-scale modeling
Published in DKUM: 17.06.2025; Views: 0; Downloads: 14
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8. Integrating Multi-Physics Modeling within Multi-Objective Optimization to Enhance the Performance and Efficiency of Permanent Magnet Synchronous Machines : doktorska disertacijaMitja Garmut, 2025, doctoral dissertation Abstract: This Dissertation focuses on the optimization of an Interior Permanent Magnet (IPM) machine for handheld battery-powered tools, aiming to enhance performance and efficiency.
The research integrates multi-physics modeling, including electromagnetic Finite Element Method (FEM) and thermal models, to evaluate machine performance under various operating conditions. The performance is evaluated according to selected Key Performance Indicators (KPIs). Further, different control methods, such as Field Oriented Control and Square-Wave Control, impact the performance significantly and are incorporated into the optimization process.
Due to the computational challenges of FEM-based performance evaluations in Multi-Objective Optimization (MOO), this work utilizes Artificial Neural Network (ANN)-based meta-models, to accelerate the optimization process while preserving accuracy.
The developed meta-models capture nonlinear machine characteristics from the FEM model. These meta-models are then used to evaluate machine performance through a combination of analytical and numerical post-processing methods.
Four MOO scenarios are presented, each aimed at optimizing the cross-sectional design of IPM machines, to enhance performance and efficiency while reducing mass and cost. Additionally, these scenarios modify the machine’s electromagnetic behavior, to ensure better alignment with the selected control method.
By comparing the optimization process of Scenario 1, which uses direct FEM-based evaluation without time reduction measures, to the approach incorporating Artificial Neural Network based meta-models, the total number of individual FEM evaluations decreased from 2.35×10^9 to 2.03×10^5, without almost any loss of accuracy. This reduced the computation time from 297 years to 9.07 days on our standard desktop computer. The obtained ANN-base meta-models can be used further for other optimizations without the need for additional FEM evaluations.
In all four optimization scenarios, the use of meta-models enabled the generation of a Pareto front of the optimal solutions, leading to improved KPIs compared to the reference design. The highest relative improvement occurred in Scenario 1, where the selected optimized machine design achieved a 30% increase in power density compared to the reference design.
Keywords: Interior Permanent Magnet (IPM) Machine, Artificial Neural Network (ANN), Meta-Modeling, Multi-Objective Optimization (MOO), Finite Element Method (FEM), Multi-Physics Modeling, Field Oriented Control (FOC), Square-Wave Control (SWC)
Published in DKUM: 15.05.2025; Views: 0; Downloads: 148
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9. Optimising energy piles: a multi-objective approach to cost and failure probabilityRok Varga, Primož Jelušič, Bojan Žlender, 2025, original scientific article Abstract: This paper presents a comparative analysis of the influence of thermal loading on the design of optimally designed floating energy piles in soft consistency soils using a genetic algorithm. The nonlinear settlement of energy piles is also considered. The deterministic optimisation model (OPT-EP) includes a cost objective function constrained by design constraints and is later extended to include the probability of failure as a second objective function to perform multi-objective optimisation. This extension was undertaken because the Eurocode 7 approach only partially accounts for uncertainties in the soil, whereas the reliability-based design (RBD) approach fully exploits these uncertainties. Consequently, a multi-objective optimisation (cost vs. failure probability) was carried out in this study. The optimal designs obtained by the two different optimisation methodologies were further analysed and it was found that when the Eurocode 7 safety factor approach was used, the conditions related to thermal loading were not crucial for the design values. On the other hand, the multi-objective optimisation based on the RBD approach showed that the thermal loading affected the design, proving the usefulness of the multi-objective optimisation and the reliability-based design.
Keywords: energy pile, multi-objective optimisation, reliability-based design, genetic algorithm
Published in DKUM: 04.04.2025; Views: 0; Downloads: 9
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10. Study of a sensitive and selective electrochemical biosensor for glucose based on ▫$Bi_2Ru_2O_7$▫ pyrochlore clusters combined with MWCNTsJelena Isailović, Aleksandra Dapčević, Milan Žunić, Matjaž Finšgar, Kristijan Vidović, Nikola Tasić, Samo B. Hočevar, 2025, original scientific article Abstract: The development of sensitive, selective, and reliable glucose biosensors remains a persistent challenge in clinical diagnostics. In this study, we exploited the advantageous (electro)catalytic properties of bismuth ruthenate (Bi2Ru2O7) pyrochlore clusters, known for their high surface activity and metallic-like conductivity, and the favorable physicochemical properties of multi-walled carbon nanotubes (MWCNTs) by combining them with glucose oxidase (GOD) in a sensitive and selective disposable glucose biosensor. The integration of Bi2Ru2O7 enabled an enhanced and more reproducible response of the biosensor along with fast and improved communication between the supporting electrode and the upper biosensing layer. The architecture of the biosensor involves the deposition of an MWCNT layer on a ferrocyanide-modified screen-printed carbon electrode (FCN-SPCE), followed by the application of a biorecognition layer including GOD and Bi2Ru2O7 clusters. The voltammetric biosensor showed excellent electroanalytical performance, capable of detecting low glucose concentrations with a detection limit of 40 μM along with a linear response across the examined concentration range of 1.0–20.0 mM. The biosensor exhibited good reproducibility with a relative standard deviation (RSD) of 1.2% and interference-free operation against several of the most common interfering compounds. The practical applicability of the biosensor was demonstrated by the determination of glucose in a real serum sample spiked with different concentrations of glucose.
Keywords: glucose sensor, glucose oxidase, Bi2Ru2O7 pyrochlore, multi-walled carbon nanotubes, voltammetry, serum
Published in DKUM: 20.03.2025; Views: 0; Downloads: 1
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