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Iskalni niz: "ključne besede" (optimization) .

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1.
Optimizing OPM-MEG sensor layouts using the sequential selection algorithm with simulated sources and individual anatomy
Urban Marhl, Rok Hren, Tilmann Sander, Vojko Jazbinšek, 2026, izvirni znanstveni članek

Povzetek: Magnetoencephalography (MEG) based on optically pumped magnetometers (OPMs) offers the flexibility to position sensors closer to the scalp, which improves the signal-to-noise ratio compared to conventional superconducting quantum interference device (SQUID) systems. However, the spatial resolution of OPM-MEG critically depends on sensor placement, especially when the number of sensors is limited. In this study, we present a methodology for optimizing OPM-MEG sensor layouts using each subject’s anatomical information derived from individual magnetic resonance imaging (MRI). We generated realistic forward models from reconstructed head surfaces and simulated magnetic fields produced by equivalent current dipoles (ECDs). We compared multiple simulation strategies, including ECDs randomly distributed across the cortical surface and ECDs constrained to regions of interest. For each simulated magnetic field map (MFM) database, we applied the sequential selection algorithm (SSA) to identify sensor positions that maximized information capture. Unlike previous approaches relying on large measurement databases, this simulation-driven strategy eliminates the need for extensive pre-existing recordings. We benchmarked the performance of the personalized layouts using OPM-MEG datasets of auditory evoked fields (AEFs) derived from real whole-head SQUID-MEG measurements. Our results show that simulation-based SSA optimization improves the coverage of cortical regions of interest, reduces the number of sensors required for accurate source reconstruction, and yields sensor configurations that perform comparably to layouts optimized using measured data. To evaluate the quality of estimated MFMs, we applied metrics such as the correlation coefficient (CC), root-mean-square error, and relative error. Our results show that the first 15 to 20 optimally selected sensors (CC > 0.95) capture most of the information contained in full-head MFMs. Additionally, we performed source localization for the highest auditory response (M100) by fitting equivalent current dipoles and found that localization errors were < 5 mm. The results further indicate that SSA performance is insensitive to individualized head geometry, supporting the feasibility of using representative anatomical models and highlighting the potential of this approach for clinical OPM-MEG applications.
Ključne besede: medical imaging, magnetoencephalography, sensor optimization
Objavljeno v DiRROS: 17.02.2026; Ogledov: 232; Prenosov: 85
.pdf Celotno besedilo (3,51 MB)
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2.
Optimisation of a sample preparation method for the determination of multi-elemental compositions in human hair by triple quadrupole ICP-MS analysis
Agneta Annika Runkel, Marta Jagodic Hudobivnik, Igor Živković, Polona Klemenčič, Darja Mazej, Milena Horvat, 2026, izvirni znanstveni članek

Povzetek: Monitoring toxic elements has a long tradition in Slovenia due to historical mining. More recently, attention has shifted to essential elements, since both deficiencies and excesses can harm health. Regular monitoring of (non-)essential elements supports risk assessment and policymaking. While urine and blood are common biomonitoring matrices, hair offers a non-invasive alternative that reflects exposure over several months, though standardised methodologies for hair analysis remain limited. This study aimed to develop and validate a sensitive and robust analytical method for the determination of 29 elements in human hair, addressing key challenges in sample preparation and contamination control. We developed a sensitive and robust method for the determination of 29 elements (Ag, Al, As, Ba, Ca, Cd, Co, Cr, Cu, Fe, Hg, K, Mg, Mn, Mo, Na, Ni, P, Pb, Rb, S, Sb, Se, Sn, Sr, Ti, U, V, and Zn) in 3 cm segments of human hair that involves a washing procedure with acetone and Milli-Q water, microwave digestion with 65% HNO3, and analysis with Triple Quadrupole Inductively Coupled Plasma Mass Spectrometry (ICP-MS/MS). Evaluation of preparation steps revealed stainless-steel scissors as a major contamination source. Glass digestion vessels were unsuitable for several elements due to high detection limits and relative standard deviations. The optimised method reduced analytical variability and improved sensitivity compared to published protocols. This validated method enables reproducible multi-elemental analysis in hair, highlights overlooked contamination risks, and is now applied in human biomonitoring studies to strengthen exposure assessment and standardisation efforts.
Ključne besede: determination of elements, optimization, human biomonitoring
Objavljeno v DiRROS: 27.01.2026; Ogledov: 182; Prenosov: 105
.pdf Celotno besedilo (1,59 MB)
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3.
Optimizing foamed glass production with machine learning
Uroš Hribar, Sintija Stevanoska, Christian Leonardo Camacho Villalón, Matjaž Spreitzer, Jakob Koenig, Sašo Džeroski, 2025, izvirni znanstveni članek

Povzetek: Foamed glass is a lightweight material commonly used for insulation. However, optimizing its properties remains a challenge due to the large number of synthesis parameters involved in its production. While previous studies have investigated synthesis conditions, a comprehensive study applying machine learning approaches is lacking in the literature. In this paper, we apply machine learning methods, i.e., random forests of predictive clustering trees and a multilayer perceptron, training them on 124 experimental data points to accurately predict the apparent density and closed porosity of foamed glass. We then apply a multiobjective optimization algorithm together with the multilayer perceptron to find optimal values for the process parameters used in foamed glass production. Our results show that the combination of machine learning and multiobjective optimization is an effective proxy for the development of novel foamed glass materials.
Ključne besede: process optimization, machine learning, foamed glass
Objavljeno v DiRROS: 18.11.2025; Ogledov: 390; Prenosov: 147
.pdf Celotno besedilo (1,51 MB)
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4.
Potential for improving the environmental sustainability of natural aggregates production (Slovenian case study)
Janez Turk, Anja Kodrič, Rok Cajzek, Tjaša Zupančič Hartner, 2025, izvirni znanstveni članek

Povzetek: The environmental performance of natural aggregates for concrete and road construction, extracted from a dolomite quarry, was investigated. Environmental hotspots were identified, and potential optimization measures to further reduce the environmental footprint were proposed. The natural aggregates extracted from the dolomite quarry have relatively low GWP and a low environmental footprint in general. The GWP of 1 tonne of natural aggregates used in concrete production is 1.13 kg CO2 equiv., while for 1 tonne of aggregates used in road construction, it is 0.97 kg CO2 equiv. The dolomite rock in the quarry in question is tectonically fractured, such that very intensive extraction is not required, taking into account the blasting of the rock and further processing. The use of non-road mobile machinery is already optimized. Additional reductions in environmental impact could be achieved by powering the screening process exclusively with electricity from renewable sources, such as a photovoltaic system. In this context, integrating on-site battery storage systems might present a promising solution for addressing the seasonal mismatch between solar energy generation and processing demands.
Ključne besede: rock extraction, global warming potential, environmental impact, optimization, sensitivity
Objavljeno v DiRROS: 15.10.2025; Ogledov: 385; Prenosov: 200
.pdf Celotno besedilo (1,14 MB)
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A study on optimistic and pessimistic pareto-fronts in multiobjective bilevel optimization via [delta]-perturbation
Margarita Antoniou, Ankur Sinha, Gregor Papa, 2025, objavljeni znanstveni prispevek na konferenci

Ključne besede: multiobjective bilevel optimization, optimistic approach, pessimistic approach
Objavljeno v DiRROS: 25.09.2025; Ogledov: 397; Prenosov: 77
.pdf Celotno besedilo (806,83 KB)
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8.
The pitfalls of benchmarking in algorithm selection : what we are getting wrong
Gašper Petelin, Gjorgjina Cenikj, 2025, objavljeni znanstveni prispevek na konferenci

Ključne besede: black box optimization
Objavljeno v DiRROS: 25.08.2025; Ogledov: 461; Prenosov: 244
.pdf Celotno besedilo (670,63 KB)
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9.
Tracing the interactions of modular CMA-ES configurations across problem landscapes
Ana Nikolikj, Mario Andrés Muñoz, Eva Tuba, Tome Eftimov, 2025, objavljeni znanstveni prispevek na konferenci

Ključne besede: single-objective continuous optimization, landscape analysis, algorithm configuration footprint
Objavljeno v DiRROS: 21.08.2025; Ogledov: 556; Prenosov: 231
.pdf Celotno besedilo (1,79 MB)
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