Search the repository

A+ | A- | | SLO | ENG

Keywords: electrical post-treatment, composite insulating silicone rubber, molecular dynamics, performance optimization
Published in DiRROS: 15.04.2026; Views: 167; Downloads: 112
Full text (2,05 MB)
This document has many files! More...

Abstract: Aims: Patients with first-degree atrioventricular (AV) block and mechanical AV dyssynchrony can present with heart failure (HF)- like symptoms. AV-optimized conduction system pacing (CSP) can improve haemodynamics and symptoms, but selection criteria remain uncertain. We aimed to identify electrocardiographic and echocardiographic predictors of an acute haemodynamic response to AV-optimized CSP in symptomatic first-degree AV block. Methods and Results: Nineteen patients (mean age 60.5 ± 21.1 years; 37% female) with symptomatic first-degree AV block underwent baseline electrocardiography and echocardiography followed by AV-optimized conduction system pacing and repeat echocardiographic assessment. Electrocardiographic parameters (PR interval, P wave duration/PR interval ratio) and echocardiographic indices (E/A wave confluence, A-Q interval, and DFT/RR ratio) were tested for association with change in left ventricular stroke volume (LVSV). The mean PR interval was 395 ± 61 ms, the mean A-Q interval 155 ± 65 ms, and the mean DFT/RR ratio 0.34 ± 0.1. E/A wave confluence was present in 15 patients (79%). AV-optimized pacing increased LVSV by 7.8 ± 3.9 ml, corresponding to an 11.8 ± 5.7% relative increase (P < .01). Echocardiographic parameters were associated with LVSV response, including A-Q interval (r = 0.63, P = .004), DFT/RR ratio (r = −0.59, P = .008), and E/A wave confluence (r = 0.57, P = .01). Electrocardiographic parameters were not associated with LVSV change. Conclusions: Echocardiographically assessed mechanical AV dyssynchrony, rather than electrocardiographic parameters, is associated with an acute haemodynamic response to pacing. Echocardiographic evaluation may help identify patients with prolonged PR interval who could benefit from AV-optimized CSP.
Keywords: atrioventricular dyssynchrony, atrioventricular conduction block, conduction system pacing, atrioventricular optimization
Published in DiRROS: 08.04.2026; Views: 200; Downloads: 150
Full text (306,59 KB)
This document has many files! More...

Abstract: 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.
Keywords: medical imaging, magnetoencephalography, sensor optimization
Published in DiRROS: 17.02.2026; Views: 369; Downloads: 190
Full text (3,51 MB)
This document has many files! More...

Abstract: 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.
Keywords: process optimization, machine learning, foamed glass
Published in DiRROS: 18.11.2025; Views: 642; Downloads: 255
Full text (1,51 MB)
This document has many files! More...

Keywords: road engineering, performance prediction, steel slag–rubber powder-modified asphalt mixture, optimization algorithms
Published in DiRROS: 03.10.2025; Views: 594; Downloads: 298
Full text (2,32 MB)
This document has many files! More...