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Theoretical modelling of phase stability, magnetic and mechanical properties of compounds based on transition metals
(Vysoké učení technické v Brně. Fakulta strojního inženýrství, ) Heczko, Martin; Zelený, Martin; Friák, Martin; Veis, Martin
In this Doctoral thesis, ab initio calculations of electronic structure within the projector augmented wave method are employed to study physical properties of several compounds containing transition metals and manifesting magnetic ordering. In particular, three material problems were studied, namely the magnetic properties of complex A2BBO6 oxides in dependence of various A, B and B metallic elements; the mechanism twinning on atomic level and its application on NM martensite of Mn-rich Ni-Mn-Ga alloys and the phase transformation and stability in Fe2C. The results of first part show that to achieve the highest Curie temperature in A2BBO6 oxides, A element must have ionic radius as small as possible while the magnetic moments of B and B must be maximized. For deformation twinning of Ni-Mn-Ga alloys it is demonstrated that Mn excess leads to more difficult twinning due to increased phase stability of non-modulated martensite and magnetic ordering of excess Mn atoms. At last but not least, the formation of orthorhombic Fe2C from tetragonal rutile structure is explained from thermodynamical, mechanical and kinetic perspectives as a stabilization due to pseudo-Jahn-Teller effect.
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Optimizing Hyaluronan-Based Lubricants for Treating Thoracolumbar Fascia Pathologies: Insights from Tribological and Pharmacokinetic Studies
(MDPI, 2025-04-16) Streďanská, Alexandra; Šimek, Matěj; Matonohová, Jana; Nečas, David; Vrbka, Martin; Suchánek, Jakub; Pavliňáková, Veronika; Vojtová, Lucy; Hartl, Martin; Křupka, Ivan; Nešporová, Kristina
In a world where the incidence of non-specific lower back pain (LBP) is steadily increasing, researchers are still searching for effective solutions for patients. Hyaluronic acid (HA) viscosupplementation is commonly used to restore lubrication in osteoarthritis (OA) and other medical applications, but its rapid metabolism limits efficacy. This study evaluates whether an HA derivative can replace native HA for the treatment of non-specific LBP while maintaining or enhancing its frictional properties and improving in vivo stability. Six HA-based lubricants, both native and derivatized, were tested in a tribological rabbit fascia model and a new synthetic model. Reduced HA derivative showed better tribological properties and longer in vivo residence time compared to native HA, as demonstrated in pharmacokinetic studies in rabbits. The 316 kDa HA and reduced HA exhibited the most stable tribological properties, which were influenced by their molecular weight and concentration. These findings suggest that both native and reduced HA are promising viscosupplements for intrafascial injection in the treatment of LBP, with reduced HA potentially enhancing effectiveness through a prolonged effect.
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Novel Second-Order Transfer Section for Frequency-Selective Response Generation in Comb Filters
(IEEE, 2025-03-07) Šotner, Roman; Semenov, Dmitrii; Andriukaitis, Darius; Svoboda, Marek; Polák, Ladislav
This article introduces a novel unified second-order filtering topology for standard band-reject (sBR) and inverting band-reject filter, enabling selective response generation. It is applied in the design of special comb filters that allow attenuation as well as amplification of specific bands. The presented solution offers several advantageous features in topology, enhanced cascadability, better parameter-setting performance, and the use of readily available off-the-shelf components, all while reducing overall costs. This is a significant improvement compared to the previous solutions in this field, which relied on a very expensive CMOS process. The topology employs a combination of an operational transconductance amplifier (OTA) and a current feedback amplifier. The intended center frequencies and transfer values of the comb filter are set to 50 Hz (-20 dB), 1 kHz (+26 dB), 10 kHz (+17 dB), and 100 kHz (-30 dB). The comb filter was experimentally verified via simulations as well as laboratory measurements utilizing LT1228 devices in a fabricated prototype.
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Propaganda agresorů
(Vysoké učení technické v Brně. Fakulta výtvarných umění, ) Huseva, Volha; Gabriel, Michal; Korbička, Pavel
Propaganda jako moderní nástroj působení na společenské vědomí v rámci východoevropského konfliktu roku 2022 je inspirací u výstupu mé diplomové práce. Lze pochopit z názvu, že středem pozornosti je válečná propaganda, která pomocí masmédií vytvořila paralelní svět. Nehledě na to, že tento svět je vytvořen z fikce a lží, existují lidé různého věku, původu a povolání, kteří ho považují za čistou pravdu. Do této skupiny lidí patří i jeden blízký člen mé rodiny. Celý tento fakt mi posloužil jako motivace k pochopení menanismu válečné propagandy, přičemž se tato osobní zkušenost stala pramenem mé inspirace, potažmo i důvodem pro výběr tohoto tématu. Válečná agresivní propaganda je děsivá tím, že omezuje jednotlivce ve vlastním myšlení, tím pádem způsobuje jeho degradaci a vytváří podmínky, aby oběť vzala „dobrovolně“ do svých rukou zbraň a šla do války. Cílem je vytvoření uměleckého projektu reagujícího na propagandu agresorů. Tohoto cíle chci dosáhnout pomocí site-specific instalace v prostoru skutečného opuštěného divadla. Jak jsem již zmínila, válečná propaganda vytvořila paralelní svět, což bylo podnětem k výběru divadla jako místa instalace; je to místo určeno k představení, k využití hereckých výkonů a fikce za cílem realizovat daný projekt postavený na smyšlence. Zvolila jsem prostor divadla Emila Králíka na Brněnském výstavišti. Strop divadla je momentálně v havarijním stavu, tudíž se může každou chvíli zhroutit, což navozuje atmosféru nebezpečí.
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Analyzing the performance of biomedical time-series segmentation with electrophysiology data
(NATURE PORTFOLIO, 2025-04-06) Ředina, Richard; Hejč, Jakub; Filipenská, Marina; Stárek, Zdeněk
Accurate segmentation of biomedical time-series, such as intracardiac electrograms, is vital for understanding physiological states and supporting clinical interventions. Traditional rule-based and feature engineering approaches often struggle with complex clinical patterns and noise. Recent deep learning advancements offer solutions, showing various benefits and drawbacks in segmentation tasks. This study evaluates five segmentation algorithms, from traditional rule-based methods to advanced deep learning models, using a unique clinical dataset of intracardiac signals from 100 patients. We compared a rule-based method, a support vector machine (SVM), fully convolutional semantic neural network (UNet), region proposal network (Faster R-CNN), and recurrent neural network for electrocardiographic signals (DENS-ECG). Notably, Faster R-CNN has never been applied to 1D signals segmentation before. Each model underwent Bayesian optimization to minimize hyperparameter bias. Results indicated that deep learning models outperformed traditional methods, with UNet achieving the highest segmentation score of 88.9 % (root mean square errors for onset and offset of 8.43 ms and 7.49 ms), closely followed by DENS-ECG at 87.8 %. Faster R-CNN and SVM showed moderate performance, while the rule-based method had the lowest accuracy (77.7 %). UNet and DENS-ECG excelled in capturing detailed features and handling noise, highlighting their potential for clinical application. Despite greater computational demands, their superior performance and diagnostic potential support further exploration in biomedical time-series analysis.