Welcome to the BUT Digital Library - an institutional repository operated by the Central Library on the DSpace system.
Do you want to deposit your article or preceedings into Digital Library? It is very simple. You can find all the information in the manual published online on BUT Portal of libraries.
Central Library supports open access to scientific publishing - Open Access.
You can also request for grant for open publishing from Open Access Fund You can find more information OA fund web page.
Into the Digital Library is integrated citation manager Citace PRO. It will allow you to easily create a bibliographic citation or save a record in the manager.
Communities in DSpace
Select a community to browse its collections.
Recent Submissions
Item
Single-Atom Colloidal Nanorobotics Enhanced Stem Cell Therapy for Corneal Injury Repair
(American Chemical Society, 2025-05-13) Ju, Xiaohui; Javorková, Eliška; Michalička, Jan; Pumera, Martin
Corneal repair using mesenchymal stem cell therapy faces challenges due to long-term cell survival issues. Here, we design cerium oxide with gold single-atom-based nanorobots (CeSAN-bots) for treating corneal damage in a synergistic combination with stem cells. Powered by glucose, CeSAN-bots exhibit enhanced diffusion and active motion due to the cascade reaction catalyzed by gold and cerium oxide. CeSAN-bots demonstrate a two-fold increase in cellular uptake efficiency into mesenchymal stem cells compared to passive uptake. CeSAN-bots possess intrinsic antioxidant and immunomodulatory properties, promoting corneal regeneration. Validation in a mouse corneal alkali burn model reveals an improvement in corneal clarity restoration when stem cells are incorporated with CeSAN-bots. This work presents a strategy for developing glucose-driven, enzyme-free, single-atom-based ultrasmall nanorobots with promising applications in targeted intracellular delivery in diverse biological environments.
Item
Digital Revolution in Spatial Planning: The Potential of Geolocation Data in Czechia
(MDPI, 2025-05-07) Jirasek, Petr; Šomplák, Radovan
This article analyzes population movement patterns in the Vyso & ccaron;ina Region, Czechia, using mobile network geolocation data. Geolocation data provide new insights into population movement and structure, capturing real-time fluctuations in population size at different times of day and days of the week. The article aims to contribute to a better understanding of spatiotemporal population dynamics and identify links between movement patterns and different types of areas. Key mobility trends, such as work commuting, seasonal migration related to second homes and tourism, and the influence of urbanization on movement patterns, are identified. A scaling approach for categorizing municipalities based on their characteristics is proposed and tested in a case study of Vyso & ccaron;ina Region municipalities. Furthermore, a case study of various municipality types demonstrates the practical application of geolocation data in spatial planning. The results highlight the value of these data for spatial planning, enabling a better understanding of population needs and optimization of public services and infrastructure.
Item
Characteristic function and moment generating function of multivariate folded normal distribution
(Springer Nature, 2025-05-10) Benko, Matej; Hübnerová, Zuzana; Witkovský, Viktor
In this study, we derive the characteristic function of the multivariate folded normal distribution, a distribution that arises when the magnitudes-but not the signs-of a normally distributed random vector are of interest. The folded normal distribution is widely applicable across various fields. Thus, obtaining an analytical expression for its characteristic function is pivotal in understanding its fundamental properties. Moreover, this allows one to facilitate numerical evaluations of complex distributions involving linear combinations of absolute values of dependent normal variables. The derivation is based on a novel expression of the moment generating function, formulated using the cumulative distribution function of the multivariate normal distribution. To validate our findings, we present two examples using our MATLAB implementation. We compare the characteristic function for the sum of the absolute values of elements of a multivariate normal vector with the simulated empirical counterpart. Additionally, we derive the second mixed moment of the bivariate folded normal distribution from the moment generating function, demonstrating its agreement with known theoretical expressions.
Item
Possibilities of K-Value Determination for Active Admixtures with Respect to Durability
(MDPI, 2025-05-12) Šperling, Petr; Hubáček, Adam; Hela, Rudolf; Stará, Tereza; Dvořák, Richard
This paper discusses the possibility of determining k-values for active admixtures concerning durability factors such as the depth of penetration of water under pressure and the depth of carbonation of cement mortars with fly ash. The k-value considers the use of active admixtures in concrete when calculating the water/cement ratio and the equivalent amount of binder. Currently, only the effect of the active admixture on the compressive strength of concrete and cement mortars is considered when determining the k-value, but not the effect of the active admixture on durability. To account for the influence of durability factors on the determination of the k-value, the mathematical functions of the property, dependent on the water/cement ratio and the age of the cement mortar, were constructed using regression analysis. From the determined functions, it was then possible to use an optimisation problem to determine the k-value so the difference between the actual measurement and calculated depth of pressure water seepage or carbonation was as small as possible. A high coefficient of determination of 0.9855 was achieved for the pressure water seepage depth function, but the coefficient of determination for the carbonation depth was lower.
Item
Deep Learning-Enhanced Ultrasound Analysis: Classifying Breast Tumors Using Segmentation and Feature Extraction
(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2025-05-09) Hamza, Ali; Mézl, Martin
Breast cancer remains a significant global health challenge, requiring accurate and effective diagnostic methods for timely treatment. Ultrasound imaging is a valuable diagnostic tool for breast cancer because of its affordability, accessibility, and non-ionizing radiation properties. This study proposes a classification method for breast ultrasound images that integrates segmentation and feature extraction. Initially, ultrasound images are pre-processed to enhance quality and reduce noise, followed by segmentation using the U-Net++ architecture. Feature extraction is then performed using MobileNetV2, and these features are used to train and validate classification models to differentiate between malignant and benign breast masses. The model's performance is assessed using accuracy, precision, recall, Mean IoU, and Dice Score metrics. The U-Net++ model achieved superior segmentation performance with a Dice Score of 0.911 and a Mean IoU of 0.838, outperforming related methods such as U-Net (0.888 Dice, 0.79 IoU) and Efficient U-Net (0.904 Dice, 0.80 IoU). In the classification task, MobileNetV2 when paired with the ANN classifier, produced the highest test accuracy at 96.58%, with a precision of 97% and recall of 96%. Our approach demonstrates superior performance compared to other models, such as RMTL-Net, which achieved 91.02% accuracy, and hybrid CAD models with 94% accuracy. This highlights the benefits of combining advanced segmentation and feature extraction techniques, with MobileNetV2 proving to be the better model, offering superior accuracy and robustness in classification tasks. This approach has the potential to support promise for supporting radiologists, enhance diagnostic accuracy, and ultimately improve outcomes for breast cancer patients. In the future, we will use comprehensive datasets to validate our methodology.