Brno University of Technology

Security of user attributes (including identity, role, real-time location and access time, etc.) show highly dynamic characteristics in mobile cloud computing. Static permission management used in current information security and access control is difficult to adapt to the dynamic commonly, resulting in permission allocation lagging behind the actual change of user attributes, increasing the risk of illegal data access. Therefore, this paper proposes a multi-level access control for secure data based on updateable attribute encryption (MAC-UAE) in mobile cloud center. First, MAC-UAE constructs an efficient and collaborative data transmission model for cloud center, which seamlessly interconnects nodes through data center switches to ensure the flexibility and reliability of data transmission. On this basis, it constructs an adaptive access control system centered on subject, object and access policy attributes, which responds to attribute updates realtime and sets access rights boundary accurately. Then, with comprehensive credibility by user attributes, optimization tree is used to change the access control policy by automatically generating and dynamically updating the set attribute key of the high-credibility user, which ensures the fineness and security of the rights management. Finally, MAC-UAE intelligently selects the optimal transmission path according to the real-time update state of the key, and realizes the flexible multi-level access control of secure data in the mobile cloud center through authentication and attribute key decryption. Experimental results show that both the credibility and efficiency of the proposed MAC-UAE is better than exist methods with multiple scales.

From 2019 to 2023, extensive innovation and modernization took place at the lock chambers of the Gabčíkovo waterworks to increase the safety and intensity of the water transport. The waterworks is located on the Danube River about 60 km southeast of Bratislava—the capital of Slovakia. There are two lock chambers (left and right) of similar dimensions. The locks have been in operation since 1992. After about 30 years of operation, their modernization and innovation were carried out. The upgrade of Gabčíkovo locks is a CEF (the Connecting Europe Facility) funded project aiming to upgrade the Gabčíkovo locks to ensure good navigation status in the Slovak part of the Danube and along the Rhine‐Danube core network corridor. The total cost of the modernization was 166 million euros. The innovation of the right and left lock was completed in December 2023. The lower gate, the upper gate, the safety flap gate, the regulation valves, the temporary protection dam of channels, floats, etc. were replaced. The paper focuses on the description of the innovated structures and especially on the description of new concrete structures used to ensure the transfer of forces from steel elements (gates) to the original concrete structure.

Electronics play an increasingly significant role in our lives. Consequently, the demand for both production volume and quality in electronics is continuously rising. Conveyor reflow ovens are the most widely used technology for soldering electronic assemblies onto printed circuit boards. The implementation of these ovens directly affects the quality of solder joints and, most importantly, the reliability of manufactured electronic assemblies, which are present in virtually all aspects of daily life. The solder joint is formed by the reflow of the solder alloy and is defined by the thermal profile, which represents temperature over time. The most common heating method relies on forced hot air convection, where the printed circuit board with mounted components moves through the oven. One of the key design requirements for these ovens is maintaining a consistent thermal profile to achieve a homogeneous temperature field across the reflow zones. To assess this condition, it is essential to measure convection under operating conditions. A new method for measuring the temperature field directly within the conveyor oven has been developed and experimentally verified during production.

This paper focuses on static solutions for the following Choquard equation with zero mass and Coulomb potential where $\mu >0$, $\frac{18}{7}<p\le 6$, $\alpha \in (0, 3)$, $\alpha +3$ is the upper critical exponent in the sense of the Hardy–Littlewood–Sobolev inequality, $I_{\alpha }{:}\,\mathbb {R}^3\rightarrow \mathbb {R}$ is the Riesz potential, and $\frac{1}{4\pi |x|}$ is the Coulomb potential. By carefully analyzing the intricate interplay between the power and Coulomb terms, we establish three types of variational geometries of the problem and prove the following existence results based on the behavior of p: the existence of two solutions, one being a local minimizer and the other of mountain-pass type, for an explicit range $0<\mu <\mathrm {Const.}$ when $\frac{18}{7}<p<3$; the existence of a positive solution if $\mu$ takes some particular value when p=3; the existence of a ground state solution for all $\mu >0$ when $4<p<6$, and for two explicit ranges $\mu >\mathrm {Const.}$ when $3<p<4$ and p=4. Furthermore, we obtain a non-existence result for the case p=6. Particularly, we identify different compactness thresholds for above three cases, and introduce three types of test functions to control the corresponding minimax levels to be less than prescribed thresholds, thereby overcoming the loss of compactness arising from the nonlocal critical term. The derivation of these strict inequalities is a novel contribution and constitutes one of the noteworthy highlights of this work, which is available and new for the limiting Sobolev critical problem as $\alpha \rightarrow 0$. We believe that the underlying ideas have potential for future development and can be applied to a broader range of variational problems with critical growth.

The optical excitation of metals initially creates short-lived non-Fermi distributions of the electrons. The electrons and holes excited far above and below the Fermi level quickly relax to hot Fermi-distributions that subsequently cool via electron-phonon scattering. Here, we show that such non-thermal charge carriers beyond the Fermi-distribution speed up the prototypical first-order antiferromagnetic-to-ferromagnetic phase transition in FeRh. In ultrafast x-ray diffraction experiments, we vary the maximum electron temperature by increasing the pump pulse duration up to 10 ps. For direct optical excitation of FeRh, ferromagnetic domains nucleate within 8 ps as soon as the successively deposited energy surpasses the site-specific threshold energy. In contrast, suppressing the direct optical excitation by an optically opaque Pt layer leads to a nucleation on a 50 ps timescale driven by the near-equilibrium heat transport. These findings unambiguously identify the photo-excitation of non-thermal electrons and not electron-phonon non-equilibria to enable the rapid phase transition in FeRh.

In this study, plasma activated water (PAW) was prepared by a pin-hole discharge, generating plasma directly in liquids with air flowing into the discharge. Radish (Raphanus sativus) plants were grown in pots filled with soil for 30 d. Pots were divided to 4 variants based on the PAW application: PAW prepared from distilled water (PAW DW), PAW prepared from tap water (PAW TW), foliar application of PAW on leaves and irrigated by TW (TW/PAW DW) and control group irrigated by TW. Results have indicated enhancement of the growth of the plant fresh matter in all variants treated by PAW. Vitality of the plants was determined by chlorophyll fluorescence. Fluorescence measurement results have shown inhibition of photosynthesis activity in case of plants treated with PAW compared to control group (TW), which means the treatment of plants with PAW lowers the overall vitality. Elemental analysis results showed that the PAW treatment of plants increased the content of nitrogen in the root part of the radish plants. The sensory evaluation showed that the PAW treatment influenced a certain taste and aesthetic characteristics of R. sativus. Overall, the foliar application of PAW seems to be more convenient option as a plant fertilizer compared to the soil irrigation.

In today’s modern society, it is difficult, nearly impossible, to work and study effectively without using the internet. With services moving into cyberspace and the ever-increasing number of users, new cyber threats are emerging with the potential to cause devastation to both organizations and individuals. For this reason, it is necessary to educate users regardless of their age, gender, and qualification. This paper addresses the challenges associated with the need for cybersecurity education and presents lessons learned from applying an interactive and gamified approach within a cyber range (CR), a controlled environment that enables the deployment of virtual machines and networks for research, training, and testing purposes. In our work, we utilized the CR platform to teach cybersecurity at the primary, secondary, and high school levels of education. Through a series of tests, different approaches, surveys, and feedback collected from students and teachers, we identified their perceptions and critical aspects of CR-based cybersecurity education. We found that gamification positively influences learning, with students emphasizing the fun aspect and teachers highlighting engagement and motivation. Both groups value interactivity for developing practical skills and reinforcing theoretical concepts. Although scoring encourages competition, some students find it stressful. Similarly, penalizing hints can motivate problem solving, but may also deter those needing assistance. These and other findings presented in this paper may be useful for building and further developing cyber ranges to improve the effectiveness of teaching, learning and training cybersecurity.

Residual stresses are considered as a significant factor influencing the stress-states in arteries. These stresses are typically observed through opening angle of a radially cut artery segment, often regarded as a primary descriptor of their stress-free state. However, the experimental evidence regarding the stress-free states of different artery layers is scarce. In this study, two experimental protocols, each employing different layer-separating sequences, were performed on 17 human common carotid arteries; the differences between both protocols were found statistically insignificant. While the media exhibited opening behaviour (reduced curvature), a contrasting trend was observed for the adventitia curvature, indicating its closing behaviour. In addition to the different bending effect, length changes of both layers after separation were observed, namely shortening of the adventitia and elongation of the media. The results point out that not all the residual stresses are released after a radial cut but a significant portion of them is released only after the layer separation. Considering the different mechanical properties of layers, this may significantly change the stress distribution in arterial wall and should be considered in its biomechanical models.

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.