http://hdl.handle.net/10563/1000007 2026-04-06T13:14:00Z http://hdl.handle.net/10563/1012778 Complete stability analysis and optimal design for dual-state-feedback delayed resonator Gao, Qingbin; Cai, Jiazhi; Wu, Hao; Zhou, Kai; Pekař, Libor We propose a dual-state-feedback delayed resonator (DFDR) by incorporating an additional acceleration-based feedback into the classical DR design. The optimal tuning of its feedback parameters is guided by two objectives: enhancing vibration suppression at a specified target frequency and maintaining overall system stability. First, we extend the Advanced Clustering with Frequency Sweeping (ACFS) methodology from the delay-only domain to the combined delay-gain domain, enabling a rigorous and complete stability analysis where feedback gains and delays interact. Second, we develop the optimal parameter tuning procedure and demonstrate that the proposed DFDR achieves improved stability margins, enhanced robustness to frequency variations, and superior vibration suppression performance compared to the classical DR. These results highlight the practical potential of DFDR as an effective and robust solution for active vibration suppression systems. 2026-01-01T00:00:00Z http://hdl.handle.net/10563/1012779 A purely algebraic proof of the omega-reducibility of pseudovarieties representing low half levels of concatenation hierarchies Volaříková, Jana We are concerned with the ω-reducibility of pseudovarieties of ordered monoids representing half levels of concatenation hierarchies. In the author’s paper (Int. J. Algebra Comput. 64(01), 87–135, 2024), the ω-reducibility of pseudovarieties representing levels 1/2 and 3/2 of concatenation hierarchies with a locally finite basic pseudovariety has been proven, using results of the paper by Place (Log. Methods Comput. Sci. 14(4:16), 1–58, 2018) on so called covering of corresponding sets of regular languages. In this paper, we prove the same results on the ω-reducibility, not using the results of the mentioned paper by Place, although still inspired by their proofs. This new method of the proofs of the ω-reducibility prepares us to their potential extension to higher half levels of concatenation hierarchies. The process of a gradual generalization is initiated in this paper. 2026-01-01T00:00:00Z http://hdl.handle.net/10563/1012774 Additively transtable conic sections with respect to fixed coefficients Cerman, Zbyněk; Vítková, Lenka The arithmetic mean has several important properties. One of them preserves the result of the arithmetic mean. That is, if one value increases and another decreases, the result of the arithmetic mean is the same. This property is called transfer stability, transtability for short. We can see its reach in several mathematical theories. The most common use is with aggregation functions. This article aims to show another use of this property, specifically in the geometry of conic sections. We have outlined how the transtability of a conic section works. The main idea was to find a common property for conic sections connected by transtability. We found that these conics have the same common intersection and the set of all centers forms a conic. 2025-01-01T00:00:00Z http://hdl.handle.net/10563/1012761 Androbank: the impact of API levels on mobile malware detection Oulehla, Milan; Dorotík, Ladislav; Komínková Oplatková, Zuzana Android is the most widely used operating system, making it a prime target for mobile malware, leading to data breaches and financial losses (e.g., Dark Herring). To address these issues, AI-based forensic tools are crucial for investigating security incidents, but their accuracy depends on high-quality mobile malware datasets. While dynamic analysis has limitations, recent research has shifted towards static analysis and AI-based methods for malware detection. However, there are three key challenges: lack of reproducibility, low dataset quality, and bias in AI datasets. This paper focuses on an overlooked bias—the incorrect API Level distribution in malware datasets. Such bias skews AI detection results, making them appear effective in tests but less applicable in real-world scenarios. To highlight the importance of dataset quality, three case studies on API Level Analysis were conducted, showing how biased datasets can distort detection results. To address this, the paper introduces methods and terms like Delayed Interception, Dataset of guaranteed quality, API Milestones, AndroBank, and Sample Unification, which aim to enhance dataset reliability and improve AI-based mobile malware detection. 2026-01-01T00:00:00Z