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Recent Submissions

Enhancement of strength properties and durability in high-performance concrete using polymer and steel fibers
(Sciendo, 2026) Němec, Jiří; Jeřábek, Jan; Gandel, Radoslav; Sucharda, Oldřich
Fiber-reinforced and high-performance concretes are elemental pillars of modern material engineering. Fibres are commonly used in combination with high-performance concrete. Important factor is to determine correct amount and type of fibres. This paper's focus is on high-performance fibre-reinforced concrete using polymer and steel varieties of fibres. For the experiment was determined main volume of fibres which was subsequently doubled in modified mixture to observe influence of highly increased number of fibres. Presented research introduces in total 4 mixtures of two different material types of fibres and two different dosages which were subjected to series of mechanical and durability tests including compressive, flexural and tensile-splitting strength, resistance against frost and defrosting chemicals and in the end were manufactured series of steel reinforced concrete beams without sheer reinforcement.The test showed a lot of mixed results, but it was determined that higher amount of steel fibres is usually beneficial for almost all measured characteristics, like compressive strength (137.3 MPa for prism fragments), mass loss after 400 cycles of resistance to frost and chemical de-icing agents (104.4 g/m2) and load tests of steel reinforced beams (125.13 kN). On the other hand, usage of polymer fibres especially in higher volume caused in some cases significant drawbacks like in the case of mass loss after 400 cycles of resistance to frost and chemical de-icing agents (116.9 g/m2) and load tests of steel reinforced beams (84.32 kN). Higher dosage of both types of fibres also influenced tensile-splitting strength in negative way.
Anthropometry and diagnostic aware deep learning for exercise assessment
(Frontiers Media S.A., 2026) Reyes Leiva, Karla Miriam; Nikelová, Pavla; Černý, Martin
Background: Correct technique during strength exercises such as squats and Romanian deadlifts (RDLs) is fundamental for performance and injury prevention. Objective: We introduce ADA (Anthropometry and Diagnostic Aware), a multimodal deep-learning framework that integrates IMU kinematics with anthropometric and diagnostic features to classify movement quality and predict movement related risk. Methods: Seventeen-sensor IMU data were collected from 15 healthy subjects performing correct and incorrect squat and RDL trials. A CNN-LSTM branch processed kinematic sequences and a fully connected branch processed static anthropometric/diagnostic inputs; feature fusion used attention weighting. Results: Incorporating anthropometry and diagnostic context increased sequence-level accuracy from 86.5% (kinematics only) to 94.8% (ADA) and enabled binary risk prediction at 97.8%. Personalized (transfer learning) fine tuning further improved accuracies (mean gains 3%–5% depending on window length). Conclusion: ADA demonstrates that subject-specific static features improve movement quality classification and risk stratification, supporting wearable-based personalized feedback in training and rehabilitation.
Functional safety-oriented risk analysis of heavy vehicle platooning
(University of Žilina, 2026) Mikula, Luboš; Famfulík, Jan
In this paper is presented platooning as a promising approach to reduce the greenhouse gas emissions, fuel consumption, and operation costs in heavy traffic. Attention is given to a risk assessment of vehicle-to-vehicle (V2V) communication in the context of ISO 26262, Edition 2: Road vehicles Functional safety. [ISO 26262-2 2018] The analysis focuses on safety-related hazards associated with convoy driving of heavy vehicles, utilizing the principles of functional safety. It applies hazard analysis and risk assessment (HARA) to classify potential risks according to their severity, exposure, and controllability. Automotive safety integrity level (ASIL) is later determined for each risk. The results provide the ASIL levels of identified hazards, which can be used for developing and validating functional safety measures for cooperative truck driving.
EBMDP: Equal Balance Message Drop Policy for QoS Optimization in Delay Tolerant Networks
(Vysoká škola báňská - Technická univerzita Ostrava, 2026) Ahmad, Zahoor; Saeed, Khalid; Anwar, Muhammad Shahid; Frnda, Jaroslav; Fajeed, Muhammad Faran; Chromý, Erik; Khan, Samiullah; Kutlimuratov, Alpamis
elay-tolerant networks (DTNs) are ad hoc in nature. It is known for characteristics such as in- termittent connectivity and dynamic topology. How- ever, a unique characteristic of DTNs is that there is no guarantee of an end-to-end connection between sender and receiver. Therefore, nodes observe long de- lays in establishing the connection. Once a connec- tion is established between the nodes, the links connect- ing the nodes are fully utilized, and the buffer mem- ory overflows, resulting in congestion that significantly compromises the quality of services (QoS). To avoid congestion, researchers have developed different buffer management policies. This research presents an effi- cient buffer management policy, known as the Equal Balance Message Drop Policy (EBMDP), designed to improve QoS in DTN. The EBMDP discourages un- necessary message drop. EBMDP drops selected mes- sages from the overflowed node, and the selection of messages for the drop from the overflowed node is based on the conditions defined by the EBMDP. The results of EBMDP are better than the drop-oldest ap- proach (DOA) and size-aware drop (SAD) regarding delivery probability (DP), overhead ratio (OR), buffer time average (BTA), and dropped messages. The de- livery probability of EBMDP obtained by simulation is 0.1861, which is higher than the delivery probabilities of SAD and DOA, which are 0.1069 and 0.1114, re- spectively. Similarly, the overhead ratio of EBMDP is lower than that of SAD and DOA. The results show a significant improvement in the buffer time average, as the buffer time average of messages using EBMDP is greater than that of SAD and DOA. The results also show lower messages dropped (MD) for EBMDP than for MD of SAD and DOA.
Bio-Inspired Optimization and Machine Learning for Multi-Band Impedance Matching Networks
(Vysoká škola báňská - Technická univerzita Ostrava, 2026) Amuda, Abdulrasaq Olanrewaju; Karataev, Tologon; Oshiga, Omotayo; Osanaiye, Opeyemi; Stittu, Moshood; Obetta, James; Araoye, Timothy Oluwaseu
The intelligent design of multi-band impedance matching networks was investigated through the integration of bio-inspired optimization and ma- chine learning classifiers. The Hippopotamus Opti- mization Algorithm (HOA) was employed in conjunc- tion with Support Vector Machine (SVM), Artificial Neural Network (ANN), and Random Forest models to derive accurate and fabrication-ready design param- eters. The optimal configuration, defined by a width of 2.7936 mm, spacing of 0.6103 mm, and length of 1.0893 mm, produced a reflection coefficient (S11) of −29.1456 dB, indicating excellent impedance matching across the target frequency band. Among the classi- fiers, the SVM achieved the highest generalization ac- curacy of 96.76% and the lowest mean squared error of 0.3174, surpassing the performance of ANN and Ran- dom Forest. The developed framework reduces reliance on computationally intensive electromagnetic simula- tions, shortens design time, and maintains high predic- tive precision. These results confirm the effectiveness of combining evolutionary optimization with machine learning for the efficient and compact design of multi- band RF matching networks.