The paper presents an optimisation workflow for modelling of a periodic mechanical structure in the form of a multi-material, axisymmetric beam. The optimisation objective is to prescribe the positions and widths of selected band gaps within a target frequency range for three basic types of structural vibrations: flexural, longitudinal and torsional. The decision variables were geometric parameters of the unit cell and material properties of selected thermoplastics assigned to successive segments of the cell. The frequency characteristics of the beam were determined using the time-domain spectral finite-element method (TD-SFEM). This model was used to perform a sensitivity analysis using the Morris method, which showed the dominant influence of the beam geometry on the position and width of band gaps, with a relatively smaller role of material variability. Due to high computational costs of the global optimisation based on a FEM solver, a surrogate regression model in the form of a residual MLP network was developed to predict the positions and widths of the first five band gaps for each vibration type. The global search was carried out using a genetic algorithm (GA) with the surrogate model and then the results were refined using a deterministic goal-attainment method with a high-fidelity model.

The article proposes an efficient line detection method using a 2D convolution filter. The proposed method was compared with the Hough transform, the most popular method of straight lines detection. The developed method is suitable for local detection of straight lines with a slope from -45˚ to 45˚. Also, it can be used for curve detection which shape is approximated with the short straight sections. The new method is characterized by a constant computational cost regardless of the number of set pixels. The convolution is performed using the logical conjunction and sum operations. Moreover, design of the developed filter and the method of filtration allows for parallelization. Due to constant computation cost, the new method is suitable for implementation in the hardware structure of real-time image processing systems.

This review summarises potential applications of artificial intelligence (AI) in histopathological diagnostics of leukemias reported in the available literature. It compares existing AI models focused on analysing image-based cellular morphology, trained on datasets of either blood smears or bone marrow slides. Key findings indicate a rising trend in research output and literature on that topic, with models achieving high accuracy rates — up to over 95% in leukemia detection and subtype classification. The implications suggest that AI can significantly enhance diagnostic precision, reduce subjectivity, and streamline workflows in hematopathology. Possible limitations and difficulties of introducing AI to routine diagnostics are also elaborated on. Overall, integrating AI into leukemia diagnostics holds promise for improving early detection, supporting clinical decision-making, and advancing treatment in haematological malignancies.

Artificial intelligence applications in histopathological diagnostics are rapidly expanding, with particular promise in complex hematological malignancies where diagnostic accuracy remains challenging and subjective. This study undertakes a scoping review to systematically map the extent of research on artificial intelligence applications in histopathological diagnostics of leukemias, examine geographic distribution and methodological approaches, and assess the current state of AI model performance and clinical readiness. A comprehensive search was conducted in the Scopus database covering publications from 2018 to 2025 (as of 12 July 2025), using five targeted search strategies combining AI, histopathology, and leukemia-related terms. Following a three-stage screening protocol, 418 publications were selected from an initial pool of over 75,000 records across multiple countries and research domains. The analysis revealed a marked increase in research output, peaking in 2024 with substantial contributions from India (26.3%), China (17.9%), USA (13.8%), and Saudi Arabia (11.1%). Among 43 documented datasets ranging from 80 to 42,386 images, studies predominantly utilized convolutional neural networks and deep learning approaches. AI models demonstrated high diagnostic accuracy, with 25 end-to-end models achieving an average accuracy of 97.72% compared to 96.34% for 20 classical machine learning approaches. Most studies focused on acute lymphoblastic leukemia detection and subtype classification using blood smear and bone marrow specimens. Despite promising diagnostic performance, significant gaps remain in clinical translation, standardization, and regulatory approval, with none of the reviewed AI systems currently FDA-approved for routine leukemia diagnostics. Future research should prioritize clinical validation studies, standardized datasets, and integration with existing diagnostic workflows to realize the potential of AI in hematopathological practice.

Mechanical periodic structures exhibit unusual dynamic behavior thanks to the periodicity of their structures, which can be attributed to their cellular arrangement. The source of this periodicity may result from periodic variations of material properties within their cells and/or variations in...