The rapid spread of unmanned aerial vehicles (UAVs) has created new challenges for airspace security, as drones are increasingly used for surveillance, smuggling, and potentially for attacks near critical infrastructure. A key difficulty lies in reliably distinguishing UAVs from visually similar birds in electro-optical surveillance channels, where complex backgrounds and visual noise often increase false alarms. To address this, we investigated recent YOLO architectures and developed an enhanced model named YOLOv12-ADBC, incorporating an adaptive hierarchical feature integration mechanism to strengthen multi-scale spatial fusion. This architectural refinement improves sensitivity to subtle inter-class differences between drones and birds. A dedicated dataset of 7291 images was used to train and evaluate five YOLO versions (v8–v12), together with the proposed YOLOv12-ADBC. Comparative experiments demonstrated that YOLOv12-ADBC achieved the best overall performance, with precision = 0.892, recall = 0.864, mAP50 = 0.881, mAP50–95 = 0.633, and per-class accuracy reaching 96.4% for drones and 80% for birds. In inference tests on three video sequences simulating realistic monitoring conditions, YOLOv12-ADBC consistently outperformed baselines, achieving a detection accuracy of 92.1–95.5% and confidence levels up to 88.6%, while maintaining real-time processing at 118–135 frames per second (FPS). These results demonstrate that YOLOv12-ADBC not only surpasses previous YOLO models but also offers strong potential as the optical module in multi-sensor fusion frameworks. Its integration with radar, RF, and acoustic channels is expected to further enhance system-level robustness, providing a practical pathway toward reliable UAV detection in modern airspace protection systems.

Tanks made of three different brands of rotational polyethylene (DOW, ELTEX, and M350) with three degrees of sintering (normal sintering (NS), incomplete sintering (ICS), and thermally degraded sintering (TDS)) and three thicknesses (7.5 mm, 8.5 mm, and 9.5 mm) were explored. It was found that the thickness of the walls of the tanks did not have a statistically significant effect on the parameters of the ultrasonic signal (USS). An increase in temperature caused a decrease in the USS parameters. According to the temperature coefficient of stability, the ELTEX brand of plastic can be distinguished (from DOW and M350). The ICS degree of the sintering of the tanks was revealed from a significantly lower amplitude of the bottom signal, compared with NS and TDS degree samples. By analyzing the amplitude of the third harmonic of the ultrasonic signal (β), three degrees of the sintering of containers NS, ICS, and TDS were revealed (with an accuracy of about 95%). Equations β = f(T, PIAT) were derived for each brand of rotational polyethylene (PE), and two-factor nomograms were constructed. Based on the results of this research, a method for the ultrasonic quality control of polyethylene tanks manufactured using rotational molding was developed.

In this work, we optimized three key factors for rotational molding composites: the recycled polyethylene (rPE) content, the pigment (Cp) content, and the process parameter-peak internal air temperature (PIAT). We studied the influence of rPE, Cp, and PIAT on various composite properties. These included mechanical properties (e.g., elastic modulus E), impact strength (MFEsp), surface characteristics (wettability measured by contact angle θ and IR spectroscopy), thermal stability (by DTA–TG analysis), environmental stress cracking resistance (ESCR in hours), and the amplitude of the third harmonic β of the ultrasonic back-wall signal. The IR spectroscopy and contact angle results indicate that adding rPE and pigment slightly increases the composite’s surface hydrophilicity. The results show that PIAT strongly influences all the characteristics of the composites studied. Depending on its percentage, the introduction of rPE can either improve or worsen these composite properties. A correlation was found between β, ESCR, MFEsp, and E, demonstrating that β can serve as a quantitative indicator of internal stress (IS) in rotomolded parts. The recommended optimal composition is rPE 30%, Cp 0.5%, and PIAT 195 °C. Under these conditions, the composite exhibits minimal internal stress and optimal performance, which in turn extends the service life of rotomolded products. Four nomograms were developed: rPE = f(MFEsp, Cp, PIAT) and rPE = f(β, Cp, PIAT), which make it possible to quickly determine MFEsp and β of a product based on the actual PIAT, taking into account rPE and pigment content in the composite (they also allow selecting the rPE and pigment content in the composition depending on the required MFEsp).

This article explores the challenges of integrating two deep learning neural networks, YOLOv5 and RT-DETR, to enhance the recognition of unmanned aerial vehicles (UAVs) within the optical-electronic channels of Sensor Fusion systems. The authors conducted an experimental study to test YOLOv5 and Faster RT-DETR in order to identify the average accuracy of UAV recognition. A dataset in the form of images of two classes of objects, UAVs, and birds, was prepared in advance. The total number of images, including augmentation, amounted to 6337. The authors implemented training, verification, and testing of the neural networks exploiting PyCharm 2024 IDE. Inference testing was conducted using six videos with UAV flights. On all test videos, RT-DETR-R50 was more accurate by an average of 18.7% in terms of average classification accuracy (Pc). In terms of operating speed, YOLOv5 was 3.4 ms more efficient. It has been established that the use of RT-DETR as the only module for UAV classification in optical-electronic detection channels is not effective due to the large volumes of calculations, which is due to the relatively large number of parameters. Based on the obtained results, an algorithm for combining two neural networks is proposed, which allows for increasing the accuracy of UAV and bird classification without significant losses in speed.

In this research, by using the finite element method, the effect of five parameters (density of a liquid mineral fertilizer (ρ), its temperature (T), tank wall thickness (L), spacing of stiffeners (K) and stiffeners height (h)) on the strength of standard polyethylene rotomolded tanks used for storage of liquid mineral fertilizers (LMF) was studied. Using the Taguchi method, it was found that these parameters are ranked according to the degree of their influence (in decreasing order) on: maximum stresses (r>L>h>T>K), maximum stresses in the tank walls (r>L>K>T>h) and deformations of the tank (for DX/DY: r>L>h>T>K and for DZ: r>L>h>K>T). Validation of the FEM strength calculations was carried out, which showed satisfactory convergence of the calculated and experimental values. Generalized equations are derived that describe the effect of all five studied parameters on P, PW and tank deformations (along the X, Y and Z axes). On the basis of the derived equations, a nomogram has been constructed, which makes it possible to choose the optimal wall thickness that will correspond to the LMF density and storage temperature. Applying the optimal wall thickness ensures a guaranteed service life of at least 50 years, minimizing the risk of environmental accidents caused by tank failure and the release of LMF and associated toxic substances into groundwater. This research offers valuable insights for designing safer and more durable storage tanks for liquid mineral fertilizers. As an optimal design of the tank for storing the most common fertilizer UAN-32 (Urea Ammonium Nitrate, 32 % nitrogen), with a density of 1.32 g/cm3 and at storage temperatures up to 40 °C, the following values of structural parameters are recommended: L=10 mm, K=38 mm, and h=4 mm

In this study, using the method of probabilistic deterministic planning (PDP), the optimum design parameters of a standard polyethylene tank used worldwide for transporting liquid mineral fertilizers (LMF) were determined. By the finite element method, the effect of the density of liquid mineral fertilizer, tank wall thickness and four motion modes (braking, acceleration, jump and landing) on the strength of standard polyethylene tanks was studied. According to the results of the study, the five most informative areas in the tank design were identified, for which the values of maximum stresses (σmax) were obtained: filler neck, pockets, walls, tap-in points and wall transition to the tank roof. As the LMF density increases, σmax in the tank increases linearly. Increasing the tank wall thickness by 1.5 times reduces the maximum stresses by 30 to 50 %. It was found that motion mode has a significant effect on the stress-strain state of a standard tank. The “heaviest” mode for a standard tank is “braking”. The “acceleration” motion mode causes σmax of no more than 60 % of the “braking” mode values. The “lightest” mode is “landing”, in which σmax is no more than 28 % relative to “braking”. Based on the PDP method, equations were derived for calculating maximum stresses depending on LMF density, wall thickness and motion mode of the tank. Nomograms were built that make it possible to quickly determine the wall thickness of a standard tank without calculations, depending on external factors. The results of the study can be used in practice when designing safe and durable tanks for transporting liquid mineral fertilizers.