During the article a hybrid named-entity recognition (NER) algorithm for Uzbek is presented. It combines rule-based modules (transliteration, dialect normalization, morphological analysis) with modern neural network models. The study is motivated by Uzbek’s agglutinative morphology, dialect diversity and the lack of specialized resources, which hinder the direct application of named entity recognition methods developed for English or other high-resource languages. As part of the work, an annotated corpus of more than three thousand sentences in the Uzbek language was formed, including legal documents, scientific articles, news materials and informal texts from social networks. The corpus is marked up according to the BIOES scheme taking into account the specific morphological and lexical features of the Uzbek language. The developed rule-oriented algorithms (transliteration, dialect standardization, morphological analysis) are integrated into a single post-processing system that complements neural network models. As a result of experiments aimed at assessing the effectiveness of the proposed approach, it was found that the hybrid approach significantly improves the accuracy and completeness metrics of named entity recognition in different thematic domains. The practical value of the study is that the proposed system can serve as a basis for automatic processing of Uzbek texts in the tasks of searching and extracting information, dialect normalization, annotating large text data and digitalization of document flow. The theoretical significance is that the work expands approaches to named entity recognition for low-resource languages, offering methods that take into account morphological-syntactic and dialectal features.
In this study, the trajectory of water droplets in a sprinkler irrigation system was studied using the optical tracking method. The analysis was carried out on the basis of 1920×1080 pixel, 60 frame/s video recordings taken in real field conditions. Image segmentation and trajectory detection algorithms were developed on the OpenCV and Python platforms to determine the trajectory of water droplets. The main focus of the study was to determine the dynamics of the movement of water droplets with a diameter of 0.002 meters after exiting a sprinkler head rotating at an angle of 360°. The following parameters were taken into account in the mathematical modeling process: exit velocity - 11 m/s, exit angle - 30°. Based on these data, the trajectory of the droplet was calculated using the equations of ballistic motion and air resistance (drag). The Random Forest model was used to assess the influence of factors. The results showed that the factors that have the greatest impact on the water spray trajectory are wind (31.2%) and terrain slope (25.6%). This means that small changes in wind speed and slope significantly reduce the water spray radius and cause uneven water distribution. The Convolutional Neural Network (CNN) model was used to spatially analyze and classify areas, achieving 93% accuracy in flat terrain and 74–79% accuracy in windy and uneven areas. This result indicates that the modeled system works with high reliability even in real field conditions. At the end of the study, the sprinkler exit angle and installation spacing were optimized, and the drift zones of water due to wind were reduced from 21.8% to 7.1%. This change has increased the stability of water distribution and allowed for a significant reduction in water consumption in crop production.
To ensure the high competitiveness of cotton products in Uzbekistan, modern approaches are being implemented to enhance both the yield and quality of cotton fiber. One such innovation is the gene knockout technology, developed by Uzbek scientists and patented in several cotton-producing countries. This advancement enabled the creation of unique genetically modified cotton varieties of the Porlock (P) series, which exhibit improved characteristics in terms of cultivation, vegetation, and fiber quality. These new genetically modified cotton varieties possess distinctive structural and volumetric properties compared to zoned varieties, necessitating adjustments in chemical finishing technologies. Technological parameters for the preparation processes have been developed with consideration of the structural and sorption characteristics of textile materials derived from Porlock cotton fiber. Cost-effective technological regimes and bath compositions for dyeing and printing preparation of textile materials (yarn and fabric) have been proposed, tailored to the structural features of these new cotton varieties. The preparation mode for dyeing cotton yarn of the P-2 and P-4 selection varieties has been successfully implemented at LLC “OSBORN TEXTILE”. As a result, high whiteness, enhanced capillarity, and improved physical and mechanical properties were achieved, aligned with the structural characteristics of the cotton yarn. Specifically, the P-2 variety, characterized by a denser structure, requiresrelatively high concentrations of alkaline agents (up to 10
This article explores modern trends and advanced foreign experience in optimizing product cost and reducing production expenses within the global textile industry. The impact of digital technologies (Industry 4.0) and lean production systems (Lean Production) on the financial stability of enterprises is analyzed. Furthermore, scientific and practical recommendations have been developed for implementing international achievements into national textile enterprises.
A small flexible self-triplexing tri-band antenna using a semi-circular half-mode substrate-integrated waveguide (HMSIW) cavity structure fabricated on a polyethylene terephthalate (PET) substrate is demonstrated. The antenna is composed of three independent 50-ohm microstrip feeds, a semicircular-shaped HMSIW cavity, one layer of PET (0.25 mm), and an inverted-V-shaped slot radiator etched in the upper conductor. Three feed/slot routes can excite three cavity modes with three slots which yield resonances at 4.446 GHz, 5.622 GHz and 5.859 GHz. The flat-state full-wave simulation shows the minimum reflection coefficient of −21.4 dB for S33 at 4.446 GHz, −21.7 dB for S 11 at 5.622 GHz, and −24.9 dB for S22 at 5.859 GHz. The simulated isolation between the ports is more than 21.9 dB with a maximum isolation of 60.7 dB, and the maximum realised gain is 3.16 dBi at 5.86 GHz. To verify the flexibility of the antenna, conformal bending simulations are performed for cylindrical bending radii $\text{Rb}=220$, 200, and 180 mm, together with fielddistribution, surface-current, and port-excitation studies. The onbody performance is also studied with a three-layer phantom (skin-fat-muscle). For the simulated 10 g, the average SAR is calculated to be 1.99 W/kg at 5.9 GHz for the specified excitation condition, which is slightly below the limit of 2 W/kg. The next validation step in the simulation study is prototype production and experimental measurements.
