Tashkent State University of Economics

Necrotizing soft tissue infections in patients with diabetes mellitus are characterized by an aggressive clinical course and a high rate of repeat surgical intervention. Objective of the study. To identify predictors of early re-necrosectomy and assess their prognostic value. Materials and methods. This single-centre cohort study enrolled 128 patients with necrotizing soft tissue infections and concurrent diabetes mellitus treated between 2016 and 2025, with re-necrosectomy within 72 hours defined as the primary endpoint. The study was conducted in accordance with the Declaration of Helsinki and applicable national regulations governing biomedical research involving human subjects. The study protocol received approval from the institutional review board of the treating facility. Prior to enrolment, all participants provided written informed consent for the use of de-identified clinical and laboratory data for research purposes. Statistical analysis was carried out with IBM SPSS Statistics 26.0 and MedCalc 20.0. The study was conducted at Bukhara State Medical Institute under the institutional research programme entitled "Early Detection, Diagnosis, and Novel Treatment and Prevention Strategies for Pathological Factors Affecting Population Health in the Bukhara Region During the Post-COVID-19 Pandemic Period (2022–2026)." Results. Re-necrosectomy within the early postoperative period was carried out in 89 patients (69.5%); this group more frequently had a prolonged history of diabetes mellitus, higher HbA1c levels and admission glycaemia, and a more pronounced systemic inflammatory response. The most substantial intergroup differences were identified through analysis of local cytological and microbiological parameters: patients who required early re-necrosectomy demonstrated a significant elevation in the neutrophil destruction index, tissue destruction index, and microbial-cellular index. This group also exhibited greater microbial mass density, a higher proportion of polymicrobial associations, and a predominance of Gram-negative flora, reflecting a more aggressive infectious process and indicating the need for broader surgical debridement. Conclusions. Comprehensive assessment of local cytological and microbiological parameters in conjunction with lactate levels enables prediction of the need for re-necrosectomy within the first 72 hours.

Hyperspectral imaging (HSI) enables real-time, non-invasive tissue characterization by capturing biochemical signatures across hundreds of spectral bands simultaneously. However, the computational demands of processing vast hyperspectral datasets create significant bottlenecks for clinical deployment. This chapter examines how edge computing and hardware acceleration technologies—including Field-Programmable Gate Arrays (FPGAs), Graphics Processing Units (GPUs), and specialized neural accelerators—enable real-time HSI processing at the point of care. We analyze architectural trade-offs, implementation strategies, and clinical workflow integration approaches that reduce processing times from minutes to milliseconds. Recent advances in embedded platforms achieve throughput rates exceeding 250 MSamples/s while maintaining power consumption below 15W, making intraoperative HSI guidance practically achievable.

The rapid increase in global cooling demand, particularly in regions with high solar potential, has emphasized the urgent need for sustainable and electricity‑independent refrigeration technologies. In response to this challenge, this work proposes a novel solar‑assisted single‑effect Lithium Bromide–Water (LiBr/H₂O) absorption refrigeration system incorporating a supersonic ejector and a triple‑layer solar thermal storage unit. The design aims to maximize energy recovery and reduce operating cost through combined thermodynamic and thermoeconomic optimization. Governing mass and energy conservation equations are established and solved using the Engineering Equation Solver (EES). Energy, exergy, and cost assessments are performed for both ejector‑assisted and conventional configurations to quantify improvements in the Coefficient of Performance (COP), exergy efficiency, and component cost rate under various solar irradiation and generator temperatures. Results reveal that ejector integration enhances COP by 12.7% and exergy efficiency by 11.3%, while reducing total investment cost by 9% compared to the baseline cycle. The optimized configuration achieves coefficient of performance of 0.74 and solar coefficient of performance of 0.58 under solar irradiation of 973 W/m², confirming marked enhancements in thermodynamic efficiency, cost effectiveness, and overall system sustainability.

This paper examines the problem of teaching students creative writing in a foreign language. The goal of the work is to develop and test a methodology for teaching creative writing in a foreign language, based on the principles of project-based learning, cooperative learning and feedback. The paper presents the theoretical foundations of creative writing and its role in teaching a foreign language, as well as a description of the developed methodology, its advantages and disadvantages. The paper also describes the conduct of experimental training using the developed methodology with a group of students studying a foreign language in non-linguistic universities. The work uses various methods of data collection and analysis, such as testing, questioning, observation and examination. The work confirms the research hypothesis and draws a conclusion about the effectiveness of the developed methodology for teaching creative writing in a foreign language. The work also provides examples of creative texts created by students during their studies, and analyzes their features, strengths and weaknesses. The paper discusses possible directions for further research and development of methods for teaching creative writing in a foreign language.

This study examines the effectiveness of teaching economic terminology through authentic materials compared with traditional textbook-based instruction in English for Specific Purposes (ESP) classes. In the context of globalization and professional communication, students specializing in economics must acquire not only general English proficiency but also discipline-specific vocabulary. However, many learners struggle to understand and use economic terminology appropriately in academic and professional contexts. The study adopts a quasi-experimental mixed-methods research design to compare vocabulary acquisition outcomes in two instructional settings. The dataset consists of 80 undergraduate students majoring in economics who participated in a six-week instructional program. Forty students learned economic terminology through authentic materials such as economic news articles, financial reports, and case studies, while forty students studied the same terminology through traditional textbook-based exercises. Quantitative analysis measured vocabulary retention, contextual usage accuracy, and terminology recognition through pre- and post-tests. Qualitative classroom observations and student interviews were conducted to explore learning experiences and engagement patterns. The findings demonstrate that students exposed to authentic materials showed higher contextual understanding and more accurate productive use of economic terms. In contrast, students in the textbook-based group demonstrated better memorization of definitions but struggled to apply terminology in real communicative contexts. The results suggest that authentic materials significantly enhance conceptual understanding and contextual vocabulary use. The study concludes that integrating authentic economic discourse into ESP instruction can improve students’ professional language competence.

Remote sensing technologies provide effective tools for monitoring and assessing the state of inland water bodies, enabling extraction of various hydrological parameters from satellite observation. Central Asian and some African countries are currently implementing practical programs aimed at mitigating water scarcity and improving the management of transboundary water resources. Rivers and their tributaries flowing across national boundaries require continuous monitoring to support early warning of droughts and floods at the basin scale.Conventional ground-based hydrological stations are traditionally used to measure water level, estimate daily river discharge, and support hydrological forecasting. However, limitations related to accessibility, data-sharing restrictions, and the high cost of installation and maintenance often constrain their spatial coverage and long-term operation. Virtual gauging station (VGS) represents a complementary remote-sensing approach, providing time series derived from the long-term satellite image archives. A VGS is defined as a free-shaped polygon on the map used to analyze data within the borders of this polygon and collect observations based on the requirements. Currently, VGS applications primarily rely on optical satellite imagery from Sentinel-2, Landsat-4, -5, -7, -8, -9 missions to estimate water surface area (WSA) using spectral water index (MNDWI, AWEI or AWEIsh). Variations in WSA serves as a proxy for surface water availability and river dynamics. In addition, VGS can be used to enrich satellite altimetry-based water level (H) time series. For this purpose, the VGS polygon is calibrated using reference altimetric observations obtained from open-access data source (e.g. SDSS, DAHITI, Hydroweb). Calibration involves estimating the parameters of a regression model describing the functional relationship between water level and water surface area. The resulting values can finally be integrated into hydrological models to support short-term river discharge forecasting. Thus, VGS provides continuous hydrological information independent of ground-based measurements, while optional validation against in-situ observations allows for the assessment of the model uncertainty. Based on the experimental analysis, optimal placement of VGS polygons is recommended dynamically active river sections that account for annual riverbed displacement, as well as in river reaches located near satellite altimeter ground tracks to improve calibration accuracy.The experiments demonstrated that correlation between ground truth and forecasted water level values is upper 0,85 and mean absolute error is lower than 0,3 m. The following result has been obtained using linear regression which shows that application of more complex forecasting models could significantly improve the results.

This study examines the prevalence and determinants of earnings management (EM) practices within Sanoat Qurilish Bank (SQB), a systemic state-owned financial institution in Uzbekistan. Amidst the country's transition toward market-oriented reforms, understanding the integrity of financial reporting is crucial. Utilizing a longitudinal quantitative approach covering the period 2013–2023, this research employs a Multiple Linear Regression model to detect potential income smoothing. The study defines Net Profit as the dependent variable (proxy for reported earnings), while Return on Assets (ROA), Return on Equity (ROE), Total Assets, and Total Equity serve as independent variables representing financial performance and firm size. The empirical results demonstrate a statistically significant relationship between fluctuations in ROE and Net Profit, signaling proactive income smoothing behavior to meet performance benchmarks. Furthermore, the observed volatility in profitability ratios (ROA/ROE)—juxtaposed against the linear expansion of assets and equity—suggests the strategic use of managerial discretion. These findings corroborate agency theory and EM patterns prevalent in emerging markets where institutional governance and audit oversight are still evolving. This research contributes to the literature on financial transparency in transitional economies and provides critical insights for regulators and investors regarding the necessity of adopting IFRS-aligned oversight in Uzbekistan’s banking landscape.

Zero-day intrusion detection is still a difficult task because of the difference between high laboratory precision and real-time deployability under strict operational constraints. This paper proposes a lightweight two-stage cascade architecture that is specifically designed for CPU-only environments and strict zero-day evaluation. The proposed architecture only uses statistical and flow-level metadata attributes, which are independent of payload analysis, to ensure compatibility with encrypted traffic. The first stage of the proposed architecture is precision oriented to detect potentially malicious traffic with a low decision threshold, and the second stage is precision oriented to enhance classification and remove false positives. To avoid optimistic bias, a strict attack-type separation protocol is employed, where testing attack types are strictly prohibited from training. The proposed method is tested on three benchmark datasets: CSIC 2012 (HTTP level), UNSW-NB15 (intra-domain), and CSE-CIC-IDS2018 (cross-domain). The experimental results show the excellent intra-domain zero-day detection capability (up to 94.81% accuracy with 0.50% FPR), controllable performance degradation in the cross-domain setting (80.53% accuracy with near-zero FPR), and extremely low FP rates on all datasets. The system provides microsecond-level inference latency (0.002–0.006 ms), a throughput of up to 470,000 requests per second, and memory usage below 6.2 MB without GPU support. These results confirm the significance of architectural optimization and thorough evaluation in building efficient zero-day detection systems.