Journal of Advanced Scientific Research (ISSN: 0976-9595)

THE ROLE OF R&D IN DIGITAL TRANSFORMATION

Khasankhonova Nodira Isametdinovna — 2025-04-09

This paper examines the impact of R&D on digital transformation processes, their importance for business and economic development, as well as the main areas of research that contribute to the creation and implementation of new technological solutions.

Hepatitis D Virus (HDV): Epidemiology, Transmission, and Advances in Screening and Management

Raxmanova A.M — 2025-03-28

Hepatitis D virus (HDV) is a satellite virus that depends on the hepatitis B virus (HBV) for replication. It represents the most severe form of chronic viral hepatitis, leading to a higher risk of cirrhosis, hepatic decompensation, hepatocellular carcinoma (HCC), and liver-related mortality. Despite its significant burden, HDV remains underdiagnosed due to inconsistent screening and a lack of standardized testing protocols. This paper explores the epidemiology, transmission, clinical progression, and recent advancements in the screening and management of HDV infection.

POSSIBILITIES OF USING RESEARCH INTELLIGENCE IN DIAGNOSTICS OF ONCOSANOUS DISEASES

Alimbekova Lobarkhon — 2025-04-09

Diagnostics plays a key role in early detection of oncological diseases. Modern artificial intelligence (AI) technologies make it possible to analyse complex biochemical data, identify hidden patterns and improve diagnostic accuracy. This paper discusses the major biochemical markers of cancer, the application of AI in the analysis of liquid biopsy, metabolomic and proteomic data, and the prospects of using machine learning algorithms for personalised medicine.

Real-Time Water Quality Analysis Using Advanced Impedance Spectroscopy and Levenberg Marquardt Optimization

Farkhat Rajabov — 2025-04-03

Real-time water quality monitoring is essential for ensuring public health and environmental safety. This study integrates advanced impedance spectroscopy with a robust compensation model using the Levenberg-Marquardt (LM) algorithm to optimize water quality parameters such as Total Dissolved Solids (TDS) and pH. The LM algorithm, applied in nonlinear least squares fitting, enables the system to dynamically adjust for environmental factors like temperature, ensuring enhanced accuracy and reliability. The experimental validation demonstrates the model’s adaptability across diverse water conditions, providing a scalable solution for real world applications. Key findings include a 35% reduction in root mean square error (RMSE) compared to traditional methods. This study presents a novel methodological framework combining advanced spectroscopy and mathematical optimization for water quality analysis.