University of World Economy and Diplomacy

This article argues that Uzbekistan could introduce mandatory pre-trial mediation for selected civil, family, labor, and low-value commercial disputes. The Law “On Mediation” (2018, effective 2019) provides a strong base, but its voluntary design limits impact. Drawing on deeper analysis of three systems like Italy’s Legislative Decree 28/2010, Turkey’s pre-suit mediation in labor and commercial disputes, and Canada Rule 24.1, this article shows how mandatory mediation can reduce court backlogs, shortens time to resolution, lowers costs, and diminishes corruption risks. It explains why these outcomes are especially valuable in Uzbekistan’s current justice environment and proposes a phased, locally adapted model. Two innovations are developed in detail: first is integrating mahalla institutions into a certified community mediation tier, and second is building a one-stop digital mediation portal linked to “my.gov” services. The article closes with a concrete legislative and institutional roadmap, risk analysis, and evaluation metrics.

The article is devoted to a comprehensive analysis of the ‘One Belt, One Road’ initiative put forward by China and its impact on modern Eurasian development. Particular attention is paid to Central Asia, which is considered as a key transit hub connecting Asian and European markets and forming a new contour of global economic interconnectivity. The study emphasises that the region has a unique geostrategic value, where the interests of not only China and Russia, but also the Gulf monarchies, seeking to build into the project through investment, energy and infrastructure partnerships, intersect. The role of the Gulf Cooperation Council (GCC) is analysed as a complement to overland routes: it is their financial resources, innovative potential and experience in developing transport and logistics hubs that allow China to expand sea and land corridors. The BRI's historical continuity with the Great Silk Road, which in the past facilitated trade, cultural exchange and the spread of knowledge between civilisations, is underlined. The modern initiative is an instrument for the formation of a new co-operation architecture based on the principles of peace and co-operation, openness and inclusiveness, mutual learning and mutual benefit. Thus, the study reveals that the pairing of Central Asia and the GCC countries within the BRI opens up prospects not only for diversifying transport routes and investment flows, but also for strengthening political and diplomatic dialogue, which makes this project an important element of the global multipolar system.

Depression is currently known to have over 280 million victims in the world, yet it has not been adequately diagnosed because it uses subjective and inaccessible diagnostic methods. The current study introduces a new two-modality model that combines the survey data on the structured PHQ-9 with the unstructured emotional self-reports to forecast the degree of depression. We use traditional machine learning classifiers and BERT model based on transformers that runs on a mental health-specific dataset. The proposed model demonstrates a classification accuracy of 87.6 percent with the use of BERT and 78 percent with the use of Random Forests with structured inputs through the methods of ensemble and comparative analysis. High-level statistical tools such as ANOVA, Pearson correlation, and PCA show that such significant predictors as sleep hours and suicidal thoughts are present. PCA plots and confusion matrices are other visualizations that can be used to enhance model interpretability. The system will provide a scalable and interpretable early mental health diagnosis tool that will be ideally applicable in under-resourced settings.

The CRISPR-Cas system has revolutionized genome editing. Among its nucleases, Cas9 and Cas12 have been studied most extensively. However, CRISPR-Cas3 stands out as a distinct nuclease, possessing both helicase and exonuclease activities. Despite this unique feature, it remains under-exploredespecially considering its potential for large-scale genome manipulation. This review emphasizes the challenge of multidrug-resistant bacteria in current antimicrobial therapy, where Cas3 may provide a breakthrough solution by degrading the entire pathogen genome, rather than targeting single genes. To compile this review, a comprehensive literature search was conducted, covering publications from 2018 to 2025 across databases such as PubMed, Web of Science, and Nature. A comparative analysis of Cas3 and other CRISPR systems was also carried out to identify key research gaps. Key findings highlight Cas3's ability to degrade long DNA segments, making it valuable for deleting large genomic regions, such as integrated viral DNA, and addressing challenges in antimicrobial therapy. Nevertheless, low targeting precision and complex delivery processes hinder its clinical translation. The review concludes that to unlock Cas3's full potential, two steps are essential: optimizing its specificity through protein engineering and developing targeted delivery systems.

The article analyzes the gender aspects of women's participation in the formation of the digital legal space. It examines the problems of digital violence, equal opportunities, and the role of women in lawmaking. The research is based on the analysis of international standards and national experiences.

This article examines the different issues involved in saving the Aral Sea, viewing them in their unity and interconnections. The causes of the Aral Sea crisis are explored, alongside a general overview of the current state of the sea and the Aral Sea region. An analysis of transboundary water resources in Central Asia is provided. The study also analyzes the evolution and progression of interstate cooperation in saving the Aral Sea and the surrounding region. Furthermore, the article examines, from a legal perspective, the regulatory frameworks underpinning cooperation efforts and discusses the main directions of activity of intergovernmental bodies tasked with addressing the problems of the Aral Sea basin. The authors claim that the Central Asian region has not yet been able to reverse the situation associated with the degradation of the former Aral Sea. The efforts of the five states of this region are clearly insufficient in dealing with this global environmental problem, and it is evident that broader external international assistance is needed. At the same time, the established regional cooperation mechanisms have led to significant accomplishments in the issue of the current management of each of the five bordering countries’ water withdrawal limits and forecast operation regimes of the reservoir cascades in the Syr Darya and Amu Darya River basins. This has played a crucial role in preventing acute water conflicts in the region, which undoubtedly would have arisen long ago without this cooperation. Following an analysis of all the factors, the article authors propose several recommendations to improve the recovery process of the Aral Sea.

Artificial intelligence (AI)-powered multi-lingual virtual assistants enable seamless communication across diverse languages. However, effective translation and speech recognition for low-resource languages (LRL) remains challenging due to limited training data and linguistic diversity. Current translation and speech recognition systems often underperform in the LRL environment, mainly due to inadequate domain adaptation and scarcity of labeled datasets. This leads to high error rates and reduced effectiveness of AI assistants in multi-lingual environments. To address these challenges, this study introduces the Domain Adversarial Neural Network-Driven Multi-lingual Adaptation Framework (DANN-MAF). The framework leverages adversarial neural networks to enhance domain adaptation, combining semi-supervised learning and distribution alignment techniques to bridge the performance gap between high-resource and LRLs. Experimental results on standard multi-lingual datasets show that DANN-MAF significantly improves translation accuracy and speech recognition performance. Metrics such as BLEU score and Word Error Rate (WER) indicate superior performance compared to baseline models, particularly in underrepresented language domains. The proposed DANN-MAF framework demonstrates the effectiveness of AdaMatch-based domain adaptation in optimizing multi-lingual AI assistants. It offers a scalable solution for enhancing LRL capabilities and fostering inclusive and equitable access to language technologies.

Agriculture is a cornerstone of Uzbekistan's economy, accounting for 25 % to the national gross domestic product and employing 26 % of the workforce. Since independence, wheat intensification has been a national priority, with cultivated land expanding from 0.63 million hectares (Mha) to 1.24 Mha and productivity increasing from 1.66 t ha −1 in 1991 to 4.55 t ha −1 in 2023. However, on-farm yields remain below attainable yield, leading to a reliance on wheat imports to meet domestic demand. Closing this yield gap is critical for achieving national wheat self-sufficiency. This study aims to identify key yield-limiting factors and develop evidence-based, agroecologically optimized bundled solutions to enhance wheat productivity in Uzbekistan. By integrating multiple analytical approaches, the research seeks to provide targeted agronomic recommendations for improving sustainability and self-sufficiency. A combination of systematic reviews, crop modeling, and machine learning was used to analyze wheat yield gaps and optimize agronomic practices. Agricultural Production Systems sIMulator (APSIM) -Wheat model was calibrated, validated and used to simulate wheat yields over 36-years across four agro-ecological zones (AEZs): Khorezm (arid saline lowland), Kashkadarya (semi-arid highland), Samarkand (semi-arid mid-altitude), and Jizzakh (arid high-altitude). The simulations optimized seeding dates, nitrogen fertilizer rates, cultivar selection, and water management practices. Additionally, a meta-analysis of 90 studies and machine learning were employed to identify key determinants of wheat yield variation. To achieve self-sufficiency, Uzbekistan requires an average wheat yield of 6.62 t ha −1 , necessitating a 45 % (2.07 t ha −1 ) increase from current levels (4.55 t ha −1 ), while the yield gap of 3.25 t ha −1 exists. The study identified nitrogen fertilization, irrigation, rainfall, cultivar selection, and seeding dates as the primary determinants of yield. Wheat yield declined significantly when plant-available water content dropped below 50 %, establishing a critical threshold for sustainable productivity. Precision nutrient management included applying 150–180 kg N ha −1 , up to 120 kg P₂O₅ ha −1 , and 75 kg K₂O ha −1 . Conservation agriculture showed a 26 % increase in yields compared to conventional tillage. High-yielding, stress-tolerant wheat varieties released after 2010 increased wheat productivity by up to 22 %. Seeding between September 15 and October 15 maximized yields, while delayed sowing reduced yield by up to 57 kg ha −1 day −1 . Seed rates of 160–180 kg ha −1 improved plant density and yields, preventing excessive competition or underutilization. This study offers a science-based framework for improving wheat productivity in Uzbekistan through AEZ-specific, resource-efficient bundled solutions. By integrating crop modeling, machine learning, and systematic reviews, this study provides scalable solutions to enhance input use efficiency, resilience to climate variability, and sustainable intensification. Beyond Uzbekistan, these findings hold relevance for wheat production in other arid and semi-arid regions facing similar food security challenges. • Data-driven approach—modeling, systematic review, and machine learning enhance wheat productivity. • Yield gap of 42 % (3.25 t ha -1 ) exists in wheat in Uzbekistan and fertilizer, irrigation, rainfall, and variety are the key yield detreminants. • Irrigation at 50 % plant available water content optimizes irrigation water use and efficiency. • Conservation agriculture increases wheat yield by 26 % over conventional in salt-affected drylands. • Tailored agro-ecological zone-specific bundled solutions enhance productivity, supporting wheat self-sufficiency