The Tashkent State University of Oriental Studies (Uzbek: Toshkent davlat sharqshunoslik instituti) is a state institution of higher education in Tashkent, the capital of Uzbekistan. Founded in November 1918, the school is the only Oriental-studies institute in Central Asia and Asia's oldest Oriental institute of higher education. It is one of the largest schools of its kind in Asia and the former Soviet Union.
This study examines the impact of AI-enabled marketing strategies on customer satisfaction, engagement, loyalty, and advocacy in Pakistan's online shopping sector. Focusing on the post-purchase phase, it investigates how AI-driven activities such as uniqueness, telepresence, delegation, interactivity, personalization, and customer relationship management shape customer behaviors and organizational outcomes. Drawing on dual concern theory, which examines the balance between egoistic and altruistic motivations, the study employs PLS-SEM analysis on data from 426 online shoppers to assess the impact of these AI strategies on key customer outcomes. The findings reveal that AI-driven marketing significantly enhances customer satisfaction, thereby improving customer engagement, loyalty, and advocacy. Furthermore, AI engagement fosters psychological rewards related to self-identity and life meaning, thereby contributing to long-term customer relationships. For executives and marketers in emerging markets, particularly Pakistan, the study underscores the need to integrate AI technologies to personalize the shopping experience and drive meaningful post-purchase engagement. This not only strengthens customer satisfaction and loyalty but also cultivates sustainable brand advocacy. The research contributes to the literature by offering practical insights into AI-driven customer engagement and by providing a deeper understanding of the psychological impact of AI marketing on customer behavior, with a focus on long-term relational benefits.
Background: Hypertension is one of the most prevalent comorbidities in patients with COVID-19 and a major contributor to chronic kidney disease (CKD). Traditional kidney injury markers, including creatinine, estimated glomerular filtration rate (eGFR) and microalbuminuria, reflect renal injury only after substantial nephron loss has already occurred. Urinary podocyte proteins, such as nephrin (nephrinuria), have been suggested as early markers of glomerular barrier dysfunction; however, their clinical behavior and diagnostic value in hypertensive patients with previous SARS-CoV-2 infection are unknown. Aim: To assess urinary nephrinuria, microalbuminuria, transforming growth factor beta 1 (TGF-beta 1), aldosterone, vascular endothelial growth factor A (VEGF-A) and renal hemodynamics across different stages of hypertension in patients with and without a history of COVID-19 and to assess the response to conventional antihypertensive and nephroprotective treatment. Methods: In a prospective comparative cohort study, 120 patients (aged 30-60 years) with stage I-III essential hypertension were stratified by COVID-19 history into a post-COVID-19 group (n = 60) and a non-COVID-19 group (n = 60); within each group, 20 patients were assigned to each hypertension stage. Comparisons were performed between the post-COVID-19 and non-COVID-19 subgroups at the same hypertension stage. Serum creatinine, cystatin-C, aldosterone, TGF-beta 1 and VEGF-A, urinary microalbumin and nephrin and intrarenal Doppler hemodynamics were measured at baseline and after six months of guideline-based treatment. Results: Nephrinuria was markedly increased in post-COVID-19 patients in all stages of hypertension, including stage I, where serum creatinine, cystatin-C and eGFR were within the normal range (126.5 +/- 9.1 vs. 91.9 +/- 8.3 pg/mL, p < 0.01). Nephrinuria was strongly correlated with renal functional reserve (r = -0.824, p < 0.001), eGFR (r = -0.797, p < 0.001), microalbuminuria (r = 0.758, p < 0.001), aldosterone (r = 0.613, p < 0.001) and VEGF-A (r = 0.589, p < 0.001). Antihypertensive and nephroprotective treatment for six months decreased nephrinuria, blood pressure and TGF-beta 1, with more limited effects in stage III disease. Conclusions: Nephrinuria was found to be an early marker of renal involvement in COVID-19, occurring before microalbuminuria and conventional functional markers and with a greater relative difference than these markers in stage I disease, suggesting podocyte injury as an early and potentially reversible mechanism of post-COVID renal involvement in hypertensive patients. Nephrinuria seems to be a potential biomarker for early renal surveillance in this population and its prognostic role for incident CKD needs to be validated in longitudinal outcome studies.
In this paper, we design and implement an intelligent interface for classification and retrieval of digital literary works within a search interface system based on standardized metadata. By building on the Dublin Core metadata schema, which supports automated indexing and retrieval processes based on attributes such as author, title, genre, language, and subject, it offers automated indexing and retrieval by way of attributes. The system is built on a modular architecture with Metadata Extraction, Indexing Engine, Search Interface components. Semantic search and multi-criteria filtering capabilities increase search accuracy and contextual relevance. Experimental analysis of 2,500 literary records validated its performance with respect to retrieval speed and semantic consistency, compared to the conventional keyword-based methods. The paper concludes by outlining future prospects for extending the system to multilingual environments and incorporating ontological models for enriched semantic representation in digital libraries for enriched semantic representation in digital libraries.
The unsupervised learning has a strong potential to discover latent patterns in historical and educational books with the researchers being able to have a chance to discover the linguistic structure and semantic correlation without the use of labeled data. They are found especially useful in investigating large corpora in which contextual depth and cultural sensitivity needs to be maintained. Nevertheless, the current methodologies tend to fail at treating data in terms of contextual integrity, noise, and distinguishing between overlapping linguistic variables. To overcome such limitations, the research will employ a new model of K-means Clustering (KmC). The structure divides words, phrases, and morphological structures into intelligible units, therefore revealing latent language patterns. With the help of KmC, the proposed methodology will help reduce the level of data sparsity, improve contextual mapping, and effectively identify the repetition of linguistic patterns in extensive textual data. Proposed methodology will be useful in the analysis of multilingual historical data, detection of thematic patterns in educational discourse, as well as differentiation between language families. Experimental evidence shows that KmC enhances the accuracy of clustering, contextual coherence, and scalability, and it can be a reliable way to develop the digital humanities research.
The article is devoted to the analysis of the definitions of the phenomenon of hybrid wars in modern international relations. An important part of the information component of this kind of wars, its elements, and the essential characteristics of special propaganda are revealed. As a case, the characteristics of the information war in the Ukrainian-Russian conflict are studied. From the standpoint of the need to resolve this conflict, the theses of the approaches of socially responsible journalism, which contributes to the promotion of peace, are also presented.
