The Institute of History of The Academy of Sciences of The Republic of Uzbekistan

Evaluating writing skills in secondary education requires accurate, automated approaches that capture both semantic meaning and linguistic quality. Traditional methods often rely on surface-level features such as grammar, vocabulary, or word count, which fail to assess deeper coherence and contextual relevance in student essays. Existing approaches struggle with subjectivity, limited generalization, and insufficient semantic understanding. To address these challenges, this study proposes a Semantic Embedding Model using a Siamese Neural Network (SNN) framework, which measures semantic similarity between student responses and reference texts. The model leverages contextual embeddings to effectively capture meaning, coherence, and writing quality beyond syntactic measures. The proposed method can be applied for essay grading, skill assessment, and providing feedback to students and educators. Experimental results demonstrate improved accuracy, fairness, and consistency in evaluating writing skills compared to traditional evaluation systems.

Speech evaluation is one of the significant parts of language learning and assessment, which involves correct pronunciation and fluency analysis. As artificial intelligence has been improved, AI-enhanced pronunciation feedback systems have come into existence to give learners automated, real-time feedback. The current systems tend to use the traditional speech recognition algorithms, which can be unable to face the variability of accents, mispronunciations, and the difference in time of speech patterns and provide incorrect feedback and prevent effective learning of the learner. To cover these shortcomings, the offered approach incorporates Dynamic Time Warping (DTW) as the fundamental approach to the evaluation of pronunciation. DTW allows the accurate alignment of the speech of the learners to the reference utterances based on the temporal differences calculation and the reduction of the distance between the sequences of the speech features. By this method, any changes in speed of speech, intonation, and articulation can be handled effectively and precise feedback can be provided by the system. To evaluate pronunciation accuracy, identify instances of mispronunciation and provide language learners with the corrective recommendations, the proposed DTW-based system is implemented. The results of the experiment show that this method increases the accuracy of feedback, minimizes the errors of alignment, and improves pronunciation abilities of the learners compared to the conventional ones.

This article analyzes the educational and educational significance of the native language subject for primary school students from a scientific and pedagogical point of view. It highlights the role of native language lessons in developing oral and written speech, expanding the scope of thinking, and forming national values and moral qualities in students. The article also shows ways to increase the effectiveness of the native language subject through modern educational technologies, interactive methods, and communicative approaches. The results of the study serve to determine the role of the native language subject in primary education in the comprehensive development of the student's personality.

Abstract. Uralov U, Kudratov J, Pazilov A, Izzatullaev Z, Khalimov F, Otakulov B, Jabbarov A, Fundukchiev S, Orziyeva Y, Suyunov G, Abduxamidovich BM, Boymurodov K, Gulboev D, Urinova X. 2025. Diversity of gastropods (Mollusca: Gastropoda) in different altitude regions of the Zarafshan Mountains and adjacent areas, Uzbekistan. Biodiversitas 26: 5012-5022. As an important part of the food chain in nature, it is of great importance to determine the systematic structure and diversity of mollusks. This study presents data obtained by assessing the current state of the taxonomic structure of gastropod mollusks in biogeographical regions with different vertical heights in the study area. The study area includes 15 locations in the Zarafshan Mountains of Uzbekistan and adjacent areas. The study sites were formed based on more than 3,000 samples collected in 2018-2025. It was found that the gastropod fauna of the region consists of 80 species belonging to 21 families and 35 genera. Using modern morphological and anatomical methods of species identification, Archaica haziratishaxika was identified for science; the following species were recorded for the first time as new species for the region, adding to the region's biodiversity: Sogdamnicola shadini, Lymnaea fragilis, Galba truncatula, Radix bactriana, Candaharia rozeni, Macrochlamys turanica, Macrochlamys kasnakowi, Vertigo antivertigo, and Chondrina intumescens. The results obtained will serve as a primary source for supplementing the data on the gastropod fauna of mountain ranges in Uzbekistan with the missing data on the Zarafshan Mountains and adjacent areas, and will provide specialists with the first information on this area.

This chapter assesses the seriousness of university deepfake scandals through a comprehensive review of some of the latest highly publicised incidents. Deepfakes have been used to replicate academic articles, impersonate teachers or students, and tarnish the reputation of institutions. The chapter presents case studies of institutions that were hacked using deepfakes and examines how these incidents impacted the institutions' reputations, public trust, and policy alignment. Regarding some of the previous cases where deepfake content was already used to victimise officers, students, and the public, the article outlines glaring loopholes present within university systems. The study employs data from incident reports, media, and university reports, utilising content analysis techniques to investigate the effects of the scandals. The findings are used to enhance safety against deepfakes and inform the response process.

Contextual Text Analytics for Knowledge Extraction in Biomedical Device Design Education addresses the growing need to efficiently process vast amounts of unstructured textual data generated during education and research. Traditional manual methods struggle to extract meaningful information from complex biomedical design documents, student feedback, and literature, causing delays in curriculum improvement and innovation. To tackle this, the paper proposes a novel framework termed Contextual Biomedical Text Analytics (CBTA) that integrates natural language processing (NLP) techniques with domain-specific ontologies to enhance contextawareness in text processing. CBTA leverages semantic embedding, named entity recognition, and topic modeling to extract relevant knowledge, trends, and competency indicators from diverse textual datasets. Experimental validation on biomedical device design course materials demonstrates CBTA's enhanced ability to identify key concepts, design challenges, and learning gaps, outperforming baseline text mining techniques by 18% in precision and 15% in recall. The results indicate that CBTA facilitates actionable insights for educators, enabling targeted curriculum updates and personalized learning paths. In conclusion, CBTA presents an intelligent, scalable solution for advancing biomedical device design education through data-driven knowledge extraction, supporting both academic excellence and industry readiness.

This article analyzes how folklore elements in the works of Abduvali Qutbiddin, especially mythological images and stylistic devices characteristic of folklore, are expressed in the literary text. The images, poetic structure, refrains, and the phenomenon of stylization used in the poet's poem "Tatabbu to Ferdowsi" are investigated. In particular, it reveals how the poet reflected modern reality through mythological symbols against the backdrop of Soviet ideology.

The rapid spread of SARS-CoV-2 and its widespread public health implications have highlighted the urgent need for effective antiviral therapies. A promising strategy is to investigate natural compounds that may inhibit the key viral targets. In this work, we demonstrated the anti-SARS-CoV-2 activity of six bufadienolides, including bufalin (A), arenobufagin (B), gamabufotalin (C), telocinobufagin (D), marinobufagin (E), and bufarenogin (F) from the venom of the Central Asian green toad, Bufo viridis. Molecular docking assays revealed that all A-F bufadienolides bind to key residues (Thr26, His41, Met49, Met161, and Gln189) in the catalytic pocket of 3-chymotrypsin-like cysteine protease (3CLpro), an essential enzyme for viral replication and polyprotein processing. The stability of the protein-ligand complexes was then tested using molecular dynamics (MD) simulations, while the binding free energies were estimated using the umbrella sampling (US) technique. Compounds A (-49.8 ± 1.0kJ/mol), C (-45.9 ± 1.8kJ/mol), E (-45.6 ± 1.1kJ/mol), and F (-45.8 ± 1.9kJ/mol) had significantly higher binding affinities than compounds B (-14.6 ± 1.1kJ/mol) and D (-10.3 ± 1.9kJ/mol). In vitro enzymatic assays also confirmed these results, demonstrating that A and C exhibited potent inhibitory activity against 3CLpro with IC50 values of 1.37 µM and 2 µM, respectively, compared to the other bufadienolides; however, they were less active than the positive control GC376 (IC50 = 0.27 µM). The experimental results are consistent with the computational observations. In silico ADME profiling also revealed good pharmacokinetic properties, indicating that bufadienolides A-F are lead compounds for further antiviral drug development. Taken together, our results support the hypothesis that bufadienolides are SARS-CoV-2 3CLpro inhibitors and elucidate their mechanism of action, thereby laying the foundation for potential therapeutic advances against COVID-19.

The Doppler effect, arising from the relative motion between the source and the observer, plays a significant role in quantum computations, particularly in the context of decoherence and the state of qubits. In quantum systems where information is encoded in the states of qubits, changes in the frequency of photons caused by the Doppler effect can lead to coherence violations and a decrease in computational accuracy. These changes can cause state mixing and complicate the management of quantum interactions, increasing the probability of errors. Understanding and accounting for the Doppler effect is critically important for designing robust quantum systems, as it can manifest in various types of qubits, including photonic, atomic, and ion qubits. To minimize the impact of the Doppler effect, it is necessary to develop error correction methods and utilize technologies such as polarizing filters or feedback systems. Thus, studying the Doppler effect deepens our understanding of the mechanisms of decoherence and contributes to the creation of more stable and efficient quantum computing systems.