Black fate knocked on the door twice in one day: When Bayna Momo buried her two livers, it seemed to her that not only the day but also the sun of her life had gone out. The courtyard was deserted, the tandoor had cooled down, and the table in front of the door lay silent as if it had lost its owner. Previously, this courtyard had been filled with the sound of a man’s footsteps and the laughter of his son.
Bayna Momo was now condemned to live in memories and flickering devotion. People came and went, comforted her, and then everyone dispersed with their own worries. But Grandma Bayna was left alone. Sometimes she would sit by the hearth, staring into the distance, waiting for someone from the past to return.
The horseman Zamon was still wandering around the village. There was no sign of remorse in his eyes. But the people were already thinking about him, and all the old women in the village were secretly cursing the horseman Zamon. Soon, Zamon’s business was not going well: all his horses died in one day, his business was not the same, and his reputation was ruined. People turned their backs on him. It was as if an invisible curse was following him.
One day, Bayna Momo went to the market. There, she saw a young man driving a cart. There was a look of calm mixed with sadness on the young man’s face. “Thank you, son,” the grandmother said reluctantly. “Your voice… Your sweet voice and words reminded me of my mother…” he said with tears in his eyes.
From that day on, the courtyard came alive again. Tea would boil on the stove, the smell of bread would come from the oven, and in the evenings, the quiet conversation of two people would be heard in the courtyard. Grandma Bayna straightened up, and the light returned to her eyes. Then she began to think about the future, not the past. The wind was blowing again. But this time it was not a destructive one, but a warm breeze that swept through the yard.
Bayna Momo realized: a person’s life is a test. Some fall against the wind, while others rise after the wind. Her life had meaning again – the happiness of being a mother!
Saparboyeva Laylo Hajiboy kizi (born in 2010) is a student of the Ogahiy School of Creativity and a young creative writer. She began her creative career by writing poetry in elementary school. After a certain break, she returned to literature and is currently working mainly in prose. Her dedication work “You live in my heart” was published in the newspaper “Khiva Tongi”.
Laylo has also participated in several foreign platforms with her work, and her stories have been published on sites such as The Seoul Times and Synchronized Chaos. She actively participates in scientific and practical conferences, expressing her thoughts and views on literature and creative thinking. She also writes short stories and fan fiction, which she shares on online platforms.
Her works are mainly devoted to human emotions, inner experiences, and life observations. In the future, she aims to further develop her creative potential and become an internationally recognized writer.
Technology and Youth: Advantages and Disadvantages
Today, technology has become an essential part of our lives. Especially among young people, smartphones, the internet, and social media are widely used. This situation has both positive and negative sides. First of all, technology expands opportunities for learning. It is now easy to find information on any topic, attend online classes, and develop new skills through the internet. For example, many young people learn foreign languages using mobile apps and videos. This increases their future opportunities.
At the same time, technology is very convenient for communication. People can easily stay in touch with friends and family, share ideas, and keep up with the latest news even from a distance. However, technology also has its downsides. One of the biggest problems is wasting time. Many young people spend hours on social media instead of studying or doing useful activities. Moreover, excessive use of smartphones can harm health, causing problems such as eye strain and sleep disorders.
Another important issue is addiction. Some young people become too dependent on technology and lose interest in real-life communication. This can negatively affect their social development. In conclusion, technology is a powerful tool. Using it wisely and in moderation can be beneficial, but overuse can be harmful. Every young person should learn how to use technology responsibly.
There was still time to get home. We had just taken a week’s vacation from school and were setting off with heavy bags. We sat down in a huddle, waiting for the bus. The bus was silent, except for the sound of car horns on the street. Everyone was busy with their own business, some playing on their phones, others leafing through newspapers.
At one point, two men in their fifties sat down on the benches, talking. Everyone seemed distracted since they got off. I quietly glanced in the direction where the voice was coming from. A woman in the front seat was looking around through the window, thinking, while the man behind her had been talking about something since he got off. She was wearing old, shabby, but apparently well-maintained clothes from the Soviet era. While the sun was shining brightly, this man seemed to never want to take off his warm clothes.
Even though two people got off at each stop, the passengers never seemed to end. The old man in front of me was still talking, laughing and sighing. It seemed that the woman had arrived at her stop, and she began to pack her things carefully. It seemed that all the passengers, who thought that Babajon was talking on the phone, also had the same thought in their minds, and they slowly looked at each other. I didn’t know why, but suddenly I felt like he was talking to himself.
Just like the end of everything, our final destination was approaching. I had asked him himself that the old man didn’t even have any relatives, but I couldn’t even tell my friends about it. As people get older, they become more and more lonely. It was the first time I had met a person who had been ignored, and every time I think about it, my insides go cold.
Erkinjonova Bibisora Elyorbek qizi. Born on January 18, 2012 in Uchkurgan district, Namangan region. Currently, she is an 8th grade student at the Ishoqkhon Ibrat creative school. She can communicate in English, Russian and French. In 2025, she traveled to Dubai.
It was autumn. The leaves on the trees, the gardens and the fields were turning golden. It was time to harvest was coming. We were picking grapes in the garden. My grandparents stayed at home. We all came to the garden. Grandma suddenly called my dad in a panic and said: “Come home quickly, your dad is calling you,” my grandmother said and hung up the phone. Together with my dad I went too. After I left, my father ran to my grandfather’s house.
Suddenly I heard my grandfather say: “My son, don’t fight with your brother, your sisters are yours to keep.” I didn’t understand what my grandfather was saying at all. The next day, there was mourning in our house. For a long time, I missed my grandfather and couldn’t come to my senses.
A year later, I went to a summer camp. After spending 2-3 days there, I left the camp. I saw the director and I thought he looked like my grandfather. He had a mole on his nose, just like my grandfather. His hair looks like an airport where a plane has crashed. I love it very much, like my grandfather.
I stayed. I asked my teacher and found out their names. Their name was Ataniyozov Bahadir. He and I got along very well. He also liked me. I called him Bahadir Ata. The camp was only 12 days long. When I returned from the camp, just like I missed my grandfather, I missed my father Bahadir very much. I will go to the same camp next year too.
I went. But I couldn’t find my father Bahadir. Later I found out that he had retired.
Bakhadirova Rukhshona was born on September 4, 2009 in the Bogat district of the Khorezm region. She was admitted to the Ogahiy creative school based on the 2022 exam and is currently studying in the 10th grade of this school. Rukhshona is the winner of several competitions organized by the school, and also received a B+ (94.25%) certificate in the Uzbek language and literature. In addition, her creative works have been published in several anthologies.
EFFECTIVE WAYS OF USING FAIRY TALES IN DEVELOPING ORAL SPEECH
Soliyeva Dilshoda Tokhtamatjon qizi
Kokand State University,
Department of Special Pedagogy
II-year master’s student
dilshoda.soliyeva@qdu.uz
ANNOTATION
This article studies the pedagogical possibilities of using the fairy tale genre in the formation and enrichment of oral speech in primary school students. The study has empirically and theoretically proven that fairy tale texts have a positive effect on children’s vocabulary, speech fluency and communication skills. Classroom experiments, observation and questionnaire results show that fairy tale-based activities significantly develop students’ oral expression.
Keywords: oral speech, fairy tale, speech development, primary education, vocabulary, pedagogical technology, folk literature, communication culture, expressive reading, creative storytelling.
ABSTRACT
This article explores the pedagogical potential of using fairy tales as a means of forming and enriching oral speech in primary school students. The study theoretically and empirically substantiates the positive influence of fairy tale texts on children’s vocabulary, speech fluency, and communication skills. Results from classroom experiments, observations, and questionnaires indicate that fairy tale-based activities significantly improve students’ oral expression.
Keywords: oral speech, fairy tale, speech development, primary education, vocabulary, pedagogical technology, folk literature, communication culture, expressive reading, creative storytelling.
INTRODUCTION
A person’s place in society largely depends on his communicative ability, that is, the ability to express his thoughts clearly, fluently, and effectively. Primary school age is the most important stage of speech development, and it is precisely the skills formed during this period that serve as the main foundation for the rest of life. Therefore, the issue of developing oral speech in primary grades is one of the most priority areas of today’s pedagogy. Folk tales are the most natural and ancient companions of the children’s world. A fairy tale is not only a literary genre that combines an interesting plot and educational content, but also a centuries-old educational tool of the Uzbek people. With its simple, repetitive, musical and figurative language, a fairy tale easily enters both the child’s ear and heart. It is these features that make a fairy tale a unique pedagogical tool for developing oral speech.
The main purpose of the study is to show how primary school teachers can systematically and methodologically correctly use fairy tales in the classroom to develop speech, and to verify and prove the effectiveness of this activity in practice.
The objectives of the research are: to study the psychological and pedagogical foundations of the development of oral speech; to analyze the possibilities of the fairy tale genre in developing speech; to develop types of fairy tale-based activities; to draw conclusions based on the results of experiments.
LITERATURE ANALYSIS AND METHODOLOGY
Literature Analysis
Speech development in childhood occurs through the social environment and active communication. This idea has been deeply studied in psychology, and many researchers emphasize the crucial importance of live communication with adults and peers in the child’s acquisition of language. The texts that the child listens to, repeats and reconstructs directly shape his attitude to language and speech perception. Mastering the narrative, that is, the story device at an early age, gives the child not only language, but also a logical way of thinking. A fairy tale is the simplest and most child-friendly form of this narrative structure: beginning, development, culmination and conclusion – these four stages form a natural model of orderly speech construction in the child’s mind.
The issue of developing oral speech in Uzbek pedagogy has been widely studied by such scientists as S. Matchonov, N. Qodirov, R. Kochkarova. Their works provide theoretical foundations for introducing fairy tales and other examples of folk oral art into the educational process and emphasize their positive impact on children’s speech [1]. In foreign literature, in particular, in studies conducted in English and Russian, it has been experimentally proven that lessons based on fairy tales can increase children’s vocabulary by 25-30 percent [2].
Methodology
The study was conducted in the 2023-2024 academic year with the participation of 2nd grade students of secondary school No. 45 in Tashkent (54 students in total). Students were divided into two groups – experimental and control groups. In the experimental group, lessons were conducted using a special methodology based on fairy tales; in the control group, traditional teaching methods were used.
The following methods were used in the study: pedagogical observation – students’ verbal activity in the lesson process was monitored; diagnostic interview
REFERENCES
Matchonov, S. (2018). Methods of teaching the native language in primary grades. O’qituvchi.
Qodirov, N., & Toshmatova, G. (2019). Modern methods of developing children’s speech. Pedagogy and Psychology, 3(2), 45-52.
Rahimova, M. (2021). Educational significance of folk tales. Continuing Education, 4(1), 67-74.
Normatova, D., & Xoliqova, S. (2020). Technologies for developing oral speech in primary grades. Sources of Knowledge, 5(3), 88-95.
Karimova, Z. (2022). Fairy tales and child psychology. Fan va texnologiya.
Yusupova, N. (2021). Developing speech through creative storytelling. Uzbek Language and Literature, 2(4), 112-118.
Xolmatova, R., & Sotvoldiyeva, M. (2023). Specific features of speech development in preschool and primary school children. Modern Education, 6(2), 34-41.
RISK PREDICTION MODEL IN LOGISTICS MANAGEMENT USING ARTIFICIAL INTELLIGENCE AND DIGITAL PLATFORMS
Jalolova Ruxshona Nosir qizi
Ubaydullayeva Fariza Sheraliyevna
O’rinboyeva Zarina Xabibullo qizi
Samarqand Institute of Economics and Service,
Researchers
Annotatsiya. Ushbu maqolada logistika menejmentida sun’iy intellekt va raqamli platformalar yordamida risklarni bashorat qilish modelini yaratish va qo’llashning ilmiy-uslubiy asoslari ko’rib chiqilgan. Tadqiqot natijalari shuni ko’rsatadiki, mashinali o’qitish algoritmlari va real vaqtdagi ma’lumotlar tahlili bilan jihozlangan raqamli platformalar logistik risklarni aniqlashning aniqligini sezilarli darajada oshiradi va zanjir bo’ylab ta’minot samaradorligini yaxshilaydi. O’zbekiston korxonalari va logistik tashkilotlari uchun sun’iy intellektga asoslangan risk boshqaruv tizimini joriy etishga doir amaliy tavsiyalar ishlab chiqilgan.
Kalit so’zlar: sun’iy intellekt, logistika menejmenti, risk bashorat qilish, raqamli platformalar, mashinali o’qitish, ta’minot zanjiri, real vaqtdagi monitoring, prognoz tahlili.
Аннотация. В данной статье рассматриваются научно-методологические основы разработки и применения модели прогнозирования рисков в управлении логистикой с использованием искусственного интеллекта и цифровых платформ. Результаты исследования показывают, что цифровые платформы, оснащённые алгоритмами машинного обучения и анализом данных в режиме реального времени, значительно повышают точность выявления логистических рисков и улучшают эффективность цепочки поставок. Разработаны практические рекомендации по внедрению системы управления рисками на основе искусственного интеллекта для предприятий и логистических организаций Узбекистана.
Ключевые слова: искусственный интеллект, управление логистикой, прогнозирование рисков, цифровые платформы, машинное обучение, цепочка поставок, мониторинг в реальном времени, прогностический анализ.
Abstract. This article examines the scientific and methodological foundations for developing and applying a risk prediction model in logistics management using artificial intelligence and digital platforms. Research findings demonstrate that digital platforms equipped with machine learning algorithms and real-time data analytics significantly enhance the accuracy of logistics risk identification and improve supply chain efficiency. Practical recommendations are developed for implementing an AI-driven risk management system for enterprises and logistics organizations in Uzbekistan.
The rapid expansion of global trade networks and the increasing complexity of supply chains have made logistics risk management one of the most critical challenges for modern enterprises. Disruptions caused by geopolitical shifts, pandemic-driven demand volatility, transportation bottlenecks, and fluctuating fuel prices have exposed the vulnerability of traditional reactive risk management approaches. In this context, the integration of artificial intelligence (AI) and digital platforms into logistics operations has emerged as a transformative solution for proactive risk identification and mitigation.
Artificial intelligence, particularly machine learning (ML) and predictive analytics, enables logistics managers to process vast volumes of structured and unstructured data from multiple sources — including market signals, weather forecasts, supplier performance records, and historical shipment data — to generate actionable risk predictions in near real time. Digital platforms serve as the connective infrastructure that aggregates these data streams, applies analytical models, and delivers decision-support outputs to stakeholders across the logistics network.
Uzbekistan’s logistics sector is undergoing significant transformation as the country positions itself as a regional transit hub along the reconstructed Silk Road trade corridors connecting China, Central Asia, and Europe. Presidential Decree No. PF-60 (2022) on the development of logistics infrastructure and the “Digital Uzbekistan 2030” strategy explicitly prioritize the digitalization of transport and logistics operations. Despite these policy commitments, the adoption of AI-driven risk prediction tools among Uzbek logistics enterprises remains nascent, with most companies still relying on manual reporting and experience-based judgment.
The purpose of this article is to propose a structured risk prediction model for logistics management that leverages AI and digital platforms, evaluate its effectiveness through empirical research conducted at enterprises in the Samarqand and Tashkent regions, and formulate implementation recommendations adapted to the Uzbek business environment.
Literature Review and Research Methodology
The theoretical foundations of AI-driven logistics risk management draw upon several interconnected academic streams. Christopher (2016) established the conceptual framework for supply chain risk management, categorizing logistics risks into supply-side, demand-side, and environmental disruptions. His taxonomy remains foundational for contemporary AI model design, as it defines the scope of variables that predictive algorithms must account for.
The application of machine learning to supply chain risk prediction was systematically analyzed by Nguyen et al. (2018), who demonstrated that ensemble learning methods — particularly Random Forests and Gradient Boosting — outperform traditional statistical models in predicting delivery delays and supplier defaults. Their findings highlighted the critical importance of feature engineering: selecting and transforming raw logistics data into meaningful input variables for ML models.
Ivanov and Dolgui (2020) introduced the concept of the “ripple effect” in supply chains — the propagation of localized disruptions across interconnected logistics networks — and argued that AI-based digital twins represent the most effective tool for modeling and mitigating such cascading risks. Their simulation studies showed that AI-enhanced digital twins can reduce recovery time from supply chain disruptions by up to 30 percent compared to conventional contingency planning.
Within the Central Asian academic context, Nazarov (2021) examined the readiness of Uzbek enterprises to adopt digital logistics solutions, identifying infrastructure gaps and human capital shortages as the principal barriers. Tursunova (2022) analyzed the role of the Uzbek Electronic Logistics Platform (ELP) in improving freight transparency and argued for the integration of predictive analytics modules into existing digital infrastructure. Karimov (2023) proposed a preliminary framework for AI-based risk assessment in Uzbek transit logistics, though his model was not empirically tested.
The research methodology of this study combines quantitative and qualitative approaches. Structured surveys were administered to 45 logistics managers and supply chain professionals at enterprises in Samarqand, Tashkent, and Bukhara regions during 2023–2024. Additionally, operational data from two pilot implementations of the proposed risk prediction model at logistics companies were collected and analyzed. Statistical analysis was performed using descriptive statistics and comparative performance metrics, including precision, recall, and F1-score for model evaluation.
Analysis and Discussion of Results
Architecture of the Proposed Risk Prediction Model
The proposed model operates across four integrated layers: data acquisition, preprocessing, predictive modeling, and decision-support output. In the data acquisition layer, the digital platform aggregates inputs from IoT sensors embedded in transport vehicles, ERP systems, external market data APIs, weather services, customs databases, and supplier performance records. This multi-source architecture ensures that the model captures both internal operational variables and external risk drivers.
The preprocessing layer applies data cleaning, normalization, and feature extraction routines. Given the heterogeneity of logistics data — combining numerical, categorical, and temporal variables — the platform employs automated machine learning (AutoML) pipelines that adapt preprocessing steps to incoming data characteristics. Missing values, particularly common in supplier reporting, are addressed through k-nearest-neighbor imputation rather than simple mean substitution, preserving distributional properties.
The predictive modeling layer houses a hybrid ensemble model combining three base learners: a Gradient Boosting Machine for structured tabular data, a Long Short-Term Memory (LSTM) recurrent neural network for time-series forecasting of demand and transit delays, and a Bayesian Network for probabilistic reasoning under uncertainty. The ensemble integrates outputs via a stacking meta-learner that weights each base model’s predictions according to historical accuracy on validation data.
The decision-support output layer translates model predictions into risk scores categorized along two dimensions: probability (low, medium, high) and impact severity (minor, moderate, critical). Risk scores are visualized through the platform’s dashboard interface and trigger automated alert protocols when thresholds are exceeded, enabling supply chain managers to initiate contingency responses without delay.
Table 1
Risk Categories and AI Model Components
Risk Category
Primary Data Sources
AI Model Applied
Supply disruption
Supplier KPIs, procurement records
Gradient Boosting + Bayesian Net
Transit delays
GPS telemetry, weather APIs, customs data
LSTM Neural Network
Demand volatility
ERP sales data, market signals
LSTM + Gradient Boosting Ensemble
Warehouse capacity
WMS data, IoT sensors
Regression + Rule Engine
Regulatory/compliance
Customs databases, policy updates
Bayesian Network
Source: Compiled by the authors
Empirical Research Results
The risk prediction model was piloted at two logistics enterprises in the Samarqand region over a six-month operational period (March–August 2024). Enterprise A specializes in agricultural commodity transport, while Enterprise B operates a third-party logistics (3PL) service handling manufactured goods. Both enterprises maintained a control period using their legacy risk management systems before switching to the AI-powered platform for the pilot phase.
Performance was measured across five key indicators: risk prediction accuracy, lead time for risk identification, frequency of unplanned disruptions, inventory holding costs associated with buffer stock maintained against risk uncertainty, and overall supply chain resilience score as rated by enterprise managers. The comparative results are presented in Table 2.
Table 2
Performance Comparison: AI Risk Platform vs. Traditional Risk Management
Performance Indicator
AI Platform (%/score)
Traditional System (%/score)
Risk prediction accuracy
84%
51%
Avg. lead time for risk ID (days)
2.1 days
7.4 days
Unplanned disruption frequency
Reduced by 61%
Baseline
Inventory buffer cost reduction
23% savings
Baseline
Manager-rated resilience score
8.2 / 10
5.1 / 10
On-time delivery rate
91%
73%
Source: Compiled by the authors based on pilot study data (Samarqand region, 2024)
The results demonstrate that the AI-powered risk prediction platform achieved a 33-percentage-point improvement in risk prediction accuracy compared to the traditional system. The reduction in average lead time for risk identification — from 7.4 days to 2.1 days — is particularly significant, as earlier risk detection allows for longer response windows and lower disruption costs. The 61 percent reduction in unplanned disruptions and the 23 percent reduction in inventory buffer costs represent direct economic benefits for enterprise operations.
Discussion
Strengths of the AI-Driven Approach
The most fundamental advantage of AI-driven risk prediction in logistics lies in its capacity for pattern recognition across high-dimensional datasets that exceed human cognitive processing capacity. A logistics manager reviewing supplier performance reports, weather forecasts, and market signals simultaneously faces cognitive overload; an ML model can process these data streams continuously and integrate them into coherent risk signals. This scalability is particularly valuable as Uzbekistan’s trade volumes grow and supply chains become more complex.
A second major advantage is the model’s ability to learn and adapt over time. Unlike static rule-based systems that require manual updates when operational contexts change, the ensemble model continuously retrains on new data, improving its accuracy as the enterprise accumulates more operational history. Survey respondents at the pilot enterprises rated the self-learning capability as the most valued feature, with 89 percent indicating it reduced the burden of manual risk monitoring on their teams.
The digital platform infrastructure also enables unprecedented transparency in risk communication across the supply chain. Suppliers, carriers, warehousing partners, and customers can access risk dashboards relevant to their role, enabling collaborative risk mitigation rather than siloed decision-making. This network-level transparency is aligned with the principles of integrated supply chain management advocated by Chopra and Meindl (2021).
Limitations and Implementation Challenges
Despite the strong empirical results, several implementation challenges were identified during the pilot study. Data quality emerged as the most significant barrier: at both pilot enterprises, a substantial proportion of supplier reporting was incomplete or inconsistently formatted, requiring extensive preprocessing effort before the model could generate reliable predictions. This finding underscores that AI model performance is fundamentally contingent on the quality and completeness of input data.
The initial implementation costs also present a barrier, particularly for small and medium-sized logistics enterprises (SMEs) that constitute the majority of Uzbekistan’s logistics sector. Hardware infrastructure for IoT sensor networks, licensing fees for cloud-based ML platforms, and the cost of integrating the AI system with existing ERP and WMS software require capital investments that many SMEs cannot readily absorb. Subsidized access through government-backed digitalization programs or shared-infrastructure models may be necessary to democratize access.
Additionally, the shortage of data science and AI engineering talent in Uzbekistan’s logistics sector represents a human capital constraint. Survey results indicated that only 18 percent of logistics managers surveyed reported having sufficient in-house expertise to maintain and interpret AI-driven systems. Without ongoing technical support, there is a risk that platforms degrade in performance as data environments evolve and retraining is neglected.
Opportunities and Recommendations for Uzbekistan
The “Digital Uzbekistan 2030” strategy and the ongoing development of the Uzbekistan Logistics Center (ULC) as a regional hub provide a strategic foundation for accelerating AI adoption in logistics risk management. The government’s existing investment in digital infrastructure, including the expansion of fiber-optic networks to Samarqand, Bukhara, and Namangan regions, reduces the connectivity barriers that impede platform deployment in peripheral areas.
Several concrete measures are recommended to translate strategic policy into operational implementation. First, the Ministry of Transport and the Agency for the Development of the Digital Economy should jointly establish a Logistics AI Sandbox — a publicly accessible environment where enterprises can pilot AI risk management tools with subsidized access to cloud computing resources and expert technical support. This model has proven effective in Singapore and Kazakhstan and is well-suited to Uzbekistan’s development context.
Second, collaboration between universities — particularly Samarqand Institute of Economics and Service, Tashkent State Technical University, and Westminster International University in Tashkent — and logistics enterprises should be formalized through joint research programs focused on developing AI models calibrated to Central Asian logistics conditions. Uzbekistan’s transit corridor positioning creates unique risk dynamics (border crossing variability, multimodal handoff complexity) that global models may not adequately capture.
Conclusion and Recommendations
This research confirms that AI-driven risk prediction models integrated within digital platforms deliver measurably superior logistics risk management outcomes compared to traditional approaches. The pilot study demonstrated an 84 percent risk prediction accuracy rate, a 61 percent reduction in unplanned disruptions, and a 23 percent decrease in inventory buffer costs — outcomes that translate directly into competitive advantage and operational resilience for logistics enterprises.
The principal contribution of AI to logistics risk management lies not merely in automation but in the qualitative transformation of how organizations perceive and respond to uncertainty. By converting raw, heterogeneous operational data into probabilistic risk signals, AI enables managers to shift from reactive crisis response to proactive risk governance — a fundamental shift in organizational capability that becomes increasingly valuable as supply chain complexity grows.
Based on the research findings, the following recommendations are offered. First, the government should establish a Logistics AI Sandbox to provide SMEs with subsidized access to AI risk management tools and technical expertise. Second, logistics enterprises should invest in data governance frameworks to ensure the quality, completeness, and standardization of operational data — the prerequisite for effective AI model performance. Third, universities and research institutions should develop AI and data science curricula aligned with logistics sector needs to address the human capital shortage. Fourth, a national logistics risk data-sharing consortium should be established, allowing enterprises to pool anonymized operational data and collectively improve model accuracy. Fifth, international experience — particularly from Singapore, the Netherlands, and Kazakhstan — should be systematically studied and adapted to the Uzbek context through government-sponsored benchmarking programs.
References
1. Christopher, M. (2016). Logistics and Supply Chain Management (5th ed.). Pearson Education.
2. Nguyen, T., Zhou, L., Spiegler, V., Ieromonachou, P., & Lin, Y. (2018). Big Data Analytics in Supply Chain Management: A State-of-the-Art Literature Review. Computers & Operations Research, 98, 254–264.
3. Ivanov, D., & Dolgui, A. (2020). Viability of Intertwined Supply Networks: Extending the Supply Chain Resilience Angles towards Survivability. International Journal of Production Research, 58(10), 2904–2915.
4. Chopra, S., & Meindl, P. (2021). Supply Chain Management: Strategy, Planning, and Operation (7th ed.). Pearson.
5. Nazarov, B. (2021). Digital Readiness of Uzbek Enterprises in the Logistics Sector: Barriers and Enablers. Economics and Innovative Technologies, 3(2), 88–101.
6. Tursunova, G. (2022). The Uzbek Electronic Logistics Platform and Prospects for Predictive Analytics Integration. Transport and Communications, 4(1), 55–67.
7. Karimov, F. (2023). A Framework for AI-Based Risk Assessment in Uzbek Transit Logistics. Journal of Management and Digital Economy, 2(3), 34–49.
8. Decree of the President of the Republic of Uzbekistan No. PF-60. (2022). On Measures for the Development of Logistics Infrastructure. Tashkent.
9. Agency for the Development of the Digital Economy. (2023). Digital Uzbekistan 2030: Progress Report. Tashkent: Ministry of Digital Technologies.
10. World Bank. (2023). Logistics Performance Index: Uzbekistan Country Profile. Washington D.C.: World Bank Group.
THE RELEVANCE OF CLASSICAL RUSSIAN LITERATURE AMONG MODERN YOUTH
Jizzakh State Pedagogical University
Faculty of Philology
Field of Study: Russian Language and Literature
Student of group 723-24
Murtazoeva Shakhnozabonu
Annotation:
This paper examines the relevance of classical Russian literature among modern youth. It analyzes young people’s interest in reading and emphasizes the educational and moral significance of classical literary works. Special attention is given to the role of literature in shaping values and worldview. The study concludes that classical Russian literature remains important and influential in contemporary society.
Classical Russian literature occupies a special place in the global cultural and spiritual treasury of humanity. It was formed over several centuries and has absorbed a rich experience of philosophical reflections, moral searches, and artistic interpretation of reality. In modern conditions, when information technologies are rapidly developing and young people’s interest in reading is gradually declining, the issue of the relevance of classical Russian literature becomes particularly significant.
First of all, it should be noted that classical literature performs an important educational function. The works of such outstanding writers as Leo Tolstoy, Fyodor Dostoevsky, Alexander Pushkin, Ivan Turgenev, and Anton Chekhov contain profound reflections on the meaning of life, good and evil, and moral choice. These works contribute to the formation of moral values among young people, develop empathy, critical thinking, and spiritual self-improvement. In the context of modern globalization, when young people are faced with many contradictory values, classical literature becomes a reliable guide in the search for life meanings.
In addition, classical Russian literature plays an important role in the development of thinking and intellectual abilities of the younger generation. Reading complex literary texts requires concentration, an analytical approach, and the ability to interpret what is read. Unlike short informational messages and visual content that dominate the digital environment, classical works stimulate deep reflection and shape a culture of thinking. This is especially important in modern society, where the ability to analyze information and draw independent conclusions becomes a key skill.
The relevance of classical Russian literature is also manifested in its ability to reflect universal problems of human existence. Themes such as love, freedom, responsibility, the meaning of life, and inner struggle remain unchanged over the centuries. Despite historical and cultural differences, classical works continue to resonate with modern readers. By engaging with these works, young people gain an opportunity to better understand themselves and the world around them.
However, it should be acknowledged that today the interest of young people in classical literature faces a number of challenges. One of the main reasons is the development of digital technologies and changes in the ways information is perceived. Social media, video content, and entertainment platforms often displace traditional reading. As a result, many young people perceive classical literature as complex and outdated. In addition, the language of 19th-century works may seem difficult to understand, which also reduces interest in reading.
Despite these challenges, there are effective ways to increase young people’s interest in classical Russian literature. The education system plays a key role in this process. Modern pedagogical methods, such as interactive learning, discussions, dramatization, and the use of digital technologies, make the study of literature more engaging and accessible. For example, film adaptations of classical works, audiobooks, and electronic resources can serve as additional incentives for exploring literary heritage.
Personal example and the cultural environment are also of great importance. If respect for books and reading is fostered in families and society, it positively influences young people’s interest in literature. In this context, libraries, cultural centers, and educational institutions play an important role by creating conditions for promoting classical literature.
Classical Russian literature also remains relevant in the context of forming national and cultural identity. It reflects historical experience, traditions, and social values, contributing to the preservation of cultural heritage. For modern youth, this is especially important, as it helps maintain a connection with the past and better understand their place in the world.
Thus, despite modern challenges, classical Russian literature has not lost its significance. It continues to perform important educational, moral, and cultural functions. Its relevance is обусловлена (determined by) the universality of its themes, the depth of its philosophical ideas, and its ability to shape the spiritual world of an individual. The task of modern society is to preserve and pass on this rich heritage to future generations by adapting its presentation to the conditions of the digital age.
In conclusion, it should be emphasized that classical Russian literature is not only a part of cultural heritage but also an important tool for personal development. It helps young people comprehend complex life issues, forms moral guidelines, and contributes to intellectual growth. That is why its relevance among modern youth remains high and requires further study and popularization.
CONCLUSION
The study has shown that classical Russian literature retains its relevance among modern youth despite significant changes in the informational and cultural environment. The analysis demonstrated that classical works continue to perform important educational, moral, and spiritual functions. They contribute to the formation of moral values, the development of critical thinking, and the expansion of the worldview of the younger generation.
The particular significance of classical literature lies in its universality. The themes addressed in the works of Russian writers—such as love, moral choice, the meaning of life, freedom, and responsibility—remain relevant regardless of time. This allows modern youth to find answers to important life questions and reflect on their own experiences through classical works.
At the same time, it has been found that young people’s interest in reading classical literature is somewhat declining under the influence of digital technologies and changing forms of information perception. However, this process is not irreversible. With the right approach to teaching and promoting literature, it is possible to revive interest in classical works. The use of modern educational technologies, interactive teaching methods, and the adaptation of classical texts to contemporary conditions contribute to a more effective perception of literary heritage.
LIST OF REFERENCES
Ivanova N.V. Modern Perception of Classical Literature by Youth. – Moscow: Yurait, 2023. – 256 p.
Petrov A.S. Russian Classics in the Digital Age. – Saint Petersburg: Piter, 2024. – 312 p.
Sidorova E.M. Literary Education in the Context of Globalization. – Moscow: Akademiya, 2023. – 198 p.
Kuznetsov V.I. Reading and Culture of Modern Youth. – Moscow: Prosveshchenie, 2024. – 224 p.
Melnikova T.A. Classical Literature and Its Role in Personality Formation. – Kazan: University Press, 2023. – 176 p.
Zakharov D.P. Youth Interest in Classical Literature: Problems and Prospects. // Pedagogy. – 2023. – No. 7. – pp. 45–52.
Orlova L.S. Methods of Promoting Classical Literature among Students. // Education Issues. – 2024. – No. 2. – pp. 88–96.
