Tashkent State University of Economics

Background In education, differentiation creates engaging and challenging experiences that improve engagement and accomplishment by customizing instruction, content, and assessment to match the needs of varied learners. Method This study examined differentiation in education across diverse national contexts, exploring strategies like task complexity differentiation, flexible grouping, and multiple intelligences, supplemented by case studies in foreign language classrooms to observe teacher adaptations for individual needs. Result While case studies demonstrated that successful implementation improves academic performance, motivation, and engagement, especially in foreign language teaching, the analysis showed that differentiation is typically framed through student-focused or teaching methods, underscoring the need for system-oriented research. Conclusion In order to address the varying needs of students, foster inclusion, and improve academic performance, differentiation in education is crucial. To develop supportive learning environments that play to each student's individual strengths, it takes system-wide research, teacher training, and resources.

This study performed a computational analysis of the outcome of Marangoni convection on the chemical reactive flow of a tetra hybrid nanofluid on the mass species and thermal energy transmission of nanoparticles containing gyrotactic microorganisms via a disc. In the water (${\text{H}}_{2}\text{O})$) base fluid, tetra nanoparticles (${\text{Al}}_{2}{\text{O}}_{3},\text{ Ag},\text{ Cu}$ and ${\text{TiO}}_{2}$) disperse to form the tetra hybrid nanofluid. Bacterial-powered micromixers, enzyme biosensors, chip-shaped microdevices like bio-microsystems, micro-volumes like microfluidic devices, and microbial fuel cells are just a variety of the systems that use gyrotactic microbes embedded in nanoparticles to boost their thermal efficiency. Through the optimization of fluid flow properties with nanoparticles, this technique can be applied to increase heat transfer in sophisticated cooling systems, such as those found in microelectronics and medical equipment. In biotechnology, designing more effective bioreactors, medication delivery methods, and environmental monitoring instruments is made easier by an understanding of how bacteria float in such intricate fluid settings. Furthermore, the research can aid in the creation of sophisticated magnetic separation methods for microbes, which are essential for the treatment of waste and water. Utilizing boundary layer theory, the governing equations were solved with a focus on the coupled system of partial differential equations incorporating boundary conditions. Ordinary differential equations are a highly nonlinear system that is generated by the transformation approach. An approximate solution is given and evaluated with the shooting method Bvp4c due to the extremely nonlinear structure of the converted equation system. For both the Xue and Yamada–Ota models, the distribution of gyrotactic microbes decreases as the Peclet number increases.

This paper analyses the impact of cultural factors on digital marketing strategies in Pakistan. Improvement of machine learning (ML) techniques combined with the Honey Bee Algorithm (HBA) has been incorporated for better solutions. Cultural differences play a vital role as consumers behave differently based on some cultural differences, demanding sensitive marketing strategies. Experimental results demonstrate that machine learning models effectively capture cultural preferences, and HBA significantly enhances marketing effectiveness, leading to a 20% increase in engagement and a 15% improvement in click-through rates (CTR). Natural Language Processing (NLP) methods are used to gain cultural insights from consumer data, while clustering algorithms segment the market based on these factors. Predictive models are applied to understand consumer behavior patterns, and HBA is utilized to optimize key marketing parameters, including content personalization and ad placement. Compared to traditional marketing approaches, our data-driven methodology results in a 25% overall improvement in consumer interactions and campaign effectiveness. The proposed framework is validated through extensive experimentation, incorporating culturally tailored A/B testing, religion-related holidays’ optimization, and personalized segmentation. These findings underscore the importance of integrating cultural insights into digital marketing strategies to enhance consumer engagement and optimize campaign performance.

Understanding and accurately quantifying thermoelastic damping (TED) in micro/nanoresonators is a major step in designing them to work well. Empirical and theoretical evidence suggests that classical elasticity theory (CET) and the Fourier heat equation break down when applied to structures with minuscule dimensions. This research presents an innovative framework to approximate TED value in miniature circular plates by leveraging both the frequency and energy-based approaches commonly applied in TED studies. The model incorporates the modified couple stress theory (MCST) and Moore–Gibson–Thompson (MGT) heat equation to enhance accuracy beyond the constraints of classical formulation at ultra-small scales. Non-classical constitutive relations and heat equation are firstly derived. Next, the MGT heat conduction equation is solved to determine the temperature distribution within the plate. In conclusion, TED is analytically formulated using two distinct approaches of frequency and energy. The agreement between these two approaches in yielding identical TED expressions reinforces the accuracy of the computations and the credibility of the developed model. The discussion in the numerical results section highlights the influence of essential parameters, especially the characteristic constants of the MCST and MGT model, on TED. The results indicate that while MCST reduces TED and the MGT model increases it, the classical framework, grounded in CET and the Fourier model, predicts a higher TED than the non-classical framework proposed in this study. This suggests that the reduction caused by MCST outweighs the increase due to the MGT model.

The ability to treat saltwater to make it suitable for human consumption has long been sought by mankind. More than three-quarters of the earth's surface is covered with saltwater. Although this water is important for some forms of transportation and fishing, it contains too much salt to sustain human life or agricultural activities. One way to desalinate water is to use solar energy-based technologies. One of these technologies is the use of a solar device to evaporate and condense water, along with the generation of electricity through a transparent photovoltaic panel, providing freshwater. The current work focused on developing a simple solar still with low-cost materials that can be built by anyone, anywhere. In addition, the study presented three-dimensional multiphase CFD models for a single-slope solar still. Computational modeling using ANSYS 15 enables precise simulation and optimization of this integrated solar system, maximizing its overall efficiency and output. This dual-purpose solar energy utilization offers a pathway to provide both clean water and electricity to communities in remote or resource-constrained regions, thereby enhancing their quality of life and promoting sustainable practices. Simulation results indicate that increasing the saline water temperature from 60°C to 70°C results in a 67% increase in water production at a wind speed of 3 m/s. A further increase in temperature from 80°C to 90°C leads to a 141% increase in water production under the same wind conditions.

Green innovation (GI) is increasingly recognized as an essential strategy for tackling urgent environmental issues, such as climate change, resource depletion, and pollution. While research is expanding on how economic policy uncertainty (EPU) affects GI, the influence of financial sector development (FSD) as a moderator in this context remains under-examined. To address this gap, we conduct an empirical analysis utilizing two decades of data (2000–2019) from five major emerging economies (BRICS). The study employs FMOLS and DOLS models to scrutinize the data. The findings indicate that EPU has a considerable adverse effect on GI, suggesting that uncertainty in economic policies can obstruct environmentally sustainable progress. In contrast, FSD demonstrates a notable positive association with green innovation, indicating that a robust financial sector can support and bolster these initiatives. Furthermore, the study identifies that FSD serves a crucial intermediary function in the EPU-GI connection. The policy implications of this study are significant, indicating that decision-makers should prioritize enhancing financial sector institutions to foster GI, particularly in times of heightened economic volatility. By providing new evidence regarding the dynamics between EPU, FSD, and GI, this investigation offers valuable insights for developing policies that harmonize economic stability with environmental sustainability. First published online 1 April 2025

The present study examines the nexus between sustainable digital entrepreneurship, open innovation, green human resource, and circular economy in the context of the Chinese fashion sector. Since fashion brands delve into the adoption of digital technologies, the opportunity to harness such innovation has become more pronounced in order to achieve sustainability. Circular economy, in recent years, has garnered the attention of scholars as they view the concept as a strategic mechanism which helps in achieving sustainable goals through cleaner production. Thus, the research assumes that digital entrepreneurship under the disguise of sustainable principles nurtures open innovation and green HRM practices, shaping circular economic goals. The study also scrutinizes the mediating role of knowledge sharing and digital orientations as they are proven critical enablers of sustainable principles. The researcher has gathered data from the full‐time employees in the HRM department of fashion companies in China; these employees included departmental heads, line managers, managers, and employees having good knowledge regarding the observed variables of the study. By employing PLS‐SEM, the findings of the study reveal that there is a significant positive association of circular economy with the firm's open innovation and green HRM practices, while it is not related to sustainable digital entrepreneurship. In light of the findings, the study offers a significant contribution by introducing a comprehensive model that fosters sustainability initiatives. However, the study is limited to the Chinese fashion industry, so its findings cannot be generalized. Therefore, future researchers are recommended to study some other industries as well.

This study examines the role of knowledge management, green social behaviour, dynamic capabilities, and green service innovation in gaining entrepreneurial success. The study has adopted a quantitative research method, targeting service sector employees based in China. A survey-based method was used to collect data, and the statistical tests were performed using PLS-SEM. The measurement model was assessed using reliability and validity methods, while the structural model was examined through structural equation modelling. Findings showed that knowledge management, dynamic capabilities, green social behavior and green service innovation significantly improve entrepreneurial success. Additionally, results revealed that green creativity mediates the association of knowledge management, dynamic capabilities and green social behavior with entrepreneurial success. The study significantly contributes to the extant literature by providing insight into organizational trust and ethical and green practices. The study provides a valuable solution to entrepreneurs and offers future directions to researchers.

The research focuses on developing an algorithm for managerial decision-making using big data and artificial intelligence (AI). It relies on such scientific research methods as structural–functional, critical, and comparative analysis and schematic visualization of organizational-economic processes as a sub-method of qualitative modeling of dynamic economic systems. The scientific novelty of the proposed algorithm lies in its two levels of decision-making (AI and manager), unlike the existing algorithm. Management is limited to the manager, with all stages seamlessly transitioning from one to another and being intrinsically linked. The theoretical significance of the developed algorithm is that it addresses the shortcomings of the existing algorithm and adapts the decision-making process to contemporary challenges. The practical significance of the author’s algorithm is that automating decision-making using big data and AI offers the following advantages for management: faster decision-making by eliminating intermediaries in the form of lower-level managers, comprehensive identification of relevant business opportunities and problems, reduced managerial workload and a smaller managerial apparatus, and the possibility of remote employment for managers. This collectively supports the humanization of managerial labor. Automated big data collection (through managerial communications, problem identification, alternative selection, optimal decision-making, and implementation) ensures improved corporate monitoring and control.

The research develops a mechanism for automating smart business through decision-making and production management based on big data and AI. Relying on the latest statistical data from Bloom Consulting and WIPO for 2023, the authors developed an econometric model for automating smart business in Central Asia. The developed model holds theoretical significance because it unveils the relatively unexplored experience of automating smart business in Central Asia and clarifies the impact of management factors on the outcomes of this automation in the region. Based on this model, the authors forecasted the automation of smart business in Kyrgyzstan, Russia, and Uzbekistan. The practical benefit of this forecast lies in its ability to facilitate more precise planning of smart business automation in these Central Asian countries. To support the practical implementation of the forecast, the authors developed a mechanism for automating smart business through decision-making and production management based on big data and AI. The new features of the mechanism include unified AI management of smart production as a special object of automation in smart business operations, storing production information in a separate big data cell to accelerate its processing, and recognizing production as the central and priority object of smart business management, with its automation progressing at its own pace. This mechanism will enhance production management efficiency and more fully realize the potential of smart production automation, underscoring its managerial significance.

The research aims to determine the impact of big data and AI on creating green jobs and making environmental decisions in Asian countries. By employing correlation and regression analysis methods, the authors developed an econometric model for environmental management in the top 15 Asian countries with the most favorable opportunities for developing a green economy in 2023. According to the model, the use of big data and AI has a significant yet contradictory influence on environmental decision-making in these countries. While it promotes more active green investments and job creation, it simultaneously limits green trade and restrains green innovations. The theoretical significance of the developed model lies in its pioneering revelation of the causal relationships between the use of big data and AI and the effectiveness of environmental decision-making in Asia, a unique region of the world. The practical significance stems from the identified potential to increase the number of green jobs and optimize environmental decision-making through the more active use of big data and AI in Kyrgyzstan. This approach enables the development of corporate plans and the implementation of programs for the green development of Kyrgyz enterprises. The managerial significance is reflected in the fact that the organizational scheme developed for creating green jobs and making environmental decisions using big data and AI will enhance contemporary environmental management practices for enterprises in Kyrgyzstan and other Asian countries.

The research focuses on the potential for organizing circular production and improving energy efficiency through decision-making based on big data and AI. The authors conducted a regression analysis on the best practices of the top 20 dynamically developing digital economies with the highest activity in applying big data and AI in 2023. The developed econometric model provided highly accurate quantitative measurements of the impact of automating investment and environmental decisions based on big data and AI technologies for the green economy, underscoring its theoretical significance. The scientific novelty of the research results is linked to the author’s classification of the consequences of automating investment and environmental decisions based on big data and AI for the green economy. Using Russia as an example, the authors demonstrated the potential for growth in energy efficiency and circularity of production by implementing big data and AI in decision-making, highlighting its practical significance by expanding opportunities for planning and forecasting the development of the green economy. The authors developed a decision-making framework for organizing energy-efficient circular production based on big data and AI. The managerial significance of the developed model is reflected in the optimization of these decisions.

The research aims to determine the contribution of transport digitalization to improving the quality of life in Central Asia and develop recommendations for maximizing this contribution, using the Republic of Uzbekistan as an example. The authors conducted a correlation analysis to explore the relationship between government regulatory factors determining the digitalization of the transport and logistics sector and the quality of life indicators associated with this sector across all 12 Central Asian countries in 2023. The analysis reveals the advantages of transport digitalization for the quality of life in Central Asia. Government regulatory measures determining the digitalization of the transport and logistics sector are ranked by their significance for the quality of life in Central Asia. Additionally, a contradiction in the government regulation of transport and logistics digitalization in Uzbekistan is identified. The authors provide recommendations to resolve this contradiction and improve government regulation of transport and logistics digitalization in Uzbekistan. The main conclusion is that the digitalization of transport significantly improves the quality of life in Central Asian countries. The theoretical significance lies in clarifying the relationship between transport digitalization and quality of life in Central Asia. The importance of the proposed recommendations for economic policy is that they will enhance the effectiveness of government regulation of transport and logistics digitalization in Uzbekistan. The practical significance of the authors’ conclusions and recommendations is that they will improve the quality of life in Uzbekistan and other Central Asian countries.

This paper studies a class of games in which players' payoffs explicitly depend on their intrinsic preferences over the set of available alternatives, level of social interaction and the global influence of the aggregate societal choices. Using the potential functions approach, we examine the conditions under which the games admit a Nash equilibrium in pure strategies with a special emphasis on the role of social interactions. The existence results are then applied to examine the welfare consequences of the introduction of common goods and the adoption of new technologies.

The research develops scientific and practical recommendations for improving intelligent decision-making support through a new technology: machine learning based on big data. The authors conducted a comparative analysis of the existing technology and proposed new technology to support intelligent decision-making. The authors also carried out a SWOT analysis of the transition to machine learning based on big data as a new technology for intelligent decision-making support. The authors developed a framework for organizing machine learning based on big data as a technology for enhancing intelligent decision-making support. The conclusion is that machine learning based on big data will enhance intelligent decision-making support by improving information support (enabled by the Internet of Things and big data), expanding the functional capabilities of smart analytical tools (artificial intelligence), and transitioning from template-based to creative solutions (enabled by machine learning). The practical significance of the developed framework for organizing machine learning based on big data as a technology for enhancing intelligent decision-making support lies in its potential to increase the efficiency of today’s organizational management.

To combat the escalating consequences of climate change issues, including severe weather phenomena, increasing global temperatures, and ecological degradation, global discussions on achieving carbon neutrality have intensified. These efforts emphasize the pressing necessity for societies to embrace sustainable solutions to resolve the climate change issue by systematically reducing carbon emissions. Meanwhile, the environmental effect of hydro energy, oil efficiency, and environmental related technology (ERT) remains underexplored, particularly in the top energy transition economies. Despite representing just 2% of the global population, these nations play vital role in advancing sustainable development, even as they account for roughly 3% of global energy-related CO2 emissions. To tackle this, this research inspects the impact of oil efficiency, hydro energy, and ERT on CO2 emissions in the top energy transition nations by using the cross-sectional dependence, slope heterogeneity, second-generation panel unit root test, Westerlund cointegration, and the Cross-Sectional Autoregressive Distributed Lag (CS-ARDL) method as the main estimator, which is capable of uncover both long and short run dynamics. Additionally, the study adopted the Augmented Mean Group (AMG) and the Dynamic Common Correlated Effects Mean Group (DCCEMG), as robustness check. The results of the CS-ARDL shows that while globalization and economic growth hinder ecological sustainability, ERT significantly mitigates CO₂ emissions. Furthermore, oil efficiency and hydro energy are identified as key drivers of carbon neutrality. These findings are reinforced by the AMG and DCCEMG estimations, alongside Granger causality analysis, which provide strong corroborating evidence. Given these insights, this study conclude that policymakers should introduce targeted incentives to accelerate investments in oil efficiency technologies and hydro energy infrastructure while simultaneously addressing regulatory and financial constraints. These nations need to establish public–private partnerships that prioritize resource allocation and enhance research and development in ERT and hydro energy, thereby promoting sustainable energy practices across sectors and attain carbon neutrality.

This study aims to investigate the impact of entrepreneurship and green investments on environmental sustainability within the scope of Sustainable Development Goals for developed economies. The study conducts an in‐depth analysis from 2001 to 2022 and reveals the possible effects of increases and decreases in entrepreneurship. Based on the results of the empirical analysis, an asymmetric relationship between entrepreneurship and environmental sustainability has been determined in the short and long run. Although decreases in entrepreneurial activities cause a reduction in environmental quality, increases in entrepreneurial activities contribute to environmental sustainability. Increases in green investments provide improvements in environmental quality. These results show that entrepreneurial activities and green investments significantly impact environmental sustainability in developed economies. Based on these results, this study recommends that policymakers in developed countries incentivize green investments, foster the growth of sustainable entrepreneurial ventures, and implement policies that assess and enhance the environmental sustainability of entrepreneurial activities.

A label‐free electrochemical immunosensor based on the zeolitic imidazolate framework‐8 (ZIF8)/bismuth ferrite (BFO) nanocomposite was fabricated for the specific and sensitive quantification of prostate‐specific antigen (PSA). The ZIF8‐BFO material not only increases the surface area effectively but also enhances the catalytic capability of the electrode through a dual amplification strategy, leading to the improved sensitivity of the probe for PSA recognition. A thin layer of l ‐cysteine was used for two reasons: providing a scaffold for the next functionalization and reducing the fouling of plasma ingredients on the surface of the probe. The mechanical and spectroscopic properties of the produced nanomaterials were characterized using different techniques such as field emission scanning electron microscopy (FESEM), x‐ray diffraction (XRD), atomic force microscopy (AFM), the Fourier transform infrared (FTIR), and dynamic light scattering (DLS)/Zeta analyzer. The electroanalytical properties of the probe were studied using square‐wave voltammetry (SWV) and cyclic voltammetry (CV). The signal of the probe decreased proportionally with increasing PSA concentration in the 100.0 pg/mL–15.0 ng/mL range, with a limit of detection (LOD) of 85 pg/mL. The proposed platform has been successfully employed to measure PSA levels in human serum samples with acceptable accuracy. The capability of the probe was evaluated in detecting PSA in patient's serum samples, with results compared to those obtained from the gold standard enzyme‐linked immunosorbent assay (ELISA). The results suggested that the ZIF8‐BFO material‐based probe could be used as a promising method for detecting PSA and tracing therapy progression in clinics.