Tashkent State University of Economics

The term metaverse is described as the next iteration of the Internet. Metaverse is a virtual platform that uses extended reality technologies, i.e. augmented reality, virtual reality, mixed reality, 3D graphics, and other emerging technologies to allow real-time interactions and experiences in ways that are not possible in the physical world. Companies have begun to notice the impact of the metaverse and how it may help maximize profits. The purpose of this paper is to offer perspectives on several important areas, i.e. marketing, tourism, manufacturing, operations management, education, the retailing industry, banking services, healthcare, and human resource management that are likely to be impacted by the adoption and use of a metaverse. Each includes an overview, opportunities, challenges, and a potential research agenda.

Soil salinization is increasing steadily in many parts of the world and causes major problems for plant productivity. Under these stress conditions, root-associated beneficial bacteria can help improve plant growth and nutrition. In this study, salt-tolerant bacteria from the rhizosphere of Uzbek wheat with potentially beneficial traits were isolated and characterized. Eight strains which initially positively affect the growth of wheat plants in vitro were investigated in detail. All eight strains are salt tolerant and have some of the following plant growth-beneficial properties: production of auxin, HCN, lipase or protease and wheat growth promotion. Using sequencing of part of the 16S rDNA, the eight new isolates were identified as Acinetobacter (two strains), Pseudomonas aeruginosa, Staphylococcus saprophyticus, Bacillus cereus, Enterobacter hormaechei, Pantoae agglomerans and Alcaligenes faecalis. All these strains are potential human pathogens. Possible reasons for why these bacteria present in the rhizosphere and establish there are discussed.

This paper examines the impact of remittances on poverty alleviation in 15 selected Asian economies. Remittances have been identified as a potential source of income for households in developing countries and a means of reducing poverty. Using panel data from 2000 to 2020, we estimate the effect of remittances on poverty levels in these economies, controlling for other relevant factors such as GDP per capita, inflation rate and population growth. Our results suggest that remittances have a significant and negative impact on poverty levels in these economies, indicating that remittances play a crucial role in poverty reduction. The findings also reveal that the effect of remittances on poverty reduction varies across economies, with some economies experiencing a stronger poverty-reducing effect than others. The findings highlight the potential benefits of policies aimed at facilitating the flow of remittances and ensuring their effective use in reducing poverty in developing countries.

Information technology (IT) is the backbone of supply chain management (SCM). However, selection of specific IT applications must be made in alignment with the organizations' competitive priorities. This article profiles differences between firms based on their level of IT usage focusing on organizational competitive priorities, choice of specific IT applications, and performance measures achieved.

To mitigate environmental challenges and fulfill the Sustainable Development Goals, a broader and holistic ecological assessment is required. As a result, this research utilizes the load capacity factor, which is a distinct proxy of environmental deterioration that offers a detailed environmental evaluation measurement by comparing biocapacity and ecological footprint simultaneously. Moreover, the load capacity factor provides the combined attributes of the demand and supply-side of environmental quality. Therefore, this research scrutinized the effect of financial globalization, urbanization, economic growth, and renewable and nonrenewable energy usage on load capacity factor for the period stretching between 1970 and 2017 in Brazil. The bounds testing procedure for cointegration in combination with the critical approximation p -values of Kripfganz and Schneider (2018) disclosed a cointegrating association between load capacity and its regressors. The outcome of the ARDL method uncovered that economic growth, non-renewable and renewable energy reduce the load capacity factor, whereas urbanization has no impact on load capacity factor in Brazil. However, financial globalization has a positive effect on load capacity factor in Brazil. Finally, the study uses the spectral causality test to assess the causality interaction between the observed parameters. The policymakers should take advantage of the opportunity by developing policies that encourage the openness of the economy to foreign investors.

Recently, sustainable development goals (SDGs) have become an international requirement that needs to be achieved and requires the focus of recent literature and regulation authorities. Thus, the current article investigates the impact of environmental, social, and governance (ESG) and economic growth on the SDG of ASEAN countries. The current study has extracted secondary data from secondary sources such as SDGs reports and world development indicators (WDI) from 1986 to 2020. The present study has used the Panel Autoregressive Distributed Lag (ARDL) to test the linkage among the variables. The results highlighted that the environmental score, social score, governance score, and economic growth positively associated with the ASEAN countries' SDGs. The current article provides help to new researchers while conducting research on achieving SDGs and guides policymakers while establishing policies regarding achieving the SDGs through ESG.

-dependent metalloproteinases and suggest ways by which these enzymes can be engineered for treating deep vein thrombosis and neurodegenerative disorders.

With an increase in both global warming and the human population, forest fires have become a major global concern. This can lead to climatic shifts and the greenhouse effect, among other adverse outcomes. Surprisingly, human activities have caused a disproportionate number of forest fires. Fast detection with high accuracy is the key to controlling this unexpected event. To address this, we proposed an improved forest fire detection method to classify fires based on a new version of the Detectron2 platform (a ground-up rewrite of the Detectron library) using deep learning approaches. Furthermore, a custom dataset was created and labeled for the training model, and it achieved higher precision than the other models. This robust result was achieved by improving the Detectron2 model in various experimental scenarios with a custom dataset and 5200 images. The proposed model can detect small fires over long distances during the day and night. The advantage of using the Detectron2 algorithm is its long-distance detection of the object of interest. The experimental results proved that the proposed forest fire detection method successfully detected fires with an improved precision of 99.3%.

Technological innovations have been a matter of contention, and their environmental consequences remain unresolved. Moreover, studies have extensively evaluated environmental challenges using metrics such as nitrogen oxide emissions, sulfur dioxide, carbon emissions, and ecological footprint. The environment has the supply and demand aspect, which is not a component of any of these indicators. By measuring biocapacity and ecological footprint, the load capacity factor follows a certain ecological threshold, allowing for a thorough study on environmental deterioration. With the reduction in load capacity factor, the environmental deterioration increases. In the context of the environment, the interaction between technological innovation and load capacity covers the demand and supply side of the environment. In light of this, employing the dataset ranging from 1980 to 2017 for the case of South Africa, the bound cointegration test in conjunction with the critical value of Kripfganz and Schneider showed cointegration in the model. The study also employed the ARDL, whose outcome revealed that nonrenewable energy usage and economic growth contribute to environmental deterioration, whereas technological innovation and globalization improve the quality of the environment. This study validated the hypothesis of the environmental Kuznets curve for South Africa, as the short-term coefficient value was lower than the long-term elasticity. Furthermore, using the frequency-domain causality test revealed that globalization and economic growth predict load capacity in the long term, and nonrenewable energy predicts load capacity factors in the long and medium term. In addition, technological innovation predicts load capacity factors in the short and long term. Based on the findings, we propose that policymakers should focus their efforts on increasing funding for the research and development of green technologies.

Authorities and policymakers in Korea have recently prioritized improving fire prevention and emergency response. Governments seek to enhance community safety for residents by constructing automated fire detection and identification systems. This study examined the efficacy of YOLOv6, a system for object identification running on an NVIDIA GPU platform, to identify fire-related items. Using metrics such as object identification speed, accuracy research, and time-sensitive real-world applications, we analyzed the influence of YOLOv6 on fire detection and identification efforts in Korea. We conducted trials using a fire dataset comprising 4000 photos collected through Google, YouTube, and other resources to evaluate the viability of YOLOv6 in fire recognition and detection tasks. According to the findings, YOLOv6's object identification performance was 0.98, with a typical recall of 0.96 and a precision of 0.83. The system achieved an MAE of 0.302%. These findings suggest that YOLOv6 is an effective technique for detecting and identifying fire-related items in photos in Korea. Multi-class object recognition using random forests, k-nearest neighbors, support vector, logistic regression, naive Bayes, and XGBoost was performed on the SFSC data to evaluate the system's capacity to identify fire-related objects. The results demonstrate that for fire-related objects, XGBoost achieved the highest object identification accuracy, with values of 0.717 and 0.767. This was followed by random forest, with values of 0.468 and 0.510. Finally, we tested YOLOv6 in a simulated fire evacuation scenario to gauge its practicality in emergencies. The results show that YOLOv6 can accurately identify fire-related items in real time within a response time of 0.66 s. Therefore, YOLOv6 is a viable option for fire detection and recognition in Korea. The XGBoost classifier provides the highest accuracy when attempting to identify objects, achieving remarkable results. Furthermore, the system accurately identifies fire-related objects while they are being detected in real-time. This makes YOLOv6 an effective tool to use in fire detection and identification initiatives.

The independent detection and classification of brain malignancies using magnetic resonance imaging (MRI) can present challenges and the potential for error due to the intricate nature and time-consuming process involved. The complexity of the brain tumor identification process primarily stems from the need for a comprehensive evaluation spanning multiple modules. The advancement of deep learning (DL) has facilitated the emergence of automated medical image processing and diagnostics solutions, thereby offering a potential resolution to this issue. Convolutional neural networks (CNNs) represent a prominent methodology in visual learning and image categorization. The present study introduces a novel methodology integrating image enhancement techniques, specifically, Gaussian-blur-based sharpening and Adaptive Histogram Equalization using CLAHE, with the proposed model. This approach aims to effectively classify different categories of brain tumors, including glioma, meningioma, and pituitary tumor, as well as cases without tumors. The algorithm underwent comprehensive testing using benchmarked data from the published literature, and the results were compared with pre-trained models, including VGG16, ResNet50, VGG19, InceptionV3, and MobileNetV2. The experimental findings of the proposed method demonstrated a noteworthy classification accuracy of 97.84%, a precision success rate of 97.85%, a recall rate of 97.85%, and an F1-score of 97.90%. The results presented in this study showcase the exceptional accuracy of the proposed methodology in accurately classifying the most commonly occurring brain tumor types. The technique exhibited commendable generalization properties, rendering it a valuable asset in medicine for aiding physicians in making precise and proficient brain diagnoses.

This study aims to explore the relationship between industry value added, renewable energy, and CO2 emissions in a sample of 44 Sub-Saharan African countries over the period 2000–2015. This study makes several important contributions to extant research. While existing research was focused on the renewable energy-CO2 emissions nexus, the current study assesses the moderating role of the renewables sector in the industrialization-CO2 emissions relationship. In addition, this study considers whether EKC relationships will hold after accounting for structural transformations (including industrial contributions to GDPs). Moreover, we are revising the existence of the EKC framework for the Sub-Saharan African countries. Using a two-step system GMM estimator, we found that the share of industry in GDP has a significant positive impact on CO2 emissions, while renewable electricity output reduces CO2 emissions. If causal, a one percentage point increase in renewable electricity output reduces carbon emissions by 0.22%. Moreover, the renewable energy sector then mediates the positive effect of industry value added on CO2 emissions. We also find evidence for the statistical significance of the inverted U-shaped relationship between GDP per capita and CO2 emissions.

Purpose – The purpose of this study is to examine the impact of gender and ethnicity of CEO and audit committee members (directors) on audit fees and audit delay in the US firms. Design/methodology/approach – Audit-related corporate governance literature has extensively examined the determinants of audit fees and audit delay by focusing on board characteristics, specifically board independence, diligence and expertise. The authors provide empirical evidence that gender and ethnicity diversity in corporate leadership and boardrooms influence a firm’s audit fees and audit delay. Findings – This study finds that firms with female and ethnic minority CEOs pay significantly higher audit fees than those with male Caucasian CEOs. The authors also find that firms with a higher percentage of ethnic minority directors on their audit committee pay significantly higher audit fees. Further, the authors find that firms with female CEOs have shorter audit delay than firms with male CEOs and firms with a higher percentage of female and ethnic minority directors on their audit committee are associated with shorter audit delay. Results indicate that female CEOs and both female and ethnic minority directors are sensitive to the market pressure to avoid audit delay. Research limitations/implications – The results suggest that gender and ethnic diversity could improve audit quality and the firms’ overall financial reporting quality. Practical implications – This study provides insights to regulators and policy-makers interested in increasing diversity within a firm’s board and top executives. Recently, the US Securities and Exchange Commission (SEC) and the European Commission have been pressing publicly traded companies to improve diversity among their directors. This study provides evidence and perspective on how diversity can enhance financial reporting quality measured by audit fees and audit delay. Originality/value – Previous studies have not given much attention on the impact of racial ethnicity in addition to gender characteristics of top executives and audit committee directors on audit fees and audit delay.

Institutional quality (IQ) plays a crucial role in achieving the Sustainable Development Goals. IQ is fundamental to SDG’s 16, which promotes peaceful and inclusive societies, provides access to justice, and builds effective, accountable, and transparent institutions. Countries with strong institutions that uphold the rule of law, protect human rights, and combat corruption are more likely to achieve this goal and promote economic development. Therefore, this study examines the relationship between IQ and economic development, as measured by the human development index (HDI), in 70 developing countries between 2002 and 2018. To achieve the above mention objective, various econometric techniques were employed, including CIPS unit root, Westerlund (2007) co-integration, and Cross-sectional Augmented Autoregressive Distributed Lag, and robustness analysis conducted using Fully Modified Ordinary Least Square, Dynamic Ordinary Least Square, Augmented Mean Grouped, Impulse Response Function, and Variance Decomposition Analysis panel estimators. The study found that IQ and globalization have a positive, while inflation, unemployment, and corruption have a negative impact on HDI. The estimates from the Impulse Response Function show that IQ positively influences HDI from 2019 to 2028. Additionally, the Variance Decomposition Analysis reported that around 38% of the variations in HDI can be attributed to changes in IQ. To improve IQ, transparency must be prioritized as it is the foundation of IQ enhancement. Similarly, measures must be taken to combat corruption, and strong administrative intervention is required to advance economic development. These findings suggest that policymakers must prioritize improving institutional quality and fighting corruption to promote economic development in developing countries.

The persistent disparity between urban and rural incomes in China poses a critical challenge to alleviating energy poverty in rural areas. This study investigates how the income gap between urban and rural regions exacerbates rural energy poverty, focusing on the period from 2005 to 2023, utilizing data from 30 provinces. By employing a two-way fixed-effects model and asymmetry analysis, the research reveals that an increase in the urban-rural income disparity significantly intensifies rural energy poverty. Notably, at higher income quantiles, the gap's effect on energy poverty is more pronounced, while at lower quantiles, its impact is less severe. Financial development, rather than alleviating the situation, is positively associated with rural energy poverty, highlighting an unintended consequence of unequal access to financial services. The results further show that rural regions with limited financial inclusion experience a deepening of energy poverty, with financial service accessibility benefiting wealthier demographics more than the impoverished rural population. These findings imply that targeted policies promoting equitable financial access, narrowing income disparities, and integrating energy poverty reduction strategies are essential to achieving China's Rural Revitalization Strategy. • Urban-rural income gap worsens rural energy poverty from 2005 to 2023 in China. • Higher income quantiles face a more pronounced effect on rural energy poverty. • Financial development unintentionally increases rural energy poverty in China. • Equitable financial access and reduced income gaps are key for energy poverty reduction.

• China’s urbanization has had a detrimental impact on the environment and increased resource, energy, and material consumption. • Urban sustainable development is an issue that is often debated in China due to all of these aspects. • Integrating environmental responsibility, green technology, green finance, and clean energy to advance sustainability goals effectively. China’s rapid urbanization has significantly impacted the environment, escalating resource, energy, and material consumption. Sustainable urban development has become a critical issue, with eco-efficiency emerging as a key metric for its assessment. This study employs eco-efficiency analysis, incorporating environmental contamination as an undesirable output, using data envelopment analysis (DEA) and a modified super-efficiency model for ranking cities. Empirical research was conducted on 30 Chinese provincial capital cities using real-world data. Results reveal that while many cities are eco-efficient, inefficiency is concentrated in underdeveloped regions of the southwest and northwest. Conversely, some eco-efficient cities exhibit high levels of pollution and intensive resource use, including land, energy, and water. The modified ranking methodology identified Yinchuan, Lanzhou, and Guiyang as the least eco-efficient cities, while Haikou, Fuzhou, and Beijing ranked as the top performers. The study highlights the need to reform the GDP-oriented development model and evaluation systems, continually upgrade industrial structures, and prevent the migration of heavy industries from more developed to less developed regions. These findings provide actionable insights for policymakers to balance urban growth with environmental sustainability.

Abstract This paper examines the impact of institutional pressure (IP), top management support (TMS), green supply chain management practices (GSCM), and supply chain competitive advantage (SCCA) on corporate environmental performance (EP). We also analyze the mediation effect of GSCM on the interplay between TMS and EP. Additionally, the paper also provides an analysis of the moderating role of SCCA between IP and EP. To attain the objective of this research, we assembled data from 710 business entities within the Shaanxi province of China utilizing a survey design approach. The structural equation model (SEM) was applied to test and assess the hypothetical outline. The study outcomes empirically show that TMS, GSCM, and SCCA positively and significantly impact EP. Interestingly, our study found an insignificant association between IP and EP. The study's results also demonstrate that IP directly relates to top management support. Moreover, the study's empirical findings reveal that GSCM positively mediates IP and EP. The study findings show that SCCA shapes IP and EP's connection. Accordingly, the practical implications of our study's findings suggest that business managers, investors, and government agencies must know the importance of adopting sustainable practices within the supply chain. Business managers must take action to integrate environmental criteria into supplier selection, evaluate suppliers' EP, and collaborate with eco‐friendly suppliers. Hence, government agencies, stakeholders, and business managers can use this information to shape regulations and policies that encourage businesses to adopt sustainable supply chain practices. Offering incentives such as tax benefits or grants for sustainability initiatives can also promote adoption. The study recommends that a business culture that targets improving EP due to IP and top management support is essential in achieving GSCM practices, thereby promising competitive advantage.

Methods for detecting emotions that employ many modalities at the same time have been found to be more accurate and resilient than those that rely on a single sense. This is due to the fact that sentiments may be conveyed in a wide range of modalities, each of which offers a different and complementary window into the thoughts and emotions of the speaker. In this way, a more complete picture of a person's emotional state may emerge through the fusion and analysis of data from several modalities. The research suggests a new attention-based approach to multimodal emotion recognition. This technique integrates facial and speech features that have been extracted by independent encoders in order to pick the aspects that are the most informative. It increases the system's accuracy by processing speech and facial features of various sizes and focuses on the most useful bits of input. A more comprehensive representation of facial expressions is extracted by the use of both low- and high-level facial features. These modalities are combined using a fusion network to create a multimodal feature vector which is then fed to a classification layer for emotion recognition. The developed system is evaluated on two datasets, IEMOCAP and CMU-MOSEI, and shows superior performance compared to existing models, achieving a weighted accuracy WA of 74.6% and an F1 score of 66.1% on the IEMOCAP dataset and a WA of 80.7% and F1 score of 73.7% on the CMU-MOSEI dataset.

This paper critically surveys the growing literature on the policy choices of local governments. First, we identify various reasons for local government policy interactions, including fiscal competition, bidding for firms, yardstick competition, expenditure spillovers, and Tiebout sorting. We discuss theoretically what parameters should be estimated to determine the reason for competition among local governments. We emphasize how the policy outcomes emerging from this competition are affected by the presence of constraints imposed by higher-level governments. Second, we integrate theoretical and empirical analyses on the effects of fiscal decentralization on mobility, spillovers, fiscal externalities, economic outcomes, and distributional issues. Third, we identify key issues that arise in the empirical estimation of strategic interactions among local governments and highlight recent quasi-experimental evidence that has attempted to identify the mechanism at work. Finally, a synthesis model, containing multiple mechanisms and fiscal instruments, resolves some puzzles and provides guidance for future research. (JEL D72,H20, H71, H72, H73, H77, R51)

Forest fires rank among the costliest and deadliest natural disasters globally. Identifying the smoke generated by forest fires is pivotal in facilitating the prompt suppression of developing fires. Nevertheless, succeeding techniques for detecting forest fire smoke encounter persistent issues, including a slow identification rate, suboptimal accuracy in detection, and challenges in distinguishing smoke originating from small sources. This study presents an enhanced YOLOv8 model customized to the context of unmanned aerial vehicle (UAV) images to address the challenges above and attain heightened precision in detection accuracy. Firstly, the research incorporates Wise-IoU (WIoU) v3 as a regression loss for bounding boxes, supplemented by a reasonable gradient allocation strategy that prioritizes samples of common quality. This strategic approach enhances the model's capacity for precise localization. Secondly, the conventional convolutional process within the intermediate neck layer is substituted with the Ghost Shuffle Convolution mechanism. This strategic substitution reduces model parameters and expedites the convergence rate. Thirdly, recognizing the challenge of inadequately capturing salient features of forest fire smoke within intricate wooded settings, this study introduces the BiFormer attention mechanism. This mechanism strategically directs the model's attention towards the feature intricacies of forest fire smoke, simultaneously suppressing the influence of irrelevant, non-target background information. The obtained experimental findings highlight the enhanced YOLOv8 model's effectiveness in smoke detection, proving an average precision (AP) of 79.4%, signifying a notable 3.3% enhancement over the baseline. The model's performance extends to average precision small (APS) and average precision large (APL), registering robust values of 71.3% and 92.6%, respectively.