This study investigates the influence of government expenditure on the economic growth of the ASEAN-5 countries from 2000 to 2021. The study employs the Pooled Mean Group (PMG) ARDL model and robust least squares method. The importance of the current study lies in its analysis of the short and long-run impact of government expenditure on economic growth in ASEAN-5. The empirical findings demonstrate a positive relationship between government expenditure and economic growth in the long run. These results align with the Keynesian perspective, asserting that government expenditure stimulates economic growth. The study also confirms one-way causality from government expenditure to economic growth, supporting the Keynesian hypothesis. These insights hold significance for policymakers in the ASEAN-5, highlighting the necessity for policies promoting the effective allocation of productive government expenditure. Moreover, it is important to enhance systems that promote economic growth and efficiently allocated economic resources toward productive expenditures while also maintaining effective governance over such expenditures.

The article examines the appearance of various unfortunate situations and tragic events in modern Kazakh novels that arise due to human and natural ecology problems. The research’s primary goal is to analyze human and natural ecology issues based on contemporary Kazakh novels. We have chosen A. Nurpeyisov’s novel “The Last Duty” as our research material, which focuses on issues of human and natural ecology, and we will discuss the large-scale issues concerning the fate of human, nature, and society as a collective. The research topic’s practical significancelies in examining Kazakh novels that address crucial issues like safeguarding the ecological environment and preserving the green earth, which directly impact the destiny and future of humanity. It also aims to highlight their role in advancing societal development, elevating human values, and safeguarding our spiritual heritage. The research method involves mentioning the names of Kazakh novels that specifically and indirectly focus on the topic of human and natural ecology and summarizing their common features. The article also employed research methods such as analysis, comparison, and discussion. The novelty of the research result: Here are some relevant points. First, in the article, the core topic of the problem of human and natural ecology, which is common to all humanity in modern Kazakh novels, was highlighted. Second, analyzing the three characters, Zhadiger, Pakizat and Azim, which reveal the actual idea of the novel “The Last Duty,” the writer’s stylistic features and skillful aspects were also mentioned during the analysis of the character image through deep psychological analysis, landscape description, clear image, and artistic language, and theoretical conclusions and analyses were presented.

This study conducts a comparative analysis of various machine learning and deep learning models for predicting order quantities in supply chain tiers. The models employed include XGBoost, Random Forest, CNN-BiLSTM, Linear Regression, Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN), Bidirectional LSTM (BiLSTM), Bidirectional GRU (BiGRU), Conv1D-BiLSTM, Attention-LSTM, Transformer, and LSTM-CNN hybrid models. Experimental results show that the XGBoost, Random Forest, CNN-BiLSTM, and MLP models exhibit superior predictive performance. In particular, the XGBoost model demonstrates the best results across all performance metrics, attributed to its effective learning of complex data patterns and variable interactions. Although the KNN model also shows perfect predictions with zero error values, this indicates a need for further review of data processing procedures or model validation methods. Conversely, the BiLSTM, BiGRU, and Transformer models exhibit relatively lower performance. Models with moderate performance include Linear Regression, RNN, Conv1D-BiLSTM, Attention-LSTM, and the LSTM-CNN hybrid model, all displaying relatively higher errors and lower coefficients of determination (R²). As a result, tree-based models (XGBoost, Random Forest) and certain deep learning models like CNN-BiLSTM are found to be effective for predicting order quantities in supply chain tiers. In contrast, RNN-based models (BiLSTM, BiGRU) and the Transformer show relatively lower predictive power. Based on these results, we suggest that tree-based models and CNN-based deep learning models should be prioritized when selecting predictive models in practical applications.

This research presents a comprehensive model for enhancing the road network in Thailand to achieve high efficiency in transportation. The objective is to develop a systematic approach for categorizing roads that aligns with usage demands and responsible agencies. This alignment facilitates the creation of interconnected routes, which ensure clear responsibility demarcation and foster efficient budget allocation for road maintenance. The findings suggest that a well-structured road network, combined with advanced information and communication technology, can significantly enhance the economic competitiveness of Thailand. This model not only proposes a framework for effective road classification but also outlines strategic initiatives for leveraging technology to achieve transportation efficiency and safety.

Comparative analysis of the development history of sports social organizations in China, Japan and South Korea from multiple perspectives, in order to provide reference suggestions for solving the existing problems of the development of sports social organizations in China as well as for the sustainable development in the future. This paper explores the optimization path of sports social organizations in China by using the literature method and comparative analysis method. The study finds that the current development of sports social organizations in Japan and South Korea is characterized by independence and autonomy, a relatively rich number and variety of organizations, mutual separation of powers and responsibilities between government agencies and social organizations, and autonomous operation and efficient governance of sports social organizations. The development of sports social organizations in China has reached a new level since the founding of New China, and the Party’s attention to and support for their development has been increasing, but China still has deficiencies in the number of organizations, organizational capacity, and policy system. The study concludes that Japan and South Korea have three development conditions for sports social organizations: a socially oriented governance system, a more complete policy and regulation system, and a standardized and efficient financial support system. The study concludes that the prosperity of sports social organizations is crucial in building a strong sports nation at the present time. Combining the successful experiences of Japan and South Korea and integrating into China’s national conditions, we strive to build a governance system that combines government and society, construct a diversified financial support system, and improve the policy support system for sports organizations to promote the progress of sports social organizations in China, and open the way for the autonomy and independence of sports social organizations in China, and put the improvement of the governance system of sports social organizations on the agenda.

The global increase in organic food consumption underscores the need for in-depth research to understand the determinants of consumer purchase intentions towards organic products. Hence, this study aims to investigate the factors influencing the consumer purchase intentions towards organic food through an analysis involving 274 participants. Additionally, it also explores the mediating and moderating effects of attitudes and environmental concerns on the purchasing intentions towards organic food, providing insights in how these psychological and environmental factors influence consumer behavior. Using a quantitative method, data was gathered from the South Korean cities of Jinju and Changwon. However, analysis was performed through Partial Least Squares Structural Equation Modeling. The research findings illustrate that subjective norm, perceived behavioral control, and ethical consciousness significantly influence attitudes towards purchase intentions of organic foods. Moreover, the results indicate that attitude positively influenced consumer purchase intention towards organic food. Results reveal that attitude serves as a mediator in the relationship between subjective norms and consumer purchase intentions for organic food. Similarly, Likewise, attitude mediates the connection between perceived behavioral control and consumer purchase intention towards organic food. Additionally, the results indicate that attitude mediates the relationship between ethical consciousness and consumer purchase intention towards organic food. Furthermore, environmental concerns are found to significantly moderate the association between attitude and consumer purchase intention towards organic foods. The insights garnered from this study shed light on consumer intentions to purchase organic foods, equipping stakeholders such as marketers, retailers, and producers with valuable insights for creating effective marketing strategies aiming at promotion of these products. This research serves as a pivotal resource for developing targeted approaches to enhance the market share and consumption of organic foods.