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.

The objective of this work was to analyze the effect of the use of ChatGPT in the teaching-learning process of scientific research in engineering. Artificial intelligence (AI) is a topic of great interest in higher education, as it combines hardware, software and programming languages to implement deep learning procedures. We focused on a specific course on scientific research in engineering, in which we measured the competencies, expressed in terms of the indicators, mastery, comprehension and synthesis capacity, in students who decided to use or not ChatGPT for the development and fulfillment of their activities. The data were processed through the statistical T-Student test and box-and-whisker plots were constructed. The results show that students’ reliance on ChatGPT limits their engagement in acquiring knowledge related to scientific research. This research presents evidence indicating that engineering science research students rely on ChatGPT to replace their academic work and consequently, they do not act dynamically in the teaching-learning process, assuming a static role.

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.

The quest for quality postgraduate research productivity through education is on the increase. However, in the context of the African society, governance structures and policies seem to be impacting on the quality level of the provided education. Hence, this conceptual study explored the roles of governance structures and policies in enhancing and ensuring quality postgraduate education programmers in African institutions of higher learning. To this end, various relevant literature was reviewed. The findings showed amongst others that governance structures and policies affect the quality of education provided. Meanwhile, other factors such as curriculum, foreign influence, lack of resources, training, amongst others contribute to the quality of education provided. The study concludes that there is need for the current structures of governance and the designed and implemented policies for postgraduate education to be reviewed and adjusted towards ensuring the desired transformation.

This study aims to use dialectical thinking to explore the impacts and responses of Artificial Intelligence (AI) empowerment on students’ personalized learning. The effect of AI empowerment on student personalization is dissected through a literature review and empirical cases. The study finds that AI plays a significant role in promoting personalized learning by enhancing students’ learning effectiveness through intelligent recommendation, automated feedback, improving students’ independent learning ability, and optimizing learning paths, however, the wide application of AI also brings problems such as technological dependence, cheating in exams, weakening of critical thinking ability, educational fairness, and data privacy protection to students. The study proposes recommendations to strengthen technology regulation, enhance the synergy between teachers and AI, and optimize the personalized learning model. AI-enabled personalized learning is expected to play a greater role in improving learning efficiency and educational fairness.

The process of internationalization and innovation (IPI) in the urban road passenger transport (URPT) sector is driven by the need to provide cities with efficient and sustainable mobility solutions. The objective of this study is to understand the perceptions of URPT employees in relation to PII, based on a comprehensive case study. By exploring how these two concepts interrelate and influence each other, the study seeks to provide valuable information that can help improve strategic planning and policy formulation in the urban transport sector. The research, based on semi-structured interviews with 20 employees, reveals significant gaps in internal communication, with only about half of the participants aware of ongoing national and international projects. Information was often limited to those directly involved, indicating a need for improved dissemination strategies. Despite these communication issues, employees positively view the company’s presence at international events and recognize the importance of involvement in European organizations, particularly for knowledge acquisition and networking. Challenges identified include inadequate internal communication and insufficient investment in international projects. However, there was strong agreement on the value of internationalization and innovation process (IIP) for both professional development and organizational growth. To enhance the company’s international presence and return on investment (ROI), the study recommends better coordination, improved information sharing, and strategic planning. These findings emphasize the critical role of effective communication and active participation in international initiatives for the sustainable growth of the organization.