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 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.

The purpose of this study is to explore new financial product’s impact on the behaviour of individual investors. To analyze investors’ risk and return expectations, this article investigates trading volumes before and after the introduction of financial product innovation. An event research technique was used to gather data from the National Stock Exchange. Data was analyzed using descriptive statistics and the Sharpe ratio approach, which were provided by different investors. The research results highlight that individual investors’ overreaction behaviour is brought out by financial product innovation. Furthermore, the study’s results imply that rising trading volumes are not entirely explained by updated risk-adjusted returns and that new financial products lead to excessive trading by investors and lowering returns. Higher trading volumes are not explained by better risk-adjusted returns. Young investors often respond irrationally to information offered by financial advisors, resulting in short-term gains at the expense of long-term gains. The study demonstrates that the development of innovative financial products does not always result in investors’ long-term prosperity. Worse outcomes and excessive trading could follow from it. The paper concludes by providing various real-world implications that the benefits and drawbacks of innovative financial products should be spelled out in detail by financial institutions and representatives. his research contributes to the implementation of individual investors’ overreaction behaviour that is brought out by financial product innovation. It highlights that higher trading volumes are not explained by better risk-adjusted returns.

Road construction and maintenance are key interventions that support economic potential in the country. However, the deplorable state of some roads in Nigeria, and in Cross River and Akwa Ibom states draws research concerns. This paper seeks to examine the impact of the Niger Delta Development Commission Intervention on road construction and economic activities in Cross River and Akwa Ibom States, Nigeria. Using the Sustainable Development Framework, a survey research design was employed, gathering data from 400 respondents across both states. The chi-square statistical technique was used to test the hypothesis that the Niger Delta Development Commission Intervention has no significant impact on road construction in Akwa Ibom and Cross River States. The result of the data analysis showed the calculated value X² = 1592 > 16.92. By this result, the null hypothesis was rejected (16.92) at 0.05 level of significance and 9 Degrees of Freedom, and the alternate was accepted. The study concludes that NDDC road projects have positively influenced economic activities and livelihoods in the states. However, it highlights the need for further improvements, particularly on the Calabar-Itu federal highway.

The article presents an answer to the current challenge about needs to form methodological approaches to the digital transformation of existing industrial enterprises (EIE). The paper develops a hypothesis that it is advisable to carry out the digital transformation of EIE based on considering it as a complex technical system using model-based system engineering (MBSE). The practical methodology based on MBSE for EIE digital representation creation are presented. It is demonstrated how different system models of EIE is created from a set of entities of the MBSE approach: requirements—unctions—components and corresponding matrices of interconnections. Also the principles and composition of tasks for system architectures creation of EIE digital representation are developed. The practical application of proposed methodology is illustrated by the example of an existing gas distribution station.