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 explores the complex dynamics of handling augmented reality (AR) data in higher education in the United Arab Emirates (UAE). Although there is a growing interest in incorporating augmented reality (AR) to improve learning experiences, there are still issues in efficiently managing the data produced by these apps. This study attempts to understand the elements that affect AR data management by examining the relationship between the investigated variables: faculty readiness, technological limits, financial constraint, and student engagement on data management in higher education institutions in the UAE, building on earlier research that has identified these problems. The research analyzes financial constraints, technological infrastructure, and faculty preparation to understand their impact on AR data management. The study collected detailed empirical data on AR data management in UAE higher education environments using a quantitative research methods approach, surveys. The reasons for choosing this research method include cost-effectiveness, flexibility in questionnaire design, anonymity and confidentiality involved in the chosen methods. The results of this study are expected to enhance academic discourse by highlighting the obstacles and remedies to improving the efficiency of AR technology data management at higher education institutions. The findings are expected to enlighten decision-making in higher education institutions on maximizing AR technology’s benefits for improved learning outcomes.

This paper explores how Saudi managers perceive the role of corporate heritage in achieving the employment goals of heritage organizations operating in Saudi and, in turn, Saudi Arabia’s Vision 2030 in relation to the Nitaqat program. Using an exploratory qualitative method, the study involved fifteen in-depth semi-structured interviews with HR managers from ten heritage-rich organizations. The analysis identified five key organizational identity traits with heritage—proficient, shelter, responsive, advancing, and centrality—that can be leveraged in employer branding to attract potential employees and enhance the employer brand of organizations operating in the Saudi market. This study is significant as it is the first to investigate corporate heritage from an employer branding perspective and in relation to national employment goals in emerging markets.

The purpose of the article is to examine the changes in cross-border cooperation between Vietnam and China as a result of the development and connectivity of cross-border infrastructure between the two countries. This article is based on a mixed-methods study that includes desk research and surveys. The article explains how the two countries’ approaches to border shifted from ‘barrier’ to the border of ‘connectivity’. Accordingly, the article examines the changes in border management cooperation between the two countries, which serves as a vital basis for cross-border development cooperation. Furthermore, the article examines the perceptions of the two countries regarding the development and connectivity of cross-border infrastructure for comprehensive cooperation between the two countries and beyond. At the same time, the article examines how the two countries promote the development and connectivity of cross-border infrastructure, both hard and soft. The article also examined some initial results and some issues facing the two countries. The paper concludes with some findings. In particular, the article concludes that increased border connectivity will encourage cross-border cooperation and integration between the two countries and help to alleviate security concerns. Although the two countries have made efforts to open their borders, in the transition from a border of ‘barriers’ to a border of ‘connectivity’ remain partly to Vietnamese people’s memories of the 1979 Sino-Vietnamese border war, as well as the impact of the two countries’ unresolved South China Sea disputes. However, Vietnam also tries to promote cross-border cooperation within a controllable level.

This study aims to take Chinese higher vocational colleges professional group leaders as the research subjects to analyze the components of their key competencies, develop the competency model of professional group leaders (PGL), and analyze the main factors influencing the model. It provides a powerful help for improving the scientific level of the construction and management of the teaching staff in higher vocational colleges and filling the gap in the research on the quality and ability of Chinese professional group leaders. A mixed research method is deployed in this study. Data are collected with the help of a self-administrated questionnaire and a semi-structured interview based on grounded theory. Data analysis involves structural equation modeling using AMOS, complemented by qualitative coding in NVivo. It concludes that the competency development model of professional group leaders comprises two main dimensions: explicit competencies and implicit competencies. Explicit competencies include cross-border adaptability (CBA), resource integration ability (RIA), innovation and development practice ability (IDPA), management leadership ability (MLA), and interdisciplinary scientific research ability (ISRA). Implicit competencies include personality attitude (PA), and intrinsic motivation (IM). The study fills a significant gap in the literature by providing a detailed model of competency for professional group leaders in the context of higher vocational education, offering a practical framework for improving the training and management of teaching staff and promoting the development of professional groups effective in vocational colleges.

This paper explores the integration of digital technologies and tools in English as a Foreign Language (EFL) learning in Jordanian Higher Education through a qualitative open-ended online survey. It highlights the perceptions of 100 Jordanian EFL instructors, each with a minimum of five years of experience, on the digital transformation in the EFL learning process. The survey, consisting of ten open-ended questions, gathered in-depth insights on the benefits, challenges, and implications of this transformation. Thematic analysis was employed to analyze the qualitative data, revealing varied levels of experience, the use of diverse digital tools, and both technical and pedagogical challenges. Key findings include the positive impact of digital tools on teaching and learning experiences, enhanced student engagement, and opportunities for personalized learning and collaboration. The study concludes that leveraging digital resources can enhance EFL learner engagement and learning outcomes, inform future pedagogical practices, and shape the landscape of digital transformation in EFL Higher Education for years to come.