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.

In older adults with disabilities, muscle weakness reduces mobility and causes postural issues. Electrical muscle stimulation (EMS) training is effective for increasing strength by inducing involuntary skeletal muscle contractions. Thus, this study aimed to examine changes in lumbar movement, muscle activity during walking, and maximum muscle strength of lower-limb joints based on an 8-week EMS exercise program for older adults with physical disabilities. Sixteen older adults (aged 56–78 years) with physical disabilities were selected and randomly assigned to either the exercise group (EG, n = 8) or the control group (CG, n = 8). EG participants were instructed to wear EMS suits and participate in an elastic band and bare-body exercise program, whereas CG participants performed the exercise program without the EMS suits. Lumbar range of motion (ROM), body circumference, partial volume, maximum muscle strength, and muscle activity were measured. Statistical analysis was performed using SPSS 28.0 for Windows, with the critical value of α = 0.05. Compared to CG participants, EG participants had a superior ROM, body volume, partial volume, maximum muscle strength (knee joints), and muscle activity while walking. These positive outcomes highlight the efficacy of combining EMS with an exercise program for strength and rehabilitation training in older adults with physical disabilities.

This study aimed at measuring the level of job burnout among King Khalid University staff. The descriptive-analytical approach was employed to describe job burnout, determine its prevalence, identify its causes, and propose ways to address it. This method was used for comparison, interpretation, and generating information to assist in understanding the phenomena of job burnout and to devise recommendations for mitigating its prevalence. The results showed that the overall mean estimation of the dimensions of the level of occupational burnout from the perspective of university staff was (2.28), with a standard deviation of (0.81), indicating a low degree. The arithmetic means of the study sample responses to the dimensions ranged from (1.98–2.66). This provides a good indicator of the prevalence of occupational burnout. The findings showed that individuals in higher ranks experience higher levels of job burnout compared to the rest of the ranks classified in the study.