A large number of publications devoted to a new class of materials - high-entropy alloys (HEA), is associated with their unique chemical, physical and mechanical properties both in cast materials and in various classes of coatings and refractory compounds. As a result of the research, the features of solid-soluble high-entropy alloys based on BCC and FCC phases have been revealed. These include the role of the most refractory element in the formation of the lattice parameter, the relationship of distortion with elastic deformation, and the contribution of the enthalpy of mixing to the strength and modulus of elasticity. This made it possible, on the basis of Hooke's law, to propose a formula for determining the hardness of the HEA based on the BCC and FCC phases. Based on the fact that with an increase in temperature in high-entropy alloys, the values of the modulus of elasticity, distortion and enthalpy of mixing will obey the same laws, a formula is proposed for determining the yield strength depending on the test temperature of solid-soluble HEA based on BCC and FCC phases. A formula based on the role of the most fusible metal in the alloy is proposed to calculate the melting point of solid-soluble materials.

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 research article explores the relationship between psychological well-being and satisfaction with life among young, athletically talented students educated through individualised programs. The primary objective is to assess whether a safe educational environment, emphasising psychological safety and individual support, positively impacts the general satisfaction and academic performance of these students. Using Ryff and Keyes’ Psychological Well-Being Scale and Diener’s Satisfaction with Life Scale, data were collected from 188 participants—Secondary and university students engaged in rigorous athletic training while completing their studies in the Czech Republic. Key findings reveal a strong correlation between self-acceptance, autonomy, coping with the environment, and enhanced satisfaction with life, indicating that well-being in young athletes is significantly influenced by psychological resilience, emotional support, and control over one’s educational journey. Research highlights that individually tailored learning environments, which provide flexibility for training and access to mental health support, contribute to a balanced development between academic and athletic goals. Additionally, the results suggest that a positive correlation within the educational environment, both with peers and instructors, further strengthens the satisfaction with life and reduces the risk of burnout. Implications underscore the need for educational institutions to adopt holistic approaches that support psychological well-being and accommodate the unique needs of athletically talented students. Recommendations include structured mentorship, flexibility in academic scheduling, and access to professional counselling. Future research should investigate the long-term impacts of such environments on academic and athletic success, considering factors such as social inclusion and the effects of digital education.

Accounting education highly affects the level of Professional Accounting Education offered in a country by academic institutions, thus determining the job market competitiveness of accounting professionals. The purpose of this paper is to determine the relationship between accounting education and accounting practices in Sri Lanka. The data for this study is obtained through a well-structured questionnaire among the Finance Managers of listed companies in the Colombo Stock Exchange (CSE). The sample size of the study was 165 Finance Managers, and of them, 122 responded to the questionnaire. This study is significant to the Sri Lankan context due to scant research in the respective research area. The results depict a moderating positive relationship, while effectiveness of accounting education determines the role and performance of accounting professionals in Sri Lanka.

The purpose of this study is to analyze how the entrepreneurial mindset, social context, and entrepreneurial ambitions of university students in the United Arab Emirates (UAE) have progressed over time in terms of starting their businesses. The research aims to investigate the evolution of the entrepreneurship mindset, considering the implementation of educational and governmental policies over the past decade to promote entrepreneurship among UAE university graduates. To collect primary data and evaluate the impact of the studied variables on the dependent variable “entrepreneurial ambitions,” a self-created questionnaire was used. The results reveal a positive correlation between personal context variables and entrepreneurial ambitions, as well as between personality traits and entrepreneurial ambitions. Furthermore, the study demonstrates the constructive effect of education, government policies, and capital availability on fostering entrepreneurial ambitions in the UAE.