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 business environment in the modern era is witnessing numerous Intellectual Changes, Technological developments, and increasingly Complex Situations, which has led to a need for effective Leadership in the Business Sectors. This leadership plays a role in transforming companies into giant corporations that serve as a true foundation for enhancing and improving Job Competencies (JC)., The study aimed to analyze the impact of the Soft Skills approach in Human Resources (analytical and critical thinking, decision-making and problem-solving, planning and organization, teamwork) on developing Job Competencies (productivity, technical, managerial) in Petroleum Sector Companies in Egypt. The researchers employed the descriptive-analytical method to study the phenomenon, conducting the study on stratified random samples consisting of 379 managers and a sample of 382 employees from Petroleum Sector Companies. The study utilized the SPSS and AMOS Software Programs. The study found statistically significant differences at the (0.01) level between the average scores of managers and employees regarding soft skills in human resources and job competencies, with managers scoring higher. Additionally, the study revealed a statistically significant direct causal effect at the (0.01) level of Human Resources Soft Skills on Job Competencies in Petroleum Sector Companies., Finally, a proposal was developed for enhancing Job Competencies in Petroleum Companies in Egypt based on the application of human resources Soft Skills, alongside future research directions and practical implications.

In the contemporary landscape characterized by technological advancements and a progressive economic environment, the utilization of currency has undergone a paradigm shift. Despite the growing prevalence of digital currency, its adoption among the Vietnamese population faces several challenges, including limited financial literacy, concerns over security, and resistance to change from traditional cash-based transactions. This research aims to identify these challenges and propose solutions to encourage the widespread use of digital currency in Vietnam. This research adopts a quantitative approach, utilizing Likert scale questionnaires, with a dataset of 330 records. The interrelationships among variables are analyzed using partial least squares structural equation modeling (PLS-SEM). The analysis results substantiate the viability of the research model, confirming the hypotheses. The findings demonstrate a positive relationship and the significance impact of factors such as perceived usefulness (PU), perceived ease of use (PEOU), perceived trust (PT), social influence (SI), openness to innovation (OI), and financial knowledge (FK) to intention to use digital currency (IUDC). Thereby aiming to inform policymakers, industry stakeholders, and the wider community, fostering a deeper understanding of consumer behavior and providing solutions to enhance the adoption of digital currency in the evolving landscape of digital finance.

5G technology is transforming healthcare by enhancing precision, efficiency, and connectivity in diagnostics, treatments, and remote monitoring. Its integration with AI and IoT is set to revolutionize healthcare standards. This study aims to establish the state of the art in research on 5G technology and its impact on healthcare innovation. A systematic review of 79 papers from digital libraries such as IEEE Xplore, Scopus, Springer, ScienceDirect, and ResearchGate was conducted, covering publications from 2018 to 2024. Among the reviewed papers, China and India emerge as leaders in 5G health-related publications. Scopus, Springer Link, and IEEE Xplore house the majority of first-quartile (Q1) papers, whereas Science Direct and other sources show a higher proportion in the second quartile (Q2) and lower rankings. The predominance of Q1 papers in Scopus, Springer Link, and IEEE Xplore underscores these platforms’ influence and recognition, reflecting significant advancements in both practice and theory, and highlighting the expanding application of 5G technology in healthcare.

As an essential principle in contract law, Indonesia has regulated good faith in the Indonesian Civil Code (the Dutch Civil Code that the Indonesian government uses based on the principle of concordance). However, the definition and benchmarks are not yet clear. There are no further provisions regarding the meaning and concept of this principle in the Indonesian Civil Code or other regulations. This absence of a single understanding of good faith principle in contract causes different opinions and legal certainty, whether from the business actor who signs the agreement or the judge as the third party who resolves contract disputes between parties. Therefore, future Indonesian contract law needs to regulate the definition and benchmarks for good faith principle. In order to find out the meaning and clear benchmarks for the good faith principle, the authors use a normative juridical method with a statute and conceptual approach. This research finds that the definition and benchmarks for the good faith principle is possible to be developed and regulated in Indonesian contract law. It shall set that good faith principle is based on honesty, decency, and fairness, which covers every agreement stage, from pre-agreement, agreement implementation, and after the agreement is completed.

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.