This study investigates the multifaceted challenges and barriers to implementing public auditor recommendations in Ghana’s public sector over an eighteen months period, aiming to enhance governance and accountability. Utilizing a qualitative research approach, the study involved semi-structured interviews with key stakeholders, including officials from the Ghana Audit Service, government ministries, and civil society organizations. The findings reveal a complex interplay of organizational, political, and attitudinal factors that impede effective implementation. Key challenges identified include the lack of clear implementation plans, insufficient resources, weak political commitment, and a pervasive culture of mistrust towards audit recommendations. The research underscores the necessity for a comprehensive and holistic approach to address these barriers, advocating for strengthened political leadership, enhanced accountability mechanisms, and improved stakeholder coordination. Additionally, fostering a sense of ownership and buy-in among implementation stakeholders is crucial for successful reform. The study contributes valuable insights into the systemic issues affecting public sector governance in Ghana and offers practical recommendations for overcoming the identified challenges, ultimately aiming to empower citizens and enhance governmental accountability. By addressing these barriers, the research highlights the potential for transformative change in the governance landscape of Ghana’s public sector.

Students from different cultures possess varying levels of skills in learning, remembering, and understanding concepts. Some terms and their explanations may seem easy for one group of students but difficult for another. Therefore, delivering educational content that aligns with student’s learning capabilities is a challenging task based on cultural orientations. This study addresses the learning challenges by developing a Thesaurus Glossary E-learning (TGE) framework method. This study introduces the TGE method which is a multi-language tool with visual associations that adapts to students’ capabilities. It also examines cultural differences and native languages, particularly aiding Arab Native to visualize appropriate terms (thesaurus) and their explanations (glossary) based on students’ learning capabilities. TGE learns from students’ term selection behavior and displays terms at a simple or advanced level that matches their learning ability. Additionally, TGE demonstrated its effectiveness as an e-learning tool, accessible to all students anytime and anywhere. The study analyzed 314 records related to student performance, out of which 114 students were surveyed to evaluate the effectiveness of the TGE method. This work presents TGE as a novel e-learning tool designed to enhance conceptual thinking within the context of modern educational practices during the digital transformation. TGE is based on artificial intelligence algorithms and associative rules that simulate the human brain, establishing logical connections between related key terms and sketching associations among diverse facets of a situation. An experiment was conducted at a private university in the Sultanate of Oman to assess the effectiveness of the proposed TGE tool. TGE was integrated with selected subjects in information systems and used by the students as a resource for e-learning methods and materials. The results show that 85% of students who used TGE improved their performance by 19%. We believe this work could establish a new smart e-learning teaching method and attract modern and digital universities to enhance student learning outcomes linked with conceptual thinking.

E-commerce plays an important role in many organizations and businesses, including small and medium-sized enterprises (SMEs). Although the body of scientific knowledge carries significant research in addressing the main drivers and challenges of e-commerce among SMEs, the Saudi market was untouched, especially after the official Saudi government classification of SMEs back in December of 2016. Therefore, this study aims to explore the most common factors and challenges of SMEs when utilizing e-commerce in Saudi Arabia. It focused on Jeddah City as the second-largest city and the main seaport of the country. This research is based on a quantitative survey carried out among 63 firms, due to the difficulty in reaching a larger number of participants who had dedicated time and budget. The examined factors were collected from the literature and classified using the Technology, Organization, and Environment Model (TOE). Out of 63 firms, only six were adopting e-commerce. This led us to focus more on the challenges that hindered the remaining 57 from utilizing e-commerce. The analysis results uncovered the status of e-commerce among a sample of Saudi SMEs and showed that the knowledge and awareness level of e-commerce potential for businesses play a significant factor in reaching this incredibly low number.

The paper examines the motivations, financing, expansion and challenges of the Belt and Road Initiative (BRI). The BRI was initially designed to address China’s overcapacity and promote economic growth in both China and in countries along the “Belt” and “Road” through infrastructure investment and industrial capacity cooperation. It took into account China’s strategic transition in its opening-up policy and foreign policy to pay more attention to the neighboring countries in Southeast Asia and Central and West Asia when facing greater strategic pressure from the United States in East Asia and the Pacific region. More themes have been added to the initiative’s original framework since its inception in 2013, including the vision of the BRI as China’s major solution to improve international economic cooperation and practice to build a “community of shared future for mankind”, and the idea of the Green Silk Road and the Digital Silk Road. Chinese state-owned enterprises and policy and commercial banks have dominated investment and financing for BRI projects, which explains the root of the problems and risks facing the initiative, such as unsustainable debt, non-transparency, corruption and low economic efficiency. Measures taken by China to tackle these problems, for example, mitigating the debt distress and improving debt sustainability, are unlikely to make a big difference anytime soon due to the tenacity of China’s long-held state-driven investment model.

The integration of Big Earth Data and Artificial Intelligence (AI) has revolutionized geological and mineral mapping by delivering enhanced accuracy, efficiency, and scalability in analyzing large-scale remote sensing datasets. This study appraisals the application of advanced AI techniques, including machine learning and deep learning models such as Convolutional Neural Networks (CNNs), to multispectral and hyperspectral data for the identification and classification of geological formations and mineral deposits. The manuscript provides a critical analysis of AI's capabilities, emphasizing its current significance and potential as demonstrated by organizations like NASA in managing complex geospatial datasets. A detailed examination of selected AI methodologies, criteria for case selection, and ethical and social impacts enriches the discussion, addressing gaps in the responsible application of AI in geosciences. The findings highlight notable improvements in detecting complex spatial patterns and subtle spectral signatures, advancing the generation of precise geological maps. Quantitative analyses compare AI-driven approaches with traditional techniques, underscoring their superiority in performance metrics such as accuracy and computational efficiency. The study also proposes solutions to challenges such as data quality, model transparency, and computational demands. By integrating enhanced visual aids and practical case studies, the research underscores its innovations in algorithmic breakthroughs and geospatial data integration. These contributions advance the growing body of knowledge in Big Earth Data and geosciences, setting a foundation for responsible, equitable, and impactful future applications of AI in geological and mineral mapping.