The debate on relocating Indonesia’s national capital from Jakarta stems from critical issues such as overpopulation, social inequality, environmental degradation, and natural disaster risks. These challenges highlight the need to reassess Jakarta’s viability as the nation’s administrative center. This study evaluates Indonesia’s readiness to address the complexities of relocation by analyzing Jakarta’s socio-economic, political, cultural, and geographical conditions. Using a systematic literature review (SLR) with a qualitative approach, the research explores key questions: Do Jakarta’s conditions necessitate relocation? What challenges might arise from the move? How prepared is Indonesia to tackle these challenges? The SLR process includes defining questions, sourcing literature from reputable databases, applying inclusion/exclusion criteria, and synthesizing data for analysis. Findings reveal Jakarta’s multifaceted challenges, including social disparities, environmental degradation, disaster risks, and governance issues, which emphasize the urgency of considering relocation. However, the study also identifies significant hurdles, such as high costs, logistical complexities, potential social conflicts, and environmental risks at the new capital site. Relocating the capital is a strategic and complex undertaking that requires meticulous planning. Indonesia must weigh Jakarta’s current issues, address potential relocation challenges, and ensure readiness for risk mitigation and sustainable development. Comprehensive and thoughtful planning is essential to achieve a successful and balanced transition.

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.

Using the Intercultural Competence and Inclusion in Education Scale (ICIES), this study examines variations in intercultural competence and inclusion between mainstream and multiethnic high schools. The sample consisted of 384 high school students, aged 17 to 18, from both rural and urban areas in Western Romania, enrolled in grades 11 and 12. The ICIES demonstrated strong reliability, with a Cronbach’s alpha of 0.721. Exploratory factor analysis revealed three distinct dimensions: Intercultural opportunities and activities, Comfort in diverse settings, and Cultural reflection and values. Independent samples t-tests identified significant differences between mainstream and multiethnic schools across several items, with students in multiethnic schools reporting higher levels of intercultural competence and inclusion. These findings highlight the critical role of multicultural educational settings in fostering students’ cultural awareness and inclusive attitudes. This study provides actionable insights for enhancing multicultural education practices and policies, including teacher training programs, inclusive curricula, and extracurricular initiatives that promote intercultural engagement and reduce intergroup biases.

Technological advancements in genetic research are crucial for nations aiming to uplift their population’s quality of life and ensure a sustainable economy. Genomic information and biotechnology can enhance healthcare quality, outcomes, and affordability. The “P4 medicine approach”—predictive, preventive, personalized, and participatory—aligns with objectives like promoting long-term well-being, optimizing resources, and reducing environmental impacts, all vital for sustainable healthcare. This paper highlights the importance of adopting the P4 approach extensively. It emphasizes the need to enhance healthcare operations in real-time and integrate cutting-edge genomic technologies. Eco-friendly designs can significantly reduce the environmental impact of healthcare. Additionally, addressing health disparities is crucial for successful healthcare reforms.

This research aims to analyze the contribution of the Industrial World Business program in improving the work skills and work readiness of people with disabilities through the Systematic Literature Review method. The involvement of businesses and industries in developing inclusive programs for people with disabilities is an important key to bridging the skills gap and employment opportunities. This research identifies various programs, best practices, and challenges in implementing these programs. Based on the results of the literature reviewed, it was found that inclusive job training programs significantly improve the technical and non-technical skills of people with disabilities while strengthening their readiness to face a competitive job market. On the other hand, there are still obstacles in the accessibility and adaptation of training programs that must continue to be optimized. However, to achieve greater inclusivity, improvements are still needed in terms of accessibility, program adaptation, and efforts to reduce discrimination in the world of work. It is hoped that the results of this research can become a basis for policymakers, industry players, and educational institutions to continue to develop inclusive programs and empower people with disabilities in the world of work. Collaboration between industry and vocational service providers is critical to improving employment outcomes and facilitating a successful transition from education to employment for people with disabilities.

Credit risk assessment is one of the most important aspects of financial decision-making processes. This study presents a systematic review of the literature on the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques in credit risk assessment, offering insights into methodologies, outcomes, and prevalent analysis techniques. Covering studies from diverse regions and countries, the review focuses on AI/ML-based credit risk assessment from consumer and corporate perspectives. Employing the PRISMA framework, Antecedents, Decisions, and Outcomes (ADO) framework and stringent inclusion criteria, the review analyses geographic focus, methodologies, results, and analytical techniques. It examines a wide array of datasets and approaches, from traditional statistical methods to advanced AI/ML and deep learning techniques, emphasizing their impact on improving lending practices and ensuring fairness for borrowers. The discussion section critically evaluates the contributions and limitations of existing research papers, providing novel insights and comprehensive coverage. This review highlights the international scope of research in this field, with contributions from various countries providing diverse perspectives. This systematic review enhances understanding of the evolving landscape of credit risk assessment and offers valuable insights into the application, challenges, and opportunities of AI and ML in this critical financial domain. By comparing findings with existing survey papers, this review identifies novel insights and contributions, making it a valuable resource for researchers, practitioners, and policymakers in the financial industry.