This paper investigates the elements affecting dividend yield in developing Southeast Asian countries—more specifically, Thailand, Malaysia, and Singapore. Examined here are the roles of financial information including debt to equity ratio, free cashflows, property, plant, and equipment (PPE) and total sales with controlling factors of size, institutional ownership, and firm age using both short-run and long-run analytical frameworks including the Error Correction Model and Engle and Granger’s approach. The results reveal different trends in the three nations. Higher debt and free cashflows lower dividend yield in Thailand; institutional shareholders benefit from maintaining greater dividend payouts. Aging companies in Malaysia are more likely to pay more dividends while rising revenues are linked to smaller short-term payouts. Leveraged and asset-heavy companies are more likely to keep paying dividends in Singapore. These discoveries have important ramifications for investors and business management trying to maximize dividend policies and improve shareholder value in developing economies.

The distress of commercial companies is considered one of the most critical stages leading to the liquidation and termination of the business. This danger increases in the context of poor management, stagnation, and the occurrence of crises and external circumstances that affect the company’s ability to cope. Rules regarding financial restructuring of distressed commercial companies may be regarded as the most prominent legal framework adopted by Emirati, Kuwaiti and French legislators to address the instability and distress of commercial enterprises and to provide solutions to mitigate the risk of bankruptcy and liquidation. It is a preventive measure aimed at reaching an agreement between the debtor and creditors to resolve the disturbances or difficulties faced by the company, which may affect its obligations to others. Therefore, financial restructuring is considered a mean of prevention and rescue for commercial companies, and the success of this rescue is linked to the debtor’s cooperation and seriousness in overcoming such issue.

This project analyzes the evolution of the manufacturing sector in Portugal from 2009 to 2021, focusing on the variations in the number of active companies across various subcategories, such as food, textiles, and metal product industries. The goal of this analysis is to understand the dynamics of growth and contraction within each sector, providing insights for companies to adjust their market and operational strategies. Key objectives include analyzing the overall evolution in the number of companies, identifying subcategories with notable changes, and providing a comprehensive analysis of observed trends and patterns. The study is based on data from PORDATA 2024, and the research employs temporal trend analysis, linear and quadratic regression, and the Pareto representation to identify patterns of growth and decline. By comparing annual data, the project uncovers periods of growth and decline, allowing for a deeper understanding of the sector’s dynamics. The findings also highlight variations in periods of economic crises and during the Covid-19 pandemic, and recommendations for action are presented to support businesses resilience and continuity. These results are valuable for companies within the manufacturing sectors analyzed and policy makers, guiding strategic decisions to navigate the complexities of the market dynamics and to ensuring long-term organizational sustainable success.

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.