The effects of climate change are already being felt, including the failure to harvest several agricultural products. On the other hand, peatland requires good management because it is a high carbon store and is vulnerable as a contributor to high emissions if it catches fire. This study aims to determine the potential for livelihood options through land management with an agroforestry pattern in peatlands. The methods used are field observation and in-depth interviews. The research location is in Kuburaya Regency, West Kalimantan, Indonesia. Several land use scenarios are presented using additional secondary data. The results show that agroforestry provides more livelihood options than monoculture farming or wood. The economic contribution is very important so that people reduce slash-and-burn activities that can increase carbon emissions and threaten the sustainability of peatland.

Divorce for female civil servants in Indonesia is more complex than for non-civil servants due to a pseudo-administrative process. This condition requires submitting a written application for divorce permission to their agency and proceeding through multiple lengthy stages. During this process, women must verbally disclose sensitive personal details to state authorities. Failure to obtain written permission or to report the divorce within a specific period can result in disciplinary action. This paper examines how female civil servants protect their privacy while seeking divorce permission, focusing on managing personal information, controlling divorce-related details at work, and handling the information turbulence that arises. The researcher collected data from 12 female civil servants at Indonesia’s Directorate General of Taxes (DGT) who had applied for divorce permission. The findings reveal the subjective experiences and strategies women civil servants use to manage sensitive personal issues. The quasi-administrative nature of the divorce permit process introduces complexities that extend beyond formal procedures. Regulations governing the submission of divorce permits, overseen by government agencies, often add to the burden these women face, neglecting their privacy and psychological well-being. Impartial individuals and gender preferences in the verification team can exacerbate distress. Therefore, revising the divorce permit regulations to enhance privacy and sensitivity is crucial. The study recommends early information about the process and communication training for maintaining privacy.

In the face of growing competition, industrial and commercial firms need more effective strategies to gain competitive advantages. This study investigates the role of enterprise risk management (ERM) as a mediator in highlighting the significance of innovation capability on profitability in industrial and commercial firms listed on the Amman Stock Exchange (ASE). Data were collected from 244 respondents using a standardized questionnaire and analyzed with SPSS software. The results indicate that the innovation capability has an impact on profitability in industrial and commercial firms, as well as their ERM practices. Additionally, ERM mediates the relationship between innovation capability and profitability. Firms that adopt distinctive innovation strategies tend to maintain formal ERM strategies, which in turn enhance market superiority and profitability. This research offers some significant managerial ramifications that may be essential for business owners, executives, and decision-makers involved in the development of firms.

The main objective of the study was to assess the impact of fiscal management on macroeconomic stability in emerging countries between 2012 and 2022. The study drew on macroeconomic theory, which postulates the importance of responsible fiscal policies for economic stability. Information was taken from ten emerging Latin American countries, and the analysis was carried out through a quantitative approach, using an econometric model. A significant relationship was found between fiscal management and macroeconomic stability, evidencing that effective fiscal policies are crucial for macroeconomic stability in emerging countries. The findings emphasize that balanced fiscal management, which avoids falling into cycles of debt and deficit, is essential for long-term stability. Practices that promote fiscal stability, such as greater efficiency in public spending and effective tax collection, can contribute significantly to economic stability and sustained growth. The results also suggest that fiscal policies should take into account human development conditions and annual particularities in order to formulate effective fiscal policies. It highlights those countries with best fiscal practices, reflected in low debt-to-GDP levels and high fiscal stability, are more likely to achieve macroeconomic stability and sustainable economic growth.

Retinal disorders, such as diabetic retinopathy, glaucoma, macular edema, and vein occlusions, are significant contributors to global vision impairment. These conditions frequently remain symptomless until patients suffer severe vision deterioration, underscoring the critical importance of early diagnosis. Fundus images serve as a valuable resource for identifying the initial indicators of these ailments, particularly by examining various characteristics of retinal blood vessels, such as their length, width, tortuosity, and branching patterns. Traditionally, healthcare practitioners often rely on manual retinal vessel segmentation, a process that is both time-consuming and intricate, demanding specialized expertise. However, this approach poses a notable challenge since its precision and consistency heavily rely on the availability of highly skilled professionals. To surmount these challenges, there is an urgent demand for an automatic and efficient method for retinal vessel segmentation and classification employing computer vision techniques, which form the foundation of biomedical imaging. Numerous researchers have put forth techniques for blood vessel segmentation, broadly categorized into machine learning, filtering-based, and model-based methods. Machine learning methods categorize pixels as either vessels or non-vessels, employing classifiers trained on hand-annotated images. Subsequently, these techniques extract features using 7D feature vectors and apply neural network classification. Additional post-processing steps are used to bridge gaps and eliminate isolated pixels. On the other hand, filtering-based approaches employ morphological operators within morphological image processing, capitalizing on predefined shapes to filter out objects from the background. However, this technique often treats larger blood vessels as cohesive structures. Model-based methods leverage vessel models to identify retinal blood vessels, but they are sensitive to parameter selection, necessitating careful choices to simultaneously detect thin and large vessels effectively. Our proposed research endeavors to conduct a thorough and empirical evaluation of the effectiveness of automated segmentation and classification techniques for identifying eye-related diseases, particularly diabetic retinopathy and glaucoma. This evaluation will involve various retinal image datasets, including DRIVE, REVIEW, STARE, HRF, and DRION. The methodologies under consideration encompass machine learning, filtering-based, and model-based approaches, with performance assessment based on a range of metrics, including true positive rate (TPR), true negative rate (TNR), positive predictive value (PPV), negative predictive value (NPV), false discovery rate (FDR), Matthews's correlation coefficient (MCC), and accuracy (ACC). The primary objective of this research is to scrutinize, assess, and compare the design and performance of different segmentation and classification techniques, encompassing both supervised and unsupervised learning methods. To attain this objective, we will refine existing techniques and develop new ones, ensuring a more streamlined and computationally efficient approach.