The fear of ghosts is a common thing that can be managed as a social condition that turns out to have an impact on the continuity of forest maintenance. Applying a qualitative approach supported by in-depth interview methods, observation, and literature study. This research does not attempt to prove the existence of ghosts or discuss the psychological conditions of people who fear ghosts. The main finding of this research is the reality of the reproduction of stories and experiences of fear of ghosts, as well as the implementation of traditions or rituals related to community activities in the forest. Stories of fear of ghosts with various forms and versions of naming not only enrich the cultural life of the community but also encourage social conditioning in the form of togetherness to agree on the fear of ghosts as a means of creating a social system in order to carry out activities in the forest. The social system is identified in the form of pamali traditions or things that should not be done in the forest, balian rituals to eliminate or treat ghost disturbances, and besoyong rituals to utilize forest products, which then have an impact on the awareness to respect the continuity of these rituals and tradition. So, even though the fear of ghosts can be overcome psychologically and disappear quickly, the reality of respect for the social system related to the forest can still survive. In addition, ghost stories’ reproduction continues to be rolled out and adapted to the times. In turn, ghosts and forest rituals continue to be conditioned into a social system that has implications for forest conservation.

This research underscores the importance of enhancing the early detection of diabetic retinopathy and glaucoma, two prominent culprits behind vision loss. Typically, retinal diseases lurk without symptoms until they inflict severe vision impairment, underscoring the critical need for early identification. The research is centered on the potential of leveraging fundus images, which offer invaluable insights by analyzing various attributes of retinal blood vessels, such as their length, width, tortuosity, and branching patterns. The conventional practice of manually segmenting retinal vessels by medical professionals is both intricate and time-consuming, demanding specialized expertise. This approach, reliant on pathologists, grapples with limitations related to scalability and accessibility. To surmount these challenges, the research introduces an automated solution employing computer vision. It conducts an evaluation of diverse retinal vessel segmentation and classification methods, including machine learning, filtering-based, and model-based techniques. Robust performance assessments, involving metrics like the true positive rate, true negative rate, and accuracy, facilitate a comprehensive comparison of these methodologies. The ultimate goal of this research is to create more efficient and accessible diagnostic tools, consequently enhancing the early detection of eye diseases through automated retinal vessel segmentation and classification. This endeavor combines the capabilities of computer vision and deep learning to pioneer new benchmarks in the realm of biomedical imaging, thereby addressing the pressing issues surrounding eye disease diagnosis.

Fiscal decentralization is one of the policy implementations of regional autonomy, which authorizes local governments to manage their local finances independently. However, with the evolution of the times and the dynamics that are taking place, the application of fiscal decentralization worldwide is changing at each time of year. Therefore, it is necessary to investigate fiscal decentralization research temporarily over the course of four decades. The study aims to explain the development of research on fiscal decentralization over a period of four decades. This research integrates Scopus database to offer a thorough conceptual and structural overview of the field by integrating bibliometric approaches and content analysis. The research procedure begins with the determination of the scope of the research, the inclusion and exclusion criteria for the selection process, the collection of data on Publish or Perish (PoP), and the execution of bibliometric analysis on VosViewer. The research shows that the type of journal with the highest productivity has sub-topics of economy, public service, development, and environmental. The development of fiscal decentralization research has a positive upward trend and most of the top-ranked journals indicate that fiscal decentralization has links and influences with other variables. It is apparent that the most often keywords emerged and studied in the research on fiscal decentralization are related to efficiency, measure, role, degree, growth, and fiscal federalism. Meanwhile, the least frequent keywords are related to poverty and inequality, health outcome, environmental pollution, Latin America, South Africa, fiscal autonomy, corruption, OECD country, determinant, and public sector. These keywords are the future lines of research that may be used for future research on the topic of fiscal decentralization.

The challenge of rural electrification has become more challenging today than ever before. Grid-connected and off-grid microgrid systems are playing a very important role in this problem. Examining each component’s ideal size, facility system reactions, and other microgrid analyses, this paper proposes the design and implementation of an off-grid hybrid microgrid in Chittagong and Faridpur with various load dispatch strategies. The hybrid microgrids with a load of 23.31 kW and the following five dispatch algorithms have been optimized: (i) load following, (ii) HOMER predictive, (iii) combined dispatch, (iv) generator order, and (v) cycle charging dispatch approach. The proposed microgrids have been optimized to reduce the net present cost, CO₂ emissions, and levelized cost of energy. All five dispatch strategies for the two microgrids have been analyzed in HOMER Pro. Power system reactions and feasibility analyses of microgrids have been performed using ETAP simulation software. For both the considered locations, the results propound that load-following is the outperforming approach, which has the lowest energy cost of $0.1728/kWh, operational cost of $2944.13, present cost of $127,528.10, and CO₂ emission of 2746 kg/year for the Chittagong microgrid and the lowest energy cost of $0.2030/kWh, operating cost of $3530.34, present cost of 149,287.30, and CO₂ emission of 3256 kg/year for the Faridpur microgrid with a steady reaction of the power system.

Recognizing the discipline category of the abstract text is of great significance for automatic text recommendation and knowledge mining. Therefore, this study obtained the abstract text of social science and natural science in the Web of Science 2010-2020, and used the machine learning model SVM and deep learning model TextCNN and SCI-BERT models constructed a discipline classification model. It was found that the SCI-BERT model had the best performance. The precision, recall, and F1 were 86.54%, 86.89%, and 86.71%, respectively, and the F1 is 6.61% and 4.05% higher than SVM and TextCNN. The construction of this model can effectively identify the discipline categories of abstracts, and provide effective support for automatic indexing of subjects.