Malaria is an infectious disease that poses a significant global health threat, particularly to children and pregnant women. Specifically, in 2020, Rampah Village, Kutambaru sub-district, Langkat Regency, North Sumatra Province, Indonesia, reported 22 malaria cases, accounting for 84% of the local cases. This study aims to develop a malaria prevention model by leveraging community capital in Rampah Village. A mixed-method sequential explanatory approach, combining quantitative and qualitative methods, was employed. Quantitative data were collected through questionnaires from a sample of 200 respondents and analyzed using structural equation modeling (SEM) with Smart PLS (Partial Least Squares) software. The qualitative component utilized a phenomenological design, gathering data through interviews. Quantitative findings indicate that natural capital significantly influences malaria prevention principles. There is also a positive and significant relationship between developmental capital and malaria prevention. Cultural capital shows a positive correlation with malaria prevention, as does social capital. The qualitative phase identified cultural capital within the Karo tribe, such as ‘Rakut si Telu,’ which signifies familial bonds fostering mutual aid and respect. The results of this study are crucial for formulating policies and redesigning community-capital-based malaria prevention programs. These programs can be effectively implemented through cross-sectoral collaboration among health departments, local government, and community members. Malaria is a communicable disease threatening global health, particularly affecting children and pregnant women. In 2020, there were 229 million cases of Malaria worldwide, resulting in 409,000 deaths. In Indonesia, specifically in North Sumatra’s Langkat Regency, Kutambaru District, Rampah Village had 22 cases (84%). The purpose of this research is to formulate a Malaria prevention model using community resources in Rampah Village, Kutambaru District, Langkat Regency. The study employed a mixed-methods sequential explanatory approach, combining quantitative and qualitative methods. Quantitative data was collected through questionnaires, with 200 respondents, and structural equation modeling (SEM) analysis using smart PLS (Partial Least Squares) software. Qualitative data was gathered through interviews. The research findings showed a positive relationship between cultural modalities and Malaria prevention (p = 0.000) with a path coefficient T-value of 12.500. The cultural modality and Malaria prevention relationship were significantly positive (p = 0.000) with a path coefficient T-value of 3.603. A positive and significant correlation also exists between development modalities and Malaria prevention (p = 0.011) with a path coefficient T-value of 2.555. Qualitative research revealed the Rakut si Telu cultural modality of the Karo tribe, meaning that family-based social connections create a sense of helping and respecting one another. The Orat si Waluh cultural modality represents daily life practices in the Karo tribe as a form of community-based Malaria prevention.

Presently, there exists a burgeoning trend of female entrepreneurs worldwide, notably within the realm of small and medium-sized enterprises (SMEs), many of which manifest as family-run enterprises. The systematic literature review endeavors to construct an integrative framework concerning the practical ramifications of female involvement in family businesses by amalgamating extant global studies. The findings elucidate the practical implications inherent in female participation across global family businesses, concurrently furnishing a reservoir of prospects for prospective investigations. The deduction posits the imperative eradication of gender disparities, cognizant that gender parity underpins economic and financial advancement and is contingent upon female involvement. Furthermore, familial enterprises are urged to acknowledge and integrate women’s contributions in entrepreneurial decision-making processes.

Vehicle detection stands out as a rapidly developing technology today and is further strengthened by deep learning algorithms. This technology is critical in traffic management, automated driving systems, security, urban planning, environmental impacts, transportation, and emergency response applications. Vehicle detection, which is used in many application areas such as monitoring traffic flow, assessing density, increasing security, and vehicle detection in automatic driving systems, makes an effective contribution to a wide range of areas, from urban planning to security measures. Moreover, the integration of this technology represents an important step for the development of smart cities and sustainable urban life. Deep learning models, especially algorithms such as You Only Look Once version 5 (YOLOv5) and You Only Look Once version 8 (YOLOv8), show effective vehicle detection results with satellite image data. According to the comparisons, the precision and recall values of the YOLOv5 model are 1.63% and 2.49% higher, respectively, than the YOLOv8 model. The reason for this difference is that the YOLOv8 model makes more sensitive vehicle detection than the YOLOv5. In the comparison based on the F1 score, the F1 score of YOLOv5 was measured as 0.958, while the F1 score of YOLOv8 was measured as 0.938. Ignoring sensitivity amounts, the increase in F1 score of YOLOv8 compared to YOLOv5 was found to be 0.06%.

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.