The Bini people of Edo State, located in the Edo South senatorial district, have been the focus of a study investigating the impact of international migration on Nigerian infrastructure. The study employed a descriptive-qualitative approach, using a survey research methodology and structured questionnaires to gather data from 401 respondents. The study used regression and thematic analysis to examine the collected data, focusing on the connection between migration and the advancement of infrastructure. The findings suggest that low incomes, job insecurity, and the development of domestic infrastructure contribute to the momentum behind international migration movements. The study suggests that remittances from migrants and investments are needed to alleviate the situation, highlighting the need for a more inclusive and sustainable approach to addressing the challenges faced by the Bini people in Edo State.

Leaf litter decomposition and carbon release patterns in five homegarden tree species of Kumaun Himalaya viz. Ficus palmata, Ficus auriculata, Ficus hispida, Grewia optiva and Celtis austalaris were investigated. The study was carried out for 210 days by using litter bag technique. In the current investigation, the duration needed for desertion of the original biomass of diverse leaf litter varied from 150 to 210 days and specifies a varying pattern of decomposition and carbon release among the species. Grewia optiva took the longest time to decompose (210 days) while Ficus hispida decomposed more quickly than rest of the species (150 days). The relative decomposition rate (RDR) was reported highest in Ficus hispida (0.009-0.02 g-1d-1) and lowest in Grewia optiva (0.008-0.004 g-1d-1). Carbon (%) in remaining litter was in the order: Ficus auriculata (24.4 %) >Ficus hispida (24.3%) > Celtis austaralis (19.8%) > Ficus palmata (19.7%) > Grewia optiva (19%). The relationship between percentage weight loss and time elapsed showed the significant negative correlation with carbon release pattern in all the species. Releasing nutrients into the soil through the decomposition of homegarden tree residuals is a crucial ecological function that also regulates the nutrient recycling in homegarden agroforestry practices.

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%.