Rapid technological advancements have driven the increasing complexity of warfare, positioning unmanned aerial vehicles (UAVs), or drones, at the forefront of military operations globally. This study examines Nigeria's adoption of drone technology in its fight against insurgencies, banditry, and communal violence. As part of its evolving security strategy, Nigeria has integrated drones into its military operations to enhance intelligence gathering, surveillance, and precision strikes. Despite the operational benefits, the use of drones raises ethical concerns, especially related to civilian casualties, and poses challenges due to limited transparency and regulatory oversight. The study employed a systematic literature review (SLR), drawing on secondary data sources and reports that address the role, effectiveness, and challenges of drones in Nigeria's defense strategies. Key findings highlight that Nigeria's military, particularly the Air Force, has successfully deployed both indigenous and foreign-acquired drones, such as the Amebo, Gulma, and Chinese CH-4, to bolster its intelligence and combat operations. However, the lack of a robust legal framework and the need for improved training and data processing infrastructure hinder the full realization of drone technology's potential. Based on the findings, the study recommends enhancing Nigeria's regulatory framework to ensure ethical drone deployment, investing in indigenous UAV development, and improving inter-agency coordination for more efficient use of drone capabilities. These steps are crucial to ensuring that drones can effectively contribute to Nigeria's national security while minimizing the associated risks.

This article analyzes the effectiveness of a new incubator design for reviving silkworm eggs. In the course of the study, an incubator based on a rotating disk was created to control the parameters of the microclimate and ensure uniform development of eggs. In this system, temperature, humidity and CO2 levels are monitored using the SCD 41 and other sensors. The results showed that using the new incubator, silkworm eggs were revived 4.1% faster than using traditional methods, and the cocoon yield increased by 5.8%. Thanks to the high efficiency of the new construction, the economic efficiency of silk production will increase and the possibility of producing high-quality silk will expand. Thus, the construction of an innovative incubator opens up opportunities for using new approaches in sericulture.

This study investigates the design and efficiency of skimmer technologies in enhancing the functionality of active grease traps. Grease traps play a critical role in wastewater management by intercepting fats, oils, and greases (FOGs) from kitchen wastewater, thereby preventing clogging and operational issues in sewer systems. However, conventional skimming methods often face challenges related to efficiency and effectiveness. This research evaluates various skimmer designs, focusing on their operational efficiency, removal rates of FOGs, and overall impact on grease trap performance. Through empirical testing and comparative analysis, the study identifies key design features that optimize skimmer functionality. The findings suggest that advanced skimmer technologies can significantly improve grease trap efficiency, leading to better maintenance practices and reduced environmental impact. This research contributes to the development of more effective wastewater management strategies in commercial kitchens and similar settings.

In the digital banking landscape, the increasing volume of online transactions has heightened the risk of fraudulent activities, necessitating the development of more effective detection systems. This study investigates the efficacy of various machine learning and deep learning algorithms in identifying fraudulent transactions, emphasizing Long Short-Term Memory (LSTM) networks. We implemented and evaluated multiple algorithms, including Logistic Regression, Random Forest, Gradient Boosting Machines (GBM), and XGBoost, on a large-scale credit card transaction dataset. Our results demonstrate that the LSTM model outperforms traditional machine learning algorithms, achieving an accuracy of 98.5%, precision of 87.2%, recall of 85.0%, and an Area Under the Curve (AUC) score of 0.94. These findings highlight the superior capability of LSTM networks to capture complex patterns in sequential transaction data, making them an asset for real-time fraud detection in banking. This research underscores the need for financial institutions to adopt advanced deep learning techniques to enhance their fraud detection systems, thereby minimizing financial losses and improving customer trust.

ZENODO DOI :- https://doi.org/10.5281/zenodo.14044576

In this study, a mechatronic system was developed for the care of mulberry silkworms in multi-layered succulents. This system allows you to monitor and monitor the humidity and temperature in each layer using the DHT11 sensor. The results showed that humidity and temperature were kept stable by a mechatronic system, which positively affected the growth of silkworms and the production of high-quality silk. Compared to traditional methods, the system has made it possible to use resources more efficiently and save energy. In short, the mechatronic system makes the silkworm rearing process efficient and stable. This suggests that the method has great prospects in the development of the silk industry.