Periyar Maniammai Institute of Science & Technology

Remote communities often lack access to reliable electricity. This study investigates the feasibility of a microgrid system tailored for Kantong Kunda, a rural community in The Gambia. The community's current energy consumption and demand are determined through data collection using the Epicollect5 survey tool to characterize the local energy consumption and demand profile accurately. HOMER Pro software was employed to simulate and optimize hybrid microgrid configuration, prioritizing both cost-effectiveness and environmental sustain-ability. The proposed system integrates 79.8 kW of Solar Photovoltaic (SPV), a 60-kW diesel generator, 374 batteries, and a 22.8 kW converter. The optimized design yields a Net Present Cost (NPC) of $251,474.80 and a Levelized Cost of Energy (LCOE) of $0.08527/kWh, which is well below the region's grid electricity tariff. This configuration yields 16.1 % excess electricity, a 10.5 % Return on Investment (ROI), a 14.2 % Internal Rate of Return (IRR), and a 6.03-year payback period, while cutting total emissions by 133,981.4 kg compared to a diesel-only baseline. The work contributes a microgrid design designed for rural African communities, and the findings demonstrate that microgrids can deliver reliable, affordable, and low-carbon electricity through decentralized energy systems for remote communities.

Aerodynamicists use the windtunnels to test the scaled models of an Aircraft, components and other vehicles placed inside the test section of it. The flow properties, Lift and drag forces over the model can be studied. An experimental jet flow setup which is similar to a low speed open circuit wind tunnel has been designed and fabricated using mil steel material. This setup is capable of producing the flow using the exhaust fan which is placed at entry portion of it. This setup was made by keeping in mind that, the educators, students and aerodynamicists to to visualize the flow pattern and to determine the flow properties over the model placed in the test section. The laminar flow can be achieved at the downstream end of nozzleand it is passed in to the test section. In this paper, the exit velocity of the nozzle for varying rpm was determined and plotted. Also, the vortex formation inside test section can be easily visualized by passing the LASER light source.

With the swift development of web mechanisms, the frequency and adaptability of web cyberattacks have also increased in a parallel manner, thus demanding improved mechanisms for robust protection. Traditional rule-supportive and signature-based mechanisms will most of the time be ineffective in focusing on evolving threats; thus, there is a need for security solutions to be smart in an adaptive way. It presents a novel framework, Feature-Fusion Convolutional Neural Network (CNN), which is an efficient method of Internet attack identification in the context of a smart cybersecurity environment. The proposed method utilizes a feature length that combines various input representations; thus, the proposed model can capture not only local but also contextual data patterns of attack behaviour. By using these representations in a smart learning model, the framework becomes more powerful in its ability to recognize slight changes between normal and malicious activities. The main goal of this method is based on its ability to enhance detection performance while at the same time decreasing the dependence on intelligent features, thus providing a scalable and adaptive solution to the problem of emerging attack vectors. The approach introduces the potential of supervised-learning-driven models to change the way web security is done, thus making them an invaluable tool in the set of external resilient, next-generation defensive measures.

The Green Metaverse is not yet eco-friendly due to limited research. This paper assesses the Green Metaverse's potential for human well-being and planetary health. Using a mixed-method approach, sustainable infrastructure accessibility and human-centred value creation are explored. Findings show that combining energy-efficient design with applications like digital learning and global collaboration offers transformative potential for a sustainable virtual future.

A machine learning model prediction of crop yield and quantify soil health using a multi-source agricultural record on twenty thousand four hundred four two crops in India. The model is a combination of the XGBoost and CatBoost models which is used to forecast the complex nonlinear relationships between soil nutrients ((N-Nitrogen), (P-Phosphorus), (K-Potassium)), pH, agricultural features, and weather conditions such as temperature, precipitation, and humidity. It is proposed that a new soil health index (SHI) from a 0-100 scale must be composed of nutrient balance indicators, pH suitability indicators, and moisture indicators. Some of the aspects that influence the sound performance of the model include rigorous preprocessing and 5fold cross-validation. Experimentally, CatBoost is greater than XGBoost in terms of R2 of 97.73 and soil health estimation of 99.13 in terms of crop yield prediction and soil health estimation, respectively. The discussion of the importance of these features has revealed that the following factors, such as production, cultivated area, rainfall and humidity. On the whole, the framework can list real agroclimatic relationships and provide valuable data on how to design and manage fertilizer, irrigation and the utilization of resources advantageously, which indicates a positive prospect of climate sensitive farming and real-time decision-support frameworks.