International Journal of Advances in Scientific Research and Engineering (IJASRE), ISSN:2454-8006, DOI: 10.31695/IJASRE

Classification of People’s Opinions on Fuel Subsidy Removal in Nigeria: An Enhancement of Unsupervised Learning Algorithm-Opinion Lexicon algorithm

Mahmood Umar — 2025-11-08

This research aims to collect Nigerians’ opinions on subsidy removal in Nigeria and classify them using an unsupervised learning algorithm, specifically corpus-based lexicon algorithm in order to enhance it prediction accuracy. The data was extracted from an online survey via social media platforms: Facebook and WhatsApp. A comprehensive literature review has been conducted on fuel subsidy removal, sentiment analysis, and unsupervised machine learning approach. The methodology involved are: data collection, preprocessing, feature extraction, model training, and evaluation. The result of this study shows that Nigerians are not happy with fuel subsidy removal, because of the highest number of negative comments over positive ones. This implies that there exist in socio-economic and security issues in Nigeria. The unsupervised learning algorithm for sentiment analysis, the Lexicon was improved with an accuracy of 84.5%. thus, enhanced by 15.27%. These results can potentially inform policymakers and stakeholders about the public's sentiments, the social and security consequences of subsidy removal in Nigeria.

Gateway Energy Aware Protocol for LoRaWAN

N’Takpé N’guessan Christian Placide — 2025-11-08

The Long Range Wide Area Network (LoRaWAN) standard was primarily proposed to connect a large number of Internet of Things (IoT) devices over wide geographical areas while maintaining low energy consumption. In these networks, end devices and the network server exchange uplink and downlink messages through gateways. The selection of the gateway for downlink transmissions is often based solely on the received signal strength. However, this approach can lead to unbalanced energy consumption among gateways, thereby reducing the overall network lifetime—particularly in contexts such as precision agriculture or environmental monitoring in remote areas, where gateways cannot be powered by the electrical grid and their batteries are difficult to replace. To address this issue, we propose a communication protocol designed to extend the lifetime of LoRaWAN networks by considering the energy constraints of the gateways. This protocol integrates the residual energy of the gateways into the downlink selection process. Experimental results demonstrate that the proposed approach increases the network lifetime by approximately seven times compared to the standard protocol, while maintaining network performance at an acceptable level of the abstract.

Artificial Intelligence in Materials Characterization: Methods, Applications, and Future Perspectives

Meric Eren Cakar — 2025-11-08

Materials characterization relies on techniques such as electron microscopy, diffraction, and spectroscopy to extract structural, chemical, and physical information from materials. While these traditional methods provide essential insights, their manual interpretation is time-consuming, operator-dependent, and difficult to scale for high-throughput analysis. In recent years, artificial intelligence (AI) has emerged as a transformative approach, enabling automation in image and pattern recognition, improving reproducibility, and accelerating property inference from experimental data. This paper reviews the current applications of AI in materials characterization, including microscopy, spectroscopy, and mechanical testing, and explains how algorithms assist in segmentation, phase identification, peak analysis, and predictive modelling. The review also discusses challenges such as data quality, model interpretability, and standardization, along with future opportunities like physics-informed learning, multimodal data fusion, and self-driving laboratories. Overall, AI is shown not only to enhance existing characterization workflows but also to redefine how materials data are generated, interpreted, and utilized in scientific decision-making.

Analytical study of Numerical Approximation for Ordinary Differential Equation

Rawaa Ibrahim Esa — 2025-11-08

This research primarily focuses on methods for solving Initial Value Problems (IVPs) for Ordinary Differential Equations (ODEs), with a particular emphasis on three key methods: the Euler method, the Fourth-Order Runge-Kutta method (RK4), and the Fifth-Order Runge-Kutta method (RK5). The latter two methods are highlighted as highly effective and practical for solving such problems. To ensure accuracy, the numerical solutions obtained using these methods were compared with exact solutions. The results showed that the numerical solutions agreed well with the exact solutions. Additionally, numerical comparisons between the Euler method and the Runge-Kutta methods were demonstrated using MATLAB. Finally, the errors of the proposed methods were studied and calculated for various examples.

Physicochemical characteristics of septic tank sludge treated with calcium oxide in Kinshasa, Democratic Republic of Congo

Nzinga Lusevakueno J — 2025-11-08

Various chemicals are used to disinfect septic tank sludge. Their influence on sludge quality is the main focus of this study. This research focuses on manual septic tank cleaners who typically use a hole dug in the ground as a disposal site for sludge. The study is conducted on a laboratory scale on the characteristics of this sewage sludge when treated with calcium oxide (quicklime). Sewage sludge from on-site sanitation systems is contaminated with pathogenic microorganisms; it is possible to modify their biotic characteristics to an acceptable level by treating them with calcium oxide. Several parameters considered to be indicators of pollution are targeted and analyzed in samples of fresh sewage sludge or sludge treated at three levels: 10%, 15%, and 20% CaO; These include total bacteria, fecal coliforms, total streptococci, fecal streptococci, staphylococci, and helminth eggs. The reduction rate for each parameter was used to evaluate the effectiveness of the treatment. The 20% treatment produced interesting results, with a reduction rate ranging from 98.00% to 100% for the bacteriological parameters. The quantities of sewage sludge and lime, their physical state, temperature, pH, contact time, and inhibitors are parameters that influence the speed of this reaction. The results show that chemical treatment with calcium oxide reduces the risk of biological contamination. Sewage sludge from septic tanks treated in this way can be used to improve agricultural soils.

Simulation-Based Analysis of Adaptive Blade Pitch Control for offshore Wind Turbines

Lyndon Bermoy — 2025-11-08

The aerodynamic efficiency and structural stability of offshore wind turbines are strongly influenced by the blade pitch angle, which governs the balance between lift generation and load mitigation. This study presents a simulation-based analysis of adaptive blade pitch control for offshore wind turbines using Computational Fluid Dynamics (CFD) and Finite Element Analysis (FEA). A three-bladed horizontal-axis turbine was modeled in COMSOL Multiphysics, adopting scaled parameters from the NREL 5 MW reference design. The aerodynamic behavior was simulated across wind speeds of 5–20 m/s for pitch angles of 0°, 10°, and 20°, and the resulting pressure fields were coupled into FEA simulations to evaluate stress and deformation responses. Results revealed that the 10° adaptive pitch configuration provided the most favorable balance between aerodynamic efficiency and structural integrity, achieving a higher lift-to-drag ratio compared to the fixed-pitch (0°) setup while reducing overall blade stress and tip deformation by more than 25%. The findings confirm that moderate adaptive pitch adjustment can enhance energy capture at low wind speeds while mitigating fatigue loads at higher velocities, aligning with prior work by Muljadi and Butterfield (2000) and Lara et al. (2023). The CFD–FEA coupling approach proved effective in quantifying aero-structural interactions, providing a computationally efficient framework for evaluating turbine design strategies. This study highlights the importance of simulation-driven design for optimizing offshore wind turbine performance and supports the integration of adaptive pitch mechanisms to improve operational reliability, extend service life, and ensure stable power generation under varying offshore wind conditions.