Mikailalsys Journal of Advanced Engineering International

Design and Analysis of Rectangular and Circular Microstrip Patch Antennas for 2.45 GHz ISM-Band Applications

Sat, 16 May 2026 18:57:22 +0800

This paper presents the design, parametric analysis, and comparative evaluation of rectangular and circular microstrip patch antennas operating at 2.45 GHz in the industrial, scientific, and medical (ISM) band for wireless communication applications. Both antenna configurations were fabricated on a low-cost FR-4 dielectric substrate (εr = 4.5, thickness = 1.6 mm) to ensure compatibility with standard printed circuit board (PCB) manufacturing processes. The rectangular patch was designed with dimensions of 38.5 mm × 29.2 mm, while the circular patch had a radius of 16.42 mm; both were optimized using cavity-model formulations and closed-form analytical equations. A 50-Ω microstrip feed line with a width of 2.88 mm was employed for impedance matching. Comprehensive parametric studies were conducted to examine the influence of geometric parameters on resonance frequency, bandwidth, and radiation characteristics. The simulation results demonstrate that both antennas achieve satisfactory impedance matching, with S₁₁ < −10 dB at the target frequency. The rectangular configuration produces a directional radiation pattern suitable for point-to-point links, whereas the circular design provides near-omnidirectional coverage with potential for circular polarization. Comparative analysis against four recent literature designs indicates that the proposed antennas achieve competitive performance in terms of compactness, fabrication simplicity, and cost-effectiveness without requiring complex modifications such as slots or parasitic elements. The study concludes that rectangular and circular microstrip patch antennas fabricated on FR-4 substrates offer practical, low-profile, and integrable solutions for WLAN, IoT, and biomedical applications requiring compact and cost-effective antenna structures.

Catalytic Hydrothermal Liquefaction of Mango Waste over Template-Synthesized NiFe₂O₄/Biochar Catalyst

Sat, 16 May 2026 19:10:11 +0800

Hydrothermal liquefaction (HTL) offers a promising pathway for converting wet organic waste into liquid fuels; however, the high oxygen content of bio-crude derived from fruit waste remains a major limitation. This study aims to valorize mango fruit waste (MFW) into upgraded bio-crude oil through catalytic HTL using a template-synthesized activated biochar-supported NiFe₂O₄ bimetallic catalyst. The feedstock and catalyst were characterized using proximate and ultimate analyses, Fourier transform infrared (FTIR) spectroscopy, thermogravimetric analysis (TGA), scanning electron microscopy (SEM), X-ray diffraction (XRD), Brunauer–Emmett–Teller (BET) surface area analysis, and gas chromatography–mass spectrometry (GC–MS). Mango fruit waste showed favorable hydrothermal conversion characteristics, including high volatile matter content, a carbon content of 48.07 wt%, and a higher heating value (HHV) of 14.32 MJ kg⁻¹. The incorporation of the NiFe₂O₄-activated biochar catalyst substantially improved bio-crude quality compared with non-catalytic HTL, increasing the carbon content to 63.53 wt% and the HHV to 16.66 MJ kg⁻¹. GC–MS analysis revealed a marked compositional shift toward aromatic hydrocarbons, phenolic compounds, and nitrogen-containing heterocycles, indicating enhanced deoxygenation, hydrogen transfer, and aromatization reactions promoted by the bimetallic catalyst. The study concludes that template-engineered biochar-supported NiFe₂O₄ catalysts are effective for upgrading oxygen-rich intermediates during fruit waste HTL. These findings contribute to sustainable waste valorization and biofuel production by demonstrating the potential of mango fruit waste as a viable feedstock for producing improved bio-crude oil.

Development of ANFIS-Based Hard Drive Failure Prediction Model for Cloud Platforms Using Intelligent Techniques

I. I. Ahmad, J. D. Jiya, MA. Baba — Sat, 16 May 2026 19:20:52 +0800

Hard drive failures remain a critical reliability concern in large-scale cloud data centres because they can lead to data loss, service downtime, and increased operational costs. Traditional threshold-based monitoring techniques often fail to capture nonlinear relationships among hard drive health indicators and may produce high false-positive rates. This study presents a conceptual framework for developing an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based hard drive failure prediction model using selected Self-Monitoring, Analysis, and Reporting Technology (SMART) attributes. It further examines the potential impact of key SMART indicators on predictive performance. By integrating fuzzy logic reasoning with neural network learning, the proposed framework is designed to improve predictive accuracy while maintaining interpretability. The study concludes that an ANFIS-based prediction framework can support proactive maintenance strategies for cloud service providers by enabling earlier identification of potential hard drive failures. This framework contributes to the development of intelligent predictive maintenance systems in cloud computing environments and offers practical implications for improving system reliability, reducing downtime, and enhancing operational efficiency.

Artificial Intelligence in Early Disease Detection: Trends, Applications, and Challenges

Sat, 16 May 2026 19:34:51 +0800

Artificial intelligence (AI) is transforming healthcare by improving diagnostic precision, reducing clinician workload, and supporting early disease detection. Early diagnosis is essential for improving patient outcomes, reducing mortality, and lowering healthcare costs. This study examines current developments in AI-assisted diagnostics, with particular attention to applications in cancer, cardiology, neurology, infectious diseases, and personalized medicine. It discusses how AI, through machine learning, deep learning, and predictive analytics, can process large-scale medical datasets, analyze medical images, and support physicians in clinical decision-making. The findings indicate that AI offers substantial benefits for healthcare practice, including improved diagnostic accuracy, enhanced patient monitoring, reduced clinical errors, and more efficient decision support. However, major barriers remain, including algorithmic bias, high implementation costs, data privacy concerns, inadequate physician training, and unresolved ethical issues. The study concludes that the effective adoption of AI in early disease diagnosis requires collaborative research, robust policy frameworks, ethical governance, and practical integration strategies. These insights contribute to current discussions on AI-enabled healthcare by highlighting both its diagnostic potential and the institutional, technical, and ethical conditions needed to optimize its implementation in healthcare delivery.

Hybrid Integral Transform Techniques for the Solution of Third-Order Nonlinear Ordinary Differential Equations

Sat, 16 May 2026 20:02:45 +0800

Third-order nonlinear ordinary differential equations frequently arise in the mathematical modeling of complex engineering and physical phenomena; however, exact analytical solutions remain difficult to obtain because of strong nonlinearities and higher-order derivative effects. Classical integral transform techniques, including the Laplace and Fourier transforms, are widely used for solving differential equations but often have limitations when extended to nonlinear systems. Although modern integral transforms such as the Sumudu, Mahgoub, and Elzaki transforms offer computational advantages, their applicability is generally restricted to linear models. This study introduces a hybrid analytical approach that integrates the Mahgoub transform with the Variational Iteration Method (VIM) to solve third-order nonlinear ordinary differential equations more effectively. The proposed method converts the governing equation into the transform domain and applies an iterative correction functional to address nonlinear terms without linearization or discretization. The resulting solutions are expressed in rapidly convergent series form. Numerical validation demonstrates strong agreement with exact solutions, confirming the efficiency, accuracy, and stability of the hybrid Mahgoub–VIM approach. The study concludes that this hybrid semi-analytical method provides a reliable framework for solving higher-order nonlinear differential equations in applied mathematics and engineering analysis. These findings contribute to the development of transform-based analytical methods by extending the applicability of the Mahgoub transform to nonlinear differential equation models through variational iteration.

Mathematical Modeling of Typhoid Fever Transmission Dynamics: A Sensitivity Analysis and Implications for Public Health Strategies

Tue, 26 May 2026 08:24:12 +0800

A comprehensive mathematical model of typhoid fever was developed to investigate the complex transmission dynamics of the disease and clarify the relationships among factors influencing its spread. The model assumes population replenishment through births and uses existing data to validate its accuracy, thereby supporting a reliable representation of disease behavior. This study aims to inform and strengthen strategies for the prevention, control, and possible eradication of typhoid fever in order to support improved public health policy and quality of life. Mathematical analysis revealed that the basic reproductive number, R₀, plays a central role in determining the global dynamics of the disease. When R₀ is less than 1, the disease-free equilibrium is locally stable, indicating that the disease will eventually die out. Conversely, when R₀ exceeds 1, an endemic equilibrium exists, suggesting that the disease will persist at a stable level. Sensitivity analysis of the model parameters provided valuable insights into the relative influence of different factors on typhoid fever transmission, thereby supporting informed decision-making and effective disease management. The model was solved using the fourth-order Runge–Kutta scheme over a 40-year time horizon and implemented in MATLAB. The study concludes that mathematical modeling is a powerful tool for understanding the transmission dynamics of typhoid fever and for guiding evidence-based strategies for disease control and prevention. This study contributes to infectious disease modeling by demonstrating how equilibrium analysis, reproductive number estimation, and parameter sensitivity assessment can support public health planning aimed at reducing the burden of typhoid fever.

AI-Driven Strategies for Rebuilding Food Security in Post-Conflict Northern Nigeria: Opportunities, Challenges, and Policy Implications

Tue, 26 May 2026 08:46:30 +0800

Years of conflict in Northern Nigeria have displaced communities, disrupted agricultural production, and weakened market systems, creating urgent challenges for food security recovery. Traditional rehabilitation approaches alone are insufficient to address these multidimensional problems. This article aims to examine the potential of artificial intelligence (AI) as a transformative tool for rebuilding food security in post-conflict settings in Northern Nigeria. The study analyzes how AI-enabled technologies, including predictive modeling, climate monitoring, automated crop assessment, and data-driven supply-chain management, can support agricultural productivity, timely decision-making, and food system resilience. The findings indicate that AI can contribute to post-conflict recovery by strengthening early-warning systems, improving agricultural planning, enhancing supply-chain coordination, and supporting more sustainable food security interventions. However, the effective adoption of AI remains constrained by inadequate infrastructure, limited technological skills, and governance challenges. The study concludes that responsible, context-sensitive, and locally adapted AI strategies can accelerate food system recovery and contribute to sustainable food security in Northern Nigeria. This article contributes to the discourse on digital agriculture and post-conflict reconstruction by highlighting the strategic role of AI in strengthening resilience, improving recovery planning, and supporting evidence-informed food security interventions in fragile contexts.

Julia and Mandelbrot Sets of Transcendental Cosine-Function Using Picard-Thakur Iteration Method

Babawuro Zuwaira, Manjak N. H, Kwami A. M, Adamu M. Y, Ismaila O. I — Tue, 26 May 2026 00:00:00 +0800

This study focuses on the generation and analysis of Julia and Mandelbrot sets for transcendental functions using the Picard–Thakur iterative scheme. It aims to examine the fractal structures produced by selected transcendental functions and investigate how parameter variations influence their topology. The study applied the Picard–Thakur iteration to generate fractal patterns and analyzed the resulting structures using the escape criterion. The findings indicate that parameter tuning produces significant transformations in fractal patterns, including the emergence of symmetrical and spiral-like structures. These results demonstrate the geometric complexity and dynamical sensitivity of transcendental Julia and Mandelbrot sets under the Picard–Thakur iterative scheme. The study concludes that the Picard–Thakur iteration provides a useful computational approach for exploring the behavior of fractal sets associated with transcendental functions. This research contributes to computational mathematics and dynamical systems by offering deeper insight into parameter-dependent fractal formation, with potential relevance to applied sciences involving nonlinear and complex dynamical structures.