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Mikailalsys Journal of Advanced Engineering International
Published by Lembaga Yasin Alsys
ISSN : 30468914     EISSN : 30469694     DOI : https://doi.org/10.58578/mjaei
Core Subject : Science, Engineering,
Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Electrical & Electronics Engineering Engineering Mechanical Engineering
Mikailalsys Journal of Advanced Engineering International [3046-8914 (Print) and 3046-9694 (Online)] is a double-blind peer-reviewed, and open-access journal dedicated to disseminating all information contributing to the understanding and development of the fields of engineering and technology across various disciplines. MJAEI aims to be a platform for researchers, scientists, and practitioners in various engineering disciplines to share their knowledge and innovative ideas, foster cross-disciplinary collaboration, and contribute to technological and scientific advancements. We invite authors from around the world to contribute to the advancement of engineering and technology fields. MJAEI publishes three editions a year in March, July and November.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 66 Documents
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Design and Analysis of Rectangular and Circular Microstrip Patch Antennas for 2.45 GHz ISM-Band Applications Abubakar, Abdulkadir; Abubakar, Aliyu Umar; Abdulkareem, H. A.; Hamza, Jamilu Bala; Sani, Zahraddeen Lawan
Mikailalsys Journal of Advanced Engineering International Vol 3 No 2 (2026): Mikailalsys Journal of Advanced Engineering International
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjaei.v3i2.9125

Abstract

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 Auwal, Abdul-Hameed Bukhari; Joel, Atuman Samaila; Raji, Yusuf Olabode; Hammari, Abubakar Muhammad
Mikailalsys Journal of Advanced Engineering International Vol 3 No 2 (2026): Mikailalsys Journal of Advanced Engineering International
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjaei.v3i2.9172

Abstract

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 Ahmad, I. I.; Jiya, J. D.; Baba, MA.
Mikailalsys Journal of Advanced Engineering International Vol 3 No 2 (2026): Mikailalsys Journal of Advanced Engineering International
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjaei.v3i2.9185

Abstract

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 Abba, Dadi Jonathan; Dudari, Mafeng Jamima; Jakawa, Jimmy Nirat; Sani, Habibu Aminu; Yona, Kudyo Deborah
Mikailalsys Journal of Advanced Engineering International Vol 3 No 2 (2026): Mikailalsys Journal of Advanced Engineering International
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjaei.v3i2.9226

Abstract

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 Aliyu, Umar Mujahid; Oyewola, David Opeoluwa; Taura, Joel John; Lukunti, Salisu; Muhammad, Hassan; Adamu, Abubakar Yahya; Ibrahim, Abdulhalim Isah; Muhammad, Mubarak; Ibrahim, Imafidor Hassan; Kolo, Mohammed Abubakar; Adamu, Isah; Piapna'an, Wallen Juliet; Mansur, Mustapha Mohammed; Adamu, Ibrahim Abubakar; Marafa, Mohammed Yusuf; Umar, Abdulwasiu; Ahmad, Sulaiman; Hashim, Nura
Mikailalsys Journal of Advanced Engineering International Vol 3 No 2 (2026): Mikailalsys Journal of Advanced Engineering International
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjaei.v3i2.9236

Abstract

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.
Time as Dimension or Illusion? A Critical Analysis within the Framework of Relativity Sah, Praveen; Shah, Neha; Sah, Dilip Kumar; Sahani, Suresh Kumar
Mikailalsys Journal of Advanced Engineering International Vol 3 No 1 (2026): Mikailalsys Journal of Advanced Engineering International
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjaei.v3i1.9337

Abstract

The nature of time remains a central problem in both physics and philosophy, particularly in light of the tension between classical and relativistic conceptions of temporality. This paper examines the question of whether time is an illusion within the framework of relativity. Whereas classical physics treats time as absolute, universal, and uniformly flowing, Einstein’s theory of relativity demonstrates that temporal intervals vary according to relative motion and gravitational fields. Building on this framework, the paper argues that time is operationally real insofar as it can be measured and modeled physically, yet the notion of a universally shared and continuously flowing present has no firm basis in modern physics. The analysis further suggests that the relativistic view of spacetime supports the coexistence of past, present, and future within a unified four-dimensional structure. It also considers whether the human experience of temporal passage arises from fundamental physical laws or from cognitive and thermodynamic asymmetries. The paper concludes that time itself is not an illusion; rather, what is misleading is the classical intuition that time flows identically for all observers. This study contributes to ongoing interdisciplinary debates by clarifying how relativity reshapes the philosophical interpretation of temporal reality.

Page 7 of 7 | Total Record : 66

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