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Asian Journal of Science, Technology, Engineering, and Art
Published by Lembaga Yasin Alsys
ISSN : 30255287     EISSN : 30254507     DOI : https://doi.org/10.58578/AJSTEA
Core Subject : Science, Art, Social, Engineering,
Arts Computer Science & IT Engineering Social Sciences
Asian Journal of Science, Technology, Engineering, and Art [3025-5287 (Print) and 3025-4507 (Online)] is a double-blind peer-reviewed, and open-access journal to disseminating all information contributing to the understanding and development of Science, Technology, Engineering, and Art. Its scope is international in that it welcomes articles from academics, researchers, graduate students, and policymakers. The articles published may take the form of original research, theoretical analyses, and critical reviews. AJSTEA publishes 6 editions a year in February, April, June, August, October and December. This journal has been indexed by Harvard University, Boston University, Dimensions, Scilit, Crossref, Web of Science Garuda, Google Scholar, and Base. AJSTEA Journal has authors from 5 countries (Indonesia, Nigeria, Pakistan, Nepal, and India).
Arjuna Subject : Umum - Umum
Articles 250 Documents
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Stochastic Optimal Control Framework for Climate-Induced Migration: Age-Structured Population Dynamics in Nigeria's Coastal Regions Adeyemo, Samuel O.; Ofomata, Amarachukwu I. O.; Duruojinkeya, Prisca
Asian Journal of Science, Technology, Engineering, and Art Vol 4 No 3 (2026): Asian Journal of Science, Technology, Engineering, and Art
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/ajstea.v4i3.8920

Abstract

This paper develops a stochastic optimal control framework for modeling age-structured population dynamics under climate-induced migration, with application to Nigeria’s Niger Delta region. Climate-related slow-onset and extreme hazards, including flooding, sea-level rise, and environmental degradation, drive internal displacement that disproportionately affects younger working-age groups and intensifies urban demographic pressure and infrastructure strain. The proposed model extends the deterministic McKendrick–von Foerster equation into a stochastic partial integro-differential system by incorporating a climate-sensitive migration kernel with multiplicative Wiener noise to represent persistent uncertainty and optional Lévy jumps to capture abrupt extreme events. Policy interventions, including relocation incentives, infrastructure capacity enhancements, and adaptive zoning, are formulated as controls to minimize an expected long-term cost functional that penalizes demographic imbalances, intervention effort, and migration-related disruptions. Optimality conditions are derived from an adapted stochastic Pontryagin maximum principle in infinite-dimensional spaces, resulting in a forward–backward stochastic partial differential equation system. The well-posedness of the state dynamics is proven using semigroup theory and fixed-point methods, the existence of optimal controls is established through compactness and continuity arguments, and long-term ergodic behavior under persistent noise is analyzed using Lyapunov functionals. Numerical solutions combine finite-difference discretization of the age variable, Euler–Maruyama time-stepping, and Monte Carlo integration for stochastic terms, with convergence demonstrated under Lipschitz and stability assumptions. A case study in Rivers State, centered on Port Harcourt and involving an estimated population of approximately 7 million, is calibrated using UN World Population Prospects age distributions, World Bank Groundswell Africa internal climate migration projections, and regional flood probability estimates. Simulations indicate that stochastic optimal policies reduce expected urban demographic overload variance by 20–35% relative to deterministic baselines under representative flood scenarios, while promoting more balanced age structures and supporting resilient urban planning. The study contributes to environmetrics by advancing uncertainty quantification for climate-induced migration modeling and provides a reproducible Python-based decision-support framework for evidence-based policy in climate-vulnerable coastal developing regions.
Phytochemical Screening and the Efficacy of Leaf Extract and Powder of Ageratum conyzoides against Callosobrochus maculatus Fabricius (Coleoptera: Chrysomelidae) on Stored Cowpea Seeds Michael, Obembe Olusola; Olamide, Kayode Emmanuel; Adeola, Adegbola Mary
Asian Journal of Science, Technology, Engineering, and Art Vol 4 No 3 (2026): Asian Journal of Science, Technology, Engineering, and Art
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/ajstea.v4i3.9126

Abstract

Cowpea weevil, Callosobruchus maculatus, is a major pest of stored grains worldwide and causes substantial postharvest losses in cowpea storage systems. This study evaluated the insecticidal efficacy of leaf powder and ethanol leaf extract of Ageratum conyzoides against C. maculatus based on adult mortality, oviposition, adult emergence, long-term storage protection, seed viability, and phytochemical composition. Fresh leaves of A. conyzoides were air-dried, ground into fine powder, and partially extracted in ethanol at 60 °C for 30 min. The extract was filtered, concentrated using a rotary evaporator, and prepared at concentrations of 2%, 4%, 6%, and 8% (v/w), while leaf powder was tested at dosages of 1, 2, 3, and 4 g (w/w). Phytochemical screening was conducted using standard procedures. The findings show that adult mortality increased with increasing extract concentration, powder dosage, and exposure time. Cowpea seeds treated with A. conyzoides powder at 8 g (w/w) recorded 92.25% mortality within 96 h, whereas 100% mortality was achieved with 8.0% (v/w) ethanol extract within the same exposure period. No oviposition occurred on seeds treated with 8.0% (v/w) extract, and no adult emergence was observed in seeds treated with 4 g (w/w) powder. Extract concentrations of 4%, 6%, and 8% (v/w) completely prevented seed damage during three months of storage, while germination tests after seven days showed 100% germinability in all treated seeds. Phytochemical analysis revealed the presence of alkaloids, glycosides, saponins, anthraquinones, tannins, terpenes, and flavonoids. The study concludes that A. conyzoides leaf powder and ethanol leaf extract are effective botanical treatments for controlling C. maculatus without compromising seed viability. These findings contribute to stored-grain pest management by supporting the use of plant-based insecticides as environmentally safer and potentially cost-effective alternatives to synthetic insecticides.
Convergence Theorems for Total Asymptotically Nonexpansive Mappings in CAT (0) Spaces Garba, Ibrahim Usman; Bello, M.I; Adamu, M.S; Ephraim, Pofi
Asian Journal of Science, Technology, Engineering, and Art Vol 4 No 3 (2026): Asian Journal of Science, Technology, Engineering, and Art
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/ajstea.v4i3.9163

Abstract

This paper proposes new iterative algorithms for total asymptotically nonexpansive mappings in CAT(0) spaces. The study aims to establish a strong convergence theorem for the proposed algorithms under suitable mathematical conditions. Using a theoretical analytical approach, the convergence properties of the iterative schemes are examined within the geometric framework of CAT(0) spaces. The results demonstrate that the proposed algorithms converge strongly to a fixed point of total asymptotically nonexpansive mappings under the stated assumptions. These findings improve and extend several recent results reported in the literature on nonlinear mappings and fixed point theory. The study contributes to the advancement of convergence theory in CAT(0) spaces by providing refined iterative methods and strengthening the theoretical foundation for analyzing total asymptotically nonexpansive mappings.
Phytochemical Screening, Proximate Composition, and Mineral Analysis of Tropical Almond (Terminalia catappa) Seeds Collected from Gombe State, Nigeria Abdulkadir, Maryam Usman; Mijinyawa, Farida Muhammad; Hammari, Abubakar Muhammad; Abubakar, Adamu Jauro; Adam, Ibrahim Abubakar; Yusuf, Saminu; Yakubu, Hanifah
Asian Journal of Science, Technology, Engineering, and Art Vol 4 No 3 (2026): Asian Journal of Science, Technology, Engineering, and Art
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/ajstea.v4i3.9173

Abstract

The growing demand for natural medicines and plant-derived nutrients has increased scholarly interest in botanical resources rich in bioactive constituents, minerals, phytochemicals, and other metabolites. Terminalia catappa, a member of the Combretaceae family commonly found in tropical and subtropical regions, is traditionally used for its antioxidant, anti-inflammatory, anticancer, and antidiarrheal properties. This study aimed to evaluate the phytochemical profile, proximate composition, and mineral content of T. catappa seed nuts. Standard analytical methods were employed for proximate analysis, while elemental content was determined using atomic absorption spectrophotometry and flame photometry. Phytochemical screening revealed the presence of saponins, steroids, phenols, and alkaloids, whereas tannins and flavonoids were absent. The proximate composition showed moisture content of 11.03%, ash content of 5.00%, crude fiber of 0.53%, crude protein of 8.28%, crude fat of 36.33%, and carbohydrates of 39.63%. Mineral analysis indicated notable concentrations of potassium at 1.1638 mg/L and calcium at 0.2046 mg/L, with sodium, manganese, zinc, iron, and copper detected in trace amounts. The study concludes that T. catappa seed nuts possess considerable nutritional and medicinal value, supporting their traditional applications and indicating their potential industrial use in food, pharmaceutical, and personal care products. These findings contribute to phytochemical and nutritional research by providing empirical evidence on the bioactive and compositional properties of T. catappa seeds.
Development of Hard Drive Failure Prediction Model for Cloud Platform Using Intelligent Techniques Ahmad, I. I.; Jiya, J. D.; Baba, MA.
Asian Journal of Science, Technology, Engineering, and Art Vol 4 No 3 (2026): Asian Journal of Science, Technology, Engineering, and Art
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/ajstea.v4i3.9186

Abstract

Disk failures in cloud platforms remain a critical reliability concern because they can cause severe data loss, service downtime, and financial losses. This study aims to develop an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based hard drive failure prediction model, investigate the impact of selected Self-Monitoring, Analysis, and Reporting Technology (SMART) attributes on predictive performance, and evaluate ANFIS against existing prediction techniques. A quantitative predictive modeling approach was employed using Backblaze SMART telemetry data, with Recursive Feature Elimination (RFE) applied for feature selection. Eight critical SMART attributes were selected, including reallocated sector count (SMART 5), seek-error rate (SMART 7), and temperature (SMART 231). The proposed ANFIS model achieved 89.4% accuracy, 91.2% precision, 87.8% recall, and an area under the curve (AUC) of 0.934. Comparative results show that ANFIS outperformed Random Forest, Gradient Boosting, Neural Networks, and Support Vector Machines (SVMs) in predictive performance. The study concludes that integrating ANFIS with RFE provides an effective and interpretable approach for hard drive failure prediction in cloud computing environments. These findings contribute to intelligent predictive maintenance research by demonstrating the value of neuro-fuzzy modeling for improving disk failure detection, supporting proactive maintenance, reducing downtime, and enhancing operational reliability in large-scale cloud platforms.
Thermal Transport Characteristics of Fractional Maxwell Fluid Model for Blood Flow in a Stenosed Artery Musa, Ali; Kwami, A. M; Madaki, A. G
Asian Journal of Science, Technology, Engineering, and Art Vol 4 No 2 (2026): Asian Journal of Science, Technology, Engineering, and Art
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/ajstea.v4i2.9188

Abstract

This study examines the unsteady heat transfer behavior of fractional Maxwell nanofluid blood flow in a stenosed artery under the combined effects of a magnetic field, thermal radiation, viscous dissipation, and internal heat generation. The study aims to provide a more realistic representation of thermal transport in pathological blood flow by incorporating fractional-order viscoelastic effects. The governing fractional energy equation is solved using a semi-analytical Laplace transform approach, while numerical inversion is carried out through the Concentrated Matrix-Exponential method. The results show excellent agreement with existing studies, confirming the validity of the proposed approach. The findings further reveal that thermal radiation, magnetic field strength, viscous dissipation, fractional order, and relaxation time increase temperature distribution, whereas higher Reynolds and Prandtl numbers reduce it. The study concludes that fractional-order modeling offers a more realistic and effective framework for analyzing thermal transport in stenosed arterial blood flow, thereby contributing to improved understanding of heat transfer behavior in pathological hemodynamic conditions.
Wind Resource Assessment and Availability Analysis Using Meteorological Data for Gombe Station, Nigeria Hassan, Muhammad Basheer; Umar, Muhammad Nasir; Burari, Felix Wilfred; Yusuf, Usama; Ahmad, Abduqadir
Asian Journal of Science, Technology, Engineering, and Art Vol 4 No 3 (2026): Asian Journal of Science, Technology, Engineering, and Art
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/ajstea.v4i3.9211

Abstract

This study investigates wind energy potential and availability in Gombe, Nigeria, using ten years of wind speed data from 2015 to 2024 obtained from the Nigerian Meteorological Station (NiMET). The objective was to analyze wind speed distribution, estimate wind power density, and determine wind availability at the station. The data were statistically examined using Weibull, Rayleigh, Normal, and Gamma probability distribution models to identify the model that best represents the observed wind characteristics. The Kolmogorov–Smirnov (KS) and Anderson–Darling (AD) goodness-of-fit tests were applied to validate model performance. The results indicate that the Gamma distribution provided the best fit, with a KS p-value of 0.44 and an AD statistic of 0.25, outperforming the other models. The Gamma distribution parameters were estimated at approximately α = 168.1 and θ = 0.0192, yielding a mean wind speed of 3.23 m/s and a standard deviation of 0.25 m/s. Based on the Gamma model, the mean wind power density (WPD) was estimated at 22.8 W/m², classifying Gombe as a low-to-moderate wind potential area suitable for small-scale or distributed wind energy applications. Wind availability analysis showed that wind speeds could support turbines operating at or above 50% of their rated capacity approximately 81.9% of the time when v₅₀ ≥ 3.0 m/s. However, turbines with v₅₀ ≥ 4.0 m/s exhibited negligible availability, indicating that only low-rated-speed and low-cut-in turbines are technically viable for the site. The study concludes that Gombe has stable and consistent moderate-speed wind conditions suitable for decentralized rural electrification and low-power applications such as water pumping and small hybrid systems. These findings contribute to wind resource assessment by demonstrating the importance of accurate statistical modeling, particularly the Gamma distribution, for characterizing low-to-moderate wind regimes and informing site-specific renewable energy planning.
Integrated Mahgoub–VIM Hybrid Transform Technique for Solving Linear, Nonlinear, and Fractional Differential Equations Aliyu, Umar Mujahid; Kwami, A. M.; Bello, M. I.; Madaki, A. G.; Okai, J. O.; Hussaini, Abubakar Assidiq
Asian Journal of Science, Technology, Engineering, and Art Vol 4 No 3 (2026): Asian Journal of Science, Technology, Engineering, and Art
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/ajstea.v4i3.9234

Abstract

This study develops an integrated Mahgoub–Variational Iteration Method (VIM) hybrid transform technique for solving linear, nonlinear, and fractional-order ordinary and partial differential equations. The study addresses the limitations of classical integral transforms in handling nonlinearities, fractional derivatives, and memory-dependent effects, while ensuring physically consistent initial conditions through the Caputo fractional derivative. The proposed Mahgoub–VIM framework was applied to higher-order nonlinear ordinary differential equations, fractional ordinary differential equations, time-fractional partial differential equations, and fractional relaxation models. The results demonstrate rapid convergence, high stability, and close agreement with exact solutions. Comparative analysis further indicates that the proposed method consistently outperforms the Sumudu transform in terms of accuracy and error control, particularly for nonlinear and fractional problems. By avoiding linearization and discretization, the technique provides an efficient analytical framework for modeling realistic phenomena, including diffusion, heat transfer, viscoelasticity, and damping. The study contributes to the development of hybrid transform-based methods by offering a robust, accurate, and versatile analytical tool for solving complex differential systems.
Retracted: Company Income Tax: A Sine Qua Non to Economic Growth of Nigeria Ikechukwu, Egbere Michael
Asian Journal of Science, Technology, Engineering, and Art Vol 4 No 2 (2026): Asian Journal of Science, Technology, Engineering, and Art
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/ajstea.v4i2.9334

Abstract

Although company income tax (CIT) remains a critical instrument for financing public services and infrastructure, its contribution to economic growth in Nigeria is constrained by persistent revenue leakages associated with capital flight. This study examines the extent to which capital flight undermines the effectiveness of CIT in supporting Nigeria’s economic growth. Drawing on an adapted model from Ichoku and Fonta (2006) and anchored in Wagner’s Law of expanding state activity, the study employs time-series data from 1994 to 2016 and applies the Error Correction Model (ECM), Augmented Dickey-Fuller unit root tests, Johansen cointegration tests, and Granger causality tests to evaluate both short-run and long-run relationships between capital flight components and CIT revenue. The findings show that over-invoicing and under-invoicing exert a statistically significant negative effect on CIT, indicating that these illicit financial practices erode the corporate tax base and weaken government revenue generation. The results also reveal both unidirectional and bidirectional causal relationships between capital flight indicators and CIT. Although debt servicing exerts a negative but statistically insignificant effect, its long-term implications for fiscal sustainability remain substantial. The study concludes that CIT is indispensable to Nigeria’s economic growth, but its effectiveness is severely compromised by unchecked capital flight. These findings underscore the need for stronger customs enforcement, improved tax administration, and enhanced international cooperation to curb illicit financial flows, strengthen tax compliance, and reinforce Nigeria’s fiscal capacity.
Deep Learning - Based Shape Recognition and Classifications of Conic Geometries in Engineering Drawing Das, Rajnandani; Shah, Neha; Sah, Dilip Kumar; Sahani, Kameshwar; Sahani, Suresh Kumar
Asian Journal of Science, Technology, Engineering, and Art Vol 4 No 2 (2026): Asian Journal of Science, Technology, Engineering, and Art
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/ajstea.v4i2.9335

Abstract

Engineering drawings frequently contain conic geometries such as circles, ellipses, parabolas, and hyperbolas, which are fundamental to mechanical design and industrial applications. Accurate identification and classification of these shapes are therefore essential for computer-aided design (CAD) systems, automated inspection, and intelligent design analysis. However, conventional geometry-based or rule-based approaches often perform poorly when drawings are noisy, complex, or partially incomplete. This study proposes a deep learning-based approach using convolutional neural networks (CNNs) to automatically extract features and classify conic shapes in engineering drawings. By learning discriminative visual representations directly from input data, the proposed method enhances classification accuracy, improves robustness, and reduces the need for manual intervention. The study concludes that CNN-based conic shape recognition offers a reliable and efficient solution for engineering and industrial contexts, with practical implications for improving automation and intelligent analysis in design-related applications.

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