Asian Journal of Science, Technology, Engineering, and Art

Land Use and Land Cover in Akure City, Ondo State, Nigeria, Using GIS and Remote Sensing

Abiodun Daniel Olabode — Mon, 04 May 2026 20:27:48 +0800

Urbanization and industrial development have intensified pressure on forest reserves in cities and settlement areas, increasing the vulnerability of urban forests to land-use change. This study examines the effects of land-use changes on forest encroachment in Akure City, Ondo State, Nigeria. A remote sensing and GIS-based approach was employed using satellite imagery obtained from the United States Geological Survey (USGS) for 2000, 2010, and 2020. The images were processed and analyzed using ArcMap 10.5 and Environment for Visualizing Images (ENVI) software to assess changes in land-use and land-cover patterns over the study period. The findings reveal notable changes in Akure City’s land-use structure, with less dense forest accounting for 41%, 66%, and 58% in 2000, 2010, and 2020, respectively, while built-up areas increased from 1.53% in 2000 to 3.28% in 2010 and 5.28% in 2020. The loss of natural vegetation also increased substantially from 4.4 km² in 2000 to 9.34 km² in 2010 and 12.01 km² in 2020. Surface water bodies were nearly absent in 2000 but accounted for 0.93 km² in 2010 and 0.66 km² in 2020. These findings indicate that continued urban expansion and associated land-use changes are likely to accelerate forest encroachment in Akure City. The study contributes to urban environmental management by highlighting the need for government authorities at all levels to implement sustainable forest conservation strategies and integrated city management policies.

Sustainable Nature-Based Landscaping and Environmental Conservation: A Case Study of Faculty of Engineering, Imo State University, Owerri

Mon, 04 May 2026 20:42:52 +0800

Although urban landscape studies have largely emphasized large-scale parks and green infrastructure, limited attention has been given to small-scale but high-impact spaces such as building entrances, particularly within educational institutions. This study aims to redesign the entrances of the Agricultural Engineering (AGE) and Civil Engineering (CVE) Departments, Faculty of Engineering, Imo State University, Owerri, using sustainable landscape practices and Nature-Based Solutions (NBS). A site assessment was conducted to develop a sustainable landscape plan that integrates ecological functionality with existing soil conditions, topography, vegetation, biodiversity, and ecosystem services. The redesign incorporated grasses and native plants to enhance site aesthetics, support rainwater absorption, and reduce flooding risk. Clearly defined walkways bordered by kerbs were introduced to guide pedestrian movement while improving functionality and visual quality, and a designated car entrance was created to ensure smooth vehicular access without disrupting the natural layout. The findings show that NBS-based entrance design can enhance aesthetics, functionality, institutional identity, ecological performance, and interdisciplinary relevance across the humanities, engineering, and arts. The study concludes that university entrance spaces can function beyond ornamental purposes by becoming dynamic ecological systems that provide environmental, educational, and psychological benefits. Its contribution lies in demonstrating that the integration of Nature-Based Solutions in institutional landscapes can strengthen climate resilience, improve visual appeal, support Sustainable Development Goals, particularly SDG 11 and SDG 15, and position entrance spaces as critical ecological nodes that shape first impressions, influence microclimatic conditions, and guide future landscape research and design.

Design and Assessment of a 3.7 KW Off-Grid Biogas Power System for Economic Optimization in Medium-Scale Nigerian Farming

Mon, 04 May 2026 00:00:00 +0800

The removal of fuel subsidies in Nigeria in May 2023 sharply increased energy costs, with Premium Motor Spirit (PMS) and Automotive Gas Oil (AGO) prices rising by more than 200%, thereby intensifying operating cost pressures in the agricultural sector, particularly for small- and medium-scale farms dependent on fossil-fuel-based power. This study evaluates the feasibility of an off-grid biogas power system as a cost-mitigation strategy through the design, capacity optimization, and techno-economic assessment of a 3.7 kW biogas-based electricity generation system. A representative medium-scale livestock farm comprising 10 cattle, 20 pigs, and 500 poultry birds was analyzed. The proposed system integrates a 30 m³ fixed-dome anaerobic digester operating under a co-digestion regime using mixed swine, bovine, and poultry substrates, with a total daily feedstock input of approximately 235 kg. System modeling indicates a biogas production potential of 13.15 m³ per day, which, when supplied to a dual-fuel generator, produces an average electrical output of 22.35 kWh per day and supports continuous operation of a 3.7 kW load for up to six hours. The economic evaluation shows an approximately 95% reduction in monthly energy expenditure, from ₦319,000 to ₦14,000, while digestate utilization as organic fertilizer provides an additional estimated monthly revenue of ₦168,000. Financial indicators reveal a payback period of 5.5 months and a gross profit increase exceeding 100% within the first year of operation. Performance assessment further demonstrates benefits in electrical output, fossil fuel displacement, operating cost savings, investment recovery, environmental impact, and waste treatment efficiency. The study concludes that small-scale biogas power systems of this capacity are technically robust and economically viable for decentralized agricultural energy supply in Nigeria, contributing to rural energy security, emission reduction, and circular resource utilization.

Stochastic Optimal Control Framework for Climate-Induced Migration: Age-Structured Population Dynamics in Nigeria's Coastal Regions

Samuel O. Adeyemo, Amarachukwu I. O. Ofomata, Prisca Duruojinkeya — Mon, 04 May 2026 21:20:29 +0800

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

Obembe Olusola Michael, Kayode Emmanuel Olamide, Adegbola Mary Adeola — Sun, 17 May 2026 00:00:00 +0800

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

Ibrahim Usman Garba, M.I Bello, M.S Adamu, Pofi Ephraim — Sun, 17 May 2026 00:00:00 +0800

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

Sun, 17 May 2026 00:00:00 +0800

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

I. I. Ahmad, J. D. Jiya, MA. Baba — Mon, 18 May 2026 08:03:47 +0800

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.

Wind Resource Assessment and Availability Analysis Using Meteorological Data for Gombe Station, Nigeria

Mon, 18 May 2026 08:25:06 +0800

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

Mon, 18 May 2026 08:45:19 +0800

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.

Adaptive Time Stepping Numerical Schemes for Stochastic Differential Equations

Sat, 23 May 2026 16:05:27 +0800

This study presents a comprehensive examination of adaptive time-stepping numerical schemes for solving stochastic differential equations (SDEs), with particular attention to methods that automatically adjust step sizes based on local error estimates. The study aims to investigate the theoretical foundations, implementation strategies, convergence properties, and practical applications of adaptive numerical methods for SDEs. The Euler–Maruyama and Milstein schemes were extended through adaptive step-size control mechanisms, and their convergence behavior was analyzed through extensive numerical experiments implemented in Python. The study also provides detailed code examples, accessible explanations, and visualizations, including convergence plots, error analysis, and performance comparisons, to support practical understanding and implementation. The findings indicate that adaptive schemes substantially improve computational efficiency while maintaining required levels of accuracy. Specifically, the results show that adaptive methods can reduce computational costs by up to 60% compared with fixed-step methods for problems involving varying stiffness. The study concludes that adaptive time-stepping offers a robust and efficient strategy for numerical SDE simulation, particularly in computational settings where accuracy and efficiency must be balanced. Its contribution lies in integrating theoretical analysis, implementation guidance, and empirical performance evaluation to support researchers and practitioners in applying adaptive numerical schemes to stochastic differential equations.

Estimation of Binary Logistic Regression Using Three Links Function (Logit, Probit, and Complementary Log Log) in Assessing the Factor That Influence HIV

Tugga H. A., Ogunmola A. O., Bamigbala O.A., Ahmad S.S. — Thu, 28 May 2026 20:09:43 +0800

Human Immunodeficiency Virus (HIV) remains a major global public health concern, with sub-Saharan Africa accounting for a substantial proportion of the global burden of infection. In Nigeria, the HIV epidemic shows geographic and demographic variation shaped by age, sex, socioeconomic status, risk behaviors, and access to healthcare services. Understanding the determinants of HIV infection is therefore essential for effective prevention, early detection, and policy formulation. This study aimed to identify significant demographic determinants of HIV infection and determine the best-fitting binary response model among patients tested at General Hospital Takum, Taraba State, Nigeria, between 2018 and 2023. Binary logistic regression models with logit, probit, and complementary log–log link functions were applied to assess the effects of age, sex, and year on HIV infection status. Model performance was evaluated using goodness-of-fit statistics, including deviance, Pearson chi-square, and Hosmer–Lemeshow tests, as well as model selection criteria based on the Akaike Information Criterion and Bayesian Information Criterion. The results indicate a consistent decline in HIV odds across the study years, significantly higher odds among females, and substantially increased odds among adults aged 30–49 years and those aged 50 years and above. Among the three models, the complementary log–log link function demonstrated the best overall fit, with the lowest AIC and BIC values and non-significant goodness-of-fit tests. The study concludes that age, sex, and year are significant predictors of HIV infection, and that the complementary log–log model provides the most reliable framework for predicting HIV status in this population. These findings contribute to epidemiological modelling by supporting more appropriate link-function selection and offer practical implications for localized HIV prevention strategies in Taraba State, Nigeria.

Two Strains of Covid-19 Model with Vaccination and the Effect of Awareness Program on Its Control

Anate A. O., Adamu M. M., Adamu M.S., Kwami A. M. — Thu, 28 May 2026 20:28:12 +0800

The Covid-19 outbreak and the subsequent emergence of different viral strains posed a serious global public health challenge. This study proposes a mathematical model to analyze the transmission dynamics of two different Covid-19 strains and examine the effect of awareness on disease control. The study established the basic mathematical properties of the model, analyzed the disease-free and disease-endemic equilibria for both strains, conducted stability analysis, and computed the basic reproduction number, defined as R₀ = max(R₁, R₂). Stability conditions were examined for the strain-specific reproduction numbers, including cases in which R₁ < 1 while R₂ > 1 and R₂ < 1 while R₁ > 1. Numerical simulations were also conducted to support the analytical results and further illustrate the model dynamics. The findings show that increased awareness enhances vaccination uptake and reduces the basic reproduction number, thereby contributing to the control of disease transmission. The study concludes that awareness-based interventions play an important role in controlling the spread of Covid-19 strains through improved vaccination behavior and reduced transmission potential. These findings contribute to mathematical epidemiology by demonstrating the relevance of awareness-driven vaccination strategies in multi-strain infectious disease models and provide practical implications for public health authorities in strengthening awareness campaigns through media and social gatherings.