Recent Advances in Natural Sciences (RANS) is an open access journal, publishing original research from across all areas of Natural Sciences. It is published biannually (June and December). Production and Hosting is by FLAYOO Publishing House LTD on behalf of the Nigerian Society of Physical Sciences (NSPS). Peer review under responsibility of NSPS. With the idea that making research openly available to the public promotes better global knowledge exchange, this journal offers direct open access to its contents, i.e., all the articles published in this journal are free to access immediately from the date of publication. RANS is available in print and online. The language used in this journal is English. ISSN(Print): 1596-0544 ISSN (Online): 1596-0757
Comparative analysis of the nutritive and antinutritive values of the pulps and kernels of Mafida (Spondias sp. cf. S. mombin L., wild variety) and Mobola plum (Parinari curatellifolia Cav.) in Akwanga, north-central Nigeria
This study comparatively evaluated the proximate composition, mineral content, antinutritional factors, and amino acid profiles of the pulps and kernels of Mafida and Mobola plum collected in Akwanga Local Government Area, Nasarawa State, Nigeria. Samples were prepared and analysed using standard analytical methods. The kernels of both fruits contained more protein, fat, and metabolic energy than their corresponding pulps. Mafida kernel contained 27.01% crude protein and 18.38% total fat, whereas Mobola plum kernel contained 33.96% crude protein and 22.32% total fat. Mafida pulp had higher moisture, carbohydrate, and crude fibre contents, and Mobola plum pulp had a notably higher carbohydrate content (53.50%) than its kernel. Both fruits contained measurable macro- and trace elements, including Na, K, Ca, Mg, Fe, Zn, and Cu; however, their overall contribution to dietary mineral requirements was limited. Oxalates, phytates, alkaloids, phenolics, and other antinutritional factors occurred at appreciable concentrations, indicating that processing may improve nutritional safety and nutrient bioavailability. All nine essential amino acids were detected, with the kernels generally showing higher total amino acid contents and better protein quality than the pulps. Mafida and Mobola plum kernels therefore have potential as protein- and energy-dense ingredients, but processing and bioavailability studies are required before their use in food formulations can be recommended.
Copyright (c) 2026 Ban John Abok, Mathew Olaleke Aremu, Jude Chinedu Onwuka, Folashade Habibat Omotehinwa https://creativecommons.org/licenses/by/4.0
Coupled optical and electrical modeling of TiO2 anti-reflective coatings for enhanced performance in GaAs thin-film solar cells
Optical reflection losses at the air--semiconductor interface limit the performance of gallium arsenide (GaAs) thin-film solar cells, whereas optical and electrical optimization are often considered separately. This study develops a coupled MATLAB-based optical--electrical simulation framework to evaluate the effect of titanium dioxide (TiO2) anti-reflective coating (ARC) thickness on GaAs thin-film solar cells. The optical response was assessed from wavelength-dependent reflectance, transmittance, absorptance, and quantum efficiency, and electrical performance was evaluated from current density--voltage characteristics, short-circuit current density ( Jsc), open-circuit voltage (Voc), fill factor (FF), and power conversion efficiency (PCE). TiO2 ARC thicknesses of 50, 75, and 100 nm were simulated under AM 1.5G illumination at 300 K. The 50 nm coating produced the lowest reflectance, highest absorptance, highest current density, and highest quantum efficiency across 300--900 nm. It also provided the best overall photovoltaic performance, with Jsc ≈ 34.7 mA cm-2, Voc approx 0.839 V, FF ≈ 0.842, and a maximum PCE of approximately 24.2%. Increasing the coating thickness to 75 and 100~nm reduced the PCE to approximately 22.95% and 21.85%, respectively. The results show that optimum photovoltaic efficiency requires a balanced combination of optical absorption, current generation, voltage, and FF. The coupled framework provides a computationally efficient tool for designing and optimizing ARCs for high-efficiency III--V photovoltaic devices.
Copyright (c) 2026 F. Aungwa, D. S. Igba, A. A. McAsule, M. B. Ochang, W. V. Zhiya, A. I. Tyom https://creativecommons.org/licenses/by/4.0
Analysis of wind-speed characteristics and energy potential across North Central Nigeria
Unreliable conventional electricity supply constrains the processing and storage of agricultural produce in North Central Nigeria, contributing to food losses and insecurity. This study evaluates the region's wind-energy potential using 20 years (2001--2020) of daily wind-speed data from the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), produced by the National Aeronautics and Space Administration. Weibull, lognormal, and Gamma probability distribution functions were fitted to data from Abuja, Jos, Lafia, Lokoja, Makurdi, and Minna. Observed wind speeds ranged from 2.5 to 6.1 m/s, and mean wind-power densities ranged from 16.8 to 45.8 W/m2, indicating potential mainly for off-grid electricity generation and other small-scale applications. Based on higher coefficients of determination and lower root mean square errors, the Weibull distribution generally provided the best fit. Average monthly wind-energy densities increased from 12.235 kWh/m2 in Abuja to 33.010 kWh/m2 in Jos, with intermediate values of 17.759, 20.254, 24.711, and 27.664 kWh/m2 for Minna, Lokoja, Lafia, and Makurdi, respectively. These results indicate that wind energy could complement conventional supply in domestic, agricultural, and industrial facilities across the region.
Copyright (c) 2026 E. V. Tikyaa, E. N. Chukwuemezie, M. O. Audu, T. Igbawua https://creativecommons.org/licenses/by/4.0
Isolation and characterization of a dihydrostilbene from the aerial parts of Indigofera conferta
Indigofera conferta, an annual herb widely distributed in northeastern and western Nigeria, is used ethnomedicinally to treat inflammation, pain, sore feet, and snakebite. Despite these uses, the phytochemical constituents of this species remain underexplored. The chloroform fraction of the methanol extract of the aerial parts of I. conferta was separated by silica gel column chromatography to afford a compound. The structure of the compound was elucidated using UV, FT-IR, one-dimensional and two-dimensional NMR (1H, 13C, HSQC, and HMBC) spectroscopy and comparison with previously reported literature data. The compound was assigned as 3,5-dimethoxy-4'-O-(2,3-dihydroxy-3-methylbutyl)-dihydrostilbene. This report documents the first isolation of this compound from I. conferta and contributes to the phytochemical and chemotaxonomic knowledge of the genus Indigofera.
Copyright (c) 2026 Maryam Isah, Asmau N. Hamza, Nkiruka Theresa Nnadozie, Edward Ayakeme Bebe, Nasir Tajuddeen, Aliyu M. Musa https://creativecommons.org/licenses/by/4.0
A systematic review of real-time monitoring systems for oil and gas pipeline leakage identification based on deep learning approaches
Leakage in oil and gas pipelines threatens environmental safety, operational integrity, and economic stability. Recent advances have integrated Internet of Things (IoT) technologies and deep learning to improve real-time leak detection and response systems. This study presents a systematic review of state-of-the-art IoT-based and deep learning approaches for pipeline leakage detection, guided by the PRISMA methodology. From 450 articles published between 2015 and 2025, 144 high-quality studies focusing on real-time monitoring were selected for in-depth analysis. Key performance metrics, including accuracy, precision, recall, F1-score, sensitivity, and specificity, were evaluated. Hybrid deep learning models, such as Deep Autoencoder with XGBoost (DAE+XGB) and Bidirectional LSTM (BiLSTM), achieved the strongest performance, with accuracies reaching 99.78%, particularly for fine leaks as small as 0.5 mm. IoT frameworks leveraging cloud computing and reinforcement learning showed strong scalability and adaptability for remote operations. Despite these advances, challenges remain, including limited real-world validation, a lack of standardized datasets, energy inefficiency, cybersecurity risks, and limited model interpretability. Future research should emphasize field deployments, energy-aware IoT designs, improved security protocols, and explainable artificial intelligence to enhance transparency and trust. This review summarizes current progress, challenges, and future opportunities in intelligent, real-time pipeline leak detection for safer and more sustainable oil and gas infrastructure.
Copyright (c) 2026 Gabriel James, Ini Umoeka, Edidiong Nkang, Emmanuel Etim, David Egete, John Odey, Bassey Ele, Ekemini Okpongkpong, Enefiok Etuk, Tope Jimoh, Idara Essien https://creativecommons.org/licenses/by/4.0
Spatial and temporal estimation of groundwater recharge in Kabago watershed using the soil and water assessment tool (SWAT) hydrological model: implications for sustainable water resource management in Northwestern Nigeria
Assessing groundwater recharge is essential for managing water resources sustainably, especially in semi-arid regions. In order to assess groundwater reserves for residential and agricultural use, this study examines the spatial and temporal distribution of recharge in the Kabago watershed in Southern Zamfara State, Nigeria. The watershed, which is roughly 5.97 km² in size and is composed of migmatite, gneiss, schist, and granitic rocks, has a Sudan savannah climate with high evapotranspiration and little annual rainfall (1164 mm). Hydrological processes during a 21-year period (1990–2010) were simulated using the Soil and Water Assessment Tool (SWAT 2012) linked with ArcGIS. SUFI-2 in SWAT-CUP was used for calibration and validation utilizing streamflow data from the Maru gauging station. With NSE values of 0.85 and 0.75, R2 values of 0.85 and 0.75, PBIAS of 2.5% and –1.3%, and RSR values of 0.43 and 0.52 for calibration and validation, respectively, the model performed satisfactorily. Soil water capacity, groundwater delay, baseflow coefficient, and surface runoff factors were among the sensitive characteristics. The results indicated that model-derived potential recharge values, which represent roughly 49% of annual precipitation, ranged from 379.93 to 678.88 mm/year with an average of 545.69 mm/year. Runoff and evapotranspiration were responsible for 15.35% (178.65 mm/year) and 33.24% (386.93 mm/year) of the water loss, respectively. Recharge variability was largely influenced by rainfall distribution, soil type, slope, and land use, with agricultural land promoting infiltration. Overall, the Kabago watershed functions as a significant recharge zone, though high evapotranspiration highlights vulnerability to land use and climate change. These findings provide a sound basis for groundwater management and policy in Zamfara and comparable semi-arid regions.
Copyright (c) 2026 A. M. Shuaibu, K. O. Musa, K. A. Murana, Mutari Lawal https://creativecommons.org/licenses/by/4.0
Bernoulli-exponential survival model for estimating proportions in randomised controlled trials with dropouts
Randomised controlled trials often experience dropouts, especially in multi-stage designs where participants must meet induction-phase eligibility requirements before progressing to the main study. Loss of information arising from early non-responders or withdrawal of clinically vulnerable subjects are not always random. Classical survival analysis methods including the Kaplan-Meier estimator, exponential models, and similar techniques assume independent censoring. When this assumption is violated, survival probabilities are overstated, hazard rates are underestimated, and median survival time become biased. Such dropout mechanisms also lead to biased estimates of the proportion of participants who would experience the clinical endpoint, thereby distorting treatment effect measures based on event counts. This study proposed a Bernoulli-Exponential (BE) model that jointly represents the occurrence of an event and the time to event, rather than treating censored subjects as if they share the same risk structure as event completers. The model incorporates a Bernoulli component to describe the probability of belonging to the event-generating subgroup and an exponential component to model event times conditional on experiencing the event. This separation enables the model to account for informative dropouts, induction-phase failures, and scenarios where some participants contribute no event time. We derive the parameter estimates, survival function, and outline procedures for estimating the median survival time, its variance, and confidence interval. Simulation studies were used to assess bias and precision under varying levels of dropout. The BE estimator consistently corrected the upward bias observed in Kaplan-Meier, intention-to-treat, and completers-only estimates under heavy or informative dropout.
Copyright (c) 2026 Ekele Alih https://creativecommons.org/licenses/by/4.0
Fabrication and characterization of dye-sensitized solar cells using Allium cepa flower extract as a means of harnessing the solar energy
Climate change and global warming have spurred a growing urgency to transition from traditional energy sources to sustainable alternatives. This article explores the realm of renewable energy, focusing on the potential of dye-sensitized solar cells (DSSCs) as an eco-friendly solution. The study examines the efficiency of a DSSC utilizing a dye extract from Allium Cepa (onion) flowers, shedding light on the material's viability as a photosensitizer. The article covers the physics of solar cells, the operation principle of DSSCs, and the specific components involved in fabricating a DSSC based on Allium Cepa extract. The findings show that the extract obtained from Allium Cepa flower exhibited maximum absorbance of 1.4793 at a wavelength of 437.74 nm in the visible region of the electromagnetic spectrum. And when adsorbed on the TiO2 semiconductor surface, the absorbance of the sample increased to the value of 1.5189 at a wavelength of 423.89 nm in the visible region. The energy bandgap of 4.21 eV was obtained for Allium Cepa extract. The fill factor of 0.61 was obtained, and a light conversion efficiency of 0.43 % was achieved. With these results, the dye extract obtained from dry Allium Cepa flowers is a good photosensitizer for making dye-sensitized solar cells.
Copyright (c) 2026 Samuel Emuvokeraye Omoyibo, Blessing Uyoyou Osolebri, Lawrence Oghenerivwe Anho, Ufuoma Alexander Odenema https://creativecommons.org/licenses/by/4.0
Spectroscopic characterization of a derivative of naphthalene isolated from Hibiscus sabdariffa calyx
Hibiscus sabdariffa (Hs) is a nutritive and medicinal herb that belongs to the Malvaceae family. It is mainly composed of organic acids, anthocyanins, polysaccharides and flavonoids. Hibiscus sabdariffa, being a natural source of bioactive compounds made up of several reactive functional groups, could serve the purpose of a starting material in the production of other compounds. Acidified aqueous ethanol extract of Hs was subjected to column chromatographic fractionation that yielded a yellow liquid isolate, which was characterized by UV–Visible, Infrared spectroscopy, Nuclear Magnetic Resonance (NMR) and High Resolution Mass Spectrometry (HRMS). The NMR spectra displayed distinct signals of a naphthalene ring linked through a carbonyl carbon to a napht 2 –yl moiety. 1H and 13C NMR indicated the presence of five aromatic protons (dH 6.98 – 7.51) resonating with ten aromatic carbon atoms (dc100.0 – 177.9), (C 189.2, indicating the presence of a carbonyl carbon coupled with a hydrogenated naphthalene ring substituent. Absorptions at 304nm, 366 nm and 506nm are due to pi- pi∗ and n - pi∗ , respectively. IR peak at 2923 cm-1 is ascribed to C − H stretch, while the peak at 1732(s) cm-1, with shoulders at 1634(w) and 1794(w), affirmed the presence of carbonyl functional group conjugated to an unsaturated naphtalen-2yl ring system. The isolated naphthalene derivative is characterized as ‘3 methyl-1, 4, 4a, 5, 6, 7, 8, 8a, octahydronaphtalen-2-yl)(naphtalen-2yl)methanone.
Copyright (c) 2026 Adewumi Idowu Adeagbo, Adekunle Abeeb Azeez https://creativecommons.org/licenses/by/4.0
Asymptotic perturbation and sensitivity analysis of malaria transmission in Nigeria: a mathematical model
This article analyzes malaria dynamics among high-risk groups in Nigeria, with the human population divided into five compartments and the mosquito population divided into two compartments. The resulting system of nonlinear ordinary differential equations is examined by asymptotic perturbation and sensitivity-analysis methods. The perturbation analysis describes the behavior of the system at the malaria-free equilibrium (MFE) and the effect of a small perturbation around that state. The first-order perturbation solution indicates exponential growth in disease prevalence, showing that the MFE is unstable when infection is introduced. The sensitivity analysis shows that preventing mosquitoes from surviving long enough to become infectious is the most effective way to reduce the malaria transmission cycle. The infected adult male compartment, IM, also acts as an important long-term reservoir and is the second most influential factor in the model. The estimated basic reproduction number satisfies R0 > 1, indicating a high potential for malaria spread and the need for effective control measures.
Copyright (c) 2026 Sunday N. Aloke, Henry O. Adagba, Uchenna E. Michael, Okorie Nwite, Theresa E. Efor, Aloysius N. Ezaka, Chika Agha https://creativecommons.org/licenses/by/4.0
Environmental and health impacts of coal occurrence and exploitation in Akunza mighili, North central Nigeria
Akunza Mighili is a community in the outskirts of Lafia widely known for extensive coal mining activities. However, no assessment has been conducted on the impact of this mining on the environment and health hazards. This research aims to investigate the environmental and health impacts of coal exploitation in Akunza Mighili. Five representative rock samples were collected during field mapping and analyzed to determine their elemental and mineralogical constituents using X-ray fluorescence (XRF) and X-ray diffraction (XRD). Additionally, two coal samples were subjected to rock eval pyrolysis. A total of twenty groundwater samples were collected and analyzed using atomic absorption spectroscopy (AAS). Measurement of groundwater physical parameters was also conducted during the field mapping. Field results indicated that the area primarily consists of shale interbedded with coal seams, ferruginous sandstone, ferruginous siltstone, and minor occurrences of mudstone. Based on the XRD and XRF results, graphite is the dominant mineral in coal while SO3 and SiO2 represent the dominant major oxides. The coal samples in the area are immature for oil generation based on their maximum pyrolysis temperature (Tmax). In the groundwater of the area, the order of parameters is pH > Total Dissolved Solids (TDS) > Temperature (T) > Electrical Conductivity (EC), while the concentration of heavy metals follows this order: Fe > Zn > Cu > Co > Cd > Mn > Cr > Ni > Pb > Hg. The assessment of heavy metal pollution indicated that the groundwater in the area is significantly polluted, which poses serious health risks.
Copyright (c) 2025 S. Iyakwari, S. Aliyu, F. O. Balogun, I. Y. Anzaku, N. Musa https://creativecommons.org/licenses/by/4.0
Green synthesis of Cu, Fe, and Cu/Fe nanomaterials using Khaya senegalensis and investigation of their photocatalytic reduction of 4-nitrophenol and antibacterial properties
The persistent environmental and health threats posed by nitroaromatic pollutants particularly 4-nitrophenol (4-NP), and the rise of multidrug-resistant bacterial pathogens demand sustainable, efficient, and eco-friendly remediation strategies. To address these challenges, this study aimed to develop monometallic (CuO and Fe₂O₃) and bimetallic (CuO/Fe₂O₃) nanoparticles via a green, one-pot synthesis using Khaya senegalensis leaf extract, leveraging its rich phytochemical content (notably flavonoids and tannins) as natural reducing and capping agents. The synthesized nanoparticles were characterized using UV–Vis spectroscopy, XRD, FTIR, and HAADF STEM, confirming the formation of spherical, monodispersed particles (20–50 nm) with distinct surface plasmon resonance peaks at 224 nm (CuO), 290 nm (Fe₂O₃), and a redshifted peak at 295 nm (CuO/Fe₂O₃). The XRD analysis revealed the crystallite sizes of CuO, Fe2O3 and CuO/Fe₂O₃ NPs to be 26, 22 and 28 nm, respectively. Bimetallic CuO/Fe₂O₃ nanocomposite demonstrated exceptional photocatalytic efficiency, achieving complete reduction of 4-NP to 4-aminophenol in just 2 minutes, over 50% faster than monometallic counterparts. Furthermore, it exhibited potent antibacterial activity, with minimum inhibitory concentrations (MIC) of 8 µg/mL against Escherichia coli and Bacillus subtilis, and 40 µg/mL against Pseudomonas aeruginosa, alongside minimum bactericidal concentrations (MBC) of 16 µg/mL and 80 µg/mL, respectively. These results underscore the multifunctional potential of K. senegalensis-derived CuO/Fe₂O₃ nanocomposites as green, high-performance agents for simultaneous environmental detoxification and antimicrobial applications. We recommend their further development for scalable water treatment and as alternatives to conventional antibiotics, particularly against resilient Gram-negative pathogens.
Copyright (c) 2025 J. I. Kuv, J. A. Attah, I Hassan, O. G. Uyi, A. D. Terna, M. E. Binin, A. H. Labulo, A. A. Idzi, M. Isah, S. M. Idris, M. A. Alabi, K. A. Ojedoja, A. Y. Danas, F. O. Ogungbemiro, F. H. Omotehinwa, J. O. Anebi https://creativecommons.org/licenses/by/4.0
Temporal investigation of effects of wildfires and aerosols on air pollution changes in Nigeria: a multivariate modelling approach
Understanding the factors that influence Aerosol Optical Depth (AOD) is essential for addressing air pollution and effectively managing air quality. This study examines the impacts of dust aerosol (DU), sea salt (SS), black carbon (BC), and burned areas (BA) on AOD across different Köppen climate zones in Nigeria from 2001 to 2019. Results from a multivariate regression analysis revealed that DU consistently had a strong and significant impact on AOD across all zones (coefficients: between 0.00106 and 0.00779, p < 0.001), indicating the influence of dust from the Sahara Desert and the Bodélé Depression. BC and SS increased AOD in southern and coastal zones, although BC had a negative impact in Aw climate zones. The influence of BC was less consistent, indicating its varied sources, including gas flaring and urban emissions. BA showed mixed effects on AOD across different climate zones. In some zones, BA had a positive but often insignificant impact on AOD, while in others, it exhibited negligible or negative coefficients. This suggests that although biomass burning contributes to aerosol levels, its direct effect on AOD may be mitigated by factors such as precipitation and aerosol interactions during the burning season. SS generally had a significant positive relationship with AOD, especially in coastal and Csb zones. Peaks in SS levels in the mid-2000s and mid-2010s correlated with higher AOD, emphasizing the maritime influence on aerosol levels in these regions. However, the impact of SS on AOD was less pronounced in the BWh zone, reflecting regional differences in aerosol composition and sources. These findings demonstrate the major impact of dust aerosols and the complex contributions of other sources, offering insights for climate-sensitive air quality management in Nigeria.
Copyright (c) 2025 Tertsea Igbawua https://creativecommons.org/licenses/by/4.0
Time series modelling of inflation and consumer price index trends in Nigeria
Inflation is a long-term, steady rise in the average price of goods and services. Its volatility weakens purchasing power, distorts income distribution, discourages investment and savings, and threatens macroeconomic stability. This study examined the relationship between the Consumer Price Index (CPI) and Financial Institutions’ Credit Deposit (FICD) ratio using regression, ARIMA, and GARCH models to forecast inflation. Data from the National Bureau of Statistics (2015-2024) and Central Bank of Nigeria were analyzed. Descriptive results showed high variability in inflation (mean = 1.29%, SD = 0.59, skewness = 1.05) and moderate variability in FICD (mean = 8.69, SD = 2.62, skewness = 0.36). Regression results indicated a negative but insignificant effect of FICD on inflation (β = −0.0177, p = 0.3977). The Augmented Dickey-Fuller test confirmed stationarity (p = 0.01). ARIMA(2,2,1) captured inflation persistence, while GARCH(2,3) identified mild volatility. Johansen cointegration tests revealed no long-run equilibrium among CPI, inflation, and FICD. Findings suggest that structural and cost-push factors drive inflation, requiring stronger monetary transmission and efficient credit allocation for price stability.
Copyright (c) 2025 Benson Ade Eniola Afere, Yahaya Baba Usman, Ekele Alih, Ekele Vincent Aguda, Latifat Enesi https://creativecommons.org/licenses/by/4.0
Diurnal solar energy and temperature changes over Nigeria using satellite data
This study investigated the diurnal variability of global horizontal irradiance (GHI) and surface temperature across Nigeria’s major climatic zones. Daily all-sky surface shortwave downward irradiance and 2-m air temperature data spanning 2001–2023 were obtained from the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). The daily datasets were aggregated into monthly means and analyzed across four climatological seasons. Daily variability was quantified using standard deviation, while long-term trends were assessed using simple linear regression, with statistical significance evaluated at p < 0.05. The relationship between GHI and surface temperature was examined using Pearson’s correlation coefficient. Results show that daily GHI variability was highest in the southern tropical monsoon and tropical rainforest zones, whereas daily temperature variability was greatest in the northern warm desert and warm semi-arid regions. Monthly analysis revealed that GHI peaked in April over northern zones but reached maximum values in February across other regions. Surface temperature was highest between March and May, but lowest in August for the tropical monsoon, tropical rainforest, and Mediterranean climates; January for the warm semi-arid and warm desert climates; and December for the tropical savanna climate. Correlation analysis indicated very weak to moderate relationships between GHI and surface temperature. Regression results revealed a very weak to weak positive relationship, with significant differences across all locations examined ($p < 0.0001$). Long-term trends showed a slight increase in GHI across all climatic zones, while surface temperature increased marginally in most regions except the northern warm semi-arid and warm desert zones.
Copyright (c) 2025 Louis Tersoo Abiem, David Inalegwu https://creativecommons.org/licenses/by/4.0
A methodological framework for evaluating ADASYN and borderline-SMOTE oversampling techniques in imbalanced epidemiological data: a proof-of-concept study for lassa fever detection
Class imbalance in epidemiological datasets poses a fundamental challenge to developing accurate predictive models, particularly for rare but critical outcomes. This proof-of-concept study presents a methodological framework for evaluating advanced oversampling techniques—Adaptive Synthetic (ADASYN) sampling and Borderline-SMOTE—in the context of imbalanced medical classification tasks. Using a controlled synthetic dataset that mimics the class distribution characteristics of Lassa Fever epidemiological data, we systematically compare these techniques’ effectiveness in preparing imbalanced datasets for machine learning. Our methodology emphasizes rigorous experimental design, including strict train-test separation before oversampling application, comprehensive ablation studies, and transparent statistical analysis. Individual machine learning models (Random Forest, XGBoost, LightGBM, and Neural Networks) and a weighted ensemble model were evaluated using appropriate metrics for imbalanced classification. This study employs synthetic data to establish a controlled experimental environment for algorithmic comparison. While results demonstrate the technical capabilities of ADASYN and Borderline-SMOTE under ideal conditions, these performance metrics should not be interpreted as clinically validated or representative of real-world performance. The primary contribution is a reusable methodological framework and comparative analysis of oversampling strategies, which requires validation on authentic clinical datasets before any deployment considerations. This work provides computational epidemiologists with evidence-based guidance for technique selection while clearly delineating the boundary between methodological demonstration and clinical applicability.
Copyright (c) 2026 Osowomuabe Njama-Abang, Denis U. Ashishie, Paul T. Bukie, Ahena I. Bassey https://creativecommons.org/licenses/by/4.0
Integrative approaches of multivariate convolutional neural networks with a dynamic conditional correlation model for forecasting stock market volatility
Machine learning and deep learning approaches have been extensively studied across various fields, significantly contributing to the successful resolution of numerous problems. Examples include image classification, computer vision applications, and natural language processing. Owing to their powerful capabilities, they have not only made significant contributions in the fields above but also increasingly been applied to time series analysis, particularly in analyzing financial time series to forecast the volatility and correlation among stock markets. Researchers have proposed hybrid models that combine machine learning and deep learning approaches with statistical and econometric models to enhance time series analysis and forecasting. In this work, the dynamic conditional correlation (DCC) model, as an econometric model, and multivariate convolutional neural networks (MCNNs), as deep learning models, are employed to construct hybrid models to enhance the forecasts of dynamic volatility and correlation among stock markets. Two methods are presented for combining the DCC model with the convolutional neural network (CNN) model. The results show that the best way to combine the DCC model and the CNN model is to use the outputs of the CNN model as inputs to the DCC model. The hybrid DCC-MCNN model demonstrates stronger performance across both in-sample and out-of-sample accuracy measures, such as the root mean square error (RMSE) and mean absolute error (MAE). Specifically, the hybrid DCC-MCNN model emerges as the top performer among the evaluated models, surpassing both the single model approaches and the hybrid MCNN-DCC model in forecasting time-varying volatility and correlation among stock markets.
Copyright (c) 2025 Anas Eisa Abdelkreem Mohammed, Henry Mwambi, Bernard Omolo https://creativecommons.org/licenses/by/4.0
Evaluation of antioxidant capacity and nutritional components of Five locally consumed fruits in Eastern Nigeria
The nutraceutical richness of locally cultivated fruits in Eastern Nigeria remains poorly documented, limiting guidance for dietary management of oxidative stress-related disorders. This study quantified the vitamin C content, total flavonoids, total antioxidants, and reducing properties of five commonly consumed fruits: cashew (Anacardium occidentale), star apple (Chrysophyllum albidum), pawpaw (Carica papaya), garden egg (Solanum melongena), and watermelon (Citrullus lanatus). Spectroscopic methods were employed to assay total antioxidants, flavonoids, and reducing properties using gallic acid, rutin, and trichloroacetic acid as standards, respectively, while vitamin C was determined by titrimetric analysis using 2,6-dichlorophenol indophenol. Results (mean ± SD, mg/100 g) showed that pawpaw had the highest vitamin C (98.50 ± 0.01), total flavonoids (103.11 ± 0.01), total antioxidants (109.17 ± 0.04), and reducing properties (63.23 ± 0.07), followed by star apple, watermelon, garden egg, and cashew. A strong positive correlation (r = 0.87) was observed between total flavonoid content and reducing power, indicating that flavonoids significantly contribute to antioxidant activity. These findings provide evidence supporting increased consumption of pawpaw, star apple, and watermelon to mitigate oxidative stress, compensate for nutrient deficiencies, and promote public health in Eastern Nigeria.
Copyright (c) 2026 C. C. Ogbuanu, O. C. Ike, C. N. Eze, C. C. Ugwoke, E. Agboeze, C. I. Diara https://creativecommons.org/licenses/by/4.0
Baobab shell powder as a bio-filler in HDPE composites: mechanical, and physical properties
This study develops and characterizes high-density polyethylene (HDPE) composites reinforced with 0–20 wt% baobab shell powder (BSP) as a sustainable bio-filler. Composites were fabricated via compression molding and assessed for mechanical, physical, and chemical properties. XRF analysis revealed high metal oxide content in BSP—K2O (29.32%), MgO (16.27%), CaO (12.42%), Al2O3 (12.12%), and SiO2 (11.73%)—indicating multifunctional potential. FTIR confirmed lignocellulosic functional groups (O–H, C–H, C=O, C–O). Tensile strength peaked at 10.996 N/mm2 (20 wt%, +6.14% vs. neat HDPE); yield strength peaked at 26.35 N/mm2 (15 wt%), suggesting improved matrix interaction. Flexural strength dropped to 2.24 N/mm2 at 10 wt% but stabilized thereafter; elongation at break fell from 37.74% to 7.87%, indicating increased rigidity. Impact energy dropped from 4.0 J to 1.63 J, while hardness and density rose from 11.36 to 28.66 N/m2 and 0.87 to 0.93 g/cm3, respectively. Water absorption remained low until day 6, then rose sharply to ~20% (20 wt%) by day 8 due to interfacial degradation. Composites with ≤10 wt% BSP showed better moisture resistance. The results demonstrated that BSP enhances stiffness, hardness, and tensile strength but reduces ductility and impact resistance, making it suitable for non-structural applications in automotive, packaging, and sustainable construction.
Copyright (c) 2025 Daniel S. Sudi, Haruna P. Wante, Ndubuisi Mbada, Isah Baba, Bamidele Boluwatife, Solomon Adeleke https://creativecommons.org/licenses/by/4.0
Synthesis and characterization of biodiesel from black date (Canarium schweinfurtii) seed kernel oil in the presence of green-synthesized Cu-Mn/TiO2 nanocatalyst using central composite design approach
One of the key challenges in scaling up biodiesel production is attaining the maximum yield while ensuring compliance with established quality standards. In this work, the potential of black date seed kernel oil as a feedstock for biodiesel synthesis was investigated using a green-synthesized Cu-Mn/TiO₂ nanocatalyst through a transesterification process. The oil was characterized using standard analytical procedures, while the catalyst was analyzed through FTIR, XRD, BET, and SEM techniques. Process optimization was carried out using Central Composite Design (CCD), considering methanol-to-oil molar ratio, catalyst loading, reaction temperature, and reaction time as the key variables. The optimum biodiesel yield of 95.07% was achieved at a methanol-to-oil molar ratio of 12:1, catalyst loading of 2 wt%, reaction temperature of 65 °C, and reaction time of 75 minutes. The quadratic model developed from the experimental design was statistically significant (p < 0.0001), confirming its predictive reliability. The high coefficient of determination (R² = 0.9904), along with adjusted R² (0.9815) and predicted R² (0.9449) values obtained from ANOVA and fit statistics, demonstrated a strong agreement between the experimental and predicted outcomes. Among the studied variables, all exhibited significant effects on biodiesel yield except the methanol-to-oil molar ratio (p > 0.05). Physicochemical analysis of the oil confirmed its suitability as a feedstock for biodiesel production. These findings highlight black date seed kernel oil as a promising non-edible and sustainable raw material for biodiesel synthesis, particularly when optimized through statistical modeling and enhanced with an efficient nanocatalyst, contributing to the advancement of renewable and eco-friendly fuel alternatives.
Copyright (c) 2026 J. Mohammed; M. O. Aremu; A. Usman https://creativecommons.org/licenses/by/4.0
Depth estimation of the residualfield of Patigi Area, Nigeria , using source parameter imaging and spectral depth analysis
Aeromagnetic depth estimation was carried out over a one-half-degree sheet in the southwestern Bida Basin, Northcentral Nigeria. The area, bounded by latitudes 8◦30′–9◦0′ N and longitudes 5◦30′–6◦0′ E, is underlain by Campanian–Maastrichtian sandstones and basement rocks of granite-gneiss and migmatites. Potential field methods were applied to investigate subsurface sedimentary thickness. Regional–residual separation was achieved using polynomial fitting, and the residual field was analyzed using source parameter imaging (SPI) and power spectrum techniques for depth estimation around Pategi and environs. The residual map was subdivided into nine spectral sections to examine depth variations from magnetic sources. SPI results revealed depths ranging from 53.4m to 2766.5m, with a mean of 284.3m. Spectral analysis indicated shallow magnetic sources between 0.730km to 1.359km (average 1.100km) and deeper sources between 1.927km to 2.840km (average 2.333km). The maximum sedimentary thickness of 2.840km was found in the north-central area, including Pategi, Baradogi, Wando, and Edogi. These results indicate significant hydrocarbon potential. Integrated geological and geophysical studies are recommended to validate reservoir structures and assess viability for exploration.
Copyright (c) 2025 S. A. Salaudeen, T. A. Adagunodo, A. O. Busari, K. O. Suleman, L. A. Sunmonu https://creativecommons.org/licenses/by/4.0
Implicit one-step optimized fourth-derivative hybrid block method for directly solving general third-order IVPs
This paper introduces a single-step optimized fourth-derivative block hybrid method specifically designed to solve general third-order initial value problems directly. By incorporating advanced optimization techniques, the method significantly improves accuracy and computational efficiency. Extensive analysis confirms that the method exhibits zero-stability, consistency, A-stability, and convergence properties. Numerical experiments conducted in this study reveal that the proposed method surpasses existing approaches in accuracy, establishing it as a significant advancement in the numerical solution of higher-order initial value problems.
Copyright (c) 2025 S. D. Yakubu, P. Sibanda https://creativecommons.org/licenses/by/4.0
Numerical method for simulation of quadratic Riccati differential equations
Differential equations widely applied in various fields of engineering and mathematical studies, particularly in control theory, optimal control, and stochastic realization. Numerous methods have been proposed for their solution. Although QRDEs pose significant analytical challenges, several approaches such as the Variational Iteration Method, Differential Transform Method, Runge-Kutta methods, and Non-Standard Finite Difference Methods have been developed to address them. In this paper, we present a computational method for solving QRDEs by employing the collocation technique in combination with a power series approximation. Fundamental properties of the method, including order, consistency, zero-stability, and convergence, are analyzed. Numerical simulations demonstrate that the proposed method provides accurate and stable results, showing strong agreement with existing methods when applied to different QRDE models. Furthermore, the findings highlight the efficiency and applicability of the approach in solving nonlinear equations of considerable complexity.
Copyright (c) 2026 Adam Ajimoti Ishaq, Folashade Mistura Jimoh, Kazeem Issa https://creativecommons.org/licenses/by/4.0
Pesticide handling practices and residue levels in yam and cassava across Nasarawa South, Nigeria
The indiscriminate use of pesticides in agriculture poses serious health and environmental risks, especially in regions with weak regulatory enforcement. This study examines pesticide handling practices among farmers and assesses pesticide residues in yam (Dioscorea spp.) and cassava (Manihot esculenta) across Nasarawa South, Nigeria. Data were collected from 430 farmers using structured questionnaires and analysed with descriptive statistics and Chi-square tests. Additionally, yam and cassava samples were analyzed for pesticide residues using QuEChERS extraction and gas chromatography-mass spectrometry (GC-MS). Residue recovery ranged from 70% to 130%, confirming method reliability. Findings indicated poor adherence to safe storage and disposal practices, with household trash disposal being the most common method, posing environmental risks. Regional disparities were noted: Lafia exhibited better compliance in pesticide storage and disposal, while Awe lagged. Residue analysis revealed organochlorine, organophosphate, carbamate, and pyrethroid pesticides in food samples. Although no pesticide residues were detected in yam from Awe and cassava from Lafia, banned pesticides like Benzene Hexa Chloride (BHC) (0.000066 mg/kg) and Dichloro Diphenyl Trichloro Ethane (DDT) (0.00013 mg/kg) were found at varying concentrations in the cassava sample. However, mean residue levels remained below maximum residue limits. These findings underscore the urgent need for improved farmer education, stricter regulations, and sustainable pest management to mitigate health risks associated with pesticide exposure.
Copyright (c) 2025 F. H. Omotehinwa, M. O. Aremu, J. C. Onwuka https://creativecommons.org/licenses/by/4.0
Simulation of optical fiber power amplification via higher-order soliton generation using the extended nonlinear Schrödinger equation
This study seeks to simulate the amplification of the optical fiber power by the generation of higher order solitons. This was achieved by solving an extended form of the nonlinear Schrödinger equation (NLSE) using the split step Fourier method (SSFM) with Gaussian functionals having multiple peaks as initial conditions to simulate the generation of higher order solitons in the optical fiber. Some key fiber parameters such as the coefficients of loss α, group velocity dispersion β and nonlinearity γ were varied and the respective effects on the optical fiber and soliton power were then observed using spatial plots, 3-D contour plots and image color maps. Results obtained showed that in all soliton orders, higher order solitons were created when β was increased from 0.05 to 0.1 fs/nm.km. This shows a broadening of the soliton to create higher order solitons when dispersion is managed within that range which results in a boost in the peak soliton power amplified from 4.5309 W to 14.9508 W and then to 15.0828W as the soliton order was increased from 1st – 2nd – 3rd order respectively using Gaussian functionals. The extra power gained is as a result of the fact that a newly created soliton takes its energy from the radiation present in the dispersed soliton even though the optical power attenuates. It was also observed that, increasing the coefficients α, β and γ from 0.1 – 1.0 results in a continuous attenuation of the optical fiber power leading to the propagation of radiation (noisy signal) in the optical fiber which scatters and exponentially decays after a short distance along the length of the optical fiber.
Copyright (c) 2025 E. V. Tikyaa, E. S. Nyiyongo , M. B. Ochang, S. D. Igba https://creativecommons.org/licenses/by/4.0
Impacts of high-dose gamma irradiation on the mechanical, structural and thermal properties of doum fiber reinforced High-Density Polyethylene (HDPe)
High-density polyethylene (HDPe) has widespread application in pharmaceutical packaging and medical devices since it is biocompatible and stable. Research on the impact of high-dose gamma irradiation on structural, thermal, and mechanical properties of Doum fiber-reinforced HDPe composites at 0 wt.%, 20wt.% and 40 wt.% is required due to the composite nature of the material. The research involves synthesis and analysis of gamma irradiation impacts properties of biomass-reinforced HDPe. The samples were irradiated at 0-150 Gy, and then subjected to mechanical testing, thermogravimetric analysis (TGA), and Fourier transforms infrared spectroscopy (FTIR). The findings illustrated that gamma irradiation at 100Gy greatly improved tensile strength, hardness, and impact strength because of enhanced fiber-matrix interaction and crosslinking effects. Tensile strength was improved from 16.93 MPa (0 Gy) to 26.80 MPa, hardness was improved from 53.83 Kgf/mm2 to 70.43 Kgf/mm2, and impact strength was optimum at 1.0187 J/mm. Mechanical properties at 150 Gy were compromised because of degradation of the polymer via chain scission. TGA analysis indicated improved thermal stability at 100Gy, as manifested by the increased onset decomposition temperature, while at 150 Gy, degradation and oxidative effects led to decreased stability. FTIR analysis indicated structural change, in accordance with higher crosslinking at 100Gy and degradation at 150Gy. The results confirm that the modest irradiation (100 Gy) is accountable for optimum mechanical and thermal properties and higher irradiations (150 Gy) cause degradation. It also optimizes the performance of Doum fiber-reinforced HDPe composites to a maximum at 40 wt.% fiber content, and to meet high-performance structural applications in medical, pharmaceutical, and aerospace applications.
Copyright (c) 2025 Mngusuur Scholastica Iorshase, Abubakar Sadiq Aliyu, Lubem James Utume, Emmanuel Ochoyo Adamu, Muhammad Sani, Wasiu Salami Oyeyemi https://creativecommons.org/licenses/by/4.0
One-pot synthesis and antimicrobial efficacy of polymeric schiff base metal complexes derived from o-phenylenediamine and terephthalaldehyde
The antimicrobial efficacies of Schiff base Polymeric Ligand (SBPL) and Polymeric Schiff base Metal Complexes (PSBMCs) have been explored. Schiff base Polymeric Ligand (SBPL) and Polymeric Schiff base Metal Complexes (PSBMCs) derived from polycondensation of terephthalaldehyde, o-phenylenediamine and metal ions (M–L, Al3+ , Cu2+ , Zn2+ , Fe3+ ) were synthesized in ethanol at room temperature through one-pot synthesis. The PSBL and PSBMCs synthesized were characterized using spectroscopic techniques: FT-IR (Fourier Transform Infrared) and Uv-vis (Ultraviolet visible). The spectroscopic data revealed that the azomethine functional group is involved in the complexation of metal ions with PSBL forming PSBMCs and geometry of PSBMCs is suggestive to be octahedral. The antibacterial and antifungi efficacies of SBPL and PSBMCs were examined on Staphylococcus aureus, Escherichia coli, Aspergillus niger and Aspergillus flavus employing the agar well diffusion method, Penicillin and omeprazole served as standard drug for antibacterial and antifungal activity respectively. The PBSMCs displayed promising activities in comparison with starting compound (SBPL). The Cu-L was found to be most potent antimicrobial agent against all tested bacterial and fungal.
Copyright (c) 2025 Wahab Adesina Osunniran, Rukayat Abdulmalik, Sulaiman Habib Adana, Yusuf Oloruntoyin Ayipo https://creativecommons.org/licenses/by/4.0
The impact of early detection and intervention on congenital heart defects in infants and children: a systematic review
Congenital heart disease (CHD) is the leading cause of mortality among congenital disabilities, affecting approximately 1% of live births. This review analyzes the critical role of early detection and intervention strategies in optimizing care for pediatric patients with CHD, utilizing a comprehensive literature search across scientific databases such as Google Scholar, PubMed,Web of Science, Springer Link, and Science Direct for articles published between 2014 and 2024. A total of 10 studies were included, representing diverse populations and healthcare systems, which provided a global perspective on CHD prevalence and diagnosis in infants and children. These studies, comprising case-control studies from Mount Sinai Medical Center, cohort studies from regions such as Denmark, Paris, Saudi Arabia, and Australia, and cross-sectional studies from China and France/Belgium, highlight the clinical challenges associated with CHD and the long-term developmental and psycho-social implications for affected children. The findings underscore the necessity of continued efforts in maternal health, early screening, and holistic care approaches to improve outcomes for children with CHD worldwide. Ultimately, the review emphasizes that early detection is crucial for enhancing survival rates and quality of life and advocates for integrating medical and psycho-social support to address the multifaceted needs of these children.
Copyright (c) 2025 R. T. Akah, J. E. Enoh, B. Nkeh-Chungag https://creativecommons.org/licenses/by/4.0
Zero-inflated and hurdle models with an application to the number of involved axillary lymph nodes in patients with breast cancer in Zimbabwe: A bootstrap resampling validation approach
Breast cancer, increasingly prevalent in Zimbabwe, underscores the need to understand the involved axillary nodal status in diagnosed patients for assessment of disease severity and its potential progression. This study was undertaken to investigate factors influencing the number of axillary lymph nodes in breast cancer patients by identifying the best fitting count regression model, validated through bootstrap resampling. A retrospective analysis using hospital-based data for patients diagnosed with breast cancer at one of the two major referral hospitals in Zimbabwe was applied. We evaluated and compared count regression models – Poisson with Negative Binomial (NB), Zero-Inflated Negative Binomial (ZINB), Zero-Inflated Poisson (ZIP), Hurdle Negative Binomial (HNB) and Hurdle Poisson (HP) which are efficient in handling over-dispersed count data to investigate the various risk factors associated with involved axillary lymph nodes. Covariates included age, tumor size, tumor grade, estrogen receptor status, progesterone receptor status and HER2 status. Model diagnostics were assessed using Aikake Information Criterion and Bayesian Information Criterion. The ZINB and HNB models outperformed other models, with the HNB model demonstrating consistency across bootstrap-resampled datasets. Bootstrap resampling validated the reliability of model estimates, addressing biases caused by small sample sizes. Age was significantly associated with the zero-inflation component of the HNB model. This study highlights the importance of selecting appropriate count regression models for analyzing medical data and demonstrates the utility of integrating bootstrap resampling to ensure robust statistical inference. The findings provide actionable insights for therapy planning and resource allocation.
Copyright (c) 2025 Bester Saruchera, Oliver Bodhlyera, Henry Mwambi, Ntokozo Ndlovu https://creativecommons.org/licenses/by/4.0
A novel one-step optimized hybrid block method for solving general second-order ordinary differential equations
This study introduces a novel one-step hybrid block method for solving both stiff and non-stiff second-order ordinary differential equations (ODEs). The method incorporates optimization techniques and higher derivative functions, addressing the computational challenges of stiff ODEs while ensuring key numerical properties such as zero-stability, A-stability, and convergence. Numerical experiments demonstrate the method’s effectiveness across various problems, including classical examples from heat conduction, electrical circuits, and the Van der Pol oscillator. The results reveal that the proposed method achieves superior accuracy and efficiency, significantly outperforming existing methods in the literature. These findings underscore the potential of this approach as a robust and versatile tool for solving a wide variety of practical ODEs in engineering and applied sciences.
Fourth-derivative, Optimized hybrid block method, Stiff ODEs