JOURNAL OF BASICS AND APPLIED SCIENCES RESEARCH
The aim of Journal of Basics and Applied Sciences Research (JOBASR) is to be one of the foremost sources of scholarly articles and research papers through the support of research publication at reasonable or at no cost in long run. This is to make research publication hassle free for financially constrained researchers and scholars. The published article will always be an open access, free under Creative Commons License 4.0 and archived for future generations. The Objectives of Journal of Basics and Applied Sciences Research (JOBASR) are: 1. To supply publishing platform to scholars and researchers from different science and technology-related fields 2. To grant research and scholarly articles available free of cost to all users without any subscription or login ID. 3. To give scholars a chance to be part of the scholars community who assists and helps others in publication and review. 4. To create research publication hassle free to ensure sharing of knowledge in due time. Publication Scheduling This journal publishes 6 issues in 1 volume a year.
Faculty of Physical Sciences, Federal University Dutsin-Ma
Kinetic and thermodynamic study of saponification of Neem oil (Azadirachta indica) with Sodium Hydroxide using Goat milk as a bioactive additive
The growing demand for natural therapeutic personal care products has intensified interest in plant-based oils and dairy additives in soap manufacturing. This study investigates the kinetics and thermodynamics of soap production using neem oil (Azadirachta indica) and sodium hydroxide with goat milk as a functional additive at elevated temperatures (303 K, 313 K, and 323 K). The saponification reaction was monitored by measuring the residual oil concentration at time intervals (10-60 seconds). Kinetic analysis revealed that the reaction follows first-order kinetics across all temperatures, with correlation coefficients (R²) of 0.995, 0.997, and 0.956 at 303 K, 313 K, and 323 K, respectively. Rate constants (k) ranged from 0.012 s⁻¹ to 0.014 s⁻¹. The activation energy (Eₐ) was determined to be 6.41 kJ·mol⁻¹ using the Arrhenius equation. Thermodynamic parameters showed positive enthalpy of activation ( ) ranging from 3.89 to 3.72 kJ·mol⁻¹, indicating an endothermic process. The negative entropy of activation ( =-261 J·mol⁻¹·K⁻¹) suggests a more ordered transition state. Gibbs free energy of activation ( ) increased with temperature from 82.94 to 88.16 kJ·mol⁻¹, indicating reduced reaction spontaneity at higher temperatures. These findings demonstrate that goat milk serves as an effective reaction facilitator by lowering the activation energy barrier compared to conventional saponification processes, while the temperature-dependent kinetic behavior provides valuable insights for optimizing industrial soap production.
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc/4.0
Crude oil biodegradation potential by mixed consortia of Pseudomonas Lurida and Bacillus Endophyticus
Crude oil contamination poses significant environmental challenges due to the persistence and toxicity of its hydrocarbon components. This study evaluated the biodegradation potential of a mixed bacterial consortium comprising Pseudomonas lurida (DSM 15835) and Bacillus endophyticus (DSM 13796) using gas chromatography–mass spectrometry (GC–MS). Sterile sediment microcosms amended with 10% Bonny light crude oil were inoculated with approximately 5 × 10⁷ CFU of the consortium and monitored over five weeks. GC–MS analysis revealed substantial degradation of hydrocarbons ranging from C7 to C44, with marked reductions in peak abundance and the emergence of intermediate metabolites such as octane derivatives and alcohols. The consortium demonstrated enhanced biodegradation efficiency compared to single strains, indicating synergistic interactions. Growth curve analysis showed rapid exponential growth followed by an early stationary phase, suggesting efficient utilization of readily degradable hydrocarbons. The findings highlight the effectiveness of mixed microbial consortia in the bioremediation of petroleum-contaminated environments.
On common fixed point for finite family on maps in a relational metric space
This research establishes new coincidence and common fixed-point theorems for finite families of self-mappings in complete metric spaces endowed with binary relation. Extending the relational framework initiated by Samet and Turinici and later developed by Shatanawi and Sintunavarat, we introduced g-comparative mappings and prove existence and uniqueness theorems for coincidence and common fixed points of families and single map g. The main results are obtained under a generalized contractive inequality where is comparison function and is auxiliary metric expression. By assuming relational regularity, weak compatibility, and the existence of an admissible initial point, we construct a modular-iterative sequence and prove its convergence to unique common fixed point. A numerical example is given to describe how the scheme works.
An enhanced convolutional neural network model for brain tumor classification using transfer learning and data augmentation
Brain tumors constitute a significant global health challenge, and accurate, timely diagnosis is critical for effective treatment planning. Manual interpretation of magnetic resonance imaging (MRI) remains time-consuming, error-prone and subject to inter-observer variability. The objective of this study was to develop and evaluate a fine-tuned VGG16 convolutional neural network (CNN), combined with a dual online and offline data augmentation pipeline, for the three-class classification of brain tumors from T1 contrast-enhanced MRI under resource-constrained computing conditions. The study used 3,064 T1 contrast-enhanced MRI slices spanning three tumor classes, namely glioma (1,426 slices), meningioma (708 slices) and pituitary tumor (930 slices), drawn from the publicly available Masoud Nickparvar Brain Tumor MRI Dataset on Kaggle. Images were normalised using min-max scaling, denoised using a Gaussian filter and resized to 224 x 224 pixels. The cohort was partitioned by stratified sampling into 2,145 training (70%), 273 validation (9%) and 646 test (21%) images. Six architectures were benchmarked, comprising a custom CNN, VGG16, VGG19, Xception, ResNet50 and DenseNet121. The best-performing backbone was then enhanced through layer freezing and fine-tuning, and finally through augmentation using random horizontal flipping applied online and histogram equalisation applied offline. The novelty of the study lies not in a new network topology but in the empirical demonstration that a selectively fine-tuned VGG16 backbone, coupled with a reproducible dual augmentation protocol, attains competitive diagnostic performance on central processing unit hardware alone. On the held-out test set, the fine-tuned VGG16 with augmentation achieved an accuracy of 98.45%, a macro-averaged sensitivity of 98.26%, a macro-averaged specificity of 99.19% and a macro-averaged precision of 98.37%. Class-wise areas under the receiver operating characteristic curve were 0.998, 0.996 and 0.999 for glioma, meningioma and pituitary tumor respectively. The custom CNN attained 95.40% accuracy and DenseNet121 attained 93.18%. These findings indicate that transfer learning based CNN models with strategic augmentation can reach diagnostically useful performance without specialised graphics hardware, offering a practical decision-support aid for radiological workflows in low-resource settings. External validation on independent clinical cohorts is required before deployment.
Invitro anticancer effect of D. Mespiliformis stem back chloroform extract against breast cancer cell lines MCF-7
Breast cancer is among the most prevalent cancers in Nigeria constituting aipproximately 27% of all new cases and representing a significant and growing public health concern. The impact of cancers continues to grow due to several factors, leading to an estimated mortality of up to 10 million annually. Medicinal plants have been utilized widely for treatment of diseases including cancer. This study evaluated the phytochemical analysis and cytotoxic effects of D. mespiliformis stem bark extract against human breast adenocarcinoma cells (MCF7). The extract was prepared using D. mespiliformis stem bark and chloroform as a solvent. The phytochemical constituents were identified using qualitative method. The anticancer effect was determined using cell titre Glo pro mega assay while total intracellular glutathione (GSH) was measured to assess whether redox imbalance contributed to the observed cytotoxic response. The phytochemical screening of the extract reveals the presence of important compounds. At 48 hours, the extract exhibited a strong cytotoxic effect (IC₅₀ of 21.75 µg/mL) against MCF-7 cells while sparing MCF-10. Mechanistic assessment showed that the extract reduced intracellular GSH levels in MCF-7 cells, indicating that glutathione depletion and oxidative stress may be involved in the cytotoxic effect. The cytotoxicity of the extract might be attributed to the presence of important secondary metabolites. The findings from the present study highlights the chemopreventive potential of D. mespiliformis stem chloroform extract against MCF-7 breast Cancer cell. Further studies are required to isolate the active constituents, confirm the molecular mechanism and evaluate safety in broader experimental models.
Synthesis, characterization and antimicrobial studies of Schiff base derived from 2-Hydroxy Acetophenone and P-Anisidine and its metal (II) complexes
Microbial infections remain a major global health challenge due to the rapid emergence and spread of antimicrobial-resistant pathogens. The misuse and overuse of conventional antimicrobial agents have accelerated multidrug resistance, highlighting the need for new therapeutic candidates with improved biological activity. In this study, Mn(II), Co(II), and Ni(II) complexes of a Schiff base derived from p-anisidine and 2-hydroxyacetophenone were synthesized, characterized, and evaluated for antimicrobial activity. The ligand and its complexes were obtained in good yields and characterized using physicochemical and spectroscopic techniques. The ligand exhibited a melting point of 197°C, while the complexes decomposed at 242–263°C, indicating enhanced thermal stability upon complexation. Molar conductivity values (6.23 – 31.40 Ω-1cm2mol-1) confirmed their non-electrolytic nature. Magnetic susceptibility measurements and effective magnetic moments of 5.5, 4.3, and 3.2 BM for the Mn(II), Co(II), and Ni(II) complexes, respectively, suggested tetrahedral geometry and paramagnetic behavior. Electronic spectra showed characteristic π→π* (230 – 265 nm) and n→π* (305 – 335 nm) transitions, while FTIR spectra revealed a shift of the azomethine C=N band from 1607 cm-1 to 1585 – 1592 cm-1 and disappearance of the phenolic O–H band at 3387 cm-1, confirming coordination through azomethine nitrogen and phenolic oxygen atoms. Job’s method established a 1:2 metal-to-ligand ratio. The ligand and complexes exhibited concentration-dependent antimicrobial activity against Staphylococcus aureus, Escherichia coli, Salmonella typhi, Candida albicans, Tinea capitis, and Aspergillus flavus. The complexes showed enhanced activity relative to the free ligand but remained less active than the standard drugs amoxicillin and griseofulvin.
Implementing a community-based health education and mobile screening intervention to improve accessibility and uptake of breast and cervical cancer screening programs in Ebonyi State, Nigeria
Breast and cervical cancers continue to be major causes of cancer morbidity and mortality among women in developing nations such as Nigeria. Inadequate knowledge about screening, lack of access to screening programs, sociocultural impediments, and poor health system performance are some factors that lead to low participation rates especially in remote areas. This study evaluated community-based education and mobile screening interventions to improve breast and cervical cancer screening access among Ebonyi women. A quasi-experimental design of community-based studies with pre-test and post-test measurements was employed among women aged 21-65 years within selected communities in Ebonyi State. The health education and mobile screening services involved culturally sensitive educational talks, and clinical breast examination and visual inspection with acetic acid (VIA) test for cervical cancer screening, respectively. Statistical analysis was done using descriptive and inferential statistics with a significant level of p < 0.05. Awareness and utilization of breast and cervical cancer screening services were low at baseline. Awareness about breast and cervical cancer screening, as well as the perceived benefits of screening, was significantly enhanced post-intervention. Utilization of screening services increased greatly among those screened for breast cancer (from 18.0% to 61.0%) and those who underwent screening for cervical cancer (from 14.5% to 55.5%) (p < 0.001). Most respondents indicated that screening services became more accessible to them after the intervention. A community health education campaign coupled with mobile screening services has proven highly effective in improving access to, and utilization of, breast and cervical cancer screening services in Ebonyi State.
Comparative analysis of some physicochemical and biological parameters of well and borehole water samples obtained in Dustin-Ma Town, Katsina State, Nigeria
Water quality continues to pose a major challenge in many urban areas particularly in the developing countries, in which Dutsin-Ma town of Katsina State is not an exception. The aim of this study was to compare the physicochemical and biological characteristics of water obtained from three wells and three boreholes located in six different areas of Dutsin-Ma Local Government Area, Katsina State, Nigeria. The analysis was carried out using standard procedure recommended by the American Public Health Association (APHA). The physicochemical parameters analysed include the pH, electrical conductivity (EC), turbidity, total dissolved solids (TDS), Total suspended solids (TSS), total solids (TS), dissolved oxygen (DO), and temperature. Bacteriological examination of the water samples was assessed using the membrane filtration technique. Findings revealed that borehole water generally had better quality than well water, with most physicochemical parameters such as pH (6.90–7.40) and electrical conductivity (300–550 µS/cm) found to be within the permissible limits set by the WHO. In contrast, well water recorded higher turbidity levels (5.2–6.8 NTU) and significant microbial contamination, with E. coli counts reaching up to 18 CFU/100 mL, exceeding recommended standards. The Water Quality Index rated borehole water from “good” to “poor,” whereas well water ranged from “poor” to “unsuitable” for consumption. Overall, borehole water was found to be relatively safer for domestic use, although treatment is still necessary for both water sources before consumption.
Physicochemical and Biological parameters
Assessment of bacterial contamination of wells and boreholes in selected students’ hostels in Nnamdi Azikiwe University, Awka Anambra State, Nigeria
Access to safe drinking water is crucial for public health, yet many students rely on potentially contaminated wells and boreholes. which can lead to serious health issues. Understanding bacterial contamination levels is essential for effective interventions, ensuring students well-being. This study assesses the bacterial contamination of water in wells and boreholes at selected students hostels in Nnamdi Azikiwe University Awka, focusing on total microbial load, total coliform counts, and the prevalence of specific bacterial species. A total of sixteen water samples were analyzed, revealing microbial load counts ranging from 1.7x10² CFU/ml to 2.78x10³ CFU/ml in well water and 2.0x10² CFU/ml to 1.87x10³CFU/ml in borehole water. Mean microbial loads were notable higher in well water (890±317.478 CFU/ml) than in borehole water (525±197.935 CFU/ml), but no significant difference existed between the two sources (F=0.9518, P=0.3458). Both sources remained below the WHO safe limit for recreational water (2300 CFU/ml). Total coliform counts were within safety limit for borehole water (1.25±1.25 CFU/ml) but exceeded WHO safe limits of 126 CFU/ml for well water, with means of 318.75±223.178 CFU/ml. Biochemical identification revealed five bacterial species: Escherichia coli, Enterobacter aerogenes, Klebsiella pneumoniae, Citrobacter freundii, and Proteus mirabilis. Klebsiella pneumoniae was the most prevalent, found in 25% of samples, while Enterobacter aerogenes was least, detected in only 6.25% of samples. This study highlights the significant public health risks associated with bacterial contamination in university water sources, and underscores the need for regular monitoring and management efforts to ensure water quality and public health safety for hostel residents.
A multiset-theoretic framework for inventory optimization in Motor garage systems
Inventory system in motor garages involve repeated spare parts such as spark plugs, filters, and bolts, where multiplicity is essential for accurate representation. Classical set-theoretic model fails to capture repeated elements structurally, requiring external quantity variables. This paper develops a multiset-theoretic framework for motor garage inventory modelling, representing inventory as multiplicity functions over a finite universe of items. Algebraic operations, such as addition, difference, union, and intersection are formalized. Structural properties such as closure, commutativity, and stability are established. A dynamic inventory update model and demand-balancing optimization are proposed. Computational analysis demonstrates linear scalability. The framework provides a robust, computationally efficient method for small- and medium- scale inventory management.
Calibration technique for compensation of non-response in domains of study with probability proportional to size
In sample survey, it is expected that the information would be collected from all the selected units in the sample, but practically, it is generally not possible because of non-response. Some of the units may not respond or may not be contacted during the survey period. This work focuses on domain estimation of population mean with sub-sampling the non-respondents using probability proportional to size. In this study, we consider calibration technique as a method of correcting non-response in domains of study by minimizing the chi-square distance function between the weight of the main estimator and the calibrated weight subject to the formulated constraint on the auxiliary variable. The bias and mean square error (MSE) for the proposed estimators are derived. The use an auxiliary variable to estimate the population mean assuming that the non-response is observed only on the study variable is adopted. The proposed estimator and the existing estimators were compared empirically in the domains with small sampling units and two populations were considered in terms of the MSE and Percentage Relative Efficiency (PRE). Also, two cases were considered, where non-responses are uniform in the two strata at approximately (30%) and a case where the nonresponse rates are different with 20% and 40% in strata 1 and 2 respectively. The proposed estimator gained more efficiency than the existing estimators. However, the proposed estimator performed better in case II than in case I in some domains but the aggregate result suggests otherwise in both populations.
Modeling and forecasting monthly Hepatitis B virus infections in Benue State using prophet time series model
Hepatitis B Virus (HBV) infection remains a major public health challenge in Nigeria, particularly in Benue State, where endemic transmission persists despite ongoing prevention programmes. This retrospective time-series study aimed to model historical trends and forecast monthly HBV infections in Benue State using the Prophet forecasting time series model while evaluating the effects of selected public health interventions. Secondary surveillance data comprising 192 monthly laboratory-confirmed HBV records collected between January 2010 and December 2025 were obtained from the Benue State Epidemiological Unit, Makurdi. Rainy season, vaccination weeks, and public health awareness campaigns were incorporated as intervention variables because they represent environmental conditions and routine control measures expected to influence HBV transmission and case detection. Descriptive statistics, the Anderson-Darling normality test, Prophet decomposition, model diagnostics, and forecast evaluation were performed. The series had a mean monthly value of 61,544 confirmed cases, with significant non-normality (A² = 2.994; p < 0.005). The fitted Prophet model identified a significant upward trend (k = 2.8553, p < 0.001), significant seasonal patterns, increased infections during the rainy season (β = 8.5085, p = 0.0001), and significant reductions during vaccination weeks (β = −3.2244, p = 0.0077) and awareness campaigns (β = −1.1253, p = 0.0060). Model performance was satisfactory (R² = 0.8716; RMSE = 3.6625; MAPE = 6.35%), with diagnostic tests confirming model adequacy. Forecasts for January 2026-December 2027 indicated persistent endemic transmission with seasonal peaks during June-August, accompanied by 95% prediction intervals quantifying forecast uncertainty. The findings demonstrate that the Prophet model provides a reliable framework for HBV surveillance, early warning, and evidence-based public health planning in Benue State.
Metal-coordinated Aldazine Schiff Base: synthesis, spectroscopic characterisation, and evaluation of antimicrobial and antioxidant potentials of CU (II), ZN (II) and AG(I) complexes
Antimicrobial resistance poses a growing threat to global health as multidrug-resistant pathogens render many conventional drugs ineffective. This has created an urgent need for alternative therapeutic approaches. In response, an aldazine Schiff base and Cu(II), Zn(II) and Ag(I) complexes were synthesised, spectroscopically characterised and evaluated for antimicrobial and antioxidant potentials. The ligand, 3EHH, was prepared from hydrazine and 3-ethoxy-2-hydroxybenzaldehyde. The Cu(II) (Cu3E), Zn(II) (Zn3E), and Ag(I) (Ag23E) complexes were synthesised and characterised by magnetic moments, NMR, IR and UV/Vis spectra. The in vitro antimicrobial and antioxidant properties were investigated using agar well diffusion, DPPH radical scavenging and ferric reducing antioxidant power (FRAP) assays, respectively. Formation of 3EHH was confirmed by the bis-azomethine protons signal at 8.69 δ ppm and the absence of amino and carbonyl protons. Coordination via azine nitrogen and phenolate oxygen was supported by shifts in v(C=N) from 1622 to 1631–1616 cm–1 and v(C–O) from 1214 to 1210–1193 cm–1, loss of phenolic protons in Zn3E NMR spectrum, and changes in π→π* transitions, together with new LMCT (423–405 nm) and d→d bands (662 nm) in the UV-Visible spectra. The inhibition zones and MIC were in the ranges of 8–24 mm and 10–0.125 mg/mL, respectively. The DPPH and FRAP antioxidant assays showed IC50 138.62–486.82 µg/mL and 9.240–22.890 AAE mg/g at 1000 µg/mL, respectively. Chelation of 3EHH with Cu (II), Zn(II), and Ag(I) improved its bioactivity, with the Cu (II) complex showing the greatest potential as a dual antimicrobial and antioxidant agent.
Predicting academic performance: a comparative study of UTME and Direct entry students using a hybrid Kernel model
This Study introduces a novel Hybrid kernel Model (HKM) that integrates weighted and unweighted kernel regression within a unified framework to predict and compared the academic performance of students admitted through two unified pathways-Unified tertiary Matriculation Examination (UTME) and Direct Entry (DE) at Sokoto State University. While prior research has employed single kernel model and machine learning techniques in educational data mining, these studies often overlook admission mode differences and integrated kernel approaches, limiting their predictive accuracy and generalizability. Using a dataset spanning 2018-2025, partitioned into training (2018-2023) and testing (2024-2025) sets, the HKM was evaluated using MSE, RMSE, MAE and . Results consistently show DE student outperforming UTME students across graduation metrics, including higher class: first class and second-class upper percentages, and lower-class rate: second lower and third-class percentage. Performance score on a 4-point scale ranged from 3.09-3.28 for DE versus 2.65-2.90 for UTME. Statistical tests confirmed admission mode as a significant predictor (P < 0,05), with prediction confidence exceeding 84% for both groups. This study fills a critical gap by disaggregating student populations and offering pathway-specific insights, challenging homogeneous treatment in existing literature. Its contributions include a replicable methodological framework and actionable recommendations for differentiated admission orientation, and support systems. The HKM proves analytics in education, supporting equitable policy and institutional quality enhancement.
Partial shading mitigation on DC link stability based on deep reinforcement learning grid-forming inverter control
Partial shading on photovoltaic (PV) arrays induces nonlinear power–voltage characteristics that cause rapid, multi-peaked fluctuations in DC-link power injection. Conventional grid-forming (GFM) inverter controllers, which rely on fixed-gain PI regulators tuned around a nominal operating point, struggle to track these fast, nonlinear disturbances: their linear control action cannot adapt quickly enough to abrupt irradiance changes, leading to sluggish transient response and, under severe shading, DC-link voltage collapse. This paper proposes a hybrid deep reinforcement learning (DRL) based GFM inverter control strategy that replaces these fixed-gain regulators with adaptive, learning-based controllers to overcome this limitation. A Deep Deterministic Policy Gradient (DDPG) agent governs the DC–DC boost converter for maximum power point tracking (MPPT), while a Twin Delayed Deep Deterministic Policy Gradient (TD3) agent replaces the conventional inner current PI regulators of the droop-controlled GFM inverter, enabling the controller to continuously learn and adjust its response to nonlinear, time-varying shading conditions rather than relying on static gains. Performance is evaluated under four irradiance scenarios, from uniform irradiation to severe partial shading, and compared against a conventional PI–PI GFM controller. Results show that the proposed GFM (PI–TD3) controller achieves faster DC-link voltage stabilization and superior voltage regulation under all shading conditions, maintaining the DC-link at 779.1 V under severe shading compared to a collapse to 290 V with the conventional controller. These findings indicate that TD3-based DRL GFM inverter control offers a robust, adaptive solution for standalone PV systems under variable irradiance conditions.
Production and characterization of biodegradable plastics from water Yam peel starch and rubber seed oil
Packaging waste forms a significant part of municipal solid waste and has caused increasing environmental concern. This research investigates the potential of producing bioplastics using the starch from water yam peels and epoxidized rubber seed oil. The starch and epoxidized rubber seed oil with additives were copolymerized to form the bioplastic. Analysis of the starch were: moisture content (10.08±0.42%), ash content (0.175±0.010%). amylase (26.93±0.01%) and amylopectin (73.07±0.10%). Physicochemical analysis of the epoxidized rubber seed oil were pH (6.50±0.01), specific gravity (0.97±0.24), viscosity (12.30±0.20 poise) acid value (22.62±0.01 mgKOH/g) and iodine value (158.51±0.03 mgl2/g). The bioplastic was subsequently produced by copolymerization of the epoxidized rubber seed oil and water yam peels starch. The tensile strengths of both bioplastic and the control were 20.54N/mm2 and 12.28N/mm2 respectively. Also, the results showed that the bioplastic had a poor resistance in acidic and water media, good resistance in basic, and alcoholic media. The biodegradability test results showed that weight loss was more evident in loamy soil whereas no change was observed for the control.
Water scarcity mitigation strategies and implementation challenges among the populace of Batagarawa Local Government Area of Katsina State, Nigeria
Scarcity of water poses serious risk to the health and livelihoods of people living in the arid and semi-arid parts of northern Nigeria. This is due to mostly climate change, poor water infrastructure, improper water management and growing populations that keep putting pressure on the limited sources of water in the areas. The objective of this study was to assess the strategies adopted in solving the problem of water scarcity in Batagarawa LGA, Katsina State, as well as to assess challenges associated with the adoption of the strategies. In this study, qualitative research methodology was utilized in the data collection process where Focus Group Discussion (FGD) and direct observation were mainly adopted. The primary data were collected using FGD and direct observation on the field to ensure a more holistic data collection. The secondary data were collected from Katsina State Water Board and from NGOs working among the people of Batagarawa. The data collected was analyzed through the use of thematic analysis technique. According to results from the interviews conducted, it emerged that borehole water is the main source of water supply to the people. Access to water differs based on whether an individual's home possesses a borehole, the proximity of one’s home to a communal borehole, and availability of pipe-borne water which in some cases may be hindered by shortage of power. People adopt personal measures in handling the situation such as water conservation and reusing water; however, financial difficulties and lengthy distances make it hard for such measures to be sustained effectively. The study conclude that households make use of several localized measures aimed at coping with water problems, including water storage, rainwater collection, and reuse of water. It was recommended that the governments should support households with financial or technical help in obtaining their own boreholes and installation of water storage systems.
Integrated mechanical and chemical control of wild rice (Oryza Longistaminata) and its impact on rice yield in upper east region, Ghana
Wild rice (Oryza longistaminata) is a pervasive weed in rice fields in the Upper East Region, significantly reducing rice yields and threatening food security. The study aimed to evaluate the efficacy of integrated mechanical and chemical control methods in Garu and Tempane Districts. The experiment was laid out in a split-plot design with three replications. The districts were assigned to the main plots and the weed control methods to the subplots. Land preparation was done manually, and Glyphosate was applied one week after, at a rate of 100 ml per 15 litres of water. One week later, Jasmine rice was planted. Data were collected on the wild rice population and rice yield parameters. The data were analyzed using GenStat 12 Edition; a two-way ANOVA was used to determine treatment differences. Means were separated using Lsd at a 5% level of significance. Results showed that glyphosate-based treatments effectively suppressed weeds. Plough + Glyphosate achieved the highest grain yields of 3.87 t/ha in Garu and 3.53 t/ha in Tempane. The weed control method was significant (p < 0.001) and reduced wild rice population by 2.7-fold. Uncontrolled wild rice infestation reduced rice yields by approximately 56%. Combined Plough + Glyphosate application is recommended for managing O. longistaminata, with Glyphosate application only where resources are limited. Awareness of the weed's rhizomatous regeneration capacity is essential for farmers to understand the control methods. Further research should examine the cost-benefit analysis of the control methods and socio-economic factors that could influence adoption.
Modeling groundwater quality and prediction in a highly urbanised coastal city of Lagos using an artificial neural network
The quality of groundwater is a serious issue worldwide. The groundwater quality status was assessed by comparing it with World Health Organization (WHO) standards, and groundwater quality parameters were predicted using an Artificial Neural Network (ANN) model with the Levenberg-Marquardt algorithm. A total of 15 samples were collected from boreholes and wells and analyzed for Electrical Conductivity (EC), pH, temperature, salinity, and chloride, and predictions were made for Total Dissolved Solids (TDS), sodium (%Na), iron (Fe), and bicarbonate. The data were normalized and then divided into training, validation, and testing sets (70%, 15%, and 15%, respectively). The results indicated significant variations in the spatial distribution; Isheri had significantly higher EC (5860 µS/cm) and TDS (3180 mg/L), which are beyond WHO limits, likely due to industrial pollution. The salinity level on Tincan Island was high (2.24 ppt), and the water was undrinkable, whereas the salinity levels on Akodo and Akeusola were low (0.1 to 0.08 ppt), and the water was drinkable. The ANN had good predictive performance with Mean Square Error of 0.6908 (training), 0.5063 (test) and 1.11 (validation). The regression value of the 10-neuron hidden layer was 1.0 (training), 0.92601 (validation), and 0.95023 (testing). Absolute Error was minimal (TDS: 0.0 to 0.06; Fe: 0.0 to 0.001), and Mean Absolute Error (TDS: 0.84; Na: 0.17; Fe: 0.0019; bicarbonate: 0.5973), confirming the model's reliability. The study shows that ANN is a powerful tool for predicting groundwater quality and can support sustainable groundwater management in coastal urban areas. The disadvantages are a small sample size (15) and the use of fixed parameters that do not account for seasonal changes. Larger data sets and dynamic environmental factors should be included in future research.
Aeromagnetic lineament mapping and structural implications of the Wase area, North-Central Nigeria
Aeromagnetic interpretation is an effective approach for delineating subsurface geological structures that control mineralization within crystalline basement terrains. This study investigates the structural framework of the Wase area, north-central Nigeria, using high-resolution aeromagnetic data obtained from the Nigerian Geological Survey Agency (NGSA). The objective was to identify structural lineaments and evaluate their implications for mineral exploration. Total Magnetic Intensity (TMI) data were processed through regional–residual separation, First Vertical Derivative (FVD), Second Vertical Derivative (SVD), and Analytic Signal Amplitude (ASA) techniques to enhance shallow magnetic anomalies and structural discontinuities. The processed data revealed numerous faults, fractures, lithological contacts, and intrusive bodies. Rose diagram analysis indicated that NE–SW is the dominant structural trend, with subordinate NW–SE, N–S, and E–W orientations, reflecting the influence of the Pan-African orogeny and subsequent tectonic reactivation associated with the Benue Trough. Lineament density analysis identified the western and northwestern parts of the study area as zones of intense structural deformation that may have served as pathways for hydrothermal fluid migration and mineralization. The results demonstrate that integrated aeromagnetic derivative techniques provide a reliable, rapid, and cost-effective approach for structural mapping and reconnaissance mineral exploration within the Nigerian Basement Complex.
Evaluation of chemical composition of Mesosphaerum suaveolen as a potential insecticide for Anopheles gambiae
Introduction: Mesosphaerum suaveolens (Lamiaceae), commonly found in tropical regions, is traditionally used for its medicinal and insect-repelling properties. Increase in resistance of Anopheles gambiae to synthetic insecticides coupled with growing concern of mammals’ health and the environment has pose a great need for safer insecticide(s). Thus, the aim of this study was to evaluate the chemical composition of M. suaveolens as a potential natural alternative for mosquito control. Methodology: Aerial parts of the plant were collected, shade-dried, pulverized, and extracted using n-hexane in a Soxhlet extractor. This was followed by the preliminary phytochemical screening, and GC-MS analysis of the crude extract at the Multipurpose Lab, ABU Zaria, Nigeria. Insecticidal efficacy of GC-MS identified compounds was evaluated using the CDC bottle bioassay on adult A. gambiae at concentrations of 2000, 4000, and 6000 µg/bottle. Findings: The preliminary qualitative phytochemical analysis revealed the presence of high concentration of terpenoids, with minute concentrations of alkaloids, tannins, saponins, and glycosides. From GC-MS analysis, eight potential insecticidal compounds were identified, including caryophyllene, caryophyllene oxide, phytol, and spathulenol. Results of insecticidal efficacy showed a dose-dependent increase in mortality: 44%, 83%, and 100% respectively within 24 hours. Probit regression gave LC₅₀ = 666.59 µg/bottle and LC₉₅ = 1,571.95 µg/ bottle. Although ANOVA showed no statistically significant differences among treatment groups (P = 0.125), the consistent mortality trend confirms the biological relevance of the extract. Conclusion: This study highlights M. suaveolens as a promising candidate for developing plant based, eco-friendly insecticides in mosquito vector control programs.
An enhanced convolutional neural network for classification of Melanoma skin cancer
Melanoma is one of the most aggressive forms of skin cancer and accounts for a disproportionately high number of skin cancer–related deaths despite its relatively low incidence. Early and accurate diagnosis is therefore essential for improving patient survival rates. This study presents an enhanced Convolutional Neural Network (CNN)-based approach for automated melanoma classification using dermoscopic images. The proposed model was developed using the HAM10000 dataset, which contains 10,015 labeled dermoscopic images categorized into melanoma and non-melanoma classes. Comprehensive preprocessing techniques, including image resizing, normalization, and data augmentation, were employed to improve model generalization and reduce overfitting. The CNN architecture integrates convolutional and pooling layers for hierarchical feature extraction, dropout layers for regularization, and a Softmax output layer for classification. The model was trained using the Adam optimizer with a learning rate of 0.001, a batch size of 32, and 100 epochs, employing an 80/20 training–testing split with 10-fold cross-validation. Experimental results show that an enhanced CNN achieved an accuracy of 96.5%, with precision, recall, and F1-score values of 96.9%, 96.0%, and 96.4%, respectively. The model outperformed baseline CNN models and selected pre-trained architectures such as ResNet50. These results demonstrate the robustness and effectiveness of the enhanced approach, highlighting its potential for integration into computer-aided diagnostic systems to support dermatologists in early melanoma detection.
Effects of biochar on some soil fertility parameters in Malumfashi local government area, Katsina State, Nigeria
The use of biochar as soil amendment is being promoted due to its ability to improve fertility and confiscate soil carbon. The study assessed the effect of biochar on soil fertility and organic carbon stock. The study area was divided in to experimental and control plots, thereby biochar made from sugarcane stalk was incorporated in to experimental plots. The OC, TN, available P, pH, E.C, exchangeable bases and CEC from thirty soil samples and raw biochar were determined in laboratory. The results were subjected to descriptive statistics and student t-test at α value of 0.05. The results revealed that biochar contain high values of pH (9.38), EC (2.63), OC (0.54), TN (0.42) Avl. P (3.84), CEC (7.50), Ca (3.84), Na (0.21), K (0.34) and Mg (0.87) than soil. This entails that these nutrients are easily release for crop utilization when biochar incorporated the soil. It was learned that the mean value of these soil fertility parameters and soil carbon stock are higher in the experimental plots. This implies that biochar is protagonist in improving soil fertility and retaining soil carbon. It was concluded that biochar application increased soil fertility and also have the capacity to sequester soil carbon.
Recent challenges on cancer health risks determinants from quality drinking water samples in some selected areas in Birnin Kudu, Jigawa State, Nigeria
Radon (222Rn) is a naturally occurring radioactive gas that constitutes one of the major contributors to natural background radiation exposure and poses significant health risks to humans. Although alpha particles emitted by radon cannot penetrate the skin, inhalation of radon released from water during domestic activities and ingestion of radon-contaminated drinking water may increase the risks of lung and gastrointestinal cancers. This study assessed the concentration of 222Rn and the associated radiological health risks in drinking water obtained from selected communities in Birnin Kudu Local Government Area, Jigawa State, Nigeria. Eight water samples, comprising four borehole and four hand-dug well samples, were collected from four locations and analyzed using a Liquid Scintillation Counter (Tri-Carb LSA-1000) at the Centre for Energy Research and Training, Ahmadu Bello University, Zaria. The mean radon concentrations were 16.48 Bq/L for hand-dug well water and 16.23 Bq/L for borehole water. These values exceed the guideline limits of 10 Bq/L recommended by the World Health Organization (WHO) and 11.1 Bq/L recommended by the United States Environmental Protection Agency (USEPA). The estimated annual effective doses due to inhalation remained below the recommended safety limits for all age groups, whereas the annual effective doses due to ingestion and the corresponding excess lifetime cancer risks exceeded the recommended values. These findings indicate that the investigated water sources may pose potential health risks if consumed without treatment. Therefore, appropriate water treatment and routine radiological monitoring are recommended to minimize radon exposure and protect public health.
Hospital power system with voltage sag mitigation mechanism
Continuous and stable electrical power is important in healthcare facilities, where critical medical equipment relies on a dependable voltage supply. Temporary reductions in voltage, commonly referred to as voltage sags, may affect the performance of sensitive medical devices. Such as ventilators, magnetic resonance imaging (MRI) scanners, computed tomography (CT) scanners, and patient monitoring systems. Although uninterruptible power supplies (UPS) and standby generators are commonly employed to enhance supply reliability, their effectiveness may be constrained by response delays and maintenance requirements. This study presents the design and simulation of a hospital power system incorporating a Static Synchronous Compensator (STATCOM) for voltage sag mitigation. The system was modelled in MATLAB/Simulink using the Simscape Electrical toolbox, and voltage sag conditions were introduced through fault simulations to assess network performance with and without compensation. The simulation results indicate that, in the absence of STATCOM support, the system voltage decreased to approximately 0.5 p.u., accompanied by poor voltage stability and a recovery time exceeding 0.2 s. Following the integration of the STATCOM, the voltage profile improved to about 0.95 p.u., the sag duration decreased from 0.1 s to 0.05 s, and the recovery time was reduced to less than 0.05s, sag reduction percentage increases from 50% to 5. These findings demonstrate the capability of the STATCOM to enhance voltage regulation, reduce the severity of voltage sag, and improve the operational reliability of hospital electrical systems. The study highlights the potential application of STATCOM technology in hospitals and other facilities containing voltage-sensitive equipment.
Prevalence of Coccidiosis among different breeds of chickens within Katsina metropolis
Coccidiosis remains one of the most economically significant parasitic diseases affecting poultry production globally. The disease, caused by protozoan parasites of the genus Eimeria, continues to undermine productivity through reduced growth, poor feed conversion efficiency, increased susceptibility to secondary infections, and mortality. This study investigated the prevalence of coccidiosis among layers, broilers, noilers, and indigenous chickens reared within Katsina Metropolis, Nigeria. A cross-sectional survey design was employed, and fresh faecal samples were collected over a two‑month period from selected birds representing the four breed categories. Laboratory examination was conducted using flotation techniques for the detection of Eimeria oocysts, while oocyst burden was assessed through oocyst-per-gram procedures. Data were analyzed using descriptive statistics and one-way ANOVA in SPSS version 25. The findings revealed widespread occurrence of coccidiosis across all breeds examined. Layers recorded the highest prevalence, followed by broilers and noilers, whereas indigenous chickens exhibited comparatively lower infection rates. Helminth co-infections involving Ascaridia spp. and Heterakis gallinarum were also identified. Statistical analysis demonstrated significant variation in prevalence among breeds (F = 6.320, p < 0.05), indicating that breed-related and management-associated factors influence disease occurrence. The study concludes that coccidiosis remains endemic in Katsina Metropolis and recommends improved biosecurity, routine disease monitoring, proper litter management, and evidence-based control programmes to reduce infection pressure and enhance poultry productivity.
Occurrences and bio-control potential of natural enemies of fall army worm, Spodoptera Frugiperda: a comprehensive review
Fall armyworm (Spodoptera frugiperda) is one of the most destructive invasive pests affecting maize and other cereal crops worldwide. Since its introduction into Africa in 2016, the pest has spread rapidly across Sub-Saharan Africa and several Asian countries, causing significant yield losses and threatening food security. This review evaluated the occurrence and biocontrol potential of natural enemies of fall armyworm through a systematic assessment of published literature obtained from scientific journals, institutional reports, textbooks, conference proceedings, and online databases. Information on the biology, distribution, natural enemies, and biological control strategies of fall armyworm was synthesized and analyzed.The review revealed that fall armyworm possesses high reproductive capacity, broad host range, and strong dispersal ability, enabling rapid establishment in newly invaded regions. Several natural enemies were identified, including parasitoids, predators, bacteria, viruses, and entomopathogenic fungi. Key parasitoids such as Telenomus remus, Trichogramma pretiosum, Chelonus insularis, and Cotesia marginiventris were found to effectively suppress egg and larval populations. Entomopathogenic fungi including Beauveria bassiana and Metarhizium anisopliae demonstrated considerable pathogenicity, with reported mortality rates ranging from 30% to 87%. Predators such as Doru luteipes, ladybird beetles, ants, and carabid beetles also contributed significantly to reducing pest populations.The effectiveness of natural enemies was influenced by environmental conditions, habitat diversity, cropping systems, and pesticide use. Conservation agriculture, habitat management, and reduced reliance on broad-spectrum insecticides enhanced biological control performance. The review concludes that integrating parasitoids, predators, and microbial pathogens into integrated pest management programs provides a sustainable and environmentally friendly approach for managing fall armyworm populations while reducing dependence on synthetic pesticides and promoting long-term agricultural productivity.
Modeling the impact of rehabilitation programs on the propagation of banditry crime: a case study of Katsina State, Nigeria
Banditry has emerged as a critical socio-economic and security challenge in north western Nigeria, particularly within Katsina State, severely disrupting rural economies and community stability. While conventional approaches heavily rely on kinetic military interventions, there is a growing paradigm shift toward non-kinetic strategies, such as rehabilitation programs. This study develops a deterministic mathematical model to investigate the transmission and control dynamics of banditry crime, explicitly incorporating a rehabilitation compartment for reformed individuals. The population is stratified into distinct epidemiological classes representing vulnerable, susceptible, actively recruited, and rehabilitated individuals. We derive the basic reproduction number for criminal propagation to evaluate the threshold conditions necessary for banditry eradication or persistence. Stability analysis of the model shows that the banditry-free equilibrium is locally asymptotically stable when ​<1, suggesting that effective rehabilitation and reduced recruitment rates can successfully curb criminal propagation. Numerical simulations are conducted using localized parameters from Katsina State to validate the analytical results. The findings demonstrate that expanding rehabilitation programs significantly reduces the active criminal pool over time, though its long-term success remains highly sensitive to the rate of relapse back into banditry. Ultimately, this model offers a predictive framework for policymakers, demonstrating that integrated rehabilitative strategies provide a more sustainable long-term solution to security challenges than punitive measures alone.
Convergence analysis of a hybrid super class of block backward differentiation formula for integrating stiff IVP
This research introduces a novel, seventh-order numerical scheme specifically engineered for the integration of first-order stiff initial value problems (IVPs). To establish the mathematical validity and reliability of the proposed method, a rigorous analysis of its structural and asymptotic properties was conducted. The fundamental necessary and sufficient conditions governing the convergence of linear multistep methods were systematically evaluated. Specifically, the order of accuracy and the error constant were analytically derived, confirming a true seventh-order algebraic convergence. Furthermore, an investigation into the stability architecture demonstrates that the scheme is both consistent and zero-stable. According to the Dahlquist equivalence theorem, the simultaneous satisfaction of these two properties guarantees the convergence of the method. Given its robust stability region and high-order precision, this scheme offers a computationally efficient and highly accurate alternative to existing solvers for handling stiff dynamical systems.
Iron-trimesate impregnated moss composites for application in petroleum recovery and remediation of petroleum-contaminated water
Crude oil spills are major environmental and economic concerns. The use of sorbents for removal of crude oil from water surfaces has remained a viable solution to crude oil spills. In this study, a novel composite material produced by impregnation of the metal-organic framework (MOF), iron-trimesate (Fe-tri), onto the moss species, Barbula indica, was investigated for its crude oil sorption capacity. Various moss/Fe-tri composition, including moss/Fe-tri 50:50, moss/Fe-tri 75:25, and moss/Fe-tri 25:75, were prepared and tested along with the pure substances for their capability to sorb crude oil. Infrared spectrometric analyses carried out for moss/Fe-tri 50:50, pure moss sample, and pristine unmodified Fe-tri (MOF) revealed characteristics bonds and functional groups such as -OH, -C=O, C-H and aromatic rings on the materials. The results obtained from the crude oil sorption tests showed relatively high sorption capacity values, which decreased in the order: moss/Fe-tri 75:25 (2.81 g/g) > pure moss sample (2.39 g/g) > moss/Fe-tri 50:50 (2.32 g/g) > moss/Fe-tri 25:75 (2.09 g/g) > Fe-tri (1.68 g/g), indicating that the impregnation of moss with Fe-tri enhanced crude oil retention capacity. Water sorption test results further demonstrated the suitability and efficiency of the materials as sorbents for removal of crude oil from water and also indicated that sorbent porosity and pore volume influenced sorption performance. The findings revealed that impregnating mosses with metal-organic frameworks could yield a composite with better crude oil retention property, and highly efficient for application in recovery of crude oil from water and remediation of water contaminated with same.