Science Research Annals - Physical is an interdisciplinary peer-review scientific journal published by Faculty of Science, Adekunle Ajasin University, Akungba-Akoko, Ondo State, Nigeria. It is an interdisciplinary peer-review scientific journal. The journal provides a forum for publishing double blind peer-reviewed research outputs from the broad field of Physical Sciences. Science Research Annals - Physical is committed to publishing original and high-quality articles in physical sciences. The Journal provides speedy and effective archival management systems for researchers, academics, and industry professionals, to access original scientific materials. Read more
Faculty of Science Adekunle Ajasin University Akungba-Akoko Nigeria
Mathematical study of the impact of vaccination on the dynamics of human papillomavirus infection using homotopy perturbation method
This study presents a mathematical model to assess the impact of vaccination on the dynamics of human papillomavirus (HPV) infection. A compartmental model is developed to describe the transmission dynamics of HPV, incorporating vaccination as a control measure. The system of equations describing the transmission dynamics was obtained. The homotopy perturbation method (HPM) was applied to obtain the analytical solution of the model’s equations. Numerical simulations of the model solution were carried out, and graphical responses of the system were presented. Numerical simulations demonstrate the effectiveness of vaccination in reducing HPV prevalence. The results show that increasing the vaccination rate slows the rate of decline in the susceptible individual and leads to a substantial reduction in the number of infected individuals. For higher vaccination rates, the infection curve flattens significantly, showing the effectiveness of vaccination in curbing HPV spread. The results highlight the importance of optimal vaccination strategies in controlling HPV transmission. This study provides valuable insights for policymakers and public health officials in designing effective HPV vaccination programs, which helps to reduce the number of health problems and subsequent deaths attributed to the infection.
Articles: Use for submitting articles for consideration in an ongoing issue
Copyright (c) 2026 SRA - Physical Sciences https://creativecommons.org/licenses/by-nc-nd/4.0
Synthesis, characterizations, and DFT studies of some vanillin Schiff base derivatives
The increase in bacterial infections has necessitated the need for the design of new drugs that can fight against multidrug-resistant bacteria. Schiff bases, especially those of vanillin, have shown promising activity against some bacterial infections. In this article, six new vanillin Schiff bases (L1–L6) were prepared by condensation of vanillin with various substituted anilines for possible medicinal applications. The compounds were characterized by infrared spectra, UV/visible spectra, and melting point measurements. Infrared spectra confirmed the formation of the compounds by the newly emerged azomethine (C=N) bands observed at 1676–1620 cm⁻¹ for the synthesized compounds. The UV/visible spectra showed the expected π–π* transition of the azomethine group at 38000–34000 cm⁻¹ and the n–π* transition at 32000–25000 cm⁻¹, which are characteristics of the molecules. The Schiff bases, L5 and L6, were obtained as thick viscous liquids, while L1–L4 came out as crystals of different colors characterized by sharp melting points. The molecules were modeled and optimized using DFT at the 6-31G(d) level of theory. The computational study shows that the compounds could effectively accept and donate protons, which is a remarkable characteristic for medicinal and other applications. The characterization results reveal that the compounds were successfully prepared. Spectroscopic and computational studies suggested that the compounds may have some medicinal and electronic applications.
Magnetohydrodynamic free convection nanofluid flow over a convectively heated vertical disk with non-uniform heat generation and nonlinear radiation
Magnetohydrodynamic (MHD) nanofluid flows have become very popular in modern alternative thermal systems, such as solar collector systems, electronic cooling devices, and high-temperature industrial processes involving high-performance heat regulation processes. In connection with these applications in various thermal systems, the current study investigates free convective MHD nanofluid flow over a vertical disk under the influence of convective heating, non-uniform heating source, and nonlinear thermal radiation. By using similarity transformations to remodel the governing equations from partial differential equations into coupled nonlinear ordinary differential equations, the complete set of these equations is numerically integrated by combining the shooting technique with a fourth-order Runge-Kutta algorithm. The impact of key physical parameters on various dimensionless profiles is analyzed through various graphs. The results of the analysis indicate that the magnetic field term decelerates velocity due to the presence of Lorentz forces, both spatial and thermal-dependent heat sources thicken the thermal bounding layer and affect heat transfer distribution in the system. More so, convective heating and nonlinear thermal radiation produce higher temperatures in the system. These results provide valuable insight into the potential improvements in terms of efficiency and control of MHD-based energy systems and industrial heat transfer applications.
Effects of viscous dissipation and micropolar heat conduction on thermally radiating MHD flow along a vertical plate
This study investigates the combined effects of viscous dissipation and micropolar heat conduction on steady magnetohydrodynamic (MHD) flow of a thermally radiating micropolar fluid along a vertical plate. The governing nonlinear partial differential equations describing momentum, microrotation, and energy transport are formulated by incorporating Joule heating, viscous dissipation, and Rosseland thermal radiation. Using appropriate similarity transformations, the system is reduced to a set of coupled ordinary differential equations and solved numerically by the shooting method alongside the Runge-Kutta-Fehlberg scheme. The influences of key dimensionless parameters, including the magnetic parameter, Eckert number, micropolar material parameter, radiation parameter, and Prandtl number, on velocity, microrotation, and temperature distributions are analyzed in detail. The results reveal that viscous dissipation significantly increases fluid temperature and the thickness of the thermal boundary layer, while enhanced micropolar heat conduction improves thermal diffusion and modifies microrotation behavior. The applied magnetic field suppresses fluid motion due to Lorentz forces but increases thermal energy through Joule heating. Furthermore, variations in radiation intensity significantly influence the heat transfer rate. A rise in the fluid material term decreases the skin friction coefficient by 17.5%. This reduction is attributed to the enhanced microstructure effects, which allow rotation of fluid particles and reduce shear stress. These findings provide useful insights for optimizing thermal management in practical systems such as MHD pumps, nuclear reactor cooling systems, metallurgical processing units, and solar thermal energy devices.
Physicochemical and antimicrobial properties of soaps formulated with essential oils of some plant species
In recent decades, there has been a growing global interest in natural and sustainable alternatives to synthetic chemical products, particularly in the area of personal hygiene. Conventional soaps often contain synthetic chemicals such as triclosan and parabens, which have been linked to antimicrobial resistance and skin irritation. Essential oils with diverse antimicrobial properties offer a promising alternative for enhancing soap formulations. This study was conducted to evaluate the physicochemical properties and antimicrobial properties of soap formulated with essential oils extracted from plant species. Essential oils from Hyptis suaveolens, Citrus aurantifolia, Curcuma longa, Citrus limon, and Thuja koraiensis were obtained through the hydrodistillation method using a Clavenger apparatus. Soap was prepared using the hot process method and was fortified with essential oils using the standard method. The physicochemical properties of the essential oil (pH, viscosity, specific gravity, and color) were analyzed. The soaps were evaluated for detergency, foaming capacity, and antimicrobial activity against some selected bacterial and fungal isolates. The results showed that the essential oils showed acceptable pH, viscosity, specific gravity, and good lathering properties comparable to conventional soaps. The antimicrobial assays demonstrated that soaps containing essential oils displayed enhanced inhibitory effects compared to the plain soap, with formulated soaps showing the broadest antimicrobial spectrum. The essential oils also demonstrated strong concentration-dependent activity against both bacteria and fungi. These confirm the synergistic antimicrobial effect of the unformulated soap, formulated soap, and essential oils, highlighting their potential for producing safe, effective, and affordable hygiene products. The study concludes that the essential oils and the formulated soaps offer enhanced cleansing, dermatological safety, and antimicrobial effectiveness, making it a promising natural alternative to synthetic soaps.
Development of a weather radar simulator for the characterisation of radar reflectivity of tropical rainfall
Weather radar systems are important tools for rainfall monitoring, atmospheric research, and the design of microwave communication systems. However, the high cost and limited availability of radar instrumentation in many tropical regions necessitate the development of alternative tools for studying radar–rainfall interactions. This study presents the development of a weather radar simulator designed to characterise the radar reflectivity of tropical rainfall and provides a cost-effective platform for radar signal analysis. The simulator integrates physically based rainfall modelling techniques, including the lognormal drop size distribution, realistic raindrop shape representations, and T-matrix electromagnetic scattering calculations. Simulations were performed for both convective and stratiform rainfall conditions at a radar frequency of 24.1 GHz. The simulator-generated reflectivity was validated using observations obtained from a Micro Rain Radar (MRR) installed at the Department of Physics, Federal University of Technology, Akure, Nigeria. The results show that convective rainfall exhibits higher radar reflectivity values than stratiform rainfall. Validation against MRR observations revealed coefficients of determination (R²) of 0.956 and 0.870 for convective and stratiform rainfall, respectively, during a 20-minute simulation period, while corresponding values of 0.809 and 0.731 were obtained for a 40-minute simulation period. The percentage difference between simulated and observed reflectivity was approximately 10% for the 20-minute simulations and approximately 35% for the 40-minute simulations. The simulator generally overestimated reflectivity, with better agreement observed for convective rainfall than for stratiform rainfall. The findings demonstrate that the developed simulator is capable of reproducing the general reflectivity characteristics of tropical rainfall and can serve as a useful tool for investigating radar–rainfall interactions. Future improvements should include the incorporation of alternative drop size distribution models, uncertainty quantification, and validation using a broader range of rainfall events.
Palynostratigraphic and palynofacies interpretations of the Muller–1 Well, Niger Delta
The sedimentary intervals of Muller-1 Well, located in the Greater Ughelli Depobelt, Niger Delta Basin, Nigeria, have been subjected to palynological and sedimentological studies, to biozonation and paleoenvironmental results. Ten (10) samples from 8920-9054 ft have been analyzed. There were a few common occurrences of pollen and spores with other organic-walled microfossils such as Botryoccus braunii, fungal spores, and diatom frustules. The sedimentological characteristics include shale intercalations with coarse-to-medium to fine-grained sandstones, indicating energy conditions that are typical of deltaic systems. The entire intervals belong to the Agbada Formation. One palynostratigraphic zone of Cyathidites sp. was established for the studied intervals using the International Stratigraphic Guide. The zone is an assemblage biozone, and a Paleocene (Thanetian) age was assigned to the interval based on the stratigraphic age range of diagnostic markers such as Longerapertites sp., Cyathidites minor, Tricolporopollenites sp., and Monocolpites marginatus. Hydrocarbon potential is suggested by deltaic deposits with terrestrial pollen/spores (likely gas-prone) and shale interbeds, consistent with the Agbada Formation’s proven gas reservoirs in the Niger Delta Basin.
Copyright (c) 2025 SRA - Physical Sciences https://creativecommons.org/licenses/by-nc-nd/4.0
Leveraging AI chat assistants for enhanced food security in Africa: A comprehensive integration of large language models, retrieval augmented generation, and vector embedding techniques
Agricultural productivity in Africa faces significant challenges due to limited access to timely, localized information and expert guidance, leading to suboptimal farming practices that threaten food security and economic development. The increasing demands of food security and sustainable agriculture necessitate cutting-edge technological solutions. This study introduces an Artificial Intelligence (AI) chat assistant powered by Large Language Models (LLMs) and AI agents, utilizing Retrieval-Augmented Generation (RAG) and vector database embeddings to transform agricultural practices. By integrating LLMs with domain-specific data through RAG, the assistant delivers precise, context-aware responses to complex agricultural inquiries. The use of vector embeddings enables efficient semantic search across extensive agricultural datasets, enhancing information retrieval and decision-making processes for frontline extension workers, agronomists, and farmers. The AI agents facilitate autonomous task execution, such as analysis of crop health, disease identification, and predictive modelling based on weather patterns. Natural language processing ensures intuitive user interactions, making advanced agricultural insights accessible regardless of technical proficiency. AI chat assistance leads to increased access to much-needed information on a wide range of agricultural practices, empowering farmers to optimize their practices, enhance crop yields, and significantly contribute to food security and sustainable economic development in Africa.
In-situ reduction synthesis and antibacterial evaluation of Cu/Ni complexes derived from hexadecylamine dithiocarbamate
This work reports on the synthesis of Ni and Cu-dithiocarbamate complexes, prepared via in situ reduction of respective metal salts with long-chain hexadecylamine in the presence of NaBH4. The as-synthesized metal complexes were obtained in satisfactorily high yields and characterized using various techniques. FTIR results indicate that the bonds between metal ions and sulfur atoms in the complexes are bidentate. SEM images revealed the presence of relatively small, irregular, and spherical particles. EDX spectroscopy confirmed the elemental composition of C, S, Cu, and Ni, supporting the successful synthesis of the metal complexes. XRD analysis shows that the dithiocarbamate (DTC) and Ni complexes are crystalline, while the Cu complex is amorphous. Thermogravimetric analysis (TGA) indicates higher stability of the Cu complex compared to the Ni complex and the DTC ligand. Antimicrobial studies demonstrated significant antibacterial activity of the Ni complex against various pathogenic bacteria, surpassing the effectiveness of the DTC ligand and the Cu complex. These findings suggest potential applications for dithiocarbamate complexes as antimicrobial agents. This research enhances the understanding of the synthesis, characterization, and biological activity of dithiocarbamate complexes, offering insights into their potential applications in various fields such as pharmaceuticals (as antimicrobial agents), agriculture (as fungicides or pesticides), and materials science (as precursors for metal sulfide nanoparticles).
In Silico evaluation of the potential of phytochemicals from Cissus populnea against Mycobacterium tuberculosis
The study investigated the anti-tubercular potential of the phytochemicals of the extracts of Cissus populnea (C. populnea) via in silico methods. Some phytochemicals from the plant were screened against Mycobacterium tuberculosis type II dehydroquinase (MTBII DHQase) and Pantothenate Synthetase (PS) using molecular docking techniques. Against MTBII DHQase, luteolin and catechin had the highest docking scores among the compounds from C. populnea, even higher than that of clofazimine, the reference drug. Against PS, the results also revealed that luteolin and poncirin had the highest scores among the compounds, also higher than that of pyrazinamide, a standard PS inhibitor. The compounds interacted with key amino acid residues in the proteins’ binding pockets. Molecular dynamic simulation of the lead compounds revealed stability of the complexes at the binding pocket of the proteins, with the average root mean-squared deviation all below 2.5 Å. The best models generated from quantitative structure-activity relationships were kpls_desc_6 and kpls_desc_47, with standard deviation values of 0.405 and 0.7829, respectively, indicating closeness between the predicted and observed values, while the R2 values were 0.7283 and 0.9156, respectively, underscoring the models’ ability in making predictions. The model kpls_desc_6 predicted a pIC50 of 5.008 μM for catechin against MTBII DHQase, while the model kpls_desc_47 predicted pIC50s of 5.407 μM and 5.362 μM for luteolin and poncirin, respectively, against PS. The fitness score generated by e-pharmacophore modeling showed that all compounds except cis-octadecanoic acid, methyl ester, scored above the standard drugs. Further clinical and preclinical investigations should be conducted on the phytochemicals from C. populnea in a bid to harness their full potential as anti-TB agents.
A combined interpretation of electrical resistivity and airborne magnetic datasets for assessment and evaluation of a geothermal reservoir in South West Nigeria
The surface occurrence and manifestation of warm and hot springs in a location are of utmost concern and require preliminary investigation to ascertain the reasons for this natural event. This event has also been observed in some other parts of Nigeria. So, the Ikogosi Warm Spring (IKGWS) area and environs were chosen as a study site located in the southwestern part of Nigeria because of their proximity. Thus, the causes and sources of the flow of hot and cold springs manifested on the surface in the area were explored. In this work, a combination of two geophysical techniques was used to describe and understand the configuration and processes of this typical event. The method involved the acquisition of resistivity data using a Terrameter (ABEM SAS 300C) from nine (9) vertical electrical soundings (VES) stations and supported by airborne magnetic data from the warm and cold sections of the study area. The VES analysis shows that the subsurface is made of four to six layers with resistivity ranging from 1.0 to 1334.7 Ωm. It also revealed that VES 1, 2 and 3 has resistivity ranging from 62.5-1082.8 Ωm with depths between 0.7-58.8 m and thickness 0.7-45.7 m while VES 4, 5 and 6 has resistivity ranging from 83.7-1334.7 Ωm with depth between 0.6-30.6 m and thickness 0.6-18.3 m. The general distribution of resistivity shows a very low value near the Ikogosi spring, revealing that resistivity ranged from 1 to 2540.3 Ωm, depth: 0.6 to 112.3 m, and thickness: 0.7 to 66.8 m. The results from heat flow computation from aeromagnetic investigations confirmed that there are zones in the IKGWS with anomalous heat flow, which have great prospects for geothermal energy exploration. The heat flow estimated for the IKGWS and environs ranged between 75 and 127 mW/m2, and the average value of 100 mW/m2, while the temperature gradient varied between 28 and 48 °C/Km and the mean value of 38 °C/Km. Likewise, the Curie point depth (CPD) estimated ranged between 12 and 21 km and mean value of 16 km.
Electrical resistivity, Aeromagnetic, Ikogosi warm spring, Geothermal energy, Curie point depth, Heat flow
Improving diagnosis of pneumonia among population: A machine learning method using k-nearest neighbors (KNN), random forest, and support vector machine (SVM) on chest X-ray images
Pneumonia is a major global health challenge, particularly affecting children under five, and remains a leading cause of mortality due to diagnostic delays and variability in clinical interpretation. This study investigates the application of classical machine learning (ML) classifiers—K-Nearest Neighbors (KNN), Random Forest (RF), and Support Vector Machine (SVM)—to enhance pneumonia detection using pediatric chest X-ray images. A dataset of 5,856 chest X-rays was preprocessed using spatial (Gaussian Blur, Histogram Equalization) and frequency-domain (Discrete Cosine Transform, High-Pass Filtering) techniques. Feature extraction was performed using a hybrid approach combining handcrafted methods (GLCM, HOG, Wavelets) and deep features via a pretrained ResNet-50. Among the models evaluated, SVM with Gaussian Blur achieved the highest accuracy (96.75%) and F1-score (0.97), followed by RF with Gaussian Blur (95.32% accuracy, F1-score 0.95). In contrast, DCT preprocessing consistently underperformed across all models. Statistical analyses, including ANOVA (p = 0.0003) and Tukey’s HSD, confirmed that model-preprocessing interactions significantly influenced diagnostic performance. While DenseNet121, a deep learning baseline, achieved slightly higher accuracy (93.1%), classical models demonstrated superior computational efficiency and interpretability, making them better suited for deployment in resource-constrained settings. This research highlights the importance of image preprocessing and feature engineering in ML-based pneumonia diagnostics, offering promising solutions for improving early detection and clinical decision-making in low-resource environments.
Stepwise extraction, functionalization, and composite formation of lignin and silica from rice husk: Synthesis and characterization
This study presents a novel approach for the stepwise extraction of lignin and silica from rice husk, followed by the synthesis of a silica-lignin hybrid composite. The rice husk was first processed to obtain rice husk ash (RHA), from which lignin and silica were sequentially extracted using alkali and acidic treatments. Lignin was isolated by acidification of the filtrate, while silica was recovered by neutralizing the remaining solution and calcining the precipitate. The silica was functionalized with amino groups via reaction with 3-aminopropyltriethoxysilane (APTES) to enhance its compatibility with lignin. The lignin-silica composite was synthesized by oxidizing the lignin with sodium periodate, followed by the addition of the amino-functionalized silica, which led to electrostatic interactions between the two components. The composite was characterized using UV-Vis, FTIR, SEM, EDX, and TGA techniques, confirming successful hybridization of lignin and silica. The obtained results indicate that the composite exhibited unique structural and thermal properties, demonstrating potential applications in areas such as adsorption, catalysis, and material science. This study provides a sustainable method for valorizing rice husk waste into high-value hybrid materials.