Journal of the Nigerian Society of Physical Sciences
Journal of the Nigerian Society of Physical Sciences is a peer-reviewed journal published quarterly (February, May, August & November) by the Nigerian Society of Physical Sciences. It offers an exciting publication outlet for Physical Science reports.
A novel parametric intuitionistic fuzzy entropy measure with applications to image edge detection
In digital image processing, edge detection is a fundamental operation used to identify intensity discontinuities in an image. In this paper, we propose a novel one-parameter generalization of an entropy measure for intuitionistic fuzzy sets (IFSs). IFSs provide additional flexibility by incorporating membership, non-membership, and hesitation information. A proof of validity is given for the proposed measure, together with numerical and graphical demonstrations. The entropy measure is applied to generate an edge map in which pixels exhibiting higher uncertainty are marked as image edges. Experimental results for standard benchmark images and real photographic images demonstrate superior edge localization and improved quantitative performance compared with existing fuzzy-entropy-based techniques.
Mathematical, Computer & Statistical Sciences
Generalized entropy in intuitionistic fuzzy sets, used it for edge detection of an image
Information theory, Entropy in Fuzzy sets, Intuitionistic fuzzy sets, Image edge detection
Copyright (c) 2026 Vaishali Manish Joshi, Javid Gani Dar (Author) https://creativecommons.org/licenses/by/4.0
Adaptive finite element analysis of thermal and elasticity problems using Zienkiewicz–Zhu recovery-based error estimation
This paper presents a verification-to-adaptivity analysis of a finite element framework for scalar and vector-valued elliptic problems that employs recovery-based a posteriori error estimation. Conforming bilinear quadrilateral (Q4) and linear triangular (T3) finite elements are used to discretise the thermal Poisson equation and the linear elasticity equations. Standard Galerkin methods are combined with uniform and adaptive mesh refinement in this approach. The approach of manufactured solutions under uniform refinement is first used for verification, where both discretizations regain the theoretical H1- and L2-norm convergence rates, proving the accuracy of the scalar and vector formulations. The Zienkiewicz–Zhu (ZZ) recovery approach is then used to incorporate adaptive refinement, using stress recovery for elasticity and gradient recovery for the thermal problems. The adaptive meshes show conservative effectivity behaviour, monotonic estimator–error correlation, and stable localisation. Differences between Q4 and T3 outcomes are consistent with their known approximation features across all test cases. The findings show that for thermal diffusion and linear elasticity issues, ZZ-driven adaptivity offers a dependable extension of a validated finite element framework.
Finite element method, Adaptive mesh refinement, Zienkiewicz–Zhu recovery, Posteriori error estimation
Copyright (c) 2026 J. K. Odeyemi, S. A. Odunlami (Author) https://creativecommons.org/licenses/by/4.0
Mathematical modelling and optimal-control strategies of schistosomiasis–typhoid fever co-infection: using the demographic setting of Makurdi, Benue State, Nigeria
Co-infection of schistosomiasis and typhoid fever presents a significant public-health challenge and complicates clinical management. Both diseases are prevalent and endemic in African regions, particularly Nigeria, but their co-dynamics remain insufficiently studied. This study developed a deterministic mathematical model to examine the co-dynamics of schistosomiasis and typhoid fever in Makurdi, Benue State, Nigeria. The basic reproduction number (R0) for the sub-models and the full co-infection model was calculated to assess the stability of the disease-free equilibrium and to conduct bifurcation analysis. The endemic equilibrium was derived. Global sensitivity analysis was conducted, and an optimal-control model was formulated with five time-dependent controls: prevention of schistosomiasis, prevention of typhoid fever, screening and treatment for schistosomiasis, screening and treatment for typhoid fever, and screening and treatment for co-infections. The model was analysed using the Pontryagin maximum principle and the forward--backward sweep method. Numerical simulations included single, pairwise, and combined interventions. The results indicate that prevention strategies for both diseases are more effective than treatment alone. The study recommends implementing concurrent control interventions for both diseases to achieve optimal co-infection management.
Copyright (c) 2026 Kazeem A. Tijani, Chinwendu E. Madubueze, Reuben I. Gweryina, Terhemen Aboiyar (Author) https://creativecommons.org/licenses/by/4.0
Weaker and deficiency of even vertex odd edge root square mean labeling graphs
This paper introduces and investigates a relaxed variant of even vertex odd edge root square mean labeling (EVOERSML), called weaker EVOERSML. For a graph G, a weaker EVOERSML is an injective labeling f : V (G) \rightarrow\ {0, 2, 4, . . . , 2(q+k)}, where k \in \mathbb\ Z+, such that all vertex labels are distinct non-negative even integers. The induced edge labels are obtained by applying either the floor or ceiling function to the square root of the average of the squares of the labels of the end vertices. This relaxed labeling framework extends the applicability of root square mean labelings to a broader class of graphs. The minimum labeling bound Kmin(G) is defined as the least integer k for which G admits a weaker EVOERSML, and methods for determining this bound are discussed. The existence of weaker EVOERSML is established for several families of graphs, and their structural properties are analyzed. The deficiency associated with weaker EVOERSML is also examined, with particular emphasis on connected graphs of orders 3, 4, and 5, yielding complete classifications and illustrative examples.
Copyright (c) 2026 K. N. Babu, S. Meenakshi (Author) https://creativecommons.org/licenses/by/4.0
Chemometric authentication of branded and crude spices using integrated GC–MS fingerprints and elemental profiling
Counterfeit and substandard spice products present significant risks to food safety, nutritional quality, and consumer confidence. This study applied an integrated chemometric approach, GC--MS chemical profiling, and elemental analysis to authenticate and discriminate among crude, genuine-branded, and potentially adulterated spice products. Twenty spice samples from curry, ginger, turmeric, nutmeg, and thyme were analysed using gas chromatography--mass spectrometry (GC--MS) for volatile compounds, flame photometry for macroelements, and atomic absorption spectrometry (AAS) for microelements, respectively. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) revealed clear separation between crude and branded spices. An optimized model framework using two principal components explained 70.55% of the total calibration variance (70.29% cross-validated variance). The hierarchical cluster analysis (HCA) dendrograms confirmed distinct clustering, highlighting close substitutability among certain spices based on their nutrient profiles. Elemental analysis indicated significant variation across samples: branded curry 3 and branded ginger 3 were rich in zinc and selenium, while branded nutmeg 1 exhibited exceptionally high iron content. Conversely, some branded products, such as branded curry powder, consistently showed low or absent levels of essential micronutrients. GC--MS profiling identified dominant fatty acids, including Octadec-9-enoic acid and Octadecanoic acid. These compositional differences underpin the discriminatory power of chemometric models, enabling reliable authentication of the crude and branded spices and the detection of potential adulteration. The study demonstrated that integrated chemical fingerprinting with multivariate analysis provides a robust framework for quality assurance and regulatory monitoring of commercial spice products.
Food Science and Technology Applied Chemistry
Copyright (c) 2026 E. Udensi, R. B. Salau, S. O. Salihu, M. M. Ndamitso, M. T. Bisiriyu (Author) https://creativecommons.org/licenses/by/4.0
Even vertex odd edge root square mean labeling of some cycle-related graphs
An injective mapping (f:V(G)\rightarrow \{0,2,4,...,2|E|\}\) is said to satisfy the even vertex odd edge root square mean labeling (EVOERSML) condition when vertices are assigned distinct even values and each edge \(uv\in E(G)\) receives a distinct odd label determined by \( \left\lceil \sqrt{\frac{f(u)2+f(v)2}\{2}} \right\rceil \quad or \quad \left\lfloor \sqrt{\frac{f(u)2+f(v)2}\{2}} \right\rfloor.\) Graphs that admit such assignments are called EVOERSML graphs. The present study examines this labeling scheme on several graph structures closely related to cycles. Constructive methods are provided for families including ladder graphs, tadpole graphs, polygon-chain graphs, dumbbell graphs, and sunlet graphs. The obtained results emphasize how parity restrictions influence labeling existence and uniqueness. This work enriches the theory of root square mean labeling and opens further scope for studying EVOERSML-type labelings in more complex graph families and applied network models.
Graph Theory, Graph Labeling, Even Vertex Odd Edge Root Square Mean Labeling (EVOERSML), Root Square Mean Labeling, Cycle Related Graphs, Ladder Graphs, Tadpole Graphs, Polygon Chain Graphs, Dumbbell Graphs, Sunlet Graphs
Discrete Mathematics or Graph Theory and Combinatorics or Pure Mathematics
Applications of quadrilateral finite element meshes with four-node, eight-node, and twelve-node for efficient microwave energy transfer in curved waveguides
This study proposes a two-dimensional higher-order finite element method for computing eigenvalues in planar and multiply connected curved domains relevant to microwave energy transfer and electromagnetic waveguide problems. A fully automated quadrilateral mesh generator implemented in MAPLE-18 is used to convert triangular finite elements into quadrilateral elements with Four-node, Eight-node, and Twelve-node so that curved boundaries can be represented with improved accuracy. The formulation combines a Galerkin finite element procedure, an automated higher-order meshing strategy, and a Gauss quadrature rule to obtain numerical solutions of Helmholtz-type eigenvalue problems. Five curved waveguide domains are considered: an annular circular domain, a circular domain with a square hole, the union of two circular disks, a circular region with two unequal holes, and an L-shaped domain with a quarter-circular curved boundary. The computed eigenvalues are compared with published numerical and analytical results. The comparisons show that increasing the element order from Four-node, Eight-node, and Twelve-node improves the accuracy of the predicted eigenvalues while reducing the number of elements required to obtain comparable convergence. The proposed method therefore provides a practical and efficient finite element framework for eigenvalue estimation in curved waveguide geometries.
Mathematics, Computational Mathematics, Applied Mathematics, Numerical Fluid Mechanics
Copyright (c) 2026 K. Lekhana, K. T. Shivaram (Author) https://creativecommons.org/licenses/by/4.0
Fuzzy semi-Markovian stochastic model for single-unit system with repairman arrival delay under Lindley lifetime distribution using bell-shaped membership function
The key objective of the present analysis is to analyze the impact of fuzziness and repairman arrival delay on the mean time to system failure (MTSF) and availability of single-unit systems. Using a semi-Markovian approach, two stochastic models were developed, and recursive relations were obtained for the reliability measures. The lifetime, failure time, and repairman arrival time are considered Lindley-distributed random variables, with a parameter represented as a fuzzy number defined by a bell-shaped membership function. The switching devices and repairs are assumed to be as good as new. The numerical results show that increasing the \alpha-cut reduces fuzziness and improves the estimates, while immediate arrival of the repairman increases system availability. The importance of the results is highlighted using a numerical example generated from a Lindley-distributed random sample of size 1000 by considering \varepsilon = 0.1 and dynamic \delta in RStudio.
Copyright (c) 2026 Monika Saini, Ashish Kumar (Author) https://creativecommons.org/licenses/by/4.0
Weibull-based reliability and profitability analysis of an industrial multi-state repairable system
Reliability is a primary concern in industrial manufacturing because operational interruptions can reduce production, cause substantial economic losses, and create workplace-safety risks. This study applies the two-parameter Weibull distribution and the regenerative point technique (RPT) to a repairable system comprising two non-identical units: a primary unit operating in parallel with a secondary unit arranged in cold standby. The model incorporates inspection, repair, and replacement activities and is used to derive transition probabilities, mean sojourn times, mean time to system failure (MTSF), steady-state availability, maintenance busy periods, and a profit function. Numerical results show that system profitability decreases as the failure rate and Weibull shape parameter increase, whereas a higher repair rate improves profitability. For the reported parameter set, profit increases from 7325.258 to 8726.043 when the repair rate rises from 0.1 to 1.0 at a=0.01, and from 4879.802 to 6970.602 at a=0.05. The analysis also identifies reported zero-profit thresholds near a=1.45, 1.38, and 1.21 for k=0.8, 1.0, and 2.0, respectively. The framework supports maintenance planning and economic assessment of multi-state repairable systems.
Copyright (c) 2026 Priya Dahiya, Jasdev Bhatti, Pankaj Thakur (Author) https://creativecommons.org/licenses/by/4.0
Complex triple-valued neutrosophic soft sets and their topological framework with AI-driven signal-template analysis
This research proposes basic operations on complex triple-valued neutrosophic soft sets (CTVNSs) and defines complex triple-valued neutrosophic soft topological spaces (CTVNSTSs) with interior and closure operators. The study also presents an application to signal-template matching using cotangent similarity. Visual comparisons of four signals (S1--S4) and four templates (T1--T4) show that T4 is the most similar template, T2 is the least similar, and T1 and T3 are moderately similar. Normalization affects dominance and makes T1 relevant in some comparisons. Correlation analysis and clustering using principal component analysis and K-means (K=2) identify one cluster (S1, S2 , and S3) and one outlier (S4). These results indicate that cotangent similarity, together with data visualization and normalization, can effectively support the analysis of complex data.
Complex triple-valued neutrosophic soft sets
Complex triple-valued neutrosophic soft topology
Complex Triple-Valued Neutrosophic Soft Sets, Complex Triple-Valued Neutrosophic Soft Topology, Cotangent Similarity Measures, Data Visualization Techniques.
Copyright (c) 2026 Raed Hatamleh, Diana Amin Mohammad Mahmoud, Haitham Qawaqneh, Hind Y. Saleh, Alaa M. Abd El-latif, Eman Almuhur, Aqeedat Hussain, Arif Mehmood, Cris L. Armada (Author) https://creativecommons.org/licenses/by/4.0
The Editor and Publisher of the Journal of the Nigerian Society of Physical Sciences hereby issue this formal retraction notice for the article titled “Fractional-order modeling of visceral leishmaniasis disease transmission dynamics: strategies in eastern Sudan” by Ghada A. Ahmed, published in Journal of the Nigerian Society of Physical Sciences, Volume 5, 2023, Article ID 1693, DOI: https://doi.org/10.46481/jnsps.2023.1693. The article has been retracted due to errors in the calculations reported in the manuscript. Since the calculations form the basis of the fractional-order mathematical modelling, disease transmission analysis, and resulting conclusions, the identified errors affect the reliability of the findings presented in the article. The retraction decision had previously been reflected on the article page; however, a separate formal retraction notice was not published at that time. This notice is therefore being published now to correct that omission, improve transparency, and ensure that the retraction is properly visible to readers, indexing services, and metadata platforms. The original article should be regarded as retracted and should not be cited as reliable scientific evidence except in discussions relating to the retraction itself. Retracted article: Ghada A. Ahmed, “Fractional-order modeling of visceral leishmaniasis disease transmission dynamics: strategies in eastern Sudan,” Journal of the Nigerian Society of Physical Sciences 5 (2023) 1693. https://doi.org/10.46481/jnsps.2023.1693 Retraction decision date: 06-12-2023 Retraction notice publication date: 26-04-2026
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
The Editor and Publisher of the Journal of the Nigerian Society of Physical Sciences hereby issue this formal retraction notice for the article titled “Preparation and Characterization of High Performance Dye Sensitized Solar Cells with Silver Nanoparticles in nanocomposite Photoanode” by Eli Danladi, Jamila Tasiu, and Lucky Endas, published in Journal of the Nigerian Society of Physical Sciences, Volume 2, Issue 1, February 2020, pages 7–11, DOI: 10.46481/jnsps.2020.24 [1]. The article was retracted after the authors notified the journal of an incorrect report of the X-ray diffraction analysis. Since the X-ray diffraction result formed part of the material characterization presented in the article, the reported error affected the reliability of the relevant findings and conclusions. Following the authors’ notification and editorial assessment, the article was retracted to maintain the accuracy and integrity of the scholarly record. The retraction decision had previously been reflected on the article page; however, a separate formal retraction notice was not published at the time. This notice is therefore being published now to correct that omission, improve transparency, and ensure that the retraction is properly visible to readers, indexing services, and metadata platforms. The original article should be regarded as retracted and should not be cited as reliable scientific evidence except in discussions relating to the retraction itself. Retraction notice publication date: 26-04-2026
Modeling the dynamics of type 2 diabetes with modifiable lifestyle influence
Type 2 diabetes mellitus (T2DM) is a multifactorial metabolic disorder driven by the interaction of insulin resistance, $\beta$-cell dysfunction, and modifiable lifestyle and environmental factors. Despite substantial advances in treatment, the global burden of T2DM continues to increase, highlighting the need for integrative frameworks that capture both biological and behavioral drivers of disease progression. In this study, we develop and analyze a deterministic compartmental model that incorporates genetic susceptibility, environmental exposure, diagnosis, treatment, and a modifiable lifestyle pressure variable representing socioeconomic and behavioral influences. The model is formulated as a system of nonlinear ordinary differential equations and analyzed using standard qualitative techniques, including positivity, boundedness, and stability analysis. We establish key dynamical properties and show that a disease-free equilibrium exists only under restrictive conditions, whereas an endemic equilibrium persists when environmental pressure and disease conversion are active. A semi-analytical solution obtained via the differential transformation method (DTM) is used to explore system dynamics and intervention scenarios. The results indicate that reducing lifestyle pressure, improving awareness, and increasing treatment initiation significantly reduce disease prevalence. Specifically, lowering lifestyle pressure or enhancing its dissipation reduces the equilibrium burden, while increasing awareness limits transitions into disease states and reduces overall disease spread.
Type 2 diabetes mellitus, population modeling, compartmental models, stability analysis, environmental pressure
Copyright (c) 2026 Opeyemi Odetunde, Tunde Alesinloye, Omoniyi Francis (Author) https://creativecommons.org/licenses/by/4.0
CropGAN: A conditional GAN framework for synthetic tabular data augmentation in crop recommendation systems
Crop recommendation systems play a crucial role in precision agriculture by enabling informed decisions about which crops to cultivate in a specific location. However, their performance is often hindered by the inherent scarcity, imbalance, and regional bias of tabular agricultural datasets. These limitations reduce the reliability and generalizability of crop recommendation models, especially in data-scarce regions. Existing synthetic data generation methods, such as the Synthetic Minority Oversampling Technique (SMOTE) and variational autoencoders (VAEs), struggle to handle high-dimensional, structured agricultural data with categorical variables. Moreover, existing generative adversarial networks (GANs) are primarily image-focused. This study proposes a re-engineered GAN, termed the Crop Recommendation GAN (CropGAN), to generate tabular, multi-crop recommendation data using a class-conditioning mechanism. The framework was designed to handle complex, non-linear datasets with both numerical and categorical variables, addressing dataset size, imbalance, and limited diversity in multi-crop datasets. CropGAN was trained on a dataset of 5,000 samples with 10 crop classes and evaluated against SMOTE and VAE using statistical data-quality and classification-performance metrics. In terms of data-quality assessment, SMOTE best preserved the original data distributions, while CropGAN introduced greater diversity in the synthetic datasets. In terms of classification performance, models trained on CropGAN-generated samples consistently achieved the highest performance, with the support vector machine (SVM) yielding the best accuracy of 99.4%. This result suggests that adversarial learning can be adapted for tabular agricultural datasets to improve the performance of crop recommendation systems in data-scarce regions.
Copyright (c) 2026 Dekera Kenneth Kwaghtyo, Christopher Ifeanyi Eke, Timothy Moses (Author) https://creativecommons.org/licenses/by/4.0
A hybrid process-based and neural network post-processing model for cowpea yield prediction under climate variability in North Central Nigeria
Agriculture has sustained human civilisation for centuries, yet it remains a sector in critical need of technological advancement. Existing crop-growth and yield-prediction methods lack a simple and generic framework that relies on climate data with minimal parameters, particularly for leguminous crops. Addressing this gap, this study develops a Crop Growth Rate Computation Model (CGRCM) to simulate crop growth with a focus on soil nitrogen utilisation. The CGRCM integrates climate variables and nine parameters to predict cowpea growth in terms of above-ground biomass and final yield, derived from biomass at maturity and harvest index. Climatic input data and soil parameters were obtained through remote sensing for Makurdi and Mokwa in North Central Nigeria, covering 32 growing seasons (1990-2021). The model was calibrated for the FUAMPEA cultivar and implemented using a Python-based neural network post-processor. Training was conducted on data from 1990--2017 and testing on data from 2018--2021. Results show that the CGRCM effectively captures biomass responses to drought, temperature and heat stress. The model achieved strong agreement with observed yields, with an MAE of 134.2, an RMSE of 153.6 and a prediction accuracy of 91.4% for Makurdi, and an MAE of 109.4, an RMSE of 113.7 and a prediction accuracy of 93.5% for Mokwa. Bootstrap confidence interval, paired t-test and Diebold-Mariano tests confirmed that the CGRCM performed better, demonstrating its reliability as a scalable and data-efficient tool for crop-growth prediction.
Climate, Crop Growth, Yield Prediction, Neural Network
Intelligent Crop growth and Yield Prediction
Computer Science and Agricultural Informatics
Copyright (c) 2026 Onyeke Idoko Charles, John Kolo Alhassan, Mohammed Danlami Abdulmalik, Kehinde Dele Tolorunse (Author) https://creativecommons.org/licenses/by/4.0
Cattle rumen content, rice husk and cow horn biochars as amendments for enhanced remediation of petroleum-hydrocarbon-contaminated soil: physicochemical characterization and performance evaluation
Petroleum hydrocarbon contamination of soil is a major environmental challenge in crude-oil-producing areas. Agricultural and abattoir wastes are promising low-cost soil amendments but remain greatly underutilized. In this study, cattle rumen content (CRC), rice husk biochar (RHB) and cow horn biochar (CHB) were converted into soil amendments and applied to petroleum-hydrocarbon-contaminated soils. Physicochemical characterization showed that CaO, SiO2 and SO3 were predominant in CRC, RHB and CHB, respectively. Quantitative phase X-ray diffraction gave 65 wt% and 56 wt% graphite in RHB and CHB, respectively, while a chaoite-like phase was tentatively observed in CRC at about 28 wt%. Brunauer--Emmett-Teller surface areas were 356.0, 254.9 and 185.9 m2 g-1 for CHB, RHB and CRC, respectively, with all materials exhibiting mesoporous structures (pore diameters 2.55-2.65 nm). RHB had a higher ash content (50%) than CHB (16%) and CRC (12%). When the amendments were applied to contaminated soil with an initial total petroleum hydrocarbon (TPH) concentration of 1307 mg kg-1 for 60 days, amendment type and application rate affected TPH reduction. RHB and CHB reduced TPH by 94% and 93%, respectively, at a 20% application rate, whereas CRC reduced TPH by 98% at a 10% application rate. Hydrocarbon-utilizing bacteria increased with time, with CRC producing the strongest stimulation. The study highlights the practical value of converting agricultural and abattoir wastes into effective soil remediation amendments for hydrocarbon-impacted areas.
Copyright (c) 2026 Faustina Onyeyirichi Iniaghe, Paschal Okiroro Iniaghe, Godswill Okeoghene Tesi, Wisdom Ivwurie (Author) https://creativecommons.org/licenses/by/4.0
On subgraph relationships between graph products
Graph products play an important role in graph theory by providing systematic methods for constructing complex graphs from simpler ones and by revealing structural relationships among different graph classes. In this paper, we investigate subgraph relations among several standard graph products, including the Cartesian, lexicographic, tensor, modular, co-normal, strong, homomorphic, and rooted products. By comparing their defining adjacency conditions, we establish a collection of inclusion results between these products. In particular, we prove that the Cartesian product is a subgraph of the lexicographic, co-normal, and strong products, while the lexicographic product is itself a subgraph of the co-normal product. We further show that the tensor product is a subgraph of the lexicographic, modular, strong, and co-normal products. In addition, the strong product is shown to be a subgraph of both the lexicographic and co-normal products. Finally, we establish that the rooted product forms a subgraph of the Cartesian, lexicographic, co-normal, and strong products. Illustrative examples are included to visualise these relationships. The results obtained provide a clearer understanding of the structural hierarchy among graph product operations and may support further investigations in algebraic graph theory and network modelling.
Copyright (c) 2026 Jinta Jose, Ninu S. Lal, Bobin George (Author) https://creativecommons.org/licenses/by/4.0
Numerical study of hall current and thermophoresis effect on the hydromagnetic casson nano fluid flow over a stretching surface
This study investigate, the numerical analysis of heat and mass transfer in Hydro magnetic Casson Nano particle flow past a stretching surface by including the effect of Hall current, thermophoresis and inclined magnetic field. The governing partial differential equations are transformed into nonlinear ordinary differential equations through similarity transformations and then solved by using the MATLAB inbuilt bvp4c method.The variations due to the key parameters occurring in the governing mathematical formulations on temperature, rate of flow and concentration profile are presented graphically and the variations in the primary attributes of the flow field namely, skin friction,Sherwood and Nusselt number are presented through a table. The outcomes of the analysis show that the Hall current parameter $(m)$ increases the transverse velocity while it decreases the temperature and nanoparticle concentration. On the other hand increasing magnetic field parameter $(M)$ enhance the nanoparticle concentration and it decreases the tangential and transverse velocities. The thermophoresis parameter $(Nt)$ results in increasing the temperature and concentration profile. The inclination angle $(\gamma)$ with the vertical axis reduces the velocity profiles. The analysis provides a valuable insight in optimizing the transport of heat and mass in hydro magnetic Casson Nano fluid flow systems which will find applications in advanced cooling and thermal management technologies.
Copyright (c) 2026 Jatin Kumar, Khem Chand, Pankaj Thakur (Author) https://creativecommons.org/licenses/by/4.0
Temperature- and frequency-dependent ultrasound response of a Poly(N-vinylcaprolactam) polymer gel dosimeter
Poly(N-vinylcaprolactam) (PNVCl) polymer gels have recently been proposed for radiation dosimetry; however, their ultrasound response remains inadequately defined. This study assessed the feasibility of pulse-echo ultrasonography for evaluating the dose response of a PNVCl polymer gel dosimeter. Gel samples composed of gelatin type A, deionized water, BIS, PNVCl, and tetrakis(hydroxymethyl)phosphonium chloride (THPC) were synthesized and irradiated with 6 MV photon beams. Dose-response measurements were performed from 0 to 30 Gy at 200 cGy min-1, and dose-rate dependence was assessed at 100, 300, and 600 cGy min-1 for doses of 5 and 10 Gy. Ultrasound measurements were acquired with transducers operating between 1 and 4 MHz. The speed of sound and attenuation coefficient were determined using pulse-echo time-of-flight and frequency-domain analyses, respectively. All ultrasonic parameters increased with absorbed dose, with the clearest linear response observed at 2 MHz. The response was temperature dependent, with higher sensitivity at room temperature than at 15 oC. Dose rate and photon beam energy showed no clear influence on the ultrasonic response within the investigated range. These findings support pulse-echo ultrasonography as a practical method for assessing PNVCl polymer gel dosimeters, provided that temperature is carefully controlled and temperature-specific calibration curves are used.
Copyright (c) 2026 Ammar A. Oglat, Khalid A Rabaeh, Mohammed Dawood Salman, Molham M Eyadeh, Kholoud Zyoid (Author) https://creativecommons.org/licenses/by/4.0
Strategic interventions in schistosomiasis control: leveraging mass drug administration, public engagement, and intermediate host control to disrupt transmission dynamics
Schistosomiasis is caused by parasitic flatworms known as schistosomes. Humans become infected when they come into contact with freshwater containing these parasites. Many communities have only a limited understanding of how schistosomiasis spreads or how mass drug administration (MDA) can help to manage it. Furthermore, some regions neglect snail control efforts, despite snails are key intermediate hosts in disease transmission. In this study, we employ mathematical equations to illustrate how schistosomiasis spreads when public enlightenment campaigns, mass drug administration, and snail control measures are implemented. We calculate the control reproduction number, $R_c,$ and examine the existence and stability of both the disease-free and endemic equilibrium states. Our results indicate that when people are aware of the disease, participate in mass drug administration, and intensify snail control efforts, the spread of the disease slows. Sensitivity analysis reveals that mass drug administration and increasing snail mortality rates are the most effective strategies for reducing $R_c.$ Simulations suggest that improved sanitation and educating people about the benefits of taking medicine can reduce the number of infected individuals. Furthermore, treating water or introducing predatory fish can help to lower the population of disease carrying snails. These findings provide valuable insights that can assist public health stakeholders in making informed decisions to control schistosomiasis.
Copyright (c) 2026 Agatha Abokwara, Chinwendu E. Madubueze, Reuben I. Gweryina, Terhemen Aboiyar (Author) https://creativecommons.org/licenses/by/4.0
Numerical simulation of Cs2TiBr6-based all-inorganic photovoltaic perovskite solar cell employing SCAPS-1D software
Lead halide perovskite solar cells (PSCs) show considerable promise, but their inherent drawbacks, including low stability and toxicity, pose environmental concerns. Therefore, exploring lead-free perovskite materials as potential alternative absorber layers is imperative. This work investigates a Cs2TiBr6/CZTSe photoactive heterostructure in which Cs2TiBr6 serves as the primary wide-bandgap absorber while CZTSe provides additional near-infrared absorption and hole transport. Numerical simulation is an effective way to identify suitable materials and device architectures for efficient photovoltaic systems, and it reduces fabrication costs and material wastage arising from extensive experimental trials. The SCAPS-1D software was employed to propose the FTO/CeOx/Cs2TiBr6/CZTSe/CuO/Pt electron transport layer-assisted (ETL-assisted) PSC, which delivered a high power conversion efficiency (PCE) of 27.12% after optimization of structural parameters. Results show an increase in PCE from 27.12% to 36.10% with increasing sunlight concentration from 1 to 50 suns. Comparatively, the optimized ETL-free counterpart design, FTO/Cs2TiBr6/CZTSe/CuO/Pt, yielded a comparable and slightly higher PCE of 27.14%. This ETL-free design represents a first attempt for the Cs2TiBr6-based perovskite absorber and provides a strategy for reducing fabrication cost and device complexity associated with multistack configurations.
Copyright (c) 2023 P. R. Jubu, A. D. Otor, A. N. Abutu, A. O. Aransiola, C. Amakom, K. I. Udofia, I. K. Nwokolo, A. A. Goje, I. I. Ayogu, M. M. Gururani, E. E. Oguzie (Author) https://creativecommons.org/licenses/by/4.0
Feature-optimized hybrid CNN–ViT architecture for sustainable vision-based condition assessment in agriculture
Early detection of structural and physiological changes in plants remains a difficult challenge for computer vision because of large intra-class variation and environmental noise. This paper integrates feature enhancement using Excess Green (ExG) and Excess Red (ExR) vegetation indices with feature compression using principal component analysis (PCA) and an asymmetric convolutional neural network (CNN)--Vision Transformer (ViT) fusion architecture for multi-crop plant-disease classification. Preprocessing involves extracting ExG and ExR, performing statistical normalization, and applying PCA-based feature compression to enhance discriminative ability and reduce redundant spectral information. The CNN component generates hierarchical texture encodings, while the ViT component produces self-attention encodings suited to capturing global associations. The complementary feature spaces are combined through a cross-domain fusion layer to improve representation capability. The proposed system achieves high classification accuracy (98%) and robustness across multiple crop datasets. Although edge efficiency and explainability still need to be addressed before deployment in real-world agricultural scenarios, these aspects are outlined as future work.
Copyright (c) 2026 Muhammad Musa Liman, Rajesh Prasad, Hauwa Ahmad Amshi (Author) https://creativecommons.org/licenses/by/4.0
A modified Dai–Yuan method with restart mechanism for nonlinear system of equations with a signal recovery
The Dai--Yuan method is an important iterative scheme for solving unconstrained optimization problems, but it often requires exact or Wolfe-type line searches to satisfy descent or sufficient descent conditions. It may also perform poorly due to the jamming phenomenon. This paper presents a modified three-term Dai--Yuan method for solving constrained systems of nonlinear monotone equations. The search direction of the proposed method includes a nonnegative parameter whose value is determined through singular-value analysis of the iteration matrix. The method also incorporates a restart mechanism that ensures global convergence regardless of the line-search procedure used. Theoretical analysis establishes the global convergence and convergence rate of the proposed scheme under suitable assumptions. Numerical experiments on constrained nonlinear equations and sparse signal reconstruction demonstrate the efficiency of the method compared with related algorithms.
Copyright (c) 2026 A. S. Halilu, M.A. Mohamed, I. A. R. Moghrabi, K. Ahmed, S. M. Ibrahim, M. Y. Waziri, S. Murtala, H. Abdullahi, M. A. Jada (Author) https://creativecommons.org/licenses/by/4.0
Velocity-in/dependent double folding analysis of 12C + 12C elastic scattering cross section at different energies
This study investigates the elastic scattering cross sections for 12 C + 12 C over a wide range of incident energies from 78.85 to 420 MeV using the velocity–dependent São Paulo Potential version 2 (SPP2) and the velocity–independent Brazilian Nuclear Potential (BNP) within the optical-model-based double folding (DF) framework. Two different density distributions for 12C, experimental matter density (ED), and the Dirac-Hartree-Bogoliubov (DHB) theoretical matter density, were employed. The analysis compares the computed potentials, obtained by folding the density distributions, and evaluates the accuracy of the results through comparison with experimental data. Two approaches were explored to determine the depths of the potentials, highlighting their performance and sensitivity to energy and density distributions. Results indicate good agreement with experimental data, with SPP2 generally outperforming BNP. Analysis of volume integrals and reaction cross sections further elucidates the behavior of the potentials and their implications on scattering phenomena. Overall, this study provides insights into the dynamics of nuclear interactions at intermediate energies, contributing to the understanding of nuclear reactions.
Copyright (c) 2026 Sunday D. Olorunfunmi, Armand Bahini, Samuel A. Adeojo (Author) https://creativecommons.org/licenses/by/4.0
A descent-safeguarded PRP-type conjugate gradient with global convergence and CUTEst benchmarks
Conjugate gradient (CG) methods are widely used for solving large-scale unconstrained optimization problems. Well-known methods, such as the Polak-Ribière-Polyak and Hestenes-Stiefel methods, may not satisfy the global convergence property. To improve this behavior, this paper constructs a new three-term CG method based on the PRP framework. The proposed method is shown to satisfy sufficient descent and global convergence properties. To study its behavior, we compare its performance with those of CG-Descent 6.8, the non-negative Dai-Liao method, and the HS+TA method by applying them to more than 180 optimization problems selected from the CUTEst library. The numerical results show that the new method performs better than the three competing methods and other recently published CG methods in terms of the number of iterations, the number of function and gradient evaluations, and the CPU time required to solve the test problems. To illustrate accuracy, we report the function and gradient values at the obtained solutions for all test problems.
Conjugate gradient method, Global convergence, Image restoration, Unconstrained Optimization
Copyright (c) 2026 Ahmad Alhawarat, Sultanah Masmali, Shahrina Ismail, Hamid El Hor (Author) https://creativecommons.org/licenses/by/4.0
Synergistic intelligence: a novel hybrid model for precision agriculture using k-means, naive Bayes, and knowledge graphs
This study presents a novel hybrid knowledge discovery model integrating K-Means clustering, Naive Bayes classification, and Knowledge Graph technology to address interpretability and data heterogeneity challenges in precision agriculture. The proposed framework first applies K-Means to segment agro-ecological zones using multi-source data (soil, climate, satellite imagery), then employs Naive Bayes to classify crop productivity tiers, achieving 89% accuracy—surpassing standalone benchmarks (Naive Bayes: 86%, Random Forest: 87.5%). A Neo4j-based Knowledge Graph contextualizes these outputs, demonstrating 95% schema completeness and efficient querying (0.1559s latency), while enabling dynamic analysis of soil-climate-crop relationships. Pilot trials confirmed actionable impacts, including 22% reduced water use and 18% less fertilizer waste in targeted farms. By unifying unsupervised/supervised learning with semantic reasoning, this work advances scalable, interpretable decision support systems for sustainable agriculture, offering a replicable template for global food security initiatives.
Copyright (c) 2025 Catherine N. Ogbizi-Ugbe, Osowomuabe Njama-Abang, Samuel Oladimeji, Idongetsit E. Eteng, Edim A. Emanuel (Author) https://creativecommons.org/licenses/by/4.0
Concentrations of heavy metal content in indoor dust and potential exposure in preschool children
Indoor dust serves as a medium for the deposition of heavy metals, and young children's rapid physical growth and hand-to-mouth behavior expose them to the impacts of heavy metals. This project aimed to measure the concentrations of heavy metals in classroom dust from the selected preschools in southwestern Nigeria. Dust samples were taken via a dust collector and analyzed quantitatively and qualitatively via the Atomic Absorption Spectroscopy (AAS) scheme. The descriptive and inferential statistical method was employed for the data analysis, and standard calibration, recovery analysis, and blank determination were carried out for the quality control measures. It was found that, in the dry season, the total metal concentrations in dust were 1.82 gg-1 Cu to 80.00 gg-1 Zn, whereas, in the wet season, the heavy metal concentrations were 0.83 gg-1 Co to 38.43 gg-1 Zn. Hence, the selected preschool dust was significantly enriched with high levels of Cd and As contamination but unpolluted for Co, Cu, Mn, and Pb across all examined metals. The results indicate elevated concentrations of lead (Pb) and cadmium (Cd) in high-traffic indoor spaces, with levels exceeding recommended safety thresholds. Lead (Pb) exposure is linked to neurodevelopmental disorders and reduced cognitive function, Cadmium (Cd) can cause kidney damage and impair bone development, while arsenic (As) is associated with immunotoxicity and an increased risk of cancer. To mitigate these risks, this study recommends practical measures, including frequent wet cleaning of floors and surfaces to reduce dust accumulation and limiting the use of materials known to contain heavy metals, such as lead-based paints and older plumbing fixtures.
Copyright (c) 2025 J. A. Akinpelu, K. P. Ojo, S. O. Salawu, G. O. Olutona, F. O. Aweda, O. O. Jegede (Author) https://creativecommons.org/licenses/by/4.0
A novel mathematical model for transmission dynamics of HPV and cervical cancer progression with cancer-reliant awareness
Human papillomavirus (HPV) is a global health problem that causes the vast majority of cervical cancers. { A novel mathematical model for HPV, as it progresses to cervical cancer, was formulated using a system of six ordinary differential equations that incorporates behavioural dynamics in the description of some control measures. In particular, this study introduces a novel chained dependency framework where the awareness parameter depends on Cancer burden, and the screening rate is a function of awareness}. The essential epidemiological features of the model, such as the positivity and boundedness of the model, the basic reproduction number $\mathcal{R}_0$, the disease-free equilibrium, and the endemic equilibrium, are derived. The disease-free equilibrium is shown to be locally and globally asymptotically stable when $\mathcal{R}_0 < 1$. The endemic level of infection is expressed in terms of $\mathcal{R}_0$, and deductions are made from their relationship. Sensitivity analysis is conducted to determine which parameters are of utmost importance using a global sensitivity analysis method called the Partial Rank Correlation Coefficient (PRCC) method. Parameters are set from the literature, and simulation is carried out using the Runge--Kutta 4th order (RK4) method to explore the impact of various parameters on model dynamics. { The results of the analyses not only reveal the impact of awareness campaigns, routine screening programmes, and vaccination in reducing HPV and cervical cancer, but also demonstrate how improved disease outcomes are directly linked to the chained awareness screening structure rather than the usual epidemic dynamics.
Bio-Maths, Applied maths, Mathematical Epidemiology
Basic reproduction number, Local stability, numerical simulation
Numerical Simulation and Mathematical Computation
Copyright (c) 2026 Ogechi Regina Amanso, Jeconia Okelo Abonyo, Phineas Roy Kiogora, Obiora Cornelius Collins (Author) https://creativecommons.org/licenses/by/4.0
Existence and uniqueness results for double jump fractional uncertain differential equations
Fractional uncertain differential equations have been used to model random processes in economics and other fields that exhibit jumps, dependency, and nonlinearities, and which possess uncertainties due to limited data and inadequate models. In this paper, a double V-jump fractional uncertain differential equation (DV-FUDE) is presented as a $\theta^{th}$-order Riemann-Liouville or Caputo fractional uncertain differential equation with the addition of two V-jump independent processes on different filtrations. The equation models systems possessing two sources of uncertain shocks attributed to internal and external factors, respectively. Exact solutions in the case of time-dependent coefficients are given in terms of the Mittag-Leffler function. Sample continuity, existence, and uniqueness for the general Riemann-Liouville and Caputo DV-FUDE are established using the Banach Fixed Point Theorem, under global Lipschitz and linear growth conditions on the coefficients. Some extensions and possible areas of application are highlighted for future research.
Fractional uncertain differential equation
fractional uncertain differential equation.
Fractional Uncertain Differential Equations
Copyright (c) 2026 Philip Ajibola Bankole, Mabel Eruore Adeosun, Sunday Emmanuel Fadugba, Tolulope Fadina, Christopher Thron (Author) https://creativecommons.org/licenses/by/4.0
Numerical solution of fractional advection-diffusion equation with generalized Caputo derivative using shifted ultraspherical collocation method
In this article, we develop an approximate technique for solving a fractional advection-diffusion equation with a generalized Caputo derivative via a finite difference scheme. This technique employed the properties of shifted ultraspherical polynomials, which combine the finite difference scheme for the temporal discretization derivative and express the approximate solution in terms of shifted ultraspherical polynomials. The convergence and the stability of the proposed technique are proved. Numerical examples are considered, and the obtained results are compared with the analytical results and those obtained in the literature to establish the accuracy of the technique. The proposed techniques, based on the generalized Caputo derivative and the properties of shifted ultraspherical polynomials, produce robust results and encompass those obtained using several other families of orthogonal polynomials as demonstrated in the results of the examples considered.
Generalized Caputo Fractional Derivative (GCFD)
Copyright (c) 2026 K. Issa, A. T. AbdulKareem, A D. Adeshola, R. A. Bello (Author) https://creativecommons.org/licenses/by/4.0