Proceedings of the Nigerian Society of Physical Sciences
Welcome to the Proceedings of the Nigerian Society of Physical Sciences! As the premier platform for showcasing cutting-edge research and innovative discoveries in the field of physical sciences, we are delighted to welcome you to our digital space. Our proceedings serve as a vital repository of knowledge, capturing the insights and contributions of esteemed researchers, scholars, and practitioners from across Nigeria and beyond. Within these pages, you will find a diverse array of scholarly works presented at our esteemed conferences, spanning a wide spectrum of disciplines within the physical sciences. From physics and chemistry to astronomy and geology, our proceedings encompass the latest advancements and breakthroughs that are shaping the future of scientific inquiry. Our commitment to excellence extends to the rigorous peer-review process undertaken by our esteemed panel of experts, ensuring that only the highest quality research receives a place within our proceedings. As a result, readers can trust in the reliability and integrity of the content presented here. Whether you are a seasoned researcher seeking to stay abreast of the latest developments or a curious enthusiast eager to explore the frontiers of scientific discovery, the Proceedings of the Nigerian Society of Physical Sciences offers a wealth of knowledge and insight to enrich your understanding. We invite you to delve into our archives, explore our latest publications, and join us on a journey of exploration and discovery. Together, let us push the boundaries of knowledge and inspire future generations of scientists to pursue their passions and make meaningful contributions to the world of physical sciences. Welcome to our community, where curiosity knows no bounds, and the pursuit of knowledge is celebrated.
Measurement of dielectric properties of prepared stable nanofluids using coated TiO2 nanoparticle-based palm kernel oil
Demand for environmentally friendly, high-performance insulating fluids has increased interest in bio-based nanofluids with improved dielectric behaviour. This study measures and analyses stable nanofluids prepared by dispersing oleic-acid-coated titanium dioxide (TiO2) nanoparticles in methyl ester obtained from palm kernel oil. Fourier-transform infrared (FTIR) spectroscopy was used to characterise the coated nanoparticles, and nanofluids were prepared at controlled concentrations using mixing and stabilisation procedures. The samples were assessed for viscosity, pour point, flash point, relative permittivity, loss factor, leakage current, and direct-current (DC) and alternating-current (AC) breakdown strength. Dielectric measurements were carried out over 20--200 kHz using a Rohde & Schwarz HM8118 programmable inductive-capacitive-reactance bridge. The results show that oleic-acid coating improved nanoparticle dispersion stability, while increasing nanoparticle concentration enhanced dielectric performance. Among the tested samples, ECNF0.6 gave the best insulating response. The findings indicate that TiO2-based palm kernel oil nanofluids are promising sustainable insulating materials for oil-filled electrical power equipment.
Copyright (c) 2026 Muhammad Ya'u Muhammad, Abdelghaffar Amoka Abdelmalik, Yusuf Musa Abubakar, Abubakar Ibrahim Balarabe (Author) https://creativecommons.org/licenses/by/4.0
Optimization of HEMA composition in polymer gel dosimeters for cost-effective radiation measurement
Polymer gel dosimeters (PGDs) are potential tools for radiation measurement in radiotherapy treatment planning systems (TPSs) and nuclear energy monitoring. Optimization of monomer composition is an essential aspect of PGD formulation because it helps achieve optimal dosimetric performance. In this study, 2 hydroxyethyl methacrylate (HEMA), in combination with N,N'-methylene-bis-acrylamide (Bis), was optimized to improve dose-evaluation efficiency and cost-effectiveness. The results show that the HEMA-free formulation containing 2% (w/w) Bis had the lowest sensitivity. When Bis and HEMA were both added, sensitivity increased linearly with increasing total monomer percentage (%T). However, when the weight fraction (WF) of Bis was fixed and the co monomer percentage (%C) of HEMA increased from 0 to 60%, sensitivity decreased beyond the optimal value because of exhaustion of the vinyl groups in Bis. The mass density of the HEMA--Bis PGDs (≈ 1.04 g cm−3), their electron density (ρe = 3.42 × 1023–3.43 × 1023 cm−3), their number of electrons per unit mass (ne = 3.28 × 1023–3.30 × 1023 g−1), and their effective atomic number (Zeff = 7.3624–7.4176) are comparable to those of water and muscle tissue, indicating water and tissue equivalence. These findings not only yield a more efficient HEMA-based PGD but also improve cost-effectiveness, thereby increasing affordability for healthcare applications.
Copyright (c) 2026 Muhammad Alhassan, Azhar Abdul Rahman, Iskandar Shahrim Mustafa (Author) https://creativecommons.org/licenses/by/4.0
Phytochemical profiles and evaluation of free radical scavenging activities of Cassiasieberiana using 1,1-diphenyl-2-picryl hydrazyl and2,2′ -Azino-bis-(3-ethylbenzothiazoline-6-sulfonic acid)
Cassia sieberiana is used in traditional medicine in West Africa, including Nigeria. This study evaluated the phytochemical profile and free radical scavenging activities of C. sieberiana root bark extracts (weighing 100 g). The powdered root bark was extracted with petroleum ether, methyl acetate, acetone, chloroform, methanol (MeOH), and 70% MeOH using a Soxhlet extractor, and the resulting extracts were concentrated under reduced pressure. Methyl acetate, MeOH, and 70% MeOH extracts were analyzed qualitatively and quantitatively using standard phytochemical methods, and their antioxidant activities were assessed using 1,1-diphenyl-2-picryl hydrazyl (DPPH) and 2,2-azinobis-ethyl benzothiozoline-6-sulphonic acid (ABTS) assays. Qualitative analysis indicated the presence of saponins, quinones, phenols, steroids, tannins, flavonoids, terpenoids, anthraquinones, cardiac glycosides, alkaloids, carbohydrates, glycosides, and coumarins in the extracts. The highest contents of flavonoids, phenolics, tannins, saponins, steroids, and alkaloids were recorded in methyl acetate (38.74 µg QE/g), MeOH (259.78 µg GAE/g), methyl acetate (228.67 µg TAE/g), MeOH (961.88 µg DE/g), 70% MeOH (108.08 µg βSSE/g), and 70% MeOH (1.81 µg CE/g), respectively. The extracts showed substantial free radical scavenging activities, with IC50 values of 30.19 µg/mL for the methyl acetate extract in the DPPH assay and 15.20 µg/mL for the methyl acetate extract in the ABTS assay. These findings support the antioxidant potential of C. sieberiana root bark and its possible value as a source of natural antioxidants.
Copyright (c) 2026 L. A. Magashi (Author) https://creativecommons.org/licenses/by/4.0
Fraud detection in Nigerian financial card transactions using a graph attention network
The growth of the digital payment ecosystem presents significant challenges to economic stability and consumer trust because of financial fraud. Traditional rule-based and machine-learning approaches often fail to capture the complex relational patterns present in fraudulent transaction networks. This study develops and evaluates a Graph Attention Network (GAT) model for detecting fraudulent card transactions in the Nigerian financial sector by using graph-based representations to capture relationships among entities. We constructed a heterogeneous graph representation of transaction data from a Nigerian bank, in which nodes represent cards, merchants, account holders, and transactions, while edges represent transaction relationships. A GAT architecture was implemented to learn node embeddings, and an ablation study was conducted to evaluate the contribution of graph structure by comparing the proposed GAT with a no-edge GAT and a randomized-edge GAT. The model was also benchmarked against GraphSAGE, a Gated Graph Recurrent Network (GRNN), a Graph Convolutional Network (GCN), and non-graph baselines including XGBoost, a feedforward neural network, and LightGBM. All models were evaluated on a real-world Nigerian banking dataset using stratified train--test splits, normalized numerical features, and median imputation for missing age values. The results show that graph structure substantially improves fraud detection: the proposed GAT achieved an F1-score of 0.9612, outperforming the no-edge GAT (0.2650) and the randomized-edge GAT (0.8769). Although the GAT achieved a higher F1-score than the GCN (0.9612 versus 0.8998), GraphSAGE achieved a higher AUC than the GAT (0.9961 versus 0.9929), indicating a trade-off between threshold-dependent performance and ranking performance.
Copyright (c) 2026 Oladayo Tosin Akinwande, Sulaimon Adebayo Bashir, Opeyemi Aderiike Abisoye, Solomon Adelowo Adepoju (Author) https://creativecommons.org/licenses/by/4.0
Prediction of heavy metal concentrations in soil using machine learning models
The application of machine learning (ML) models is increasingly used to predict pollutant levels in environmental samples, particularly heavy metals. This study predicted lead (Pb), zinc (Zn), and cadmium (Cd) concentrations in soil samples collected near a plastic recycling facility in Ogun State, Nigeria. Atomic absorption spectrophotometry (AAS) showed low heavy metal concentrations (mg kg−1 ), with Pb ≤ 0.38, Cd ≤ 0.40, and Zn ≤ 7.55, below the regulatory limits considered in this study. Five regression models—linear regression, Random Forest, Extra Trees, XGBoost, and CatBoost—were evaluated using soil physicochemical properties as predictors. XGBoost produced the highest R2 values for Pb (0.973) and Cd (0.971), whereas Extra Trees produced the highest R2 value for Zn (0.957). CatBoost and Random Forest also showed strong predictive ability, with generally low root mean square error (RMSE) and mean absolute error (MAE) values. The feature-importance results indicated that nitrogen, total organic carbon, and organic matter were important predictors of heavy metal concentrations. The findings suggest strongly nonlinear relationships between soil properties and heavy metal concentrations and support ensemble ML models as useful tools for rapid preliminary monitoring. The results should, however, be interpreted cautiously because of the limited number of experimental samples and the use of rule-based synthetic data augmentation.
Copyright (c) 2026 Temitope M. Osobamiro, Samuel O. Sipeolu, Emmanuel F. Ayo, Sakeenah O. Abdullah (Author) https://creativecommons.org/licenses/by/4.0
Digital money and youth spending behavior in Ebonyi State, Nigeria
This paper examines digital money adoption and use among youths in Ebonyi State, Nigeria, and how it influences their spending behavior. A convergent mixed-methods design was adopted, and data were collected through structured questionnaires administered to 400 respondents and 20 in-depth interviews. The study used both qualitative and quantitative data-collection methods. Thematic analysis was used to analyze the qualitative data, whereas descriptive statistics, multiple regression, and correlation analysis were used to analyze the quantitative data. The findings reveal a high adoption rate of 72% among youths, driven by education, income, financial literacy, social influence, and trust. Digital money use substantially increased spending, especially on mobile data, airtime, and peer-to-peer transfers. Adoption is constrained by trust and risk concerns, and youths deploy various strategies to mitigate these risks. The paper recommends adequate financial education, infrastructural improvement, and stricter financial regulatory interventions to strengthen trust and promote responsible use. These findings contribute to understanding digital money adoption and its behavioral implications in sub-national Nigerian contexts.
Copyright (c) 2026 Sylvester Agbo Igwe, Jeremiah Chukwu (Author) https://creativecommons.org/licenses/by/4.0
Exploring fixed point results for condensed Kannan-type cyclic maps
Condensed Kannan-type contraction has recently been introduced to investigate the properties of nonlinear operators in classical metric spaces. However, certain classes of nonlinear operators cannot be adequately analyzed within this standard framework because of its restrictive geometric structure. To address this limitation, this paper introduces a new class of mappings, namely qpb-cyclic condensed Kannan-type contraction mappings, in the setting of quasi-partial b-metric spaces. The main aim is to employ this novel approach to establish fixed point existence and uniqueness results under qpb-condensed Kannan-type contractive conditions. By integrating cyclic mapping techniques with the generalized geometry of quasi-partial b-metric spaces, the proposed framework extends and unifies several existing results in the literature. The effectiveness and applicability of the obtained results are further illustrated with appropriate examples.
Copyright (c) 2026 Salaudeen Alaro Musa, Musiliudeen Adisa Anise, Olalekan Taofeek Wahab, Saheed Kunle Ajibade (Author) https://creativecommons.org/licenses/by/4.0
Effect of concentration on the crystallographic and FTIR properties of lead sulphide (PbS) thin films
This study reports the use of the successive ionic layer adsorption and reaction (SILAR) technique as a low-cost and rapid method for depositing lead sulphide (PbS) thin films. The films were deposited on soda-lime glass (SLG) substrates by varying the molar concentrations of lead ions (0.3, 0.5, and 0.7 M) at a constant SILAR cycle number of 25. X-ray diffraction (XRD) patterns showed that the deposited PbS thin films were crystalline and had a cubic structure. Fourier-transform infrared (FTIR) spectroscopy indicated nanocrystalline PbS thin films with weak-to-medium M--O bonding. The O--H stretching vibration appeared at 3419.10 cm-1. Bands at 1338.90 and 1220.10 cm-1 were associated with improved crystallinity and C--O stretching vibrations. N--H group vibrations were observed at 1686.42 and 1597.87 cm-1, whereas CH2 bending vibrations occurred between 1482.79 and 1441.44 cm-1.
Copyright (c) 2026 Qasim A. Adeniji, Taiwo O. Roy-Layinde, Dasola B. Adeniji, Temitope O. Fowodu, Ayobamidele D. Adelaja, Abdulganiu D. Adeniji, Muhammad A. Salihu, Oludele Adegboyega, Kola Odunaike (Author) https://creativecommons.org/licenses/by/4.0
A stratified ransomware mitigation model based on zero trust and network segmentation architectures
Ransomware poses a significant threat to information technology because of its ability to spread laterally across computer networks. This paper presents the design and implementation of a stratified mitigation model that combines Zero Trust Architecture (ZTA) with network segmentation to impede ransomware propagation. The proposed model integrates continuous verification through ZTA with the structural containment provided by network segmentation. It was implemented using pfSense, VMware, and GNS3, and evaluated using actual flow patterns extracted from a Ryuk ransomware packet-capture (PCAP) dataset. The model demonstrated automated containment based on real ransomware activity patterns, including distinctive Server Message Block (SMB) traffic profiles and rapid byte-transfer rates. Detection and containment were achieved within sub-second timescales, with a time-to-detect (TTD) of 0.31 s and a time-to-contain (TTC) of 0.32 s. These results outperform standalone ZTA (TTD: 1.50 s; TTC: 2.50 s) and standalone network segmentation (TTD: 0.65 s; TTC: 0.65 s). Across 20 controlled simulation runs, the model achieved a detection accuracy of 85.0%, precision of 81.8%, recall of 90.0%, an F1-score of 85.7%, and a false positive rate of 10%. The results show that the hybrid approach offers a pragmatic and measurable improvement over individual strategies for securing networks against ransomware.
Copyright (c) 2026 Justine Utsu Undiandeye, Moses Adah Agana, Bassey Igbo Ele (Author) https://creativecommons.org/licenses/by/4.0
Synthesis of CuO-ZnO/banana stalk-derived activated carbon composite for efficient adsorptive removal of Cresol Red from aqueous solutions
The increasing discharge of toxic and non-biodegradable dyes from textile wastewater poses a significant environmental challenge, necessitating the development of efficient and sustainable treatment technologies. This study investigates the adsorption performance of a CuO--ZnO nanocomposite supported on banana stalk-derived activated carbon (BSAC) for the removal of Cresol Red (CR) dye from aqueous solution. The composite was synthesized via co-precipitation and characterized using X-ray diffraction (XRD), Raman spectroscopy, scanning electron microscopy (SEM), and thermogravimetric/differential thermal analysis (TGA/DTA), confirming the formation of a porous, thermally stable, and structurally integrated material. Batch adsorption experiments were conducted to evaluate the effects of pH (2--10), contact time (5--180 min), adsorbent dosage (0.01--0.10 g), and initial dye concentration. Maximum adsorption was achieved at an equilibrium concentration of 100 mg L-1, pH 6, 0.01 g dosage, and 30 min contact time, with an adsorption capacity of 178.8 mg g-1. Kinetic analysis revealed that the adsorption followed the pseudo-second-order model (R2 =0.9932), indicating chemisorption as the dominant mechanism. Equilibrium data were best described by the Temkin isotherm, while the Freundlich model further suggested multilayer adsorption on a heterogeneous surface. The results demonstrate that the CuO--ZnO/BSAC composite is a low-cost, sustainable, and efficient adsorbent with strong potential for application in dye-contaminated wastewater treatment.
Copyright (c) 2026 Akeem Adebayo Jimoh, Wahaab Oluwatobi Waheed (Author) https://creativecommons.org/licenses/by/4.0
First-principles analysis of electric field, spin–orbit coupling and lithium adsorption in graphene
This study presents a first-principles examination of the effects of external electric fields (EEFs) on ion transport and adsorption processes in graphene. The Perdew--Burke--Ernzerhof (PBE) and local modified Becke--Johnson (lmBJ) exchange--correlation functionals, as implemented in WIEN2k, were used to investigate a 4 x 4 monolayer graphene supercell and lithium adsorption on graphene under electric fields ranging from 0.5 to 5 V/Å. The results indicate that the external electric field and spin--orbit coupling (SOC) modify the electronic structure of graphene, with lmBJ enhancing bandgap resolution. Lithium adsorption generated localised states, improved charge transfer and tuned the energy gap as the external field varied. The findings suggest that controlled electric fields can alter graphene's electronic properties, supporting possible developments in nanoelectronics and energy storage.
Copyright (c) 2026 Zakar Ya'u Shuaibu, Lawal Mohammed, Abdulsalam Ismaila Galadima (Author) https://creativecommons.org/licenses/by/4.0
Machine learning-based prediction of climate-driven sickle cell anemia crises across Nigerian regions using synthetic multimodal data
Sickle cell anemia (SCA) is a genetic hemoglobinopathy that imposes a major health burden in sub-Saharan Africa, particularly in Nigeria. Vaso-occlusive crises (VOCs), the principal acute manifestations of SCA, may be influenced by environmental factors such as temperature, low humidity, harmattan dust, and rainfall. Although associations between VOCs and environmental conditions have been reported, prediction models remain difficult to develop for data-constrained clinical settings. We developed a proof-of-concept VOC prediction framework for six Nigerian ecological regions using a synthetic multimodal health--environment dataset comprising 12,000 patient-month observations. Random Forest, support vector machine (SVM), XGBoost, neural network, and logistic regression models were evaluated. Logistic regression achieved the best overall performance (accuracy: 0.78; F1-score: 0.74; area under the receiver operating characteristic curve [ROC-AUC]: 0.84), followed by Random Forest (0.77, 0.72, and 0.83), SVM (0.78, 0.73, and 0.82), neural network (0.77, 0.72, and 0.82), and XGBoost (0.76, 0.70, and 0.82). Regional analysis showed a north--south gradient in simulated crisis occurrence: the North-East had the highest probability (80.1%), associated with temperature and particulate matter with aerodynamic diameter below 2.5, mu m (PM2.5), whereas the South-South had the lowest probability (10.4%). Temperature, PM2.5, humidity, and previous hospitalization were the most influential predictors. These findings establish the methodological feasibility of a climate-sensitive prediction pipeline that should be externally validated with clinical and meteorological data before use in health-surveillance applications.
Sickle Cell Anaemia, Machine Learning, Artificial Intelligence, Vaso-Occlusive Crisis, Synthetic Multimodal Data, Climate Science
Artificial Intelligence, Machine Learning, Computer Science, Health, Climate changes..
Copyright (c) 2026 Mary Ofuru Kama, Ifeyinwa Angela Ajah, Sylvester Agbo Igwe, Anayo Chukwu Ikegwu, Chinatu Michael Anyanwu (Author) https://creativecommons.org/licenses/by/4.0
Effect of non-conducting nanoparticles on PVDF for improved insulating properties
This study investigates the effect of incorporating non-conducting calcium carbonate (CaCO3) nanoparticles into polyvinylidene fluoride (PVDF) to improve its insulating properties. PVDF is widely used as an electrical insulation polymer because of its dielectric performance, thermal stability, and mechanical strength; however, these properties can be further improved for advanced applications through nanoparticle modification. PVDF--CaCO3 nanocomposite samples were prepared, characterized, and tested for dielectric and mechanical properties. The results show that adding CaCO3 nanoparticles improves the insulating response of PVDF, especially at 0.5 wt% and 1.0 wt%, making the nanocomposites promising materials for high-performance insulation in electrical and electronic devices. The findings demonstrate the potential of nanoparticle-modified PVDF for improving energy efficiency and reliability in insulation systems.
Copyright (c) 2026 Aliyu Abdulraheem, Abdeghaffar Amoka Abdelmalik, Sadiq Umar, Elvin William (Author) https://creativecommons.org/licenses/by/4.0
A design science approach to semantic alignment: quantifying the curriculum–market gap using NLP-driven vectorization
The demands of modern society have increased expectations about the quality of university graduates, especially in technology-driven disciplines. Nigerian graduates appear to be struggling to meet labour-market expectations as emerging technologies continue to evolve. Graduates of Information and Communications Technology (ICT) programmes are expected to acquire employable digital skills, yet curriculum reform has not always kept pace with industry requirements. The National Universities Commission (NUC) introduced the Core Curriculum and Minimum Academic Standards (CCMAS) 70:30 policy, which permits universities to use a 30% institutional window to introduce innovative and market-responsive content. Since the policy was unveiled in 2023, however, limited use has been made of this provision. This study found that market-validated innovation was approximately 1%, far below the 30% allowance. The researchers developed a diagnostic artifact that processes curriculum learning outcomes and industry job postings through a four-stage natural language processing (NLP) pipeline comprising compound normalisation, whitelist filtering, rule-based semantic inference, and set-intersection scoring. The artifact computes a Programme Alignment Score (PAS) for each university. The study analysed learning outcomes from 150 university handbooks and 1,500 real-time ICT job postings. The results reveal statistically measurable misalignment, with a national average PAS of 28% and a legacy burden of 69%, indicating that most technical skills required by the modern workforce are absent from Nigeria's formal ICT curricula.
Set-Theoretic Analysis, NUC 70:30 Policy, Curriculum Agility, Python, AI, Machine Learning, CCMAS
Copyright (c) 2026 Teddy Aitan, Ishaq Oyefolahan, Abdullahi Mohammed, Johnson Opateye (Author) https://creativecommons.org/licenses/by/4.0
SmoteGAN and TabNet: a hybrid framework for detecting pump-and-dump schemes in cryptocurrency markets
This paper describes a new method for detecting pump-and-dump (P&D) schemes in cryptocurrency markets, an important cybersecurity problem because of the large financial losses suffered by investors as a result of market manipulation and cyber-enabled attacks. Current P&D detection methods often fail to keep pace with changing manipulation strategies, particularly because transaction data are highly imbalanced. This study proposes a hybrid approach that combines generative adversarial networks (GANs) and TabNet to address these limitations. In the proposed framework, a GAN variant, SmoteGAN, is used to create synthetic P&D transaction samples and augment the original training data for the TabNet classification model. This mitigates class imbalance and allows TabNet to learn feature relationships sequentially through its attention mechanism. Evaluation on 25-second intervals of cryptocurrency P&D transaction data shows that the developed model achieved a precision of 98%, recall of 83% and F1-score of 90%, outperforming several existing state-of-the-art methods for detecting pump-and-dump schemes. The findings provide a hybrid method for improving cybersecurity in cryptocurrency markets by enhancing the detection of market manipulation and supporting a safer trading environment.
Copyright (c) 2026 Umar Faruq Abdulrazaq, Muhammad Nazeer Musa (Author) https://creativecommons.org/licenses/by/4.0
Pursuit differential game with high-order dynamic
This study examines a pursuit–evasion differential game with finitely many pursuers and a single evader moving inside a nonempty compact convex region of $\mathbb{R}^{2}$ The dynamics of all participants are governed by ordinary differential equations of degrees $n$ and $m$, while their control inputs satisfy coordinate-wise integral constraints. Capture is said to occur when at least one pursuer reaches the same position as the evader. Sufficient conditions ensuring that the pursuers can guarantee capture are derived, and constructive strategies are proposed to realize this outcome.
Copyright (c) 2026 Hassan Abdullahi, Bashir Mai Umar, Bilyaminu Muhammad, Sani Musa Tsoho (Author) https://creativecommons.org/licenses/by/4.0
Heavy metal bioaccumulation in food crops grown around Babban Tsauni gold mines and assessment of farmland soil contamination
Heavy metal bioaccumulation in food crops is a serious environmental and public health concern, especially in regions affected by artisanal mining. This study assessed the bioaccumulation potential, expressed as bioaccumulation factor (BF), of selected heavy metals---lead (Pb), arsenic (As), nickel (Ni), chromium (Cr), manganese (Mn), zinc (Zn), cobalt (Co), and iron (Fe)---in twelve food crops, comprising four tubers and eight grains cultivated around the Babban Tsauni gold mines. The contamination status of nearby farmlands was also evaluated. Heavy metal concentrations were determined using instrumental neutron activation analysis (INAA) and X-ray fluorescence (XRF). The mean BF values for tubers were 19.176 (Pb), 0.015 (As), 0.101 (Ni), 0.796 (Cr), 0.079 (Mn), 2.839 (Zn), 1.334 (Co), and 0.879 (Fe), while those for grains were 43.290 (Pb), 0.258 (As), 0.419 (Ni), 2.162 (Cr), 0.152 (Mn), 3.827 (Zn), 0.529 (Co), and 0.653 (Fe). Pb showed the highest bioaccumulation in both crop groups, followed by Zn, indicating strong plant uptake in contaminated soils. The pollution load index (PLI = 2.25) indicated soil pollution, whereas the overall potential ecological risk index (RI = 87.61) indicated low ecological risk. The occurrence of Zn and Mn may be attributed mainly to natural geogenic sources rather than human activity. The elevated accumulation of Pb and Zn calls for appropriate remediation strategies, including land-use restriction, crop selection, and supervised mining in mining-impacted agricultural areas.
Heavy metals, Bioaccumulation, Farm Soil and contamination
Physics (Environmental and human health risk)
Copyright (c) 2026 K. A. Odelami, Y. Musa (Author) https://creativecommons.org/licenses/by/4.0
Numerical assessment of economic crisis on consumers purchasing power in Nigeria
The uncertainties influencing consumers’ purchasing power in Nigeria and other developing economies have become recurring issues which pose serious threat to the survival of its citizens and business collaborations. Waves of instabilities in vital economic indicators significantly affect consumer purchasing power and spending decisions in the market. This study focuses on modeling the resulting impact of Nigerian economic crisis on the purchasing power of consumers using second derivative block Adams Moulton methods (SDBAMM). Following the instability display of the absolute wave errors (AWEs) of the modeled equation, the discrete schemes of partition p=4 of SDBAMM gave better and faster computational solutions than other lower partitions p=2 and 2 when compared with other applied numerical methods in literature. This study recommends that adopting stronger monetary policy tools by the Nigerian government to manage tariff uncertainty, inflation and exchange rate waves will improve consumers’ purchasing power and reduce economic fluctuations in the Nigerian markets.
Second Derivative Block Adams Moulton Methods (SDBAMM)
Numerical Analysis and Financial Mathematics
Copyright (c) 2026 Chigozie Chibuisi, Prisca Udodiri Duruojinkeya, Christie Yemisi Ishola, Bright Okore Osu (Author) https://creativecommons.org/licenses/by/4.0
Influence of a topological defect on energy and partition function for a Yukawa potential
This study obtains the solution of the Schrödinger equation for a Yukawa potential interacting with a cosmic string as a topological defect (TD). The calculated energy levels are used to obtain the partition function (Z) under the TD. Some thermodynamic properties such as enthalpy (H), entropy (S) and Gibbs free energy (G), are studied under the influence of the TD. The results are presented for systems with and without the defect. They show that the presence of the TD consistently increases the energy eigenvalue as a function of both the potential strength and the screening parameter. The presence of the TD lowers S, increases Z and G, and has no effect on H as a function of temperature. The defect also increases Z and G as functions of the potential strength. Generally, the TD affects the energy, Z, S and G but has no effect on H because the topological parameter cancels out during computation.
Copyright (c) 2026 C. A. Onate (Author) https://creativecommons.org/licenses/by/4.0
A novel class of Obrechkoff-type methods for solving boundary value problems
A new family of high-order Obrechkoff-type block methods for the direct solution of second-order boundary value problems is proposed in this study. To discretize the differential equations, the methods are built using piecewise linear and constant approaches, as well as interpolation and collocation techniques based on Hermite splines of both first and higher orders. They are further developed using truncated power series expansions, achieving eighth order accuracy with favourable stability properties. The method captures key characteristics of the problem and is computationally efficient. A thorough theoretical analysis confirms that it is consistent, convergent, and zero-stable. A single illustrative example is considered and comparison with the well-known Runge–Kutta method demonstrates its superiority in accuracy and error reduction.
Copyright (c) 2026 Sunday Emmanuel Fadugba, Taiwo Eniola Fayode, Malesela Clifford Kekana (Author) https://creativecommons.org/licenses/by/4.0
Forensic investigation of flagged import goods
Trade is central to social and economic activity, but imported goods must be screened carefully to protect public safety and national security. In this study, a consignment declared as household products was flagged by national security agencies for forensic investigation. Initial field tests at the port of entry used portable X-ray fluorescence (XRF), spectroscopic screening, and radiation surveys. The radiation survey showed no significant deviation from background levels, and the initial XRF measurements did not reveal suspicious elements. Spectroscopic screening, however, indicated high nitrate/nitrite levels in a material labelled as bath salt, where such levels were not expected. The samples were therefore subjected to laboratory analysis by high-accuracy energy-dispersive X-ray fluorescence (EDXRF), instrumental neutron activation analysis (INAA), ultraviolet--visible (UV--Vis) spectroscopy, gas chromatography--mass spectrometry (GC--MS), and Fourier-transform infrared (FTIR) spectroscopy. INAA and XRF showed that the product labelled as bath salt (magnesium sulphate) contained magnesium at only about 0.23% and sulphur at 6.27%, which is inconsistent with the declared composition. UV--Vis spectroscopy confirmed nitrate/nitrite at 15.7% (m/v), while FTIR revealed nitrate absorptions together with hydrocarbon and amine features. GC--MS supported these findings by identifying complex hydrocarbon chains and related fragmentation patterns. Taken together, the results showed that the flagged goods were not household products but hazardous materials with potential ballistic applications. The investigation provided critical support to national security infrastructure by preventing suspicious materials from bypassing regulatory control.
Copyright (c) 2026 T. Muhammad, D. J. Adeyemo, J. A. Yusuf, D. B. Hasan, A. Said (Author) https://creativecommons.org/licenses/by/4.0
Evaluation of financial models for portfolio optimization in Nigeria’s non-interest real estate sector: Integrating mean-variance optimization, the capital asset pricing model, and Value-at-Risk analysis
This study evaluates how modern financial-modeling tools can improve investment decision-making in Nigeria's non-interest real estate sector. Secondary data covering 2018--2024 were obtained from the Central Bank of Nigeria (CBN), Nigerian Exchange Group (NGX), and Real Estate Investment Trusts (REITs). Three models were applied within an Islamic-finance framework: Mean-Variance Optimization (MVO), the Capital Asset Pricing Model (CAPM), and Value-at-Risk (VaR). The empirical results indicate that appropriately modeled non-interest portfolios outperformed conventional benchmarks. On average, optimized portfolios achieved a 17.6% improvement in expected return and a 12.9% reduction in risk. The inclusion of Sukuk-backed instruments significantly stabilized portfolio returns, contributing to an overall 12.9% reduction in portfolio risk and lower drawdown compared to conventional benchmarks. Regression and sensitivity analyses yielded R2 = 0.88 and p < 0.05, indicating strong explanatory power. The study concludes that structured financial modeling can support ethical investment and long-term wealth creation in Nigeria's real estate sector. It recommends wider use of data-driven investment tools, gradual adoption of advanced analytical methods such as machine learning, and stronger coordination among the CBN, Securities and Exchange Commission (SEC), and National Insurance Commission (NAICOM) to improve transparency, regulation, and portfolio management in Nigeria's expanding non-interest financial system.
estate management and valuation/maths and statistics
Copyright (c) 2026 Jeremiah Adamu Rikaya, Hannatu Lucius Datukun, Pheobe Oine Rikaya, Job Olanipekun Israel (Author) https://creativecommons.org/licenses/by/4.0
Assessment of heavy metals and natural radionuclides in locally produced soaps used in Benue State, Nigeria: implications for human health risk
Human exposure to ionizing radiation and toxic metals remains a major concern for physicists, chemists, environmentalists, and the public. In this study, heavy metals (HMs: Ni, Pb, Co, Cr, and Cd) and natural radionuclides (NRs: 238U, 232Th, and 40K) were analyzed in thirty locally produced soaps used in Benue State, Nigeria. The HMs were determined using Atomic Absorption Spectrophotometry (AAS), while NRs were measured using a NaI(Tl) detector. The concentration ranges of Ni, Pb, Co, Cr, and Cd were 0.100--0.400, 0.124--1.235, 0.003--0.034, 0.001--0.010, and 0.0037--0.115 ppm, respectively. These values were below the recommended safe limits set by the WHO and the U.S. FDA, indicating no immediate health risk except under prolonged exposure that could lead to HM accumulation. The results further showed that only three of the thirty surveyed soaps posed a severe radiological health concern because of elevated NR concentrations. The margin of safety, systemic exposure dose, and carcinogenic-risk estimates at 50% and 100% bio-accessibility indicated a higher health risk for children than for adults. Overall, the findings support safer production and use of locally produced soaps in Benue State and may encourage the growth of micro, small, and medium enterprises (MSMEs).
Environmental Science, Radiation Physics, Radiation Exposure, Heavy metals Exposure, Analytic Chemistry
Physics, Radiation Physics, Analytical Chemistry, Environmental Science
Copyright (c) 2026 Moses Owoicho Audu, Inikpi Ojochenemi Agada, Julius Nyiayem Tsaviv, Aondohemba Paschal Achu, Ishaq Shaibu Eneji (Author) https://creativecommons.org/licenses/by/4.0
Tetrahydrofuran-based recycling of furan–lignin foams: comparative characterization of regenerated and virgin foams
Foams are used widely in furniture, construction, automotive, and related industries because of their mechanical performance and durability. However, the poor recyclability of conventional polyurethane (PU) foams has raised environmental concerns because of their persistence and contribution to solid-waste accumulation. Bio-based furan-lignin foams (FLFs) offer a promising alternative because they are derived from renewable feedstocks and may provide improved end-of-life options. This study investigated the recycling of rigid FLFs through solvent-assisted dissolution. Virgin foams were prepared from furfuryl alcohol, used as a cross-linker, and lignin-based polyol obtained from corn husks and palm fruit shells, respectively. Glyoxal was used as a hardener and diethyl ether as a blowing agent. Eight solvents were evaluated for their dissolution indices and recyclability potential. Tetrahydrofuran (THF) gave the highest dissolution index (93.24%), followed by ethylene glycol and polyethylene glycol-400, whereas water gave the lowest value (1.10%). Four recycled furan-lignin foams (RFLFs) were then produced from the THF-derived dissolution product, which was used as recycled polyol. Comparative characterization showed that RFLF2 produced the highest char residue (24.45%), whereas RFLF4 showed the highest degradation temperature (556.80 oC), compressive stress, and density (0.876 g cm-3). The regenerated foams retained, and in some cases improved, the mechanical and thermal performance of the virgin FLFs.
Copyright (c) 2026 Alaba Joseph Adebayo, Olugbenga O. Oluwasina, Joseph Kolawole Ogunjobi, Labunmi Lajide (Author) https://creativecommons.org/licenses/by/4.0
Assessment of solar radiation variability and its impact on estimated photovoltaic electricity output over Kano, Nigeria
This study assessed the variability of solar radiation and its impact on photovoltaic (PV) energy generation in Kano, a Sahelian city in Nigeria located at latitude 12.05oN and longitude 8.20oE. Thirty-one years (1990-2020) of meteorological data obtained from the Nigerian Meteorological Agency (NiMet) were used. Monthly, seasonal, and annual variations in global solar radiation (GSR), sunshine duration, and mean temperature were analysed to evaluate their influence on solar-energy output. The average annual GSR was 17.86 MJ m-2 day-1, the mean sunshine duration was 7.55 h day-1, and the mean annual temperature was 26.78oC. Using a 21.9%-efficient monocrystalline panel with an area of 2.58 m2, the estimated mean monthly energy output was 2721.44 kWh, with peak generation occurring during the rainy season. Correlation analysis showed a strong positive relationship between GSR and energy output. Among the empirical models tested, Garcia's temperature-based model performed best, with R2 = 92.2%. The findings confirm that solar-radiation variability significantly influences PV generation and that temperature-based models provide superior estimation accuracy in Sahelian climates, supporting large-scale solar deployment in northern Nigeria.
Copyright (c) 2026 Abba Babagana, Davidson Odafe Akpootu, Muhammad Bello Abdullahi, Ahmad Isa Tambuwal (Author) https://creativecommons.org/licenses/by/4.0
Multisectoral data lakehouse for climate-resilient agriculture and sustainable food securityin Nigeria
Nigeria, the most populous country in sub-Saharan Africa, faces multifaceted, overlapping problems of economic instability, security concerns, and an acute energy deficit, further aggravated by the ever-increasing impacts of climate change on its agricultural systems, a primary source of livelihood for millions. These hydra-headed challenges require a scientific, data-driven, informed response. The paper examines how principles from the physical sciences, particularly in data management, modelling, analytics, and systems integration, can be applied to promote efficient energy resource distribution for climate-resilient agriculture (CRA), thereby boosting the economy and ensuring long-term food security. A mixed-methods approach, combining literature reviews, AI tools, case studies of climate adaptation projects, ontology-driven data harmonisation, and stakeholder consultations, was used in the research. Findings revealed that an integrated National Climate–Water-Agriculture Data Lakehouse can significantly enhance climate-resilient agriculture and energy resource sharing, thereby improving the economy and food security. The hybrid Semantic Machine learning Framework provide accurate food security analytics and predictions. The initiative becomes a scientific laboratory and a decision-support ecosystem, transforming siloed databases into actionable insights that policymakers, researchers, and practitioners can use to enhance the economy. The paper concludes that a multisectoral ontology-driven National Climate-Water-Agriculture Data Lakehouse transforms physical science data into practical tools for climate-resilient agriculture by connecting laboratories to farmlands and datasets to decision-making, thereby enabling evidence-based agricultural governance and smart-agriculture to increase food production and boost the economies of the major and small-holding farmers in the face of emerging climatic chaos compounded by security concerns.
Copyright (c) 2026 Adam Omeiza Yusuf, Taiwo Kolajo, Emeka Ogbuju, Francisca Oladipo (Author) https://creativecommons.org/licenses/by/4.0
Influence of solution matrix on the adsorption capacity of NaOH-modified Carica papaya seed for Congo red and malachite green removal
Underutilized agro-based waste Carica papaya seed (CPS) was processed into a base-modified adsorbent for the remediation of malachite green (MG)- and Congo red (CR)-contaminated water. CPS was treated with 300 mL of 0.1 M NaOH for 24 h at room temperature, and the treated adsorbent was denoted as BCPS. BCPS was characterized using scanning electron microscopy (SEM), Fourier transform infrared (FTIR) spectroscopy and pH point of zero charge (pHpzc). Batch adsorption studies were carried out as functions of contact time, adsorbent dose, solution pH, initial adsorbate concentration and temperature. Adsorption isotherm, kinetic and thermodynamic studies were also performed. FTIR analysis indicated surface groups including O--H, C≡C, C=C, C≡N, C--O, C--C and C--H; O--H, C=O, C≡N and C--H were implicated in MG and CR adsorption. The pHpzc of BCPS was 6.2. BCPS showed higher adsorption capacity for MG than CR under most experimental conditions, except pH, where the optimum values for MG and CR adsorption were pH 9 and pH 3, respectively. Maximum adsorption capacities for MG in single and binary systems were 42.37 and 7.62 mg/g, respectively, whereas those for CR were 6.95 and 5.58 mg/g, respectively. Langmuir and pseudo-second-order models best described the isotherm and kinetic data, respectively, and the adsorption processes were thermodynamically feasible.
Copyright (c) 2026 Saheed Ademola Adesokan, Adetayo Damola Adelakin, Abdur-Rahim Adebisi Giwa, Deborah Olubunmi Aderibigbe (Author) https://creativecommons.org/licenses/by/4.0
Healthcare delivery in Bayelsa State through health systems modeling
In Nigeria, improving healthcare delivery of course is still exceedingly difficult, in areas with resource-constrained settings like Bayelsa State especially. Indeed, in order to ascertain the factors affecting satisfaction of outpatients as a stand-in for healthcare delivery effectiveness, this research employed a health systems modeling technique. A structured questionnaire was used to gather data from 200 outpatients at Federal Medical Centre, Yenagoa. Multiple regression analysis was then used to evaluate the impact of waiting times, travel times, education levels, and healthcare system capacity and access on patient satisfaction. The statistical techniques: Tukey, Durbin-Watson, Breusch-Pagan, Shapiro-Wilk and Variance Inflation Factors were respectively employed to assess linearity, independence of errors, homoscedasticity, normality and multicollinearity. The robustness of the model was achieved by the diagnostic results, which reveal that the main assumptions were mostly achieved, justifying the reliability of the model. The results from the regression model explain about 42.3% of the variation in outpatient satisfaction and are significant statistically. The biggest positive predictor variable of satisfaction was capacity and access, emphasizing the significance of sufficient staffing, infrastructure, drug availability, and pricing. Service delays were determined as a major constraint in the delivery of healthcare, and waiting times were found to have a considerable negative impact on satisfaction. Travel time revealed a weaker negative influence, whereas level of education significantly did not affect satisfaction once factors of system-level were considered. To maximize healthcare delivery and raise patient satisfaction in Bayelsa State, waiting times must be decreased and the healthcare system's capacity must be strengthened.
Copyright (c) 2026 Jude Opara, Uche Celestine Agwi, Chidimma Udo Osuagwu (Author) https://creativecommons.org/licenses/by/4.0
Modeling spatiotemporal chromatic energy using attention-enhanced CNN-LSTM networks for deepfake video detection
As deepfake videos become increasingly difficult to distinguish from authentic footage, the proliferation of artificial-intelligence (AI)-generated synthetic media poses severe threats, including identity theft, misinformation campaigns, and political manipulation. Synthetic videos are frequently indistinguishable from genuine footage to human observers, rendering conventional forensic approaches insufficient. This paper presents a robust detection system capable of identifying subtle spatiotemporal anomalies in manipulated videos. We develop and evaluate a hybrid deep learning architecture that employs convolutional neural networks (CNNs) for spatial feature extraction and bidirectional long short-term memory (BiLSTM) networks for temporal sequence modeling. An attention mechanism enables the model to focus on the video segments most informative for classification. A key innovation is the introduction of spatiotemporal chromatic energy distributions (SCED) as input features, which model harmonic relationships in authentic video and demonstrate sensitivity to artifacts introduced during synthetic video generation. The proposed hybrid CNN-BiLSTM-attention model achieves 98.7% accuracy, 96.2% precision, 96.4% recall, and a 97.7% F1-score, substantially outperforming standalone CNN (88.5% accuracy) and LSTM (91.4% accuracy) baselines. This integration offers an effective approach for deepfake video detection and contributes to digital media security and reliability.
Spatiotemporal chromatic energy distributions
Copyright (c) 2026 Clive Ebomagune Asuai, Ofualagba Mamuyovwi Helen (Author) https://creativecommons.org/licenses/by/4.0
Enhancing cloud data security using quantum key distribution (QKD) and quantum cryptography
Cloud computing systems face serious security risks that are intensified by advances in quantum computing because powerful quantum algorithms such as Shor's algorithm can break widely used public-key schemes which include RSA and ECC. The cloud environment needs Post-Quantum Cryptography (PQC) and Quantum Key Distribution (QKD) as countermeasures but their individual deployment creates problems because scalability and infrastructure requirements become obstacles for multi-tenant systems. This research proposes a hybrid security architecture that uses both hardware-based QKD for symmetric key generation over optical fibers and PQC for authentication through quantum-resistant key encapsulation using CRYSTALS-Kyber and digital signatures with CRYSTALS-Dilithium. The hybrid design uses XOR to combine the QKD-generated secret with the PQC-derived key material which creates the final session key yet security remains intact if either component gets compromised. The system tested on a private cloud testbed with 10 virtual machines and a 50 km commercial QKD link through optical fiber measured performance against Classical TLS 1.3 and PQC-TLS and QKD-only configurations. The results showed that QKD-only achieved the lowest handshake latency of 5.2 ms while the hybrid approach delivered the strongest security through a 19.8 ms overhead which was only 1.7 ms slower than pure PQC. After creating the secure channel all systems achieved similar data throughput of about 942 Mbps which shows that quantum-security mechanisms affect the connection setup phase instead of persistent data transfer.
Copyright (c) 2026 O. Sarjiyus, Umaru Faruku Adamu, Salamatu Umar (Author) https://creativecommons.org/licenses/by/4.0