African Scientific Reports is a peer reviewed, open access, inter- and multidisciplinary scientific journal that is dedicated to expanding access to African research, increasing intra-African scientific collaboration, and building academic research capacity in Africa. The journal aims to provide a modern, highly-visible platform for publishing pan-African research and welcomes submissions from all areas of the natural sciences, psychology, medicine and engineering. With the idea that making research openly available to the public promotes better global knowledge exchange, this journal offers direct open access to its contents, i.e., all the articles published in this journal are free to access immediately from the date of publication. ASR is available in print and online. The language used in this journal is English.
Application of modified analytical embedded atom method to the study of lattice vibration in bcc transitional metals
Section: PHYSICS SECTION Categories: PHYSICS Keywords: Transition metal, Lattice dynamics, Nearest neighbour, Modified analytical embedded atom method, Force constant matrix Disciplines: Condensed Matter Physics The phonon dispersion curve of four bcc transition metals—(fe), (W), (Mo), and (Cr)—along the principal symmetry [1,0,0], [1,1,1], and [1,1,0] of the Brillouin Zone (BZ) up to the second nearest neighbors is reproduced using the modified analytical embedded atom method (MAEAM) in conjunction with the theory of lattice dynamics. The results obtained are consistent with experimental results, and for all the bcc transition metals taken into consideration, the force constants generated with the MAEAM indicate that the force constants corresponding to the second nearest neighbors were less than the force constants corresponding to the first nearest neighbors. Additionally, the elements of the force constant matrix along φxy, φxz, and φyz are all zero (0). All of the resulting dynamical matrices are diagonal. The generated dispersion curves correlate well with the experimentally generated ones. This demonstrates that the MAEAM is a very dependable instrument for researching the lattice dynamics in bcc transition metals when combined with the theory of lattice dynamics.
Modified analytical embedded atom method
Copyright (c) 2025 Akpata Erhieyovwe, Ernest Ojegu, Aroghene Edison Enaibe https://creativecommons.org/licenses/by/4.0
Anthropometric variables, physiological parameters and physical activity levels of undergraduate students from the University of Fort Hare, South Africa
Section: HEALTH & MEDICAL SCIENCES SECTION Categories: HEALTH & MEDICAL SCIENCES Keywords: Anthropometrics, Body mass index, Blood pressure, Physical activity, University students The aim of this study was to evaluate the anthropometric variables, physiological parameters and physical activity levels of undergraduate students. 132 undergraduate students aged 18 – 25 years from the University of Fort Hare who signed the consent forms participated in this cross-sectional study. Anthropometric variables were assessed using the International Society for the Advancement of Kinanthropometry (ISAK) standard procedures. Physical activity (PA) levels were evaluated using the international physical activity questionnaire – long form (IPAQ-LF). Blood pressure (BP) was measured using a digital BP monitor (Omran Corporation, model no-IA2 HEM 7001-c-1). 8 %, 59.8 %, 28.0 % and 11.4 % are underweight, normal weight, overweight and obese, respectively. The skinfolds, percentage body fat and body mass index (BMI) of female participants were significantly (p < .001) higher than the male participants with a large effect sizes (d ≤ -2.3). Likewise, the waist-to hip ratio was significantly (p < .001) higher in male participants with a large effect size (d = 1.2). The result also showed that 59.8 % participants had normal BP, 13.6 % had high systolic blood pressure (SBP), 6.8 % had high diastolic blood pressure (DBP), 7.6 % had prehypertension and 12.1 % had hypertension, respectively. Male participants had higher SBP while female participants had higher DBP which had no significant (p > 0.001) effect and with low effect sizes (d= 0.1). Again, 72.1 % of participants do not engage in sufficient PA of 300 mins per week which is reflective of less physical activities as compared to the recommended standard (of 300 minutes of moderate-intensity aerobic PA per week) by the world health organization (WHO). We recommend an urgent intervention programs that will help improve the PA levels, reduce unhealthy BMI and BP levels of university students.
Copyright (c) 2025 Nikita Phuza, Sibulele Moni, Iviwe Sigojo, Ayamnkele James, Mere Idamokoro, Leon Van Niekiek https://creativecommons.org/licenses/by/4.0
Physico-chemical, heavy metal, and microbiological analysis of effluent from a confectionery company in Lagos State, Nigeria
Section: CHEMISTRY SECTION Categories: CHEMISTRY Keywords: Effluent, Confectionery, Wastewater, Heavy metals, Coliform count Disciplines: Chemistry Wastewater from the confectionery industry exhibits marked daily and seasonal variations, complicating its management and disposal. This study investigated the physico-chemical characteristics, heavy metal content, and microbial load of effluents from a confectionery processing plant using American Public Health Association (APHA) standard procedures. At the discharge point, the effluent showed temperature (25.02±0.15 °C), turbidity (1.00 NTU), pH (6.20±0.29), electrical conductivity (2295.00±3.62 µs/cm), total dissolved solids (1708.00±19.70 mg/L), total suspended solids (6252.00±0.30 mg/L), total hardness (CaCO3) (1141.30±0.03 mg/mL), oil and grease (5.60±0.00 mg/L±), dissolved oxygen (4.76±0.02 mg/L), COD (8900±0.00 mg/L), BOD (181.80±0.37 mg/L), free chlorine (0.25 mg/L), phosphate (9.418 mg/L), nitrate (387.60 mg/L). Heavy metals (mg/mL) included Mg (25.71), Zn (<0.0010), Pb (<0.0100), Cu (0.0100), Mg (0.0564), Ni (0.0064), and Cd (<0.0020). The total plate count was (238 cfu /100 mL), while the total coliforms count exceeded 1600 MPN/ 100 mL. Comparison with FEPA and NESREA discharge standards indicated that most parameters met regulatory limits, except TDS, TSS, alkalinity, nitrate, chloride, phosphorus, oil and grease, total coliforms, Fe, BOD, and COD, which exceeded permissible thresholds. The release of such inadequately treated effluents poses environmental and public health risks. Strengthened regulatory enforcement is recommended in industrial zones such as Lagos State, Nigeria.
Copyright (c) 2025 Anthony E. Aiwonegbe, Odunayo D. Adeyemi, Faith I. Akhidenor https://creativecommons.org/licenses/by/4.0
A mathematical framework for assessing food fraud intentions
Section: MATHEMATICAL SCIENCES SECTION Categories: BIOLOGICAL SCIENCES, MATHEMATICAL SCIENCES Keywords: Food fraud, Ethical fading, Unethicality, Bounded ethicality, Malicious intent, Similarity distance Disciplines: Applied Mathematics, Food Science, Behavioural sciences This work presents a framework for assessing subjects’ intentions to engage in food fraud. The model demonstrated that the decision to commit fraud arises from psychological mechanisms of malicious intent (Π) and ethical fading-bounded ethicality (Ω). Based on these psychological processes, the framework utilises a survey tool to generate finite sets of binary responses, fT (theorised response set derived from governing moral notions) and fA (actual response set given by subjects). The similarity function, fT ϕ fA, maps a dichotomous variable, with arguments (p, q), to a continuous variable, f [(p, q) 7→ f ]. The model metrics have Jaccard index characteristics with a range of 0.0-1.0, where fraud intention is low when f → 0.0 and high when f → 1.0. The number of questions used in a survey indicates special characteristics of the model, for instance, its rate of change (Rf ) and the number of possible response patterns (Γ). Survey data (N=54) was used to validate the model. The results show that the f values for respondents range from 0.21-0.69 with a mean of 0.44. Indicating that the respondents have moderate to neutral inclinations towards fraud. The statistical difference between f -values obtained from Π and Ω data indicates that they have the same effect on food fraud intention (p > 0.05). The model is essential when assessing the fraud intention of an individual or population by examining and understanding factors contributing to fraud and their numerical impacts. This is a significant step towards developing a fraud prevention framework.
Food fraud, Ethical fading, Unethicality, Bounded ethicality, Malicious intent, Similarity distance
Copyright (c) 2025 Shadreck Muyambo, Jack A Urombo, Garry K. Masoha, Livingstone Jenya https://creativecommons.org/licenses/by/4.0
Transformative approach in Lassa fever diagnostics: an innovative integrative strategy for early detection and outcome prediction
Section: MATHEMATICAL SCIENCES SECTION Categories: ENGINEERING, MATHEMATICAL SCIENCES Keywords: Lassa fever, Hybrid machine learning, Diagnostic models, Risk prediction Disciplines: Information Systems, Computer Science, Information technology, Cyber Security, Software engineering Lassa fever remains a critical public health concern in West Africa, with Nigeria facing the highest burden. Timely and accurate diagnosis is essential for reducing mortality and controlling outbreaks, yet existing diagnostic methods are often ineffective due to resource constraints and limited capacities to handle complex datasets. This study develops and evaluates a hybrid machine learning model to improve the early detection and risk prediction of Lassa fever using Nigerian patient datasets. The research employs three robust base models—Random Forest, LightGBM, and XGBoost optimized individually and then integrated into a stacked hybrid model for enhanced prediction accuracy. Model performance is assessed using metrics such as accuracy, precision, recall, F1-score, and AUC-ROC curves. The results demonstrate that the hybrid model outperforms individual machine learning models, achieving significant improvements in precision and recall for the minority class. Feature importance analysis highlights key clinical predictors, such as fever duration and hemorrhagic symptoms, for distinguishing patient outcomes. These findings underscore the potential of hybrid machine learning frameworks in addressing diagnostic challenges for infectious diseases in resource-limited settings. This study presents a scalable approach for data-driven diagnostics, offering meaningful advancements in public health interventions and disease monitoring systems.
Lassa fever, Hybrid machine learning, Diagnostic models, Risk prediction
Copyright (c) 2025 Denis U. Ashishie, Endurance O. Obi, Osowomuabe Njama-Abang, Ahena I. Bassey https://creativecommons.org/licenses/by/4.0
Hyperparameter optimisation for support vector machine-based disease detection in maize leaf variants
Section: MATHEMATICAL SCIENCES SECTION Categories: MATHEMATICAL SCIENCES Keywords: farmers, livestock, machine learning, RSA, SVM Disciplines: Mathematical Sciences One of the main staple crops farmed and extensively consumed in Africa is maize. However, despite being widely used as a human diet and a raw material for animal feed, several diseases on leaves endanger their productivity and result in a sizable yield loss. However, Nigerian farmers typically employ antiquated techniques to detect plant diseases, which are labour-intensive and prone to mistakes, making the constant need for more effective solutions necessary. Although numerous researchers have developed classification models using Support Vector Machine (SVM) to identify and classify diseases in crop leaves. However, optimising a Support Vector Machine (SVM) is critical because it allows for fine-tuning of its parameters to achieve the best possible performance on a given dataset. Therefore, to optimise Support Vector Machines, this study created a hybrid model that combines Binary Particle Swarm Optimisation (BPSO) with a Reptile Search Algorithm (RSA). The Kaggle village datasets provided images of the leaves of maize. After being converted to grayscale, the pictures were improved with bi-histogram equalisation methods. After segmenting the leaf’s affected area using the Sobel edge detection method, texture, shape, and colour features were extracted using Gray Level Spatial Dependence and colour moment. Every classification model was trained and tested using the 10-fold approach. The performance of the suggested method was compared with a few other machine learning and deep learning models that are currently in use. Regarding identifying maize diseases, the results showed that the BPSO-RSA-SVM model performed better than all other optimised support vector machine models and some deep learning state-of-the-art models. The model demonstrates its efficiency in advancing agricultural disease detection with an average accuracy of 97.01% and a false positive rate of 3.85% compared with BPSO-SVM and RSA-SVM which achieved 96.65% & 95.44% and 3.30% & 4.60% for accuracy and false positive rate, respectively.
farmers, livestock, machine learning, RSA, SVM
Copyright (c) 2025 O. B. Ayoade, M. O. Raji, A. A. Akindele, K. J. Yusuf-Mashopa, M. F. Abdulrauf, I. A. Raji, F. B. Musah https://creativecommons.org/licenses/by/4.0
Persuasive power of artificial intelligence-generated ads: exploring online behaviour among Gen Z internet users in Nigeria
Section: MATHEMATICAL SCIENCES SECTION Categories: ENGINEERING, MATHEMATICAL SCIENCES Keywords: Artificial intelligence, AI-generated ads, Gen Z, Online behaviour, Persuasive technology Disciplines: Computer Science, Artificial Intelligence Advertisements created by artificial intelligence (AI) models are becoming increasingly common in digital marketing, prompting inquiries about their impact on consumer behavior. This research analyzes the effectiveness of AI-created advertisements among Generation Z (Gen Z) Nigerian online users, a group recognized for its significant online activity and distinctive behaviour patterns. The study examines key elements such as personalization, emotional attraction, platform-specific differences, and trust to analyze how these advertisements affect user behavior. Using persuasive technology and a mixed-method strategy, the study integrated an extensive examination of 25 pertinent studies obtained from Google Scholar, IEEE Xplore, and SpringerLink databases with a survey of 205 Gen Z participants carried out over six months (July–December 2024). Data were examined to identify trends in user feedback across various platforms, emotional resonance, and demographic traits. The outcome of the research indicated that personalization and emotional resonance greatly improve the effectiveness of AI-generated advertisements, with humour and relatability having especially strong effects. Instagram and TikTok became the leading platforms, with gender disparities showing a bit more engagement from male users. Even with a fair amount of trust in these advertisements, doubt continues to be a significant obstacle to wider acceptance. The research uncovered that AI-created ads have a moderate impact on the online behaviour of Gen Z Nigerians, indicating significant room for enhancement. This study adds to the expanding understanding of artificial intelligence's influence in contemporary marketing and its effects on society.
Computer Science, Artificial Intelligence
Copyright (c) 2025 Ignatius Nwoyibe Ogbaga, Henry Friday Nweke https://creativecommons.org/licenses/by/4.0
Role of Van der Waals correction on the catalytic performance of 1T-TiS₂ electrocatalyst
Section: PHYSICS SECTION Categories: PHYSICS Keywords: Vander Waals interactions, Catalytic performance, Density functional theory, Adsorbed hydrogen In this paper, the role of Van der Waals (vdW) correction on the catalytic performance of 1T- TiS2 material was investigated within the framework of density functional theory (DFT) and dispersion-corrected density functional theory (DFT-D3). The exchange-correlation functional was approximated using generalized gradient approximation (GGA) as parameterized by Perdew-Burke-Ernzerhof (PBE). Based on our results, the calculated lattice parameters were a =3.33 Å and a =3.32 Å upon calculations without and with inclusion of vdW correction, respectively, which indicated a slight reduction of ∼ 0.3% when vdW correction was included. In both cases, the value of a was in good agreement with previous experimental and theoretical data. However, the distances between the adsorbed hydrogen (H) and the surface of the catalyst were affected by the vdW correction. Our findings also showed that the vdW correction has an impact on the catalytic performance of 1T-TiS2. The Gibbs free energy change for hydrogen adsorption (ΔGH∗) calculated for the most stable adsorption location was ∼ - 0.55 eV and ∼ - 0.53 eV without and with vdW correction, respectively. This revealed that the one calculated with the inclusion of vdW correction is closer to the optimal value. Therefore, this emphasized the need of including a vdW correction in any DFT study that involves catalytic properties of this material and its related members for more accurate and reliable results.
Copyright (c) 2025 Shamsuddeen Sani Alhassan, Mahmud Abdulsalam, Abdullahi Tanimu, Ibrahim Muhammad Bagudo https://creativecommons.org/licenses/by/4.0
Quasi-subordination for a subclass of non-Bazilevic functions connected with a q-derivative operator
Section: MATHEMATICAL SCIENCES SECTION Categories: MATHEMATICAL SCIENCES Keywords: Non-Bazilevic functions, Modified Opoola q-differential operator, q-calculus, Quasi-subordination, Fekete-Szego inequality Disciplines: Pure Mathematics This study examines a broad subclass of non-Bazileviˇc functions that includes several subclasses of q-bounded turning functions and q-Sakaguchi functions. We link the definition of the class with a modified Opoola q-derivative operator, quasi-subordination, and a few number of mathematical concepts such as q-calculus and infinite series formations. Among the achievements in this work are the estimates for the early upper coefficient bounds and the Fekete-Szego inequalities having complex parameters. In general, this unique class reduces to various recognized classes of non-Bazilevic functions when some of the parameters take values within their interval of definition.
Non-Bazilevic functions, Modified Opoola q-differential operator, q-calculus, Quasi-subordination, Fekete-Szego inequality
Copyright (c) 2025 Risikat Ayodeji Bello, Ayotunde Olajide Lasode, Rasheed Olawale Ayinla, Adediran Dauda Adeshola https://creativecommons.org/licenses/by/4.0
Challenges and advances in anti-rabies vaccine development in sub-saharan Africa
Section: HEALTH & MEDICAL SCIENCES SECTION Categories: HEALTH & MEDICAL SCIENCES Keywords: Rabies, Anti-rabies, Vaccine, Sub-saharan Africa Disciplines: Medical Microbiology. Zoonotic infection, such as rabies disease, causes devastating brain disorders. Rabies causes almost 100 % fatality rate in humans, and it is also 100 % preventable. Thousands of people die from rabies each year, with the majority occurring in Sub-Saharan Africa.The disease is one of the deadliest viral diseases known to humans, which claims about 60,500 human lives annually, mostly in countries with low income. Over 90 % of rabies infections are transmitted via dogs.Rabies vaccines are the major means to prevent and tocontrol rabies.The vaccines can be administered both pre- and post-exposure prophylaxis.The rabies vaccine was first developed witha virus-infected nerve tissue. In its evolutional stages, different types of vaccines have evolved, such as toxoid vaccines, live attenuated vaccines, inactivated vaccines and subunit vaccines. Future trends of novel vaccines development are targeted towards nuclei-based vaccine and therapeutic vaccine for specific diseases. These advances in technology arelimited in Sub-Saharan Africa, owing to lack of infrastructure, high cost low power supply and economic constraint. The vaccine market size is small, and those with the greatest need for vaccine, cannot afford to purchase them at market price.The delivery systems remain another important area of focus, which when addressed will help in effective shipping and access of the product. This review examines the key challenges and recent advances in addressing vaccine development in Sub-Saharan Africa, with the aim of highlighting effective strategies to improve vaccine quality and acceptance coverage.
Copyright (c) 2025 M. C. Owosen, A. W. Jik, D. D. Odonye, E. B. Ogundeji https://creativecommons.org/licenses/by/4.0
Measurement and simulation of indoor radon concentration distribution in students’ residential hostels around Ladoke Akintola University of Technology, Ogbomoso, Nigeria
Section: PHYSICS SECTION Categories: HEALTH & MEDICAL SCIENCES, PHYSICS Keywords: Indoor radon, Air change rate, Annual absorbed dose, Annual effective dose, Radon monitoring Disciplines: Radiation and Health Physics Radon is a harmful gas and class I carcinogen posing global challenge to human health. Being an inert gas, it is impossible to perceive its presence by any of the sense organs, thus the application of Computational Fluid Dynamics (CFD) simulation in radon study is enhancing human understanding of the behavior and distribution of radon in indoor air. Hence, this work measured the indoor radon concentration (Ri) in student’s residential hostels and evaluate radon gas distribution in indoor air. In-situ measurement of the Ri in 10 student’s residential hostels was carried for a period of one month using RAD7. The CFD code ANSYS Fluent 2024R2, based on the finite volume method was employed in modeling the geometry of the student’s room, calculate, predict and visualize the concentration and distribution of radon inside the room. The average measured Ri was 45.89 Bq/m3 with a corresponding annual effective dose of 1.042 mSv/yr. The simulation revealed an average value of 45.51 Bq/m3 and the annual effective dose from the inhalation of radon when ACH is 2, 3 and 4 h-1 respectively was calculated to be 1.03 mSv/yr, which is lower than the recommended value by ICRP. The simulation revealed that radon gas is more concentrated at the building window compare to other parts of the building. It is therefore recommended that the indoor radon induced air be diffused with external or fresh air by ensuring adequate ventilation and maintaining healthy air circulation.
Copyright (c) 2025 E. A. Oni, C. O. Ajiboye, P. S. Ayanlola, O. O. Oloyede, A. A. Aremu, T. A. Olajide, O. O. Oladapo, O. P. Oyero, A. E. Oladipo, M. K. Lawal https://creativecommons.org/licenses/by/4.0
Development of a visual analogy model using transfer learning techniques
Section: MATHEMATICAL SCIENCES SECTION Categories: ENGINEERING Keywords: Visual analogy models, Transfer learning, Conceptual change, Machine learning, Model development Disciplines: Information Systems, Machine Learning, Artificial intelligence This study presents a novel transfer learning architecture for automated visual analogy generation to improve the effectiveness of visual analogies in facilitating learners’ comprehension of complex concepts within educational settings. While visual analogies are widely recognized as powerful tools for enhancing understanding and promoting conceptual change, existing automated models often fall short due to insufficient focus on critical design phases. To address these limitations, this research employs transfer learning technique, incorporating enriched datasets to generate visual analogies that are clear, contextually appropriate, and effective for diverse learning audiences. The computational approach integrates advanced data preprocessing, neural network architecture design, and PyTorch implementation, alongside transfer learning from Stable Diffusion. The model’s performance is assessed through a combination of quantitative metrics and qualitative evaluations. Quantitative results demonstrate that the enhanced VAM significantly outperforms the baseline, with an average FID score of 139.2091 compared to 4511.81 for the baseline, and an average CLIP similarity score of 35.3981 for the enhanced model compared to 15.6767 for the baseline. These findings underscore the potential of automated visual analogy creation to transform educational practices, offering actionable insights for educators and designers to seamlessly integrate visual analogies into teaching materials and technologies, ultimately enhancing the communication of complex ideas in classrooms.
Information Systems, Machine Learning, Artificial intelligence
Copyright (c) 2025 Osowomuabe Njama-Abang, Denis U. Ashishie, Emmanuel A. Edim, Moses A. Agana https://creativecommons.org/licenses/by/4.0