African Scientific Reports
type: channel
description: 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.
publisher: Nigerian Society of Physical Sciences
language: en
publication name: African Scientific Reports
issn: 2955-1625
items: Measurement and simulation of indoor radon concentration distribution in students’ residential hostels around Ladoke Akintola University of Technology, Ogbomoso, Nigeria
items: Anthropometric variables, physiological parameters and physical activity levels of undergraduate students from the University of Fort Hare, South Africa
items: Aspect ratio of thermo-hydraulic performance of periodically prescribed semi-circular groove in rectangular channel
items: Role of Van der Waals correction on the catalytic performance of 1T-TiS₂ electrocatalyst
items: Application of modified analytical embedded atom method to the study of lattice vibration in bcc transitional metals
items: A mathematical framework for assessing food fraud intentions
items: Physio-chemical properties of Tamiya clay deposit for production of insulating refractory bricks using mixture of rice husk and waste paper as additive
items: ECG anomaly detection: a deep learning perspective with LSTM encoders
items: Investigation of direct normal irradiance and total cloud cover connection over Nigeria using satellite data
items: Hyperparameter optimisation for support vector machine-based disease detection in maize leaf variants
items: Persuasive power of artificial intelligence-generated ads: exploring online behaviour among Gen Z internet users in Nigeria
items: Assessment of mycotoxin presence and distribution in maize grains across North central states of Nigeria
items: Physico-chemical, heavy metal, and microbiological analysis of effluent from a confectionery company in Lagos State, Nigeria
items: Quasi-subordination for a subclass of non-Bazilevic functions connected with a q-derivative operator
items: Challenges and advances in anti-rabies vaccine development in sub-saharan Africa
items: Transformative approach in Lassa fever diagnostics: an innovative integrative strategy for early detection and outcome prediction
items: Intrusion detection in a controlled computer network environment using hybridized random forest and long short-term memory algorithms
items: Development of a visual analogy model using transfer learning techniques
items: Assessment of potentially harmful elements in mine spoils of Jos and environs, central Nigeria

Measurement and simulation of indoor radon concentration distribution in students’ residential hostels around Ladoke Akintola University of Technology, Ogbomoso, Nigeria
type: item
description: 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.
subject: PHYSICS SECTION
  topic: section
subject: HEALTH & MEDICAL SCIENCES
  topic: category
subject: PHYSICS
  topic: category
subject: Indoor radon
  topic: keywords
subject: Air change rate
  topic: keywords
subject: Annual absorbed dose
  topic: keywords
subject: Annual effective dose
  topic: keywords
subject: Radon monitoring
  topic: keywords
subject: Radiation and Health Physics
  topic: disciplines
creator: Emmanuel A. Oni
creator: Christianah O. Ajiboye
creator: Paul S. Ayanlola
creator: Oluwaseun O. Oloyede
creator: Adewale A. Aremu
creator: Temitayo A. Olajide
creator: O.O Oladapo
creator: O.P Oyero
creator: Adeshina E. Oladipo
creator: Mustapha K. Lawal
rights: 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
date: 2025-09-07
publication date: 2025-09-07
starting page: 328
ending page: 328
doi: 10.46481/asr.2025.4.3.328

Anthropometric variables, physiological parameters and physical activity levels of undergraduate students from the University of Fort Hare, South Africa
type: item
description: 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, reduc…
subject: HEALTH & MEDICAL SCIENCES SECTION
  topic: section
subject: HEALTH & MEDICAL SCIENCES
  topic: category
subject: Anthropometrics
  topic: keywords
subject: Body mass index
  topic: keywords
subject: Blood pressure
  topic: keywords
subject: Physical activity
  topic: keywords
subject: University students
  topic: keywords
creator: Nikita Phuza
creator: Sibulele Moni
creator: Iviwe Sigojo
creator: Ayamnkele James
creator: Mere Idamokoro
creator: Leon van Niekerk
rights: Copyright (c) 2025 Nikita Phuza, Sibulele Moni, Iviwe Sigojo, Ayamnkele James, Mere Idamokoro, Leon Van Niekiek https://creativecommons.org/licenses/by/4.0
date: 2025-09-02
publication date: 2025-09-02
starting page: 313
ending page: 313
doi: 10.46481/asr.2025.4.3.313

Aspect ratio of thermo-hydraulic performance of periodically prescribed semi-circular groove in rectangular channel
type: item
description: Section: ENGINEERING SECTION Categories: ENGINEERING Keywords: Convective heat enhancement, Artificial technique, Semi-circular groove, Friction factor, Nusselt number Disciplines: Mechanical Engineering A rectangular channel including an angled groove on the end wall was modeled in an air tunnel to assess the augmentation of heat transfer and the friction factor. The groove exhibited a 45-degree angle relative to the flow direction, with a pitch-to-groove depth ratio of 6.5. Three distinct aspect ratios, 0.025, 0.03, and 0.05, were evaluated. The Reynolds number varied from 1000 to 11000, including laminar to turbulent flow regimes. The pressure reduction and thermal transfer were quantified in the primary flow of the experimental portion. The 0.05 aspect ratio yielded the highest performance, with optimal values of 22%, 30%, and 48% in the laminar, transition, and turbulent areas, respectively. The non-dimensional air temperature of the flow at the conclusion of the test portion demonstrated the dispersion of the fluid, signifying uniform turbulence within the channel. The impacts of various plate factors on enhancing heat transmission were regulated. This was ascribed to the channel aspect ratio, mild pressure penalties, and the significant thermal increases of the groove endwalls. It reduced the thickness of the fluid layer at the surface, facilitated prolonged smooth flow over the groove surfaces, and is essential for enhancing the efficiency and sizing of flat plate heat exchangers in applications such as solar panels, fuel cells, electronic components, motor/generator jackets, bearing jackets, and turbine blades. The findings indicated that angled grooves can be beneficial in several contemporary applications, including turbine blades, solar cells, and processing equipment.
subject: ENGINEERING SECTION
  topic: section
subject: ENGINEERING
  topic: category
subject: Convective heat enhancement
  topic: keywords
subject: Artificial technique
  topic: keywords
subject: Semi-circular groove
  topic: keywords
subject: Friction factor
  topic: keywords
subject: Nusselt number
  topic: keywords
subject: Mechanical Engineering
  topic: disciplines
creator: Samson A. Aasa
creator: Regan K. Dunne
creator: Dawood A. Desai
rights: Copyright (c) 2025 Samson A. Aasa, Regan K. Dunne, Dawood A. Desai https://creativecommons.org/licenses/by/4.0
date: 2025-12-22
publication date: 2025-12-22
starting page: 205
ending page: 205
doi: 10.46481/asr.2025.4.3.205

Role of Van der Waals correction on the catalytic performance of 1T-TiS₂ electrocatalyst
type: item
description: 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.
subject: PHYSICS SECTION
  topic: section
subject: PHYSICS
  topic: category
subject: Vander Waals interactions
  topic: keywords
subject: Catalytic performance
  topic: keywords
subject: Density functional theory
  topic: keywords
subject: Adsorbed hydrogen
  topic: keywords
creator: Shamsuddeen Sani Alhassan
creator: Mahmud Abdulsalam
creator: Abdullahi Tanimu
creator: Ibrahim Muhammad Bagudo
rights: Copyright (c) 2025 Shamsuddeen Sani Alhassan, Mahmud Abdulsalam, Abdullahi Tanimu, Ibrahim Muhammad Bagudo https://creativecommons.org/licenses/by/4.0
date: 2025-11-15
publication date: 2025-11-15
starting page: 356
ending page: 356
doi: 10.46481/asr.2025.4.3.356

Application of modified analytical embedded atom method to the study of lattice vibration in bcc transitional metals
type: item
description: 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.
subject: PHYSICS SECTION
  topic: section
subject: PHYSICS
  topic: category
subject: Transition metal
  topic: keywords
subject: Lattice dynamics
  topic: keywords
subject: Nearest neighbour
  topic: keywords
subject: Modified analytical embedded atom method
  topic: keywords
subject: Force constant matrix
  topic: keywords
subject: Condensed Matter Physics
  topic: disciplines
creator: Akpata Erhieyovwe
creator: Ernest Ojegun
creator: Aroghene Edison Enaibe
rights: Copyright (c) 2025 Akpata Erhieyovwe, Ernest Ojegu, Aroghene Edison Enaibe https://creativecommons.org/licenses/by/4.0
date: 2025-09-07
publication date: 2025-09-07
starting page: 329
ending page: 329
doi: 10.46481/asr.2025.4.3.329

A mathematical framework for assessing food fraud intentions
type: item
description: 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.
subject: MATHEMATICAL SCIENCES SECTION
  topic: section
subject: BIOLOGICAL SCIENCES
  topic: category
subject: MATHEMATICAL SCIENCES
  topic: category
subject: Food fraud, Ethical fading, Unethicality, Bounded ethicality, Malicious intent, Similarity distance
  topic: keywords
subject: Applied Mathematics
  topic: disciplines
subject: Food Science
  topic: disciplines
subject: Behavioural sciences
  topic: disciplines
creator: Shadreck Muyambo
creator: Jack A Urombo
creator: Garry K. Masoha
creator: Livingstone Jenya
rights: Copyright (c) 2025 Shadreck Muyambo, Jack A Urombo, Garry K. Masoha, Livingstone Jenya https://creativecommons.org/licenses/by/4.0
date: 2025-10-18
publication date: 2025-10-18
starting page: 314
ending page: 314
doi: 10.46481/asr.2025.4.3.314

Physio-chemical properties of Tamiya clay deposit for production of insulating refractory bricks using mixture of rice husk and waste paper as additive
type: item
description: Section: ENGINEERING SECTION Categories: ENGINEERING Keywords: Tamiya clay, Rice husk, Waste paper, Thermal conductivity, Porosity Disciplines: Metallurgical and Materials Engineering The study aimed at investigating impact of the processing parameters on the physico-chemical and mechanical properties of insulating refractory bricks made from Tamiya clay with admixture of rice-husk/paper-waste. The molding was done with a compression hydraulic press. The molding of the bricks was done by incorporating the varied proportions of the additive of the mixture of rice-husk/paper-waste (10%, 15% 20wt %) into the clay matrix. The firing temperature varied from 850oC, 950oC, 1000oC. The results were optimized using Taguchi design of experiment method and the effect of the processing parameters (clay, additive, firing temperature) was established using the response Table and means effects plots. The properties of the produced bricks investigated after firing at 850oC, 950oC and 1000oC indicated that samples of mixture of rice husk and waste paper had percentage total apparent porosity increment ranging from 15.90%, - 26.1%. Similarly, samples of the bricks had cold crushing strength ranging from 2.04 MN/m2 – 3.45 MN/m2. The estimated refractoriness using Shuen’s formula revealed 1554.612°C as a range of refractoriness while the result of the refractoriness using Pyrometric Cone Equivalent (PCE) indicated that samples had refractoriness of 1200°C. On the other hand, thermal insulation improved from 1.759531Wmk to 0.51WmK. Taguchi-based optimization experiment identified Tamiya clay content and temperature as the most considerable factors affecting the cold crushing strength. However, mixture of rice husk and waste paper and temperature are significant parameters influencing thermal conductivity.
subject: ENGINEERING SECTION
  topic: section
subject: ENGINEERING
  topic: category
subject: Tamiya clay
  topic: keywords
subject: Rice husk
  topic: keywords
subject: Waste paper
  topic: keywords
subject: Thermal conductivity
  topic: keywords
subject: Porosity
  topic: keywords
subject: Metallurgical and Materials Engineering
  topic: disciplines
creator: Rayyan Dodo
creator: Godwin Joshua
creator: Terver Ause
creator: Zahradeen Musa
creator: Kamilu Bello
creator: Jimoh Ohinoyi Gaminana
creator: Ibrahim Abubakar
rights: Copyright (c) 2025 Rayyan Mamuda Dodo, Godwin Joshua, Terver Ause, Zahradeen Musa, Kamilu Bello Adeyemi, Jimoh Gaminana Ohinoyi, Ibrahim Abubakar Ibrahim https://creativecommons.org/licenses/by/4.0
date: 2025-12-22
publication date: 2025-12-22
starting page: 291
ending page: 291
doi: 10.46481/asr.2025.4.3.291

ECG anomaly detection: a deep learning perspective with LSTM encoders
type: item
description: Section: MATHEMATICAL SCIENCES SECTION Categories: MATHEMATICAL SCIENCES Keywords: Anomaly, Anomaly detection, LSTM, Autoencoder, Electrocardiogram, Deep learning Disciplines: Computer Science, Artificial Intelligence, machine learning, Deep Learning An electrocardiogram (ECG) is a procedure that measures the heart’s electrical activity. Each heartbeat generates an electrical impulse, causing the heart muscle to contract and pump blood. ECG data is a classic example of time series data, where the timing of the upper and lower chambers of the heart is analyzed by medical professionals. The use of unsupervised learning techniques, such as K-Means Clustering and Hierarchical Temporal Memory (HTM) to ECG anomaly detection often results in poor performance due to their dependability on pre-defined features. The performance of a multilayer convolutional neural network adapted to the unsupervised task is also constrained by lack of quality feature extraction and guidance from labelled data. This research proposes a deep learning approach using Long-Short Term Memory (LSTM) Networks for the ECG signal analysis to improve the accuracy of anomaly detection in heartbeats. The proposed method was evaluated on the ECG5000 dataset. The model achieved an impressive accuracy of 99.23%, Specificity of 99.25% and Area under curve (AUC) of 98.00%, thereby outperforming existing models. The results demonstrate that the LSTM Autoencoder-based approach effectively learns expressive representations of ECG sequences, leading to improved performance compared to previous methods.
subject: MATHEMATICAL SCIENCES SECTION
  topic: section
subject: MATHEMATICAL SCIENCES
  topic: category
subject: Anomaly, Anomaly detection, LSTM, Autoencoder, Electrocardiogram, Deep learning
  topic: keywords
subject: Computer Science, Artificial Intelligence
  topic: disciplines
subject: machine learning
  topic: disciplines
subject: Deep Learning
  topic: disciplines
creator: Adenike Adegoke-Elijah
creator: Theresa Omolayo Ojewumi
creator: Kudirat Oyewumi Jimoh
rights: Copyright (c) 2025 Adenike Adegoke-Elijah, Theresa Omolayo Ojewumi, Kudirat Oyewumi Jimoh https://creativecommons.org/licenses/by/4.0
date: 2025-12-29
publication date: 2025-12-29
starting page: 359
ending page: 359
doi: 10.46481/asr.2025.4.3.359

Investigation of direct normal irradiance and total cloud cover connection over Nigeria using satellite data
type: item
description: Section: PHYSICS SECTION Categories: GEO SCIENCES, PHYSICS Keywords: Direct normal irradiance, Total cloud cover, Seasonal variability, Long-term trend Disciplines: Atmospheric Physics, Solar energy This study assessed the connection between Direct Normal Irradiance (DNI) and Total Cloud Cover (TCC) throughout Nigeria, a region with considerable yet underexploited solar energy potential. Daily DNI data from Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2) and TCC data from the ERA5 reanalysis of the European Centre for Medium-Range Weather Forecasts (ECMWF) were aggregated into monthly means and analyzed across four climatological seasons. Seasonal variability was evaluated using standard deviation, while long-term trends were examined through simple linear regression, with statistical significance appraised at p < 0.05. Pearson’s correlation coefficient was used to estimate DNI–TCC relationship. Results show that TCC peaked during JJA in the Af and Am climates, whereas DNI peaked in DJF and SON, particularly in the BSh and BWh regions. Variability in both DNI and TCC was highest in DJF and lowest in JJA. DNI variability was highest in the Aw zone and lowest in Am, while TCC variability peaked in the Aw and BWh zones and was lowest in Af and Am. Regression analysis revealed a strong inverse relationship between DNI and TCC in the Csb and BWh zones, while Af and Am exhibited complex interactions. Correlation analysis showed strongest negative relationship during DJF and JJA (mean r = –0.73), and weakest during MAM (r = –0.33). Trend analysis indicated a modest increase in DNI across all climate zones, with TCC decreasing except in Af. DNI values exceeding 400 W/m² were most likely in northern zones during DJF and SON but low in the south.
subject: PHYSICS SECTION
  topic: section
subject: GEO SCIENCES
  topic: category
subject: PHYSICS
  topic: category
subject: Direct normal irradiance
  topic: keywords
subject: Total cloud cover
  topic: keywords
subject: Seasonal variability
  topic: keywords
subject: Long-term trend
  topic: keywords
subject: Atmospheric Physics
  topic: disciplines
subject: Solar energy
  topic: disciplines
creator: Tersoo Abiem
creator: Tertsea Igbawua
creator: Jacob Adawa
rights: Copyright (c) 2025 Louis Tersoo Abiem, Tertsea Igbawua, Jacob Tersugh Adawa https://creativecommons.org/licenses/by/4.0
date: 2025-12-22
publication date: 2025-12-22
starting page: 332
ending page: 332
doi: 10.46481/asr.2025.4.3.332

Hyperparameter optimisation for support vector machine-based disease detection in maize leaf variants
type: item
description: 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 wit…
subject: MATHEMATICAL SCIENCES SECTION
  topic: section
subject: MATHEMATICAL SCIENCES
  topic: category
subject: farmers, livestock, machine learning, RSA, SVM
  topic: keywords
subject: Mathematical Sciences
  topic: disciplines
creator: O. B. Ayoade
creator: M. O. Raji
creator: A. A. Akindele
creator: K. J. Yusuf-Mashopa
creator: M. F. Abdulrauf
creator: I. A. Raji
creator: F. B. Musah
rights: 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
date: 2025-10-28
publication date: 2025-10-28
starting page: 320
ending page: 320
doi: 10.46481/asr.2025.4.3.320

Persuasive power of artificial intelligence-generated ads: exploring online behaviour among Gen Z internet users in Nigeria
type: item
description: 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.
subject: MATHEMATICAL SCIENCES SECTION
  topic: section
subject: ENGINEERING
  topic: category
subject: MATHEMATICAL SCIENCES
  topic: category
subject: Artificial intelligence
  topic: keywords
subject: AI-generated ads
  topic: keywords
subject: Gen Z
  topic: keywords
subject: Online behaviour
  topic: keywords
subject: Persuasive technology
  topic: keywords
subject: Computer Science, Artificial Intelligence
  topic: disciplines
creator: Ignatius Nwoyibe Ogbaga
creator: Henry Friday Nweke
rights: Copyright (c) 2025 Ignatius Nwoyibe Ogbaga, Henry Friday Nweke https://creativecommons.org/licenses/by/4.0
date: 2025-10-15
publication date: 2025-10-15
starting page: 301
ending page: 301
doi: 10.46481/asr.2025.4.3.301

Assessment of mycotoxin presence and distribution in maize grains across North central states of Nigeria
type: item
description: Section: BIOLOGICAL SCIENCES SECTION Categories: BIOLOGICAL SCIENCES Keywords: Grains, Fungi, Mycotoxins, LC-MS, Hygiene, Prevalence Disciplines: Microbiology, Biocontrol of Microbes, Endophytes This research identified the prevalent fungal species and determined the concentration and distribution of mycotoxins in maize grains consumed in the North-central states of Nigeria. Six hundred composite samples were collected and screened for fungal contamination. The fumonisins, aflatoxins, ochratoxin, trichothecenes, and zearalenone concentrations were quantified in the samples using liquid chromatography-mass spectrometry (LC-MS). Kogi State had the highest concentration of fumonisin B1 (FB1) (755.7±56.7 µg/kg), Deoxynivalenol (DON) (1211 µg/kg), zearalenone (ZEA) (313 µg/kg) and aflatoxin B1 (AFB1) (7.5±1.6 µg/kg) and B2 (1.6±0.3 µg/kg) respectively. Ochratoxin A (OTA) value in Benue State (3.2±0.7 µg/kg) exceeded the European Union (EU) recommended amount of 3 µg/kg, while AFB1 concentrations in Kogi State (7.5±1.6 µg/kg) and Benue State (6.8±1.4 µg/kg) exceeded the EU and Standard Organization of Nigeria (SON) recommended amount of 2 µg/kg and 4 µg/kg respectively. Trichothecenes concentrations were all low compared to the EU-recommended amount in maize grains meant for the table.
subject: BIOLOGICAL SCIENCES SECTION
  topic: section
subject: BIOLOGICAL SCIENCES
  topic: category
subject: Grains
  topic: keywords
subject: Fungi
  topic: keywords
subject: Mycotoxins
  topic: keywords
subject: LC-MS
  topic: keywords
subject: Hygiene
  topic: keywords
subject: Prevalence
  topic: keywords
subject: Microbiology
  topic: disciplines
subject: Biocontrol of Microbes
  topic: disciplines
subject: Endophytes
  topic: disciplines
creator: Eno Chongs Mantu
creator: Olukayode Olugbenga Orole
creator: Aleruchi Chuku
creator: Femi Gbadeyan
creator: Tosin Okunade
rights: Copyright (c) 2025 Eno Chongs Mantu, Olukayode Olugbenga Orole, Aleruchi Chuku, Femi Gbadeyan, Tosin Okunade https://creativecommons.org/licenses/by/4.0
date: 2025-12-29
publication date: 2025-12-29
starting page: 370
ending page: 370
doi: 10.46481/asr.2025.4.3.370

Physico-chemical, heavy metal, and microbiological analysis of effluent from a confectionery company in Lagos State, Nigeria
type: item
description: 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.
subject: CHEMISTRY SECTION
  topic: section
subject: CHEMISTRY
  topic: category
subject: Effluent
  topic: keywords
subject: Confectionery
  topic: keywords
subject: Wastewater
  topic: keywords
subject: Heavy metals
  topic: keywords
subject: Coliform count
  topic: keywords
subject: Chemistry
  topic: disciplines
creator: Anthony Aiwonegbe
creator: Odunayo Adeyemi
creator: Faith Akhidenor
rights: Copyright (c) 2025 Anthony E. Aiwonegbe, Odunayo D. Adeyemi, Faith I. Akhidenor https://creativecommons.org/licenses/by/4.0
date: 2025-11-15
publication date: 2025-11-15
starting page: 333
ending page: 333
doi: 10.46481/asr.2025.4.3.333

Quasi-subordination for a subclass of non-Bazilevic functions connected with a q-derivative operator
type: item
description: 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.
subject: MATHEMATICAL SCIENCES SECTION
  topic: section
subject: MATHEMATICAL SCIENCES
  topic: category
subject: Non-Bazilevic functions, Modified Opoola q-differential operator, q-calculus, Quasi-subordination, Fekete-Szego inequality
  topic: keywords
subject: Pure Mathematics
  topic: disciplines
creator: Risikat Ayodeji Bello
creator: Ayotunde Olajide Lasode
creator: Rasheed Olawale Ayinla
creator: Adediran Dauda Adeshola
rights: Copyright (c) 2025 Risikat Ayodeji Bello, Ayotunde Olajide Lasode, Rasheed Olawale Ayinla, Adediran Dauda Adeshola https://creativecommons.org/licenses/by/4.0
date: 2025-09-22
publication date: 2025-09-22
starting page: 326
ending page: 326
doi: 10.46481/asr.2025.4.3.326

Challenges and advances in anti-rabies vaccine development in sub-saharan Africa
type: item
description: 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.
subject: HEALTH & MEDICAL SCIENCES SECTION
  topic: section
subject: HEALTH & MEDICAL SCIENCES
  topic: category
subject: Rabies
  topic: keywords
subject: Anti-rabies
  topic: keywords
subject: Vaccine
  topic: keywords
subject: Sub-saharan Africa
  topic: keywords
subject: Medical Microbiology.
  topic: disciplines
creator: Mojisola Owoseni
creator: Albert Jik
creator: Odonye Dantani
creator: Ebenezer Ogundeji
rights: Copyright (c) 2025 M. C. Owosen, A. W. Jik, D. D. Odonye, E. B. Ogundeji https://creativecommons.org/licenses/by/4.0
date: 2025-10-14
publication date: 2025-10-14
starting page: 302
ending page: 302
doi: 10.46481/asr.2025.4.3.302

Transformative approach in Lassa fever diagnostics: an innovative integrative strategy for early detection and outcome prediction
type: item
description: 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.
subject: MATHEMATICAL SCIENCES SECTION
  topic: section
subject: ENGINEERING
  topic: category
subject: MATHEMATICAL SCIENCES
  topic: category
subject: Lassa fever, Hybrid machine learning, Diagnostic models, Risk prediction
  topic: keywords
subject: Information Systems
  topic: disciplines
subject: Computer Science
  topic: disciplines
subject: Information technology
  topic: disciplines
subject: Cyber Security
  topic: disciplines
subject: Software engineering
  topic: disciplines
creator: Denis U. Ashishie
creator: Endurance O. Obi
creator: Osowomuabe Njama-Abang
creator: Ahena I. Bassey
rights: Copyright (c) 2025 Denis U. Ashishie, Endurance O. Obi, Osowomuabe Njama-Abang, Ahena I. Bassey https://creativecommons.org/licenses/by/4.0
date: 2025-10-28
publication date: 2025-10-28
starting page: 338
ending page: 338
doi: 10.46481/asr.2025.4.3.338

Intrusion detection in a controlled computer network environment using hybridized random forest and long short-term memory algorithms
type: item
description: Section: MATHEMATICAL SCIENCES SECTION Categories: MATHEMATICAL SCIENCES Keywords: Intrusion, Detection, Security, Hybrid Disciplines: Cyber Security. The unending reliant on network access in everything we do, and with the increasing dominance of online communication, there is urgent need to address computer network security challenges now, more than ever. Most of the existing Intrusion Detection Systems (IDSs) struggle to keep pace with the ever-changing characteristics of newly emerging threats. This research proposes a hybrid model that is effective in detecting both majority and minority attack classes using Random Forest (RF) and Long Short-Term Memory (LSTM) algorithms in a controlled computer network environment. Individually, the RF and LSTM models have limitations, but their individual strengths were extracted and subsequently hybridized to cover each other's weaknesses. To handle the challenge of class imbalance, a class weight was applied to the model’s loss function. This approach prompts the model to give extra attention to the minority class attacks. The meta classifier optimized the RF-LSTM combination and offered a more improved model that is effective in detecting both majority and minority classes simultaneously. The hybrid model was analyzed using the Neural Simulator Language- Knowledge Discovery in Databases (NSL-KDD) dataset. The model was deployed in a virtual network environment consisting of three operating systems and a host. The RF – LSTM hybrid model performed exceptionally well by achieving a prediction accuracy of 98.3%, precision of 96.98, recall and F1score of 96.58 and 97,03 respectively, all after 100 epochs at 0.01 learning rate . This outcome addresses shortcomings and lapses hitherto suffered by most intrusion detection models (assessing minority class attacks).
subject: MATHEMATICAL SCIENCES SECTION
  topic: section
subject: MATHEMATICAL SCIENCES
  topic: category
subject: Intrusion
  topic: keywords
subject: Detection
  topic: keywords
subject: Security
  topic: keywords
subject: Hybrid
  topic: keywords
subject: Cyber Security.
  topic: disciplines
creator: Ahena Bassey
creator: Moses Agana
creator: Emmanuel Edim
creator: Njama-Abang
rights: Copyright (c) 2025 A. I. Bassey, M. A. Agana, E. A. Edim, O. Njama-Abang https://creativecommons.org/licenses/by/4.0
date: 2025-12-01
publication date: 2025-12-01
starting page: 327
ending page: 327
doi: 10.46481/asr.2025.4.3.327

Development of a visual analogy model using transfer learning techniques
type: item
description: 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.
subject: MATHEMATICAL SCIENCES SECTION
  topic: section
subject: ENGINEERING
  topic: category
subject: Visual analogy models
  topic: keywords
subject: Transfer learning
  topic: keywords
subject: Conceptual change
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Model development
  topic: keywords
subject: Information Systems, Machine Learning, Artificial intelligence
  topic: disciplines
creator: Osowomuabe Njama-Abang
creator: Denis U. Ashishie
creator: Emmanuel A. Edim
creator: Moses A. Agana
rights: Copyright (c) 2025 Osowomuabe Njama-Abang, Denis U. Ashishie, Emmanuel A. Edim, Moses A. Agana https://creativecommons.org/licenses/by/4.0
date: 2025-10-28
publication date: 2025-10-28
starting page: 304
ending page: 304
doi: 10.46481/asr.2025.4.3.304

Assessment of potentially harmful elements in mine spoils of Jos and environs, central Nigeria
type: item
description: Section: GEOSCIENCES SECTION Categories: GEO SCIENCES Keywords: Index of geo-accumulation, Pollution, Contamination, Mining, Environment Disciplines: Geology One of the main human activities that has greatly deteriorated the environment and people's health is mining. This study aimed to determine the level of potential harmful element contamination in mine spoils collected from Jos, Central Nigeria. Forty-five samples were collected, air-dried, pulverized, and sieved to collect fine particles and analyzed for As, Ba, Co, Cr, Cu, Mo, Mn, Ni, Pb, Sb, Th, U, V, and Zn using Inductively Coupled Plasma Mass Spectroscopy (ICP-MS). The average concentrations of the potentially harmful elements in the spoils were; As: 5.70 ppm; Ba: 97.31 ppm; Be: 3.02 ppm; Co: 7.48 ppm; Cr: 124.64 ppm; Cu: 19.16 ppm; Mo: 6.19 ppm; Mn: 165 ppm; Ni: 28.84 ppm; Pb: 38.72 ppm; Sb: 0.51 ppm; Th: 36.17 ppm; U: 10.94 ppm; V: 119.84; Zn: 19.15 ppm. Pollution indices such as contamination factor (CF), pollution load index (PLI) and index of geo-accumulation (Igeo) were used to determine the level of contamination in the samples. The CF values show that the soils are considerably contaminated with As, Ba, Be, Co, Cr, Cu, Mo, Mn, Ni, Pb, Sb, V, and Zn while it is highly contaminated with Th and U. Conversely, the PLI of 0.439 shows that the site is unpolluted. Based on the index of geo-accumulation, the mine spoils are moderately- uncontaminated with As, Ba, Be, Co, Cr, Cu, Mo, Mn, Ni, Pb, Sb, Th, U, V, and Zn. Most of these elements are sourced from natural or geogenic sources, and some are remobilized from mining activities.
subject: GEOSCIENCES SECTION
  topic: section
subject: GEO SCIENCES
  topic: category
subject: Index of geo-accumulation
  topic: keywords
subject: Pollution
  topic: keywords
subject: Contamination
  topic: keywords
subject: Mining
  topic: keywords
subject: Environment
  topic: keywords
subject: Geology
  topic: disciplines
creator: Sunday Sani Daku
creator: Victor Diyelmak
creator: Ernest Okafor
rights: Copyright (c) 2025 Sunday Sani Daku, Victor Bulus Diyelmak, Ernest Anayo Okafor https://creativecommons.org/licenses/by/4.0
date: 2025-12-08
publication date: 2025-12-08
starting page: 352
ending page: 352
doi: 10.46481/asr.2025.4.3.352