Journal of Science and Information Technology
Journal of Science and Information Technology (JOSIT) is an inter- and interdisciplinary scientific journal devoted to improving scientific collaboration, enhancing access to Nigerian research, and enhancing academic research capability in Nigeria and African as a whole. The journal welcome papers from all scientific disciplines in the general categories listed below with the purpose of providing a contemporary, highly visible venue for publishing Nigerian research: Biological Sciences Chemical Sciences Computer Science and Information Technology Health Education Human Kinetics Mathematics Physical Sciences Telecommunication Science The journal accepts full-text research article submissions and reviews.
College of Science and Information Technology (COSIT), Tai Solarin Federal University of Education (TASUED)
An Explainable Hybrid Learning Model for Stock Price Prediction: Integrating Prophet and Linear Regression in a Web-Based Investment Decision Support System
Stock price prediction remains a challenging problem due to the volatile and nonlinear nature of financial markets. This study developed an explainable hybrid learning model that combined Prophet and Linear Regression, integrated into a web-based investment decision support system. The objectives included designing the hybrid model with technical indicators, evaluating its performance using standard metrics (MAE, MSE, RMSE, and R²), integrating the model into a user-friendly interface, and assessing usability. A mixed-method approach was adopted, incorporating quantitative techniques (for model development and performance evaluation) and qualitative usability testing. Historical data from five major tech companies (AAPL, MSFT, TSLA, AMZN, and GOOGL) were used alongside indicators like RSI, Daily Return, Volatility, and Bollinger Bands. SHapley Additive exPlanations (SHAP) was employed to interpret the influence of features on predictions. Results showed that the weighted hybrid of Prophet and Linear Regression models outperformed individual models such as Long Short-Term Memory (LSTM), Support Vector Machine (SVM), Prophet, and Linear Regression achieving lower error rates (e.g., MAE of 4.99 and R² of 0.83 for AAPL) and improved reliability across multiple stocks. Usability feedback confirmed high user satisfaction, clarity, and trust in the system's predictions and explanations. This study successfully bridged prediction performance and explainability to support better investment decisions.
Copyright (c) 2025 Journal of Science and Information Technology
Identifying Significant Structural Factors associated with Knee Pain Severity in Patients with Osteoarthritis using Hybrid Bio-BERT Bi-LSTM CNN Model
Knee osteoarthritis (OA) is a progressive, long-term joint disorder marked by pain, stiffness, and impaired mobility, significantly diminishing patients’ quality of life. Accurately predicting the severity of knee pain is challenging due to the complex, multifactorial aetiology of OA and the heterogeneity in both structural changes and clinical symptoms among patients. This study introduces a hybrid deep learning model which integrates Bidirectional Encoder Representations from Transformers for Biomedical Text Mining (Bio-BERT), Bidirectional Long Short-Term Memory (Bi-LSTM), and Convolutional Neural Network (CNN) models to predict knee OA pain severity by combining clinical text and Magnetic Resonance Imaging (MRI) datasets. Using a multimodal Kaggle datasets which contains 163,064 clinical records and 2000 MRI scans, the model achieved a maximum test accuracy of 98.11%, with strong precision, recall, and F1-scores, particularly for severe pain cases. These results demonstrate its effectiveness in synthesizing textual and visual features, supporting potential clinical applications in diagnosis, early intervention, and treatment planning. Future work will focus on improving interpretability, validating external generalizability, enabling real-time Electronic Health Record (HER) integration, and ensuring ethical AI practices, including patient privacy and transparency.
Copyright (c) 2026 Journal of Science and Information Technology
Assessing the Need for the Development of a Problem-Posing Multimedia Package for Teaching Hydrocarbon Concepts: Perspectives from Secondary School Chemistry Teachers
This study presents a need analysis conducted as the first phase of the ADDIE instructional design model to justify the development of a problem-posing multimedia package for teaching hydrocarbons in organic chemistry. This investigation is rooted in the documented complexity of hydrocarbon concepts for secondary students, including abstract molecular structures and reaction mechanisms, which often lead to persistent misconceptions and low engagement. Coupled with the increasing emphasis on interactive, student-centered pedagogies and digital integration in modern science curricula, there is a pressing need to explore innovative instructional solutions. A preliminary study was conducted involving interviews with six experienced chemistry teachers with teaching qualifications and at least 10 years work experience. The teacher interviews explored current pedagogical practices, challenges in teaching hydrocarbons, student misconceptions, and perceptions of multimedia and problem-posing approaches. Five themes emerged from teacher interviews: importance of hydrocarbon concepts, challenges in teaching and learning hydrocarbons, student misconceptions in Organic chemistry, teaching methods resources, and strategies for improving engagement. Findings revealed significant gaps in foundational knowledge, limited use of interactive pedagogies, persistent misconceptions, and inadequate laboratory and digital resources. The results justify the need for a problem-posing multimedia package to support conceptual understanding and active student engagement. Recommendations include integrating visualizations, contextualized problem-posing tasks, inquiry-based activities, and improved resourcing. The study contributes to literature on chemistry education by providing evidence-based justification for multimedia-supported problem-posing pedagogy in the teaching of hydrocarbons.
Metric space is a set in which a notion of distance between elements is defined in a precise and consistent way. Metric space provides a general and flexible framework for studying the idea of distance which underlies much of mathematics and its applications. In 1922, Stefan Banach was able to use the space alongside with contraction to prove the existence and uniqueness of solutions of ODE. This study opened the eyes of many researchers to this area of study. In this paper, we introduce the concept of metric quasigroups and metric loop with examples. This will enrich the space. We also introduce homomorphism in this concept. Our work unify, extend and generalize existing work in literature.
Incorporating Conceptual Clarity and Topic Coverage into Flesch-Kincaid Readability Model for University Course Content Analysis
While the Flesch-Kincaid (FK) formula remains a standard for readability assessment, current research highlights its limitations in measuring actual text comprehension due to its focus on surface-level linguistic features. This study addresses this gap by developing the Conceptual Clarity and Topic Coverage (CCTC) enhanced Flesch Reading Ease (FRE) model, which integrates semantic coherence and syllabus alignment into the traditional readability framework. The system, implemented via a Flask-based web application, automates the evaluation of university course materials using a pipeline of Latent Dirichlet Allocation (LDA) for topic distribution and TF-IDF cosine similarity for conceptual clarity. Validation was performed using a dataset of Computer Science courseware benchmarked against expert human ratings. Statistical analysis via ANOVA and Pearson correlation demonstrated that the CCTC-enhanced FRE score achieves a significantly higher correlation with human judgment (τ=0.86, ρ<0.05) compared to traditional metrics such as the standard FRE (τ=0.86), Gunning Fog (-0.56), and SMOG (-0.77). These findings provide empirical evidence that incorporating semantic and thematic depth significantly improves the accuracy of automated readability assessments in technical academic contexts. This research offers a robust framework for educational quality assurance and data-driven curriculum design.
Assessment of Potassium Bromate Levels and Dietary Health Risk in Commonly Consumed Breads in Ijebu-Ode, Ogun State, Nigeria
The continued use of potassium bromate as a dough improver in bread production is often driven by its technological advantages, including enhanced loaf volume and improved texture; however, its potential adverse health effects remain a major public health concern. In addition, contamination of bread with heavy metals may further compromise consumer safety. This study therefore quantified potassium bromate and selected heavy metals (Pb, Cd, Cr, Ni, Fe, and Zn) in commercial bread samples and evaluated associated dietary risks. Potassium bromate concentrations ranged from 1.52 ± 0.64 to 22.38 ± 0.98 mg/kg (mean: 9.35 ± 0.58 mg/kg), with 100% exceedance of the maximum permissible limit established by the World Health Organization and Food and Agriculture Organization. Heavy metals (mg/kg, mean ± SD) were: Pb (0.11 ± 0.052), Cd (0.04 ± 0.020), Cr (0.17 ± 0.060), Ni (0.12 ± 0.051), Fe (20.55 ± 5.42), and Zn (10.75 ± 2.70). Hazard Index values ranged from 0.062–0.197 (adults) and 0.291–0.923 (children). Although heavy metal exposure remained within non-carcinogenic safety thresholds (HI < 1), the widespread occurrence of potassium bromate classified as a possible human carcinogen by the International Agency for Research on Cancer indicates significant regulatory non-compliance and potential long-term health risks.
Assessing Artificial Intelligence Knowledge, Skills, and Utilization for Psycho-Educational Development in Selected Private Universities in Ogun State, Nigeria
Artificial Intelligence (AI) has emerged as a strategic innovation with potential to enhance teaching effectiveness, student engagement, and psycho-educational development in higher education. Despite its growing relevance, empirical evidence on stakeholders’ awareness, skills, and utilization of AI within Nigerian universities remains limited. This study investigated the level of awareness, skills, and application of AI for psycho-educational development among university stakeholders in Ogun State, Nigeria. The study examined stakeholders’ understanding of AI technologies, factors influencing adoption in teaching and learning, and the relationship between effective AI use and students’ engagement, academic performance, and psycho-educational development. A descriptive survey research design was adopted. Data were collected using a researcher-designed instrument titled Knowledge, Skills and Uses of Artificial Intelligence for Psycho-Educational Development Questionnaire (KSUAPD). The sample comprised 200 respondents—lecturers, administrators, and undergraduate students—from Caleb University and South Western University, selected through systematic sampling. Data were analysed using descriptive statistics. Findings revealed a relatively high level of general AI awareness, alongside gaps in practical skills and confidence. Resource availability, institutional support, and resistance to change significantly influenced AI adoption. Effective AI use was positively associated with students’ engagement, academic performance, and psycho-educational development. The study concludes that although perceptions of AI are largely positive, limitations in skills, infrastructure, and institutional frameworks constrain effective utilization. It recommends strengthened AI literacy, curricular integration, and ethical governance to support responsible and sustainable AI integration in Nigerian universities.
Addressing the Out-of-School Syndrome in Nigeria: Leveraging Technology and Vocational Education for Youth Empowerment, National Development and Security
This study examines the out-of-school syndrome in Nigeria, a multifaceted crisis affecting human capital development, economic growth, social cohesion, and national security. Driven by poverty, insecurity, socio-cultural barriers, gender inequality, and infrastructural deficits, the phenomenon leaves millions of children and youth marginalized and vulnerable. The research explores technology-enabled learning and vocational education as viable interventions to enhance access, equip youth with market-relevant skills, and foster empowerment. Findings indicate that digital platforms, mobile learning initiatives, and skills-based programs can bridge educational gaps, promote economic self-sufficiency, and support social integration. The study further highlights the importance of a coordinated, multi-stakeholder implementation framework, adequate financing, and robust monitoring and evaluation to ensure sustainability and impact. Recommendations include expanding technology-driven education, aligning vocational programs with labour market needs, strengthening certification systems, and integrating peace building and gender-sensitive approaches. The study underscores that holistic, evidence-based strategies are essential to reintegrate out-of-school youth, promote national development, and enhance security.
Competency Improvement Needs of Craftsmen in Motor Vehicle Mechanic Work for Enhancing Employability in Automobile Industries: Evidence from Nigeria
The study examined the competency improvement needs of craftsmen in motor vehicle mechanic work for enhancing employability in Ogun State, Nigeria. Specifically, the study sought to identify areas where craftsmen required improved competencies in order to remain relevant in the dynamic automobile industry. Three research questions guided the investigation, and a descriptive survey research design was adopted. The population consisted of 121 craftsmen currently engaged in motor vehicle mechanic work across industries in the study area. Data were collected through a structured questionnaire, whose internal consistency was determined using Cronbach Alpha, yielding a reliability coefficient of 0.84, indicating high reliability. Data analysis was carried out using weighted mean and improvement Needed Index (INI). Findings revealed that craftsmen demonstrated significant competency deficiencies in critical areas, particularly in engine maintenance, braking systems and auto-electrical operations. The study concluded that these competencies are vital and should be systematically integrated into motor vehicle mechanic curricula in technical colleges to better prepare future graduates for industrial relevance. It was recommended that industries, training institutions, and professional associations should collaborate to organize regular hands-on workshops, refresher courses, and continuous professional development programs to ensure craftsmen keep pace with technological advancements in the automotive sector.
Lessons from the Genomes: Rhizobacterial Ecology and Genomics
The rhizosphere is a dynamic microbial hotspot where plant roots interact with diverse microorganisms, including plant-growth-promoting rhizobacteria (PGPR). Recent advances in genomic technologies, particularly genome sequencing, functional genomics, and metagenomics, have enhanced understanding of rhizobacterial ecology by revealing genetic traits associated with root colonisation, nutrient acquisition, and stress resilience under environmentally variable conditions. This review synthesises studies published over the past decade to examine the genetic basis of plant–microbe interactions. It evaluates the roles of horizontal gene transfer (HGT), niche adaptation, and the interplay between core and accessory genomes in structuring rhizobacterial communities. The review further highlights that gene presence does not consistently translate to in situ activity, reflecting context-dependent gene expression and environmental modulation of microbial functions. Key gaps include limited functional validation of candidate genes, constraints in linking genomic potential to ecological performance, and challenges associated with extrapolating laboratory-based findings to complex field environments. These insights inform ongoing efforts to develop genomics-guided microbial inoculants for sustainable agricultural systems.
Machine Learning Models for Predicting ICU Length of Stay: A Comparative Analysis
Proper estimation of intensive care unit (ICU) length of stay (LOS) is important for effective resource utilization, patient flow management, and clinical decision assistance. In this study, a comparative analysis of machine learning models: Linear Regression, Random Forest, XGBoost, and Multilayer Perceptron (MLP) were conducted to assess the prediction efficiency of these models on the publicly available MIMIC-IV dataset. The information, demographic, clinical, and lab features, were pre-processed and divided into training, validation, and test sets (70%, 15%, and 15%). Performance of the model was measured in terms of various regression metrics including mean absolute error (MAE), root mean squared error (RMSE), coefficient of determination (R²), mean absolute percentage error (MAPE), and median absolute error (MedAE). Results showed that the best in prediction were ensemble-based models, namely XGBoost (MAE = 2.14 days, RMSE = 2.95, R² = 0.61) over Random Forest and MLP, and the worst was Linear Regression with no ability to detect nonlinear relationships. This research emphasizes the clinical value of achieving an average error of less than two days, which allows hospitals to predict ICU demand and enhance staffing and resource allocation. Additionally, the importance of interpretability was mentioned, particularly regarding the necessity for transparent models that foster clinician trust and encourage their adoption. Overall, the findings point to the promise of future machine learning systems in designing better ICU resource management and also suggest that greater emphasis needs to be given to transparency and generalizability in the application to clinical environments.
Rhizobacteria as Biofungicides: Mechanisms and Applications
Plant pathogenic fungi are a major cause of agricultural losses worldwide, stressing the need to transition toward biologically derived fungicide alternatives that pose less risk to environmental and human health. Rhizobacteria are beneficial root-associated microbes with strong biocontrol capacity, acting against fungal pathogens via competition, antibiosis, parasitism, and triggering systemic resistance in plants. This review consolidates current insights into the mechanisms underlying rhizobacterial biofungicides and explores their practical applications in crop protection. It discusses notable successes in field trials, key obstacles to commercialisation, and emerging strategies such as genetic engineering and nano-formulation technologies to enhance efficacy and scalability.
Design and Implementation of an AI Chatbot for Prospective Student Support
Traditional communication channels in higher education often struggle to handle the high volume of admission-related inquiries, leading to delays and limited access to accurate information. This study presents the design, implementation, and preliminary evaluation of an AI-powered chatbot developed to support prospective undergraduate students at Tai Solarin University of Education, Ijebu-Ode. The system integrates an institution-specific knowledge base with a Large Language Model (LLM) to respond to queries on admission requirements, application procedures, available programs, and general university information. The chatbot was implemented via a web-based interface with Firebase backend services and Google-based authentication, with natural language responses generated using the Google Gemini API. Evaluation was conducted using 20 admission-related queries derived from real-world student information needs. Responses were assessed by the researcher and two evaluators based on accuracy and relevance, using official university information as a reference. Results show that 19 out of 20 responses were fully accurate and one was partially accurate, reflecting strong performance within the defined scope. The system demonstrated consistent relevance across all queries, with response times ranging from 2 to 10 seconds depending on network conditions. Limitations include the small dataset and qualitative assessment approach. These findings establish the chatbot as an effective proof of concept for structured admission inquiries. Further research involving larger datasets and quantitative metrics is needed to validate scalability and real-world effectiveness.
Comparative Performance Evaluation of Artificial Neural Network and Hidden Mackov Model for Predicting Substance Abuse Neuron Disorder Rate
Researchers in the past have adopted different techniques such as the traditional statistical models, hidden mackov models for forecasting into the distance future but this approach is becoming outdated and gradually reaching their limitation in application due to incomplete precision and accuracy, for the purpose of complete accuracy and precision, neural network model technology has been used in this application work where the problem domain mainly involves prediction model This study embraced the use of neural network model technology to forecasting precision for the control of Substance Abuse Neuron Disorder (SAND) rate. This enormous task methodology involve first the theoretical gathering of psychiatric life data from World Health Organization (WHO), then using different well-known neuron disorder diseases data were mathematically models and simulated in matrix laboratory tools with Artificial Neural Network (ANNM) and Hidden Mackov Model (HMM) as examples. Secondly, consider major variables upon which new model which is more realistically represents actual disease prediction is built. Three components are implemented: SAND; is a targeted non communicable disease with Routine surveillance, modeling the disease risk with historical data surveillance with contemporary environmental data; and Forecasting future risk with comparative evaluation using two predictive metrics models Mean Square Error (MSE), Standard Deviation (SDEV) as paramount tools in epidemiological surveillance. Average MSE and SDEV of HFSANDR for each periods was 0.7 and 0.8 which is close to (1.0) while that of AFSANDR performance was between 0.3 to 0.4 less than (0.5) which indicates the weakness of HFSANDR for prediction Finally the evaluation shows that ANN prediction model performs better than HMM with more than 18 percent in term of accuracy
Removal of Methylene Blue Dye from Aqueous Solution using Cotton (Gossypium Spp.) Leaf as an Adsorbent
Synthetic dyes like methylene blue (MB) when released into water bodies, are highly toxic, persistent, and have low biodegradation, which is dangerous to the environment and the people. In this paper, we have used cotton (Gossypium spp.) leaf powder, a readily available and low-cost agricultural waste, as a new biosorbent for the effective extraction of methylene blue from aqueous solutions. Fourier-transform infrared spectrophotometry (FTIR) and scanning electron microscopy (SEM) were used to characterise the adsorbent and the presence of key functional group (–OH, –COOH), and a porous surface structure that bound the dye. To test the impact of solution pH, contact time, and the initial dye concentration, nearly all experiments were run in batch adsorption. Optimal absorbance was realized at pH 7, with adsorption that rose proportionally to contact time and dye concentration attain its optimum at 300 minutes. The pseudo-first-order model gave the best fit to kinetic data (R²= 0.99), implying that the rate-limiting step was physiosorption. The Langmuir isotherm was most suitable as the adsorption equilibrium character: R² was close to 1 (0.99), illustrating monolayer adsorption with the highest adsorption capacity of 75.00 mg/g. The results illustrate that cotton leaf powder is a potential, sustainable, and environmentally friendly adsorbent to be used to decolorize methylene blue dye in wastewater. The research offers a good avenue to the valorisation of agricultural residues in low-cost water treatment systems.
A Hybrid Machine Learning Framework for Predicting Academic Performance and Mental Health Risks in Students
The increasing prevalence of poor academic performance and mental health challenges among students has created a critical need for intelligent, data-driven intervention systems. This study presented a novel hybrid machine learning framework for the simultaneous prediction of academic performance and mental health risk, addressing the limitations of existing approaches that treat these problems independently. The proposed model employs a stacked ensemble architecture that integrates Support Vector Machine, Random Forest, Artificial Neural Network, and Gradient Boosting as base learners, with Logistic Regression as a meta-learner to enhance predictive accuracy and robustness. An integrated dataset comprising 3,000 student records was constructed from academic data and survey-based psychological assessments, incorporating features such as CGPA, attendance, study habits, stress levels, and sleep quality. To ensure data quality and model reliability, comprehensive pre-processing and validation procedures were applied, including normalization, missing value imputation, and stratified k-fold cross-validation. Experimental results demonstrate that the proposed framework significantly outperforms individual baseline models, achieving an accuracy of 95% for academic performance prediction and 93% for mental health risk classification, with an F1-score of 0.94 and ROC-AUC of 0.96. Furthermore, SHAP-based explainable artificial intelligence techniques were employed to provide interpretable insights into model predictions, revealing key factors influencing student outcomes. The study is limited by its reliance on a single integrated dataset and self-reported psychological measures, which may affect generalizability. This study contributes a scalable and interpretable predictive framework that bridges academic analytics and mental health assessment, offering practical implications for early intervention and decision-making in educational systems.
Child Labour Practices as Predictors of Educational Involvement of School-Age Children in Ogun State, Nigeria
Child labour remains a pervasive socioeconomic challenge in Nigeria. Despite being regarded as educationally advanced, Ogun State continues to see school-age children engaged in economic labour during school hours, raising serious concerns about educational participation and child welfare. This study examined child labour practices as predictors of educational involvement among school-age children in Ogun State, Nigeria. A descriptive survey design was employed. Using a multistage sampling procedure combining purposive, judgemental, and snowball sampling techniques, 1,685 school-age children were recruited from markets and motor parks across all three senatorial districts of Ogun State. Data were collected using two validated instruments, the Prevalence of Child Labour Questionnaire (PCLQ; α = 0.814) and the Child Labour and Education Questionnaire (CLEQ; α = 0.875), yielding an overall reliability index of 0.944. Descriptive statistics and multiple regression analysis were conducted at the 0.05 level of significance. Child labour was highly prevalent, with hawking the most frequently reported activity (mean = 3.46). Regression analysis revealed that child labour practices collectively accounted for 4.8% of the variance in educational outcomes (R² = 0.048, F = 28.499, p < 0.05). Hawking (β = −0.210) and domestic services (β = −0.208) negatively predicted educational involvement, while load-carrying showed an unexpected positive association (β = 0.552) that requires further qualitative investigation. Child labour significantly constrains educational participation among school-age children in Ogun State. The government should provide accessible and affordable educational opportunities and enforce the Child Rights Act of 2003 to reduce child labour.
Conceptualizing Stadium Safety and Security in African Professional Football: A Theoretical and Empirical Synthesis of Administrative, Personal, and Structural Determinants in the Nigerian Professional Football League
This paper provides a theoretical and empirical synthesis of stadium safety and security within African professional football, focusing specifically on the Nigeria Professional Football League (NPFL). Drawing extensively on the author’s recent empirical study of NPFL stakeholders (Adebayo, 2025), as well as global frameworks and local realities, the synthesis examines administrative, personal, and structural determinants that influence safety outcomes in Nigerian stadiums. It highlights persistent challenges such as inadequate crowd management, poor stewardship, biased officiating, and infrastructural deficiencies, which have led to repeated incidents of violence, declining attendance, and reputational damage for the NPFL. The synthesis incorporates Gustave Le Bon’s Crowd Theory to explain the psychological underpinnings of collective behaviours, and underscores the importance of context-specific interventions tailored to sub-Saharan Africa’s unique socio-economic and cultural environment. Despite the existence of comprehensive safety guidelines by FIFA and CAF, implementation gaps continue to expose stakeholders to preventable risks. This synthesis addresses the under-researched nature of stadium safety in Africa and proposes actionable recommendations for policy alignment, capacity building, and infrastructure enhancement. By extending the empirical findings of Adebayo (2025), the paper bridges international best practices with local realities and offers pathways for the Nigeria Football Federation and related bodies to foster safer, more secure stadium environments that can restore spectator confidence, improve attendance, and attract investment.
A Multifunctional Nano Fe-NPK Fertiliser: Synthesis, Characterisation, and Applications in Plant Growth
Iron is a critical micronutrient that supports photosynthesis, respiration, enzyme activity, and nucleic acid synthesis in plants, yet its bioavailability in soils is often limited. Nanotechnology provides new opportunities to address this challenge by enabling controlled nutrient delivery and improved uptake efficiency. Building on advances in metallo-agriculture, this study evaluates the agronomic performance of a hybrid nano Fe-NPK fertiliser previously synthesised through the thermal reaction of calcium phosphate, potassium chloride, and urea with nano-iron produced via chemical reduction of FeSO₄·7H₂O using NaBH₄. The nanofertiliser, characterised by AAS, EDX, XRD, FTIR, UV–Vis, and SEM, exhibits an N:P:K:Fe ratio of 1:2:1:2 and a core–shell nano-architecture containing both zero-valent and oxide phases of iron. In this application study, the nano Fe-NPK formulation was applied to maize (Zea mays L.) seedlings for 21 days to assess its functional performance. Treated plants demonstrated enhanced chlorophyll production, accelerated shoot and root growth, and greater biomass accumulation relative to the untreated control. Leaf length in treated plants increased by approximately 33% relative to the control by day 21, and potassium uptake reached 0.48 mg/kg compared with 0.36 mg/kg in the control group. These improvements are attributed to increased iron bioavailability and improved uptake of N, P, and K facilitated by the nanoscale size and controlled-release behaviour of the formulation. Overall, the results highlight the potential of hybrid nano Fe-NPK fertiliser to improve early-stage crop development, improve nutrient use efficiency, and support sustainable agricultural productivity.
Effect of an 8-Week High Intensity Interval Training Programme on Cardiorespiratory Parameters of Male Athletes of Tai Solarin Federal University of Education Ijagun Ogun State
The main objective of the study was to determine the effects of an 8-week high-intensity interval training program on the cardiorespiratory parameters of male athletes at Tai Solarin University of Education in Ijagun, Ogun State. The study's target population consisted of all male undergraduate athletes at Tai Solarin Federal University in Ijagun, Ogun State. Fourteen volunteers were chosen at random from the population to make up the study's sample. This study adopted the one group Pre-test/Post-test research design. It is one of the pre-experimental designs where a single group is observed after a treatment, often without a control group. The participants were exposed to pre-test measurements. After the pre-test measurement, participants were exposed to 8-week interval training. Data was collected for pre-test and post-test using the following standardized instruments: 400-metre running track (for Cooper's 12-minute run test for Vo2 max), the PCV micro-haematocrit centrifugation, and the sphygmomanometer for measuring Heart rate and blood pressure were all standardised instruments. The acquired data was evaluated using the descriptive statistics of mean and standard deviation, and the hypotheses were analysed at the 0.05 level of significance using the inferential statistics of the Student t-test. Five hypotheses in all were developed, tested, and all of them were found to be false. According to the study, male athletes' resting heart rate, resting systolic and diastolic blood pressure, packed cell volume, and maximum oxygen absorption were all affected by 8-week interval training. Given the results, it was recommended that male athletes should Coaches should include high-intensity interval training to improve cardiorespiratory variables, while proper enlightenment campaign should be organized for male athletes on the effectiveness of interval training at high intensities programme in reducing/improving the resting heart rate which is an evidence of improved cardiovascular health and endurance.
Big Data Analytics and Crime Mapping in African Cities: Implications for Security Governance and Evidence-Based Policing
With an emphasis on the consequences for security governance and evidence-based policing, this study attempts to investigate the use of Big Data analytics and crime mapping in African cities. Although data-driven policing is becoming more and more popular worldwide, little context-specific study has been done on the particular socioinstitutional factors influencing its application in African metropolitan settings. Using comparative case studies of Johannesburg, Nairobi, Accra, and Lagos, the study employs a qualitative, doctrinal, and comparative analytical method, drawing from recent literature, policy papers, and institutional reports (2024–2026). Routine Activity Theory, Crime Pattern Theory, and Socio-Technical Systems Theory serve as its theoretical foundations. The results show that crime hotspot identification, resource allocation, and proactive decision-making are much improved by Big Data tools, such as Geographic Information Systems (GIS) and predictive police models. The study contributes to knowledge by integrating criminological theory with digital policing practices in African contexts. It concludes that sustainable adoption requires not only technological investment but also robust governance structures to ensure accountability, legitimacy, and public trust in urban security systems.
Enhancing Evidence Integrity in Nigeria’s Criminal Justice System: A Doctrinal Analysis of Blockchain Technology Adoption
Blockchain technology offers a revolutionary solution to Nigeria's criminal justice system's enduring problems with evidence integrity. Weak chain-of-custody protocols, human error, and the increasing use of digital evidence all pose threats to Nigeria's legal system. The potential of blockchain as a governance and procedural tool is examined in this paper, with an emphasis on how it might improve accountability, transparency, auditability, and procedural reliability. It examines current academic literature and legislation provisions, such as the Evidence Act of 2023 and the Administration of Criminal Justice Act of 2015, which have decided Nigerian cases involving electronic evidence using a qualitative doctrinal approach. While propositions assess blockchain's potential to enhance evidence integrity and court confidence, research questions look at its operational viability, institutional preparedness, and legal compatibility. Comparative insights from China, the European Union, and the United States inform lessons for Nigeria. The paper makes a theoretical contribution by characterising blockchain as a socio-technical governance infrastructure, a doctrinal contribution by incorporating it with evidence law, and a practical contribution by providing policy recommendations for the transformation of the justice sector. Studies have shown that using blockchain technology can greatly improve evidence management, reduce procedural errors, and boost trust while bringing up ethical and human rights issues. Doctrinal restrictions and the quick advancement of technology are among the limitations, pilot testing and the integration of AI and blockchain are suggested.
Haemoglobin Polymorphism and Genetic Diversity of Indigenous Nigerian Cattle Breeds: Insights from Gudali, Red Bororo, and White Fulani Populations
Polymorphism is the occurrence of varieties attributed to biochemical differences which are under genetic control. It has provided a medium for genetic improvement of farm animals, because it is a useful tool for characterization of livestock breeds and population. The degree of resemblance or differences within and between breeds can be ascertained, and they are important raw materials for genetic improvement of animals. This study was carried out to analyse the haemoglobin (Hb) polymorphism in 380 cattle comprising of three different breeds; 126 Gudali (G), 106 Red Bororo (RB), and 148 White Fulani (WF), at the general abattoir, Ibadan. The samples were subjected to Cellulose Acetate Electrophoresis (CAE) to determine the genetic variants of Haemoglobin using Tris EDTA borate buffer. The result showed that Haemoglobin (Hb) exhibited two alleles A and B, with three genotypes (HbAA, HbAB, and HbBB). In the G, the allele frequencies were A (0.588) and B (0.412) respectively, while A (0.491) and B (0.509) were observed in the RB, A (0.466) and B (0.534) were observed in WF, and in the overall population A (0.514) and B (0.486). The genotype frequencies obtained in all populations showed that genotype HbAB had the highest frequency in all the breeds, and highest individually in Gudali (0.476), White Fulani (0.527), and Red Bororo (0.453), while HbBB was lowest in Gudali (0.175), and also lowest in the overall population (0.243). All the breeds studied were found to be in Hardy-Weinberg Equilibrium.
Detection of Polymorphism in Insulin-Like Growth Factor I Gene in Fulani and Yoruba Ecotype Chickens
This research examined polymorphism in the insulin-like growth factor I (IGF-1) gene of native Nigerian chickens (Fulani and Yoruba ecotypes) using polymerase chain reaction-restriction fragment length polymorphism (PCRRFLP) analysis. This study examined 98 chickens (50 Fulani and 48 Yoruba ecotypes) with approximately 2 mL of blood collected from the wing vein for DNA extraction. Genetic variation was tested by digesting PCR products with PstI. Two alleles (C and T) and three genotypes (CC, CT, and TT) were identified in both ecotypes, confirming that the IGF-1 locus was polymorphic. Frequencies of the T allele and TT genotype were higher in the populations. Chi-square test revealed a departure from the Hardy- Weinberg equilibrium in the Fulani ecotype, but not in the Yoruba ecotype. Observed heterozygosity was significantly lower than the expected, implying inbreeding. The genetic diversity was fairly high while the genetic distance was low, suggesting high genetic similarity between the two ecotypes. Additional studies are needed to determine the relationship between IGF-1 polymorphism and productivity traits.
Design and Implementation of an Intelligent, Modular, and Secure Human Resource Management System using Laravel Framework
This study presents the design and implementation of an intelligent, modular human resource management systems developed using the Laravel framework. The system integrates core human resource functions such as employee and department management, attendance tracking, leave management, and reporting within a secure and scalable architecture. Utilizing the Incremental Development Model and Model-View-Controller pattern, the research ensures modularity, maintainability, and user-centered design. Advanced security mechanisms, including role-based access control, authentication, and Cross-Site Request Forgery protection, were incorporated to safeguard sensitive human resource data. The system was evaluated through unit, integration, and feature testing, all of which demonstrated functional accuracy, data integrity, and high usability. Results revealed that the system effectively addressed common inefficiencies in traditional human resource workflows, including data redundancy, administrative delays, and inadequate reporting. Furthermore, the integration of privacy-sensitive design and automation capabilities enhanced transparency and efficiency across human resource operations. This research contributes to the body of human resource technology by demonstrating how modern web frameworks can be leveraged to develop adaptable and secure HRMS platforms suitable for resource-constrained environments such as developing economies.
Numerical Evaluation of Fractional-Order Behaviour in a Nonlinear Climate Forecasting Model for Nigeria Temperature Anomalies Using Atangana-Baleanu Predictor-Corrector
Understanding long-term climate variability remains essential for environmental planning and sustainability in developing countries such as Nigeria. This study develops a nonlinear climate forecasting model with fractional-order capability for the simulation and prediction of annual temperature anomalies in Nigeria from 1960 to 2050. Annual temperature anomaly data referenced to the 1961–1990 climatological baseline were obtained from Berkeley Earth, while radiative forcing data were sourced from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The model incorporates external radiative forcing, nonlinear thermal stabilization, and an Atangana–Baleanu fractional derivative framework. Numerical simulations were implemented in MATLAB R2016a using a predictor–corrector algorithm, while model performance was evaluated against Euler, Runge–Kutta fourth-order (RK4), and autoregressive AR(1) approaches using RMSE and coefficient of determination metrics. Parameter estimation yielded a = -0.023822, b = 0.060654, c = 0.072747, and ∝ = 1.00000. The convergence of the fractional order toward unity indicates that the calibrated annual anomaly series behaves predominantly as a classical nonlinear climate system with limited evidence of strong long-memory effects at the national aggregation scale. Sensitivity to fractional order showed ∝<1 unpredicted observed warming, only ∝ =1 tracked the trend.