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- Inaugural lectures by Professors from different Faculties of Lead City University
Recent Submissions
Microbial and Physicochemical Characterization of Effluent and Waste Water around a Detergent Manufacturing Complex in Ibadan, Nigeria
(Lead City University, Ibadan, 2025-12) Tayo Elijah EGBEDEYI
Industrialization has contributed immensely to economic growth, urban development, and improved standards of living across the world. The detergent manufacturing industry is one of the most polluting industrial sectors due to its high chemical input, complex production processes, and generation of large volumes of wastewater. The aim of this study is to characterize the microbial and molecular profile of effluent, workers wash water, and receiving surface water from a detergent manufacturing complex in Ibadan, Nigeria. Effluent and wastewater samples were collected from three distinct sites associated with the detergent manufacturing complex. Physicochemical parameters were measured and Microbial characterization involved both cultural and molecular techniques were carried out. The results of this study demonstrated that effluents and wastewater discharged from the detergent manufacturing complex in Ibadan are heavily polluted, with significant deviations from recommended physicochemical standards and a high microbial burden dominated by coliform bacteria. The predominance of Bacillus spp. (33 %), Staphylococcus sp. (15 %), Klebsiella spp. (3.0 %), and Escherichia coli. (6.1 %) among isolates is consistent with previous reports of the presence of microbial contamination in industrial effluents. Physicochemical analyses confirmed that the effluent was not compliant with World Health Organization (WHO) standards, particularly with respect to pH, BOD, COD, and nutrient load. Out of the three isolates tested, blaCTX-M, was detected in 2 (66.7 %) of the isolates, indicating a moderate prevalence of extended-spectrum β-lactamase (ESBL) producers in the sampled environment. The SUL1 gene, was identified in all isolates (100 %), suggesting widespread dissemination of sulfonamide resistance within effluent-associated bacteria. Overall, the study highlights the urgent need for stricter regulatory enforcement, improved effluent treatment technologies, and continuous monitoring of industrial wastewater.
Keywords: Effluents, Industrial discharge. Microorganisms, Ibadan, Pollutants, Contaminants
Word Count: 269
A Comparative Analysis of Machine Learning models in the Classification Techniques in the Prediction of Autism in Children
(Lead City University, Ibadan, 2025-12) Ebierimunu ABULE
Autism Spectrum Disorder (ASD) is a mild cognitive impairment known to affect a person’s language, communication, thought process and social behaviour. Some recent advances in machine learning made it possible to predict ASD using behavioural, demographic, and medical data, but the choice of optimal algorithms and feature selection techniques remains an open research question. This study used the ASD for children dataset obtained from the University of California, Irvine repository to investigate the performance of the selected machine learning models through a comparative analysis. Eight nonparametric models were compared in total, with four models SVM, KNN, GNB, and MLP being base learners, and the other four ensemble methods RF, XGBoost, Bootstrap Aggregating (Bagging), and Stacking. Hyperparameters of the models were autotuned, and GridSearchCV was employed to choose the model with the best combination of parameters. All base models performed brilliantly, with SVM being the best and most precise in this experiment with a result of 1, amongst all classifiers even before hyperparameter tuning. After hyperparameter tuning, SVM and MLP
ranked highest with all metrics returning scores of 100%. KNN returned 96.2% accuracy and recall and GNB trailed at 95.26% for both metrics for the ensemble methods, XGB and Bagging with SVM performed best at 100% for all metrics. RF produced an accuracy and recall of 98.1% while Bagging with KNN and GNB had both metrics at 95.2%. The classification performance of Bagging with MLP and the stacking classifier were equivalent across all metrics though, producing accuracy and recall of 99.5%. Based on the findings, it is recommended that thorough feature selection should be conducted to eliminate features that could lead to overfitting, such as "Q_Chat_10" in this study cross-validation technique was used to assess the generalizability of models to unseen data.
Keywords: Accuracy, Classifier, Defaults, Financial, Models, Predicting, Validation
Word Count: 300
Effects of Educational-Based Intervention on Knowledge and Skill of Breast Self- Examination among Nursing Mothers in Selected Secondary Health Facilities in Osun State
(Lead City University, Ibadan, 2025-12) Modupe Aduke AINA
Despite regular healthcare contact, there is limited empirical evidence on how structured educational interventions improve nursing mothers’ knowledge and skills of breast self- examination in Osun State. The study assessed the effect of an educational-based intervention on the knowledge and skills of breast self-examination (BSE) among nursing mothers in selected secondary health facilities in Osun State. A quasi-experimental design with control and experimental groups was adopted, involving 156 participants selected through multistage sampling procedure. Data collection employed a structured test paper and a teaching module. Instruments were validated through expert review and pilot testing. Data collection occurred in four stages: pre-intervention, planning, intervention, and post-intervention, with the intervention group receiving the teaching module and the control group routine health education. Data were analysed using SPSS version 27, applying descriptive statistics and independent t-tests at a 0.05 significance level. The study found that before the intervention, most nursing mothers in both groups had poor knowledge and skills of breast self-examination (BSE). After the intervention, 98.6% of the experimental group attained good knowledge, while the control group remained poor. Similarly, 82.4% of the experimental group gained good skills and 17.6% intermediate, while the control group showed no improvement. Independent t-test results showed no significant pre-intervention differences in knowledge (t(141)=0.379, p=0.705, d=-0.063) and skills (t(141)=0.553, p=0.581, d=-0.093). However, post-intervention results revealed significant improvements in knowledge (t(141)=40.889, p<0.001, d=6.843) and skills (t(141)=55.784, p<0.001, d=9.336) among the experimental group, indicating a strong intervention effect. The
study concluded that structured educational interventions significantly improved nursing mothers’ knowledge and skills in breast self-examination, unlike the control group. The study recommended integrating BSE training into maternal health services, regular follow-ups, staff training, and community outreach.
Keywords: Breast Self-Examination, Nursing Mothers, Nurse-led Intervention, Breast Cancer
Word Count: 276
Investigation and Health Risk Assessment of Potentially Toxic Elements (PTEs) in Selected Hair Dye Products
(Lead City University, Ibadan, 2024-12) Sakirat Dasola DANJUMA
Hair dye products have recently been implicated as another human exposure route to PTEs. This study investigated the presence and levels of PTEs in five different brands of hair dye products (labelled; A1(Black), A2(Green), A3(Gold), A4(White), and A5(Wine)respectively) using Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES) and the attending health risks. The concentrations of 22 PTEs; Silver, Arsenic, Boron, Cerium, Cadmium, Cobalt, Chromium, Cesium, Copper, Iron, Mercury, Magnesium, Manganese, Molybdenum, Nickel, Palladium, Lead, Sulphur, Antimony, Selenium, Silicon and Zinc were determined. The studied PTEs concentrations were ranged; Cs > Mg > Si > Fe > Pd > Pb > Ag > Zn > Mn > Se > Ni > Ce > Sb > As > Cr > B > Cu > Mo > Co > Cd > S > Hg respectively. The average PTEs concentration ranged as (1074.149 > 882.715 > 720.246 > 689.242 >7.845) ppm for A4 >A1 >A5 > A2 and A3 respectively. The values for Ag, Ce, Cs, Fe, Mg, Mn, and Si were all above the United State Food and Drugs Administration (USFDA) limits, while those of As, B, Cd, Co, Cr, Cu, Hg, Mo, Ni, Pd, Pb, S, and Zn were below the limits in all the samples except for Se in A2, and A3 with the values of 1.21 and 1.18 respectively. Lifetime cancer risk (LCR) values of PTEs through inhalation were in decreasing order of Cr > Co > As > Ni > Cd > Pb and were all above the limits except for Ni in A5. The values of LCR dermal analyzed for adults were found in the order of A5 > A4 > A1 > A2 > A3. In Conclusion, frequent use of these hair dyes products may pose cancer risks and elemental toxicity through dermal contact, ingestion and inhalation.
Keywords: Dermal Exposure, Heavy metals, Hazard Index, ICP-OES, Life-time Cancer Risk
Word Count: 296
Public- Private Partnerships in Electricity Distribution: Lessons from IBEDC’s Operations in Oyo State (South-West)
(Lead City University, Ibadan, 2024-12) Damilola Olaoti MUSTAPHA
In recent time Public-Private Partnerships (PPPs) have emerged as a prominent strategy to enhance efficiency, innovation, and resource mobilization and also to improve effective service delivery in Nigeria. The aim of the study is to comprehensively access the role of Public Private Partnership in improving public service delivery in Oyo State, Nigeria, focusing on the case of Ibadan Electricity Distribution Company (IBEDC) from 2019 to 2024. To achieve this study it adopted the mixed method that is both qualitative and quantitative method. For further understanding theory like the stakeholder theory was adopted. The findings revealed general agreement on the importance of service delivery improvement, financial investment, and leveraging private sector expertise in adopting PPPs in the power sector. Based on the findings the study concluded that PPPs play a critical role in enhancing public service delivery in Oyo State. And it further recommends that the key factors such as transparent governance, innovative financing mechanisms, and effective community engagement are essential for the success of PPP initiatives.
Keywords: Public- Private Partnerships, Electricity Distribution, IBEDC’s, Oyo State
Word Count: 166