FACULTY OF PHYSICAL SCIENCE

Attendance management in academic tertiary institutions is a critical administrative task that directly impacts the credibility of academic records. Traditional methods such as manual roster calls, paper-based attendance sheets, and ID card verification have proven inefficient, timeconsuming, and vulnerable to impersonation attendance fraud. This project highlights the necessity for automated attendance systems using modern technologies such as biometric verification, Radio Frequency Identification (RFID) tracking, and facial recognition. Considering the operational constraints and specific requirements of the University of Benin, Department of Computer Science, this project proposes a Web-Based Facial Recognition Attendance System as an optimal solution. The project focuses on implementing a functional prototype of the facial recognition attendance system, where students register their facial biometrics during enrollment and subsequently mark attendance by scanning their faces via a web-based application. The system follows an objectoriented approach to system analysis and design, utilizing use case diagrams, class diagrams, and sequence diagrams to model the system architecture. These designs form the foundation for a system capable of handling the complete attendance process from student authentication to generating real-time attendance reports for courses offered by the Department of Computer Science. The key features of this attendance system include real-time face detection, liveness verification to prevent bypass attempts, and geolocation validation to ensure attendance is marked within authorized locations. The system also provides administrative dashboards for attendance monitoring and analytics. By implementing this solution, the University of Benin would probably have achieved a more secure, efficient, and fraud-resistant attendance management system compared to conventional methods

Aseni clay was obtained from Kogi State, Nigeria. Adsorption studies of Iron (III) ions (Fe3+) was carried out on the clay and Atomic Absorption Spectrometry (AAS) was employed in analysis of equilibrium concentration of Fe3+ions in aqueous solution. Batch experiment involving varied initial concentration adsorbent dosage, contact time and pH were conducted. quilibrium data showed that as initial Fe3+ concentration increased from 10 to 50 mg·L-¹ the adsorption capacity increased from 0.96 to 3.62 mg·g-¹ while percentage removal decreased from 95.7% to 72.4%, indicating progressive site saturation at higher loadings. Increasing the adsorbent mass from 0.2 to 1.0 g (per 100 mL) improved removal efficiency from 57.67% to 83.23%, demonstrating the positive effect of greater available surface sites. Contact time produced rapid initial uptake, with the amount adsorbed rising from 22.97 mg·L -¹ at 5 min to 26.03 mg·L-¹ at 120 min and percentage removal from 76.57% to 86.77%, indicating approach to equilibrium within the experimental timeframe. pH trials (4–9, initial concentration 100 mg·L-¹) returned very high removal (>99%); however, experimental notes indicated Fe hydrolysis/precipitation during base addition which likely affected measured concentrations and must be considered when interpreting pH-dependent results. Equilibrium modelling revealed strong fits to both Langmuir and Freundlich isotherms, with a marginally better fit to the Freundlich model (R² = 0.9815 versus Langmuir R² = 0.979), consistent with adsorption on a heterogeneous surface. Kinetic analysis showed that the pseudo-second-order model provided an excellent description of the adsorption behaviour (linear t/qt versus t relationship; very high R²), suggesting that chemisorption and surface complexation are dominant rate-controlling steps. The findings indicate that Aseni clay is a viable, low-cost adsorbent for Fe +³ removal under the tested laboratory conditions, especially at low to moderate contaminant concentrations, while highlighting the need for care in pH control to avoid precipitation artefacts and for further work on regeneration and real-waste testing.

Hibiscus sabdariffa L. (Roselle) is a medicinal plant grown in different countries, including India, Africa, Thailand and Mexico. It is known as zobo in Nigeria, Jamaica flowers, Sorrel and Karkade (in Egypt), and is a member of the Malvaceae family. It can be used as a colorant for foods, flavoring for sauces, jellies, marmalades and soft drinks. The study researched the phytochemical constituents and acute toxicity profile of the ethanol extract of Hibiscus sabdariffa stem in mice. Phytochemical screening was done using standard and qualitative methods to identify the presence of bioactive compounds. The acute toxicity assessment followed OECD guidelines, where mice were given increasing doses of the extract, and mortality was recorded. The phytochemical evaluation showed the presence of Glycosides, flavonoids, terpenoids, alkaloids, saponins, and phenolic compounds, which
are known for their therapeutic benefits. The acute toxicity study showed no mortality at doses up to 1600 mg/kg, while a slight toxicity effect (16.66% mortality) was observed at 2900 mg/kg. These results suggest that the ethanol extract of Hibiscus sabdariffa stem is relatively safe at moderate doses and contains bioactive compounds that may contribute to its therapeutic potential.

The persistence of atrazine in agricultural runoff and groundwater has raised serious environmental and public health concerns due to its chemical stability and resistance to conventional water treatment methods. This study investigates the synthesis, modification, characterization, and adsorption performance of zinc–aluminum layered double hydroxide (Zn–Al LDH) and its disodium EDTA-modified derivative for the removal of atrazine from aqueous solutions. The Zn–Al LDH was synthesized by the coprecipitation method at a Zn²⁺:Al³⁺ molar ratio of 3:1 under alkaline conditions and aged at 110 °C. Modification with disodium EDTA was achieved via anion exchange, producing a hybrid adsorbent with enhanced surface functionality and interlayer chemistry. Characterization with Fourier transform infrared spectroscopy (FTIR), X-ray fluorescence (XRF), and thermogravimetric/differential thermal analysis (TGA/DTA) confirmed the formation of a highly crystalline and thermally stable Zn–Al LDH structure. FTIR spectra revealed new carboxylate bands at 1600 cm⁻¹ and 1390 cm⁻¹, indicating successful EDTA incorporation, while XRD patterns showed an expansion of basal spacing from 7.6 Å to 9.8 Å, signifying effective interlayer modification XRF analysis indicated a significant increase in aluminum content and compositional uniformity after modification, confirming Zn–Al integration. Batch adsorption studies were conducted to evaluate the influence of contact time and adsorbent dosage on atrazine uptake. The adsorption process exhibited a pattern, characterized by a rapid initial phase attributed to surface adsorption followed by a slower diffusion-controlled phase. Increased adsorbent dosage enhanced the removal efficiency due to the greater availability of active sites. The EDTA-modified Zn–Al LDH demonstrated superior adsorption capacity compared to the unmodified form, owing to improved surface reactivity and functional group availability. overall, the study establishes that EDTA modification enhances the structural integrity, surface chemistry, and adsorption performance of Zn–Al LDH, positioning it as a promising low-cost and eco-friendly material for the remediation of atrazine-contaminated water.

This study investigates the extraction and acetylation of cellulose from Sporobolus pyramidalis, commonly known as Giant Rat Tail Grass, an abundant yet underutilized plant species. Cellulose was extracted through a series of chemical treatments, including alkali and bleaching processes, to remove lignin, hemicellulose, and other non-cellulosic components. The extracted cellulose was
characterized using Fourier-transform infrared spectroscopy (FTIR), which showed peaks closely matching those of commercial cellulose. Scanning electron microscopy (SEM) was also employed to confirm the structure of the extracted cellulose. The cellulose was then acetylated using acetic anhydride and sulfuric acid to enhance its thermal stability, hydrophobicity, and solubility. FTIR analysis confirmed the successful acetylation, with peaks closely aligning with those of commercial cellulose acetate. The acetylated cellulose exhibited improved properties, including enhanced solubility in organic solvents and thermoplasticity, making it suitable for use in bioplastics, coatings, and other biodegradable materials. This research highlights the potential of Sporobolus pyramidalis as a renewable source of cellulose and contributes to the development of sustainable, biomass- based materials.

The research work explores the comparison of various methods for estimating variance components in a two-way random effects model, a critical task in experimental data analysis. The methods assessed include classical Analysis of Variance (ANOVA), Restricted Maximum Likelihood (REML), and Bayesian estimation. The experiment was designed with treatments (3 levels) and blocks (4 levels), with each combination replicated 5 times, resulting in 60 observations. The objective was to estimate variance components attributable to treatments, blocks, and errors. The results were compared across the three methods: ANOVA produced variance components of σ²α = 3.84, σ²β = 2.43, and σ²ε = 3.58, while REML and Bayesian estimates were σ²α = 4.805 and 4.75, σ²β = 2.4067 and 2.60, and σ²ε = 3.58 and 3.60, respectively. While the three methods yielded similar results, minor differences were observed, reflecting their respective properties. ANOVA, though simple and interpretable, may be biased in small samples or unbalanced designs, whereas REML offers better performance in such situations, and Bayesian estimation provides flexibility with credible intervals to quantify uncertainty. The research work highlights the importance of method selection depending on sample size, design, and the need for uncertainty quantification, suggesting future work on more complex or larger-scale experiments.

This project proposes the creation and implementation of an Integrated Alumni Networking and Departmental Associations Management System. The platform will allow alumni to connect with students for mentorship and career advice. It will also serve as a hub for departmental associations to share news, plan events, and collaborate on projects, while providing students with access to job and internship opportunities. The system will include user role management (students, alumni, administrators), profile creation, association hubs, event and job postings, and a basic messaging system. The project will employ a modular software engineering approach, integrating backend development, database management, and a user-friendly interface. By using modern web technologies, the system will be scalable, accessible, and easy to maintain. The expected outcome is a functioning platform that enhances alumni-student relationships, facilitates departmental associations, and fosters career development at the university. Beyond its immediate use, the project may also serve as a model for alumni engagement and student support systems in other universities.

The synthesis of Manganese Oxide (MnO) nanoparticles using plant extracts has acquired attention as a sustainable and eco-friendly alternative to traditional chemical methods. This study explores the synthesis of Manganese Oxide nanoparticles using Dacryodes erulis seeds extract, investigating their antioxidant and phytochemical properties. The Phytochemical composition of
Dacryodes erulis seeds, along with the reduction and stabilization properties of plant-based compounds, played a crucial role in the synthesis of nanoparticles. The synthesized Manganese Oxide Nanoparticles were characterized using various techniques, including UV-Vis spectroscopy, X-ray diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR), and Dynamic Light Scattering (DLS). X-ray diffraction analysis revealed that the MnONPs were crystalline in nature. Dynamic Light Scattering (DLS) indicated its polydispersity with a PDI value of 0.250 and an average particle size of 48.78nm. Fourier Transform Infrared Spectroscopy (FTIR) indicated the presence of a hydroxyl (-OH), Carbonyl (C=O), and carboxyl (-COOH) functional group. Antioxidant activities of the Manganese Oxide Nanoparticles were assessed using DPPH, while phytochemical properties were evaluated through quantitative analysis. The results suggested that the synthesized Manganese Oxide Nanoparticles exhibited significant antioxidant properties making them promising candidates for applications in medicine, environmental cleanup, and energy production. Overall, this study demonstrated the potential of using African bush pear seed extract for the green synthesis of MnONPs

The work is intended to study the moments of distributions and in particular pareto-II distribution named after Italian scientist, Vilfred pareto. This study was guided by the following objectives; to obtain the rth moment, obtain the mean, variance, skewness and kurtosis of the pareto-II distribution and finally obtain some numerical results of the moment. The study employed the knowledge of differential and integral calculus with transformation of variables to obtain several expressions as we shall be seeing. Statistical software "R" was used to run analysis and obtain numerical results of the moments. Finding revealed that the parameters of the distribution are important in determining the behavior of the moments. From the findings, it implied that the study of moments is important and applicable to study of distributions. Keywords Moments, Parameter, Distribution, Mean, variance, skewness, kurtosis.

Malaria remains a global health crisis, particularly in low-resource regions, where traditional diagnostic methods face challenges such as human error, resource constraints, and delayed detection. This project addresses these limitations by leveraging supervised machine learning (ML) to enhance malaria diagnosis and outbreak prediction. The motivation stems from the urgent need for scalable, accurate, and cost-effective solutions to reduce the disease’s burden, which claims over 600,000 lives annually. The objective is to develop robust ML models capable of automating malaria diagnosis using blood smear images and patient metadata while improving outbreak forecasting through environmental and epidemiological data analysis. Methodologically, the study employs supervised learning algorithms, including convolutional neural networks (CNNs) for imagebased detection and random forests for tabular data. Datasets were preprocessed to handle class imbalance and missing values, followed by hyperparameter tuning and cross-validation to optimize performance. Results demonstrated that CNNs achieved 96% accuracy in classifying infected blood cells, outperforming traditional methods like microscopy. Random Forest models yielded 92% recall and 89% precision in predicting malaria risk from clinical data, highlighting their utility in early diagnosis. Additionally, stratified k-fold cross-validation ensured model generalizability across diverse datasets. This work underscores the transformative potential of supervised ML in malaria control, offering tools that enhance diagnostic speed, accuracy, and accessibility. By bridging technological innovation with public health needs, the project contributes to global efforts toward malaria eradication, particularly in endemic regions