FACULTY OF PHYSICAL SCIENCES

The Geophysical survey involving electrical resistivity method has been carried out at
Technical road in Benin, A total of five(5) Vertical Electrical Resistivity (VES) were
established within the site .The Schlumberger configuration was used for the data acquisition. The quantitative interpretation of the VES involved the plotting on a graph and also used
1X1D software iteration technique. The geoelectric sections drawn from the results of the
interpretation revealed a maximum of six layers which comprises of top soil, silt and
sandstone.

This study focused on factorial experiments and its applications in industries. In this study, we examined the application of a two-factor factorial design to determine the significant difference in the mean yield of onion with respect to the effect of fertilizers and plant densities. Primary data (yield of onion) was collected from the Department of Crop science, Faculty of Agriculture, University of Benin. This research work covers only two factors which are fertilizers at three levels (PM, OM and NPK) and plant densities at two levels (LOW and HIGH). The analysis techniques employed was a 2*3 replicated factorial design with 6 replicates per cell. Data collected was analyzed using SPSS version 22. The hypothesis tests were carried out at a (5%) significance level and the decision rule was to reject the null hypothesis if the calculated significance value (p-value) is less than the α (5%). Results from the analyses revealed among others that there is significant difference in the fertilizer effect and plant densities effect on the yield of onion with a significance level of 0.0001 and 0.016 respectively. In addition, there is no significant interaction effect between fertilizers and plant densities with significance value of 0.840 on the yield of onion.

The purpose of this study is to analyze sandstone deposit using 2-Dimensional Electrical resistivity tomography survey investigation of the subsurface and getting detailed structures, properties, deposited volume and it’s economic importance, using the study area as a case study. Two dimensional Electrical Resistivity Tomography (ERT) survey each of 200m in length was laid in College of Education, Abudu, Edo State to locate and delineate sandstone deposits present at igarra. Wenner-alpha array configuration was employed to acquire field data for the various transverses which were generated in parallel and perpendicular directions using dipole-dipole electrode configuration with electrode spacing of 10m. The 2D data were collated and inverted using a computer software known as RES2DINV to obtain true resistivity images of 2D model for each line, which has various options that could be altered to obtain models that correlate with the known geology of the study area. Pasi- Earth Resistivity meter was used to measure the apparent resistivity in order to obtain a 2-D image of the subsurface for each line. These geophysical survey area were observed to be composed of sandstone, clay, limestone, shale, with resistivity values ranging between 259 mΩ to 2160 mΩ for the unit electrode spacing adopted during the period in the survey areas. The data gotten has depth ranging from 2.50m to 40m beneath earth surface and width of 200m. The first transverse has resistivity values between 20 Ωm to 1000 Ωm indicating various subsurface minerals having resistivity within that range, these indicate the presence of sedimentary rocks formation, a large percentage of shale was discovered alongside clay, sandstone and limestone in mall proportions.

A geophysical survey was carried out at Edo State Development and Property Agency (EDPA)Housing Estate, Benin City to determine the depth to water table at four points within the study area. The Geophysical method used was the Electrical Resistivity Method. The Vertical Electrical Sounding technique was used in the survey with the Schlumberger Array being the electrode setup of choice. Four electrical soundings were carried out with the aid of ABEM SAS 300 terrameter set. The reduction of the acquired data was done with a spreadsheet software. The Interpretation was done using software such as IX1D. 3D field was used to generate subsurface maps.
The results of our interpretation show that in every VES sounding a prolific aquifer was located at relatively shallow depths. The depth to groundwater values from VES 1, 2, 3, and 4 show us a depth to water table of 87, 107, 85, and 93 metres respectively. The groundwater flow direction was also determined and the groundwater is considered to be safe as the depth to groundwater is high enough to be out of contact with underground sewage storages and also, the clay beds above the aquifers serve as filtering medium for infiltrating groundwater and escaping sewage.

The study was concluded to produced fruit juice and its blend using some Nigeria selected fruits. The fruits were purchased in Edo Street, Ekosodin, Benin City. The fruit juices were produced and analyzed using standard method. From the analysis, it was observed that the pH ranges from 3.30 – 5.80 without pulp and 3.50 – 5.70 with pulp. A reversed case occurred on these samples for titratable acidity with a range of 0.030% - 0.88% without pulp and 0.32% - 1.82%. The pH and titratable falls within acceptable range

In this project work, we look at how the least-square polynomial regression model is used to fit a non-linear relationship between a response variable and an explanatory variable in curve fitting. Finding a mathematical equation or model that best fits a noisy data has experience some drawback in curve fitting. i.e. finding an appropriate fit that best depicts the behaviour of the data. The purpose of this project is to show how the polynomial regression model can be used to show the relationship that exist between two variables; where the linear regression model is inadequate in describing such a relationship. The method of curve fitting used in this study is the least square polynomial regression method. It is designed in a way that, the model parameters are estimated by minimizing the residual term of the polynomial regression model; and then used the model to find the line that best fits the data points of the data set. This method was validated by modelling a data extracted from Nigeria Stock Exchange; and the model was able to predict over 80% of the relationship that existed in the data. It was discovered that the inadequacies of the simple linear regression model in describing the relationship that existed in a data set could be easily tackled by fitting a polynomial regression line. This is done by increasing the power of the independent variable to a higher power until we get a best fit.

Taking safety into account, the levels of Cu, Cr, Mn, Pb, Zn, Cd, and Fe in Bitter leaves and Scent leaves have been assessed. This study involves the collection of fresh vegetable samples from Uselu market, Edo State, Nigeria. The samples were prepared using stander methods to quantify the concentrations of heavy metals present. The analytical finish was done by Atomic Absorption Spectrophotometer (AAS) model Buck Scientific VGP210 to obtain accurate measurements of heavy metal content in Scent leaves and Bitter leaves. Analytical data available shows a pattern of Cu 0.20mg/kg, Cr 0.10mg/kg, Mn 7.50mg/kg, Pb 0.00mg/kg, Zn 5.15mg/kg, Cd 0.05mg/kg, Fe 6.50mg/kg for Scent leaves and Cu 0.20mg/kg, Cr 0.15mg/kg, Mn 5.00mg/kg, Pb 0.05mg/kg, Zn 4.85mg/kg, Cd 0.05mg/kg, Fe 4.50mg/kg
for Bitter leaves. These data were compared to World Health Organization (WHO) and National Agency for Food, Drugs Administration and Control. The results showed that the levels complied with regulatory standards. The findings of this research will provide valuable insights into the levels of these selected heavy metals in Scent leaves and Bitter leaves, allowing for a comprehensive assessment of potential risks associated with their consumption. Furthermore, this study will contribute to enhancing public awareness about food safety and help formulate recommendations to mitigate heavy metals contamination in vegetables.

The contamination of water bodies by synthetic dyes such as methyl orange from industrial effluents poses a significant environmental challenge due to their toxicity, persistence, and resistance to biodegradation. This study investigates the adsorption capacity of Uteh-Uzalla clay for the removal of Methy orange from aqueous solutions. . The clay was characterized using SEM, BET, XRD, TGA, and FTIR to determine its surface morphology, elemental composition, crystalline phases, thermal stability, and functional groups. BET analysis revealed a surface area of 170.571 m²/g, a pore volume of 0.109cm³/g, and an average pore size of 2.411nm, indicating a mesoporous structure suitable for adsorption. Thermogravimetric analysis (TGA) showed significant weight loss between 200°C and 600°C, attributed to the dehydroxylation of clay minerals, confirming its thermal stability.FTIR analysis indicated the presence of kaolinite with functional groups such as O–H stretching at 3693.8 cm⁻¹, Si–H stretching at 2117.1 cm⁻¹, and H–O–H bending at 1636.3 cm⁻¹. SEM imaging revealed a porous, agglomerated structure that could facilitate adsorption. Elemental analysis revealed that silicon (64.23%) and aluminum (29.98%) were the dominant elements, consistent with kaolinite's composition. XRD analysis showed moderate crystallinity, with peaks corresponding to kaolinite and quartz. The raw clay was then modified into sodium clay using sodium chloride, and further treated with humic acid to enhance its adsorption properties. Adsorption studies were conducted to evaluate the effects of key parameters such as pH, contact time, and temperature, on dye removal efficiency was quantified using UV-visible spectrophotometry at 464nm. Adsorption experiments showed that the optimal adsorption condictions are temperature of 70°C, pH of 10 and Contact time of 120 mins. The study concludes that modified Uteh-Uzalla clay is an effective and low-cost adsorbent for methyl orange removal and offers potential for wastewater treatment applications.

The use of machine learning techniques in the process of power transformer fault diagnosis represents a significant advancement in technology. This abstract of this project provides an overview of the key features of this work, displaying the profound benefits of machine learning in power transformer fault detection and prediction.
In recent years experiment have been conducted in this area using traditional methods like the Key Gas Method, Duval triangle method and others and utilizing the data gotten from DGA (Dissolved Gas Analysis) to detect faults in the power transformers. These traditional methods had a draw back of poor accuracy in detection and fault prediction. In this project machine learning techniques like the Naive Bayes, Support vector Machine, C4.5 Decision Tree and their ensembles also included in this is the Majority voting were introduced to solve the problems.
In this project a model is created to train and test data using this machine learning techniques. The results gotten from these experiments show that the machine learning techniques presents a brighter future for professionals and experts in this field of work.

ABSTRACT
Numerical schemes for the integration of stiff initial value problems are required to
possess wide region of absolute stability which include the entire left of the complex
plane. Numerical schemes that are explicit usually do not attain the requirement for
integration of stiff initial value problems. In this study, implicit second derivative RungeKutta methods are constructed for the integration of stiff initial value problems.
A family of generalised second derivative mono-implicit Runge-Kutta (GSDMIRK)
method is derived using the method of Tailor series expansion.
The proposed GSDMIRK methods are 𝐴-stable for stage 𝑠=3 𝑎𝑛𝑑 4 and 𝐴(∝)-stable for
𝑠=5 𝑎𝑛𝑑 6. Numerical experiments show that the GSDMIRK methods perform better
when compared to some numerical algorithms in the literature.