FACULTY OF PHYSICAL SCIENCES,

In this work, the structural, mechanical, vibrational and electronic properties perovskite materials are investigated in detail using spin-polarized DFT, using the Ultra Soft Pseudopotential (USPP) method in the Quantum Espresso (QE) software package, the total energy was calculated and the lattice constants optimized using the Perdew-Burke-Ernzerhof (PBE) formulation of the eneralized Gradient Approximation (GGA). In excellent agreement with previously published theoretical values, the study produced optimized equilibrium lattice parameters, band structures, elastic constants, and elastic moduli. Additionally, the Density of States (DOS) and band structures were analyzed in order to comprehensively study the electrical characteristics. The findings support the efficacy of the computational techniques used and offer a thorough understanding of the structural, mechanical, and electrical properties of perovskites. These discoveries add to the growing corpus of information on perovskite materials and provide insightful information for upcoming studies and technological uses

This research undertakes a comprehensive statistical analysis of Nigeria's Gross Domestic Product (GDP) spanning a decade, with a focus on estimating Autoregressive (AR) models using two prominent statistical methods: the Yule-Walker method and the Least Squares method. The study aims to provide statistical insights into the underlying dynamics of Nigeria's economic performance during this period. The research commences by delineating the statistical framework of ARmodels, which offer a statistical representation of a time series based on its past values. Subsequently, the Yule-Walker method is introduced, a statistical technique leveraging autocorrelation functions to estimate AR model parameters. The statistical properties of Yule-Walker estimators are elucidated in the context of Nigeria's GDP data. In contrast, the Least Squares method is presented as an alternative statistical approach, characterized by its objective to minimize the sum of squared prediction errors. A statistical framework for the least squares estimators is outlined, providing insights into the statistical properties of parameter estimates and their significance in explaining variations in Nigeria's GDP. The core of the research involves the statistical analysis of Nigeria's GDP time series data over the 10-year period. Both the Yule-Walker and Least Squares methods are applied to estimate AR models tailored to the GDP data. The statistical comparison is based on goodness-of-fit statistics, such as the Akaike Information Criterion (AIC), to evaluate the models' adequacy in capturing the statistical patterns within the GDP dataset.

his study looks into the spending behavior of students at the University of Benin, Nigeria, in order to establish the relationship between demographic factors such as gender, age, and academic level on expenditure behaviour. The purpose of the study was to gain a better understanding of how students’ money is spent in areas of food, transport, housing, academic materials, and personal expenses. A quantitative approach was used in the study where 228 questionnaires were completed and descriptive and inferential analysis were employed on the data collected. The results show that the amount of money spent by the students on food, transport, and housing is highest. The results of the statistical analysis indicate that there is no significant association between the gender of the respondent and the primary source of income, however, more males are likely to save than females. The result of the study shows that the academic level affects the spending and higher-level students spend more money on the necessities. The transport expense also depends on the age of students, the older students spend more money on transport.
The results conclude that, among students, academic level and age are important predictors of spending behaviour; the impact of gender is limited to saving behaviour. For practice, it is suggested that financial literacy be enhanced, housing and transportation be made cheaper, and financial aid be increased. Further research could be directed towards the patterns of spending at different universities, changes over time, the effect of financial aid and part-time employment on spending. This research can help to explain student financial problems and offers some practical suggestions for students, administrators and policymakers.

The production of biodiesel from waste cooking oil presents a sustainable approach to waste management and alternative fuel generation. This study investigates the use of Manganese Magnesium Binary Oxide (MMBO) nanoparticles as a catalyst for biodiesel synthesis. The physicochemical properties of waste cooking oil were analyzed before and after esterification, revealing significant reductions in acid value (2.973 mg KOH/g to 1.85 mg KOH/g) and free fatty acid content (1.4865 mg KOH/g to 0.925 mg KOH/g), which improved feedstock suitability for biodiesel production. A comparative analysis of biodiesel produced using MgO and MMBO nanoparticles demonstrated the superior catalytic efficiency of MMBO-NP, yielding 84.6% biodiesel compared to 65% with MgO. The biodiesel produced with MMBO-NP exhibited favorable fuel properties, including a lower kinematic viscosity (3.89 mm²/s), reduced acid value (0.35 mg KOH/g), and improved density (875 kg/m³), aligning with ASTM D6751 and EN 14214 standards. Despite meeting most standard requirements, the flash point (77°C) and cold flow properties (cloud point: 32°C, pour point: 39°C) indicate potential areas for further optimization. These results highlight the potential of MMBO nanoparticles as an efficient catalyst in biodiesel production, promoting a more effective and environmentally friendly conversion of waste cooking oil into biodiesel.

In kernel density estimation, the choice of kernel plays a crucial role in accurately
estimating the underlying probability density function. This project focuses on
comparing three commonly used kernels: Gaussian, Epanechnikov, and Biweight. The objective is to plot a graph that visually demonstrates the differences between these kernels and evaluate their efficiency using the mean square error metric. First, the theoretical foundations of kernel density estimation are explored, emphasizing the importance of choosing an appropriate kernel. The Gaussian kernel, known for its smoothness and symmetry, is widely used due to its
desirable properties. The Epanechnikov kernel, with its compact support and
optimal bias-variance trade-off, is another popular choice. Lastly, the Biweight
kernel, which balances robustness and efficiency, is considered. To compare these kernels, a graph is plotted to visualize their shapes and characteristics. This graphical representation allows for a clear understanding of how each kernel affects the density estimation. Additionally, the mean square error metric is employed to quantitatively assess the efficiency of each kernel. By calculating the squared differences between the estimated density and the true density, the mean square error provides a measure of accuracy. Through this analysis, valuable insights into the strengths and weaknesses of each kernel can be gained. The graph and mean square error comparisons reveal how the choice of kernel impacts the estimated density function. This information can guide researchers and practitioners in selecting the most suitable kernel for their specific applications. Overall, this project contributes to a deeper understanding of the choice of kernel in kernel density estimation. By focusing on the Gaussian, Epanechnikov, and Biweight kernels, both their graphical representations and efficiency evaluations shed light on their performance in estimating probability density functions

Web applications have become a mainstay of modern life, powering significant services such as e-commerce, online banking, and health systems. As a result of their pervasiveness and sophistication, however, they are enticing targets for cyber-attacks. Flaws in web applications, such as compromised access control, injection flaws, and insecure design, can lead to significant consequences, such as data breaches, service disruptions, and unauthorized data access. The Open Web Application Security Project (OWASP) Top 10 is a comprehensive list of the most critical web application vulnerabilities, which can be utilized as a foundation for comprehension and prevention of these threats. This study explores the evolving web application vulnerability environment, such as current trends including API vulnerabilities, single-page application (SPA) vulnerabilities, and cloud-native application threats. It discusses established techniques of vulnerability assessment, i.e., black-box, white-box, and gray-box testing, and industry best practices and standards, e.g., the Penetration Testing Execution Standard (PTES) and NIST SP 800-115. In addition, the study is comparable to familiar tools and platforms applied in vulnerability analysis, including Metasploit, Burp Suite, and OWASP ZAP, and establishes gaps in past research, including the need for increased automation, AI implementation, and specialty tools accommodating emerging technologies like IoT and serverless architecture. Through a review of these topics, the current research identifies the necessity for proactive management of vulnerabilities and ongoing development of techniques for examining vulnerabilities. The findings strive to offer insights that assist in the creation of more secure and agile security products that can aid organizations in protecting their web applications more effectively against emerging cyber threats.

Quinoline or benzopyridine, a nitrogen containing compound has been known to possess antimalarial, anti-bacteria, and anti-fungal properties. In this study, a derivative of quinoline-N-oxide-4-thiol was obtained by a five step reaction. The compounds synthesized in the stepwise reactions were: quinoline-N-oxide, 4- nitroquinoline-N-oxide, 4-chloroquinoline-N-oxide and quinoline-N-oxide-4-thiol. Furthermore, the latter was coupled with benzoyl chloride under basic condition to obtain quinoline-N-oxide-4-benzoylsulphide. The physical properties of the compound obtained are percentage yield 1.86%, melting point 96-98 oC, and retention factor value 0.83.

This project focuses on exploring iterative techniques for solving linear systems. The goal is to examine the efficiency and accuracy of iterative methods such as Jacobi and Gauss-seidel method used in solving linear systems commonly found in scientific applications

There has recently been an increase for the usage of clay minerals. Hence, the driving force for this research work which was carried out to determine the chemical composition and basic geotechnical and physical characteristics of Ogiso and Okhoro clay deposits in Edo State, Southern Nigeria. In this study, ten (10) fresh clay samples were randomly collected, three(3) from Okhoro and seven(7) from Ogiso and were subjected to X-ray-Diffraction (XRD), Xray-Fluorescence (XRF) and geotechnical analysis to determine their mineralogy, chemical composition and engineering behaviour so as to evaluate their economic potential. The XRD analysis revealed that the basic mineralogy of the samples studied is made up of kaolinite, quartz and hematite with traces of zircon, illite and anatase occurring in minor amounts in some of the samples. The XRF analysis revealed the occurrence of 10 elemental oxides and 10 elemental trace elements. The major elemental oxides include SiO2 with a range of 55.82 – 61.41wt %, Al2O3 with a range of 21.12 – 24.42% and Fe2O3 with a range of 6.05 – 9.06% while the major elemental trace elements are zircon with a range of 0.89 – 1.78wt%, zinc with a range of 0 – 1.51%, copper with a range of 0 – 1.39wt% and chromium with a range of
0.01 – 0.11wt%. The geotechnical analysis revealed that the samples are of low to medium plastic inorganic clay with percentage fines fraction of 76.84 – 78.45%

In today's digital age, the security of online accounts and sensitive information has become a paramount concern. A critical component of this security is the strength of passwords used to protect these accounts. A password is "a series of letters, numbers, etc. that you must enter into a computer or computer system to use it". Password strength is a term that refers to how effective a password is in preventing unauthorized access to a computer system or account. A time cracking estimator is a tool that calculates how long it would take to break a password by trying every possible combination of letters, numbers and symbols until finding a match. This project aim is to develop an advanced Password Strength and Time Cracking Estimator that enhances password security by accurately assessing the strength of passwords and providing realistic estimations of the time required to crack them. The Password Strength and Time Estimator tool has been successfully developed and tested. Users can input their passwords through a web-based interface and the tool provides an instant assessment of the password's strength and an estimate of the time it would take for a brute-force attack to compromise it. The tool's accuracy has been validated through extensive testing with a wide range of passwords, and it consistently provides reliable results. The project frontend was built with HTML, CSS, while the backend was built with Java Script. The Password Strength and Time Estimator project addresses a critical aspect of cybersecurity by empowering users to make informed decisions about their online security. By understanding the strength of their passwords and the potential risks, individuals and organizations can take proactive steps to enhance their digital security posture. This project contributes to the ongoing efforts to create a safer online environment and emphasizes the importance of strong, unique passwords in safeguarding sensitive data.