FACULTY OF PHYSICAL SCIENCE

In this study, we explore the power and importance of log-rank statistics in survival analysis.Our research not only enhances our understanding and implementation of thisstatisticalmethod, but also sparks curiosity for future investigations. The applications of log-rankstatistics are vast and diverse, extending beyond disciplinary boundaries andprovidingvaluable insights into the complexities of survival phenomena. Fromlife-savingclinicaltrials to bolstering technological advancements, log-rank statistics have a profoundandfarreaching impact.

The population of man is a germane thing to know and analyze, from the north of the world to the South, the world is compose of huge numbers of human. From the East of the world to west of it, it is comprises of enormous numbers of man. knowing the world population is not enough but also being able to project its future reality. In this project we consider using Malthusian model in estimating relative population reality by the 2050. It’s very interesting because we really present the estimation in two categories; first, by continent, and then globally. This project serve its aim as it give a relative result that the present population can work with to expect and probably tackle the challenges that may arises.

Geochemical and mineralogical determination of clay deposits with
integrated sedimentological characterization of their physical properties are fundamental in bridging the knowledge gap and raw material feed for industrial processes and applications. In this study, I investigated clays at locations with relatively little or no investigations carried out to discover the untapped mineral deposits. The clay deposits situated at Iguoriakhi, Ofunmwengbe and environs were investigated using several analytical techniques; X Ray Diffractometry (XRD), X Ray Fluorescence Spectroscopy (XRF), Hydrometer Method, Wet Sieving Analysis and other geotechnical techniques. The average abundance of minerals in the samples derived from the XRD Peaks indicates that Clay minerals present include; Kaolinite, ranges between (19.52% - 22.33%) and (24.24% - 28.60%), Montmorillonite (10.49% and 4.6%), Illite (5.24% and 4.07%) and associated non clay minerals include; Mica (5.50% - 7.02%) and (4.88% - 6.03%), Feldspar (6.02% - 6.53%) and (3.03% - 3.50%), quartz (49.77% - 52.80%) and (54.11.0% - 55.99%) for Iguoriakhi and Ofunmwengbe samples respectively. Geochemical signatures Al2O3/TiO2 ratio for clastic rocks used to determine origin indicates that the Al2O3/TiO2 for Iguoriakhi and Ofunmwengbe clays range between (23.73 – 50.40) and (22.92 – 51.88) respectively signifying that the clays originated from
intermediate to felsic igneous rocks. The high Chemical Index of
Alteration (CIA) ranges between 86.14 - 93.57 and 86.05-93.47, Chemical Index of Weathering (CIW) between 93.59 - 97.71 and 93.55
- 97.67 and Weathering Index of Parker (WIP) between 10.27 - 12.02
and 10.04 - 12.1.

This work entails extraction, characterization and thermal stability studies with tigernut (Cyperus esculentus) oil. The Tigernut used in this work was obtained from an open market in Benin city. Oil extraction was carried out by soxhlet extraction using n-hexane as a solvent. Parameters studied were free fatty acid (FFA) content giving 0.48% as result, peroxide value (PV) with results of 1.96meq/kg and 7.80meq/kg for the raw oil and treated oil respectively, refractive index (RI) test- 69% and 69.5% brix for both raw and treated oil respectively and measurement of absorbance of the raw and treated oil at 234nm and 270nm. Additionally, Fourier-transform infrared spectroscopy (FTIR) was used to identify the functional groups present in the oil. Findings indicate that the oil is thermally stable when treated with heat. Due to the highly nutritional properties of this oil, it has found wide usefulness in culinary arts, pharmaceutical products and even cosmetics

Natural clay minerals are abundant and versatile, offering a broad range of applications across various industries due to their unique physical and chemical properties. Their adsorption capacity and catalytic capabilities were enhanced through specific treatments. This study examined the effect of temperature on base-activated clay from the Gegu-Egba region, Kogi State, Nigeria. Clay minerals, valued for their high surface area and structural properties, were widely used in catalysis, adsorption, and refining. In this research, the Gegu-egba clay samples were treated with 30% NaOH and heated at 200°C (F3A) and 400°C (F3B) to assess structural and chemical changes. Characterization techniques, including X-ray diffraction (XRD), scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), energydispersive X-ray spectroscopy (EDX), and Brunauer-Emmett-Teller (BET) analysis, were employed to evaluate their structural, morphological, and textural properties FTIR analysis indicates progressive dehydroxylation, with the disappearance of free hydroxyl groups and shifts in Si–O and Al–O–Si vibrations, suggesting kaolinite transformation into metakaolin. XRD results confirm a decline in kaolinite content (23% to 3.1%) and increased feldspar influence, supporting amorphization at 400°C. SEM-EDX analysis shows increased porosity and redistribution of elemental composition, notably a decrease in Si and Ti with a rise in Al content. BET surface area decreases from 249.577 m²/g at 200°C to 214.149 m²/g at 400°C, indicating structural densification. These findings emphasized the role of optimized thermal treatment in enhancing base-activated clay for industrial applications such as catalysis, adsorption and wastewater treatment

This project examines the evolution of cryptography from ancient methods to modern techniques, and it serves as a practical guide for beginners. The study starts with classical encryption methods like the Caesar and Atbash ciphers, which laid the groundwork for secure communication, and then discusses advanced systems such as the Enigma machine. Modern cryptographic methods are explored through symmetric algorithms like DES and AES and public-key systems including RSA and ECC. The work also covers digital signatures used in Bitcoin, secure messaging protocols like TLS/SSL, and file encryption methods such as PGP/GPG. All these techniques were implemented using Python and thoroughly tested for functionality, security, and performance. The project emphasizes the importance of using trusted cryptographic standards, secure key management, and following guidelines from organizations such as NIST. Overall, this study not only provides valuable insights into the strengths and
limitations of various cryptographic approaches but also offers an accessible guide for beginners to understand and apply these techniques in real-world scenarios.

This project implements a deep learning algorithm for image recognition, focusing on traffic sign classification. Traditional machine learning methods struggle with manual feature extraction and dataset diversity. To address these limitations, a robust convolutional neural network (CNN) with residual blocks, dropout layers, and global average pooling is utilized. Preprocessing techniques like normalization and data augmentation enhance accuracy and
generalization. Using TensorFlow and Keras, experiments were conducted on the German Traffic Sign Recognition Benchmark (GTSRB) and the Chinese Traffic Sign Dataset. The model achieved 99.54% validation accuracy and 94.95% test accuracy on GTSRB, but overfitting led to 60.38% accuracy on the smaller Chinese dataset. The study highlights CNN effectiveness in pattern recognition, with strengths in GPU acceleration and modular architecture. Challenges like overfitting and computational constraints persist. Future research should explore transfer learning, ensemble methods, and real-time optimization to enhance performance. This study advances deep learning-based
image recognition for applications in autonomous driving and traffic management. This project implements a deep learning algorithm for image recognition, focusing on traffic sign classification. Traditional machine learning methods struggle with manual feature extraction and dataset diversity. To address these limitations, a robust convolutional neural network (CNN) with residual blocks, dropout layers, and global average pooling is utilized. Preprocessing techniques like normalization and data augmentation enhance accuracy and generalization. Using TensorFlow and Keras, experiments were conducted on the German Traffic Sign Recognition Benchmark (GTSRB) and the Chinese Traffic Sign Dataset. The model achieved 99.54% validation accuracy and 94.95% test accuracy on GTSRB, but overfitting led to 60.38% accuracy on the smaller Chinese dataset. The study highlights CNN effectiveness in pattern recognition, with strengths in GPU acceleration and modular architecture. Challenges like overfitting and computational
constraints persist. Future research should explore transfer learning, ensemble methods, and real-time optimization to enhance performance. This study advances deep learning-based image recognition for applications in autonomous driving and traffic management.

Color detection is a simple task for humans, but for computers it is not easy. In any industry, individual effort has to be implemented when a computer is dealing with colors. Previous system has primarily relied on paid labor input and manually color-coding items or any given data which most of the time could be monotonous and painstaking. Hence, this project developed a deep learning mechanism program for detection of multiple color in real-time using Python which is a high-level general-purpose programming language and Open-Source Computer Library (OpenCV). The Proposed system provides any computer device the ability to recognize multiple colors in real-time which can be useful in various industries such as big pharma, self-driving vehicle manufacturing companies and robotics which will reduce production time and significantly cut down paid labor expenses

The web-based sectional result computation system abstracts the complexities of calculating and generating academic results in a University or educational institution. It provides a user-friendly interface that simplifies data management, computation algorithms, result generation, security, access control, and integration with other systems. By abstracting these components, the system streamlines the result computation process and ensures accuracy, reliability, and timeliness. It simplifies data storage, retrieval, and organization, automates grade calculations and performance indicators, and generates result sheets and reports in customizable formats. The system also abstracts security measures, ensuring authorized access to student results, and integrates with other institutional systems for seamless data synchronization. This abstraction allows users, such as faculty, staff, and administrators, to focus on result analysis, interpretation, and decision-making, rather than getting overwhelmed by technical details. The web-based nature of the system enables easy access from anywhere with an internet connection, enhancing accessibility and convenience. Overall, the result computation system's abstraction simplifies and automates result processing, improves efficiency, reduces errors, and provides standardized and reliable frameworks for handling academic outcomes. It empowers users with accurate and timely student results, facilitating informed decision-making and enhancing the overall academic experience within the institution.

The result for proximate analysis on Semolina (Triticum turgidum L. var. durum) showed carbohydrates 76.31±0.51, crude fibre 2.43±0.10, Ash 1.86±0.43, crude fat 1.14±0.05, protein 9.14±0.22 and moisture 9.11±0.07. From the results Semolina has high carbohydrates content and the low value of moisture content indicates the longer shelf life. The results of the mineral content showed the presence of calcium 5.57±0.25, magnesium 0.94±0.27, potassium 116.57±3.96, copper 0.02±0.01, zinc 0.97±0.05 and iron 0.87±0.21 in the sample.