FACULTY OF PHYSCIAL SCIENCES

In today's world, the rise of modernization and industrialization has quietly reshaped ecosystems—the rapid expansion of industries and unchecked urbanization continue to disrupt fragile environments, leading to the persistent challenge of heavy metal contamination in soil. This study investigates the green synthesis and characterization of an MgO-MnO-biochar ternary nanocomposite as well as the MgO-MnO Nanopparticle using an eco-friendly co-precipitation method, and highlightening its potential heavy metal remediation applications. The synthesis involved the bottom- up fabrication of magnesium and manganese oxides in a green solvent system, followed by integration with biochar. The characterization of the MgO-MnO-biochar nanocomposite and MgO-MnO nanoparticle system revealed significant structural, compositional, and morphological differences. FTIR analysis showed the nanocomposite had prominent O–H stretching at 3951.1, 3641.1, and 3790.2 cm⁻¹, C–H stretching at 2907.3 cm⁻¹, CO₂ adsorption at 2110.3 cm⁻¹, C=C stretching at 1611.8 cm⁻¹, and Mg–O and Mn–O bonds at 946.3 and 864.7 cm⁻¹, while the nanoparticle system exhibited fewer functional groups, with CO₂ adsorption at 2102.2 cm⁻¹ and C=C stretching at 1598.5 cm⁻¹. EDX analysis revealed high carbon content (48.72 wt%) in the nanocomposite, absent in the nanoparticle system, alongside higher Mn (61.62 wt%) and Mg (32.24 wt%) concentrations in the nanoparticle system compared to 24.47 wt% Mn and 11.34 wt% Mg in the composite. XRD analysis identified Lindergerbite (59.00%) and Periclase (13.00%) in the nanoparticle system, while the nanocomposite featured Flagstaffite (52.40%), Graphite (1.84%), and Cryoptohalite (7.96%). BET analysis showed the nanoparticle system had a higher surface area (282.000 m²/g vs. 216.400 m²/g), pore volume (0.173 cm³/g vs. 0.128 cm³/g), and BJH surface area (354.200 m²/g vs. 265.400 m²/g), though pore diameters were similar (2.132 nm vs. 2.129 nm). SEM analysis revealed the nanocomposite's porous, fibrous structure with well-dispersed nanoparticles, while the nanoparticle system exhibited a denser, more aggregated morphology with reduced porosity. The characterization results revealed that the MgO-MnO-biochar ternary nanocomposite possesses significant structural and compositional properties, suggesting its potential as a sustainable, cost-effective material for future heavy metal remediation applications.

Facies prediction refers to the task of determining the type of rock or sediment in a particular area, based on various physical and chemical properties. Machine learning techniques can be used to build predictive models for facies prediction, using data
on the characteristics of different rock types and the corresponding measurements made at various locations. These models can then be used to make predictions about the facies of new, previously unseen locations. There are several benefits to using machine learning for facies prediction. One benefit is that the models can be trained on large amounts of data, allowing them to make highly accurate predictions. Additionally, machine learning models can be updated as new data becomes available, enabling them to improve over time.

The logistics monitoring system, developed as a university project, seeks to enhance resource management and transportation logistics. It offers real-time tracking of assets and facilities, facilitates efficient coordination of transportation services, and provides comprehensive reporting and analytics for informed decision-making. By improving efficiency and transparency, the system contributes to enhancing operational effectiveness, benefiting various stakeholders involved.

Thorough intensive effort have been made to design powerful and outstanding e-learning platforms or environment but little or no attention have been paid to creating also environment that will provide assessment to test the knowledge and skills which learners may acquire form
these platforms. Competency based testing can be applied to various instance including real life or e-learning programmes with a focus of skill based learning outcomes. This study however is concerned with creating a competency-based e-learning platform for the regular desktop that teaches web development to students or interested learners. Web development here is a larger learning goal and is however divided or broken down into smaller component courses called competencies. Students or users of the platform must however go through each phase and must be classified Advanced, Competent or Master in order to advance to a next level as advancement to a next level component or competency would depend on the students tested competency level.
Included also in this study is a model for testing student’s competency level in relation with the 5-level scale which is used to classify student according to their competency leve