Magnetohydrodynamic (MHD) flow past a flat plate over an outer flow turbulence subjected to a defect-layer has been examined. It finds a novel approach to a Blasius equation with a view to analyze outer flow turbulence by analytical method, neglecting numerical method to describe the physical situation on outer flow which does not seems to appeared in the literature. In a defect-layer it is rigorously stated that outer flow in a defect layer is independent of Reynolds number. To solve Blasius equation subject to boundary conditions it is stated that numerical results are obtained by analytical method. A graphical representation shows that the velocity distribution is merged with different values of Hartmann number (magnetic pressure) so that velocity increases with indefinite period. In this situation, outer flow turbulence in a defect layer holds stress free so that the existence of a magnet field dominates the entire outer flow situation. In relating to the physical situation of interest, the universe is expanding slowly and slowly subject to a Hot Big Bang with a decisive importance to a microwave background of radiation.

The article is devoted to one of the main problems of the evolution of the earth's crust, where from the position of the concept of the dynamics of the evolution of the earth's crust (CDEEC) the nature of numerous geological processes is clarified. Including the origin of geodynamic forces and those geotectonic processes that directly occur under the influence of these forces: such as volcanoplutonic; seismotectonic processes; global deep fault networks; divergent and convergent zones; active and passive margins; riftogenic processes; the origin of arc systems, etc. The origin, mechanism of formation, as well as their distribution patterns on the face of the Earth and other characteristic features of these natural processes are clarified from the position of CDEEC. From the position of this concept, geodynamic forces are formed during the rotation of the Earth around its axis and they are distributed on the face of the Earth with certain patterns, which are predetermining factors in the development of geological processes. These geological processes, both in scale and in form of distribution, have their own specific features, which are important in the formation of various genetic types of mineral deposits. Therefore, the study of these processes is one of the priority areas of geological research.

Public building projects in the South-South Region, Nigeria have earned a bad reputation for poor performance. Construction Industry findings point to lack of front-end planning as the main cause of poor project performance. This paper focuses on systematic literature review and semi-structured interviews to identify the factors that affect project definition and front-end factors that influence project performance. The search revealed that the project environment interacts with organizational structures, project resources, Front-end processes and others (frontend management framework, funding, technology, integration, coordination and control) to influence project performance. These findings lead to the development of the conceptual framework, for enhancing project performance in the South-South Region, Nigeria.

In the evolving landscape of higher education, data-driven approaches have become pivotal in enhancing academic performance and institutional decision-making. This study investigates the application of supervised machine learning algorithms to predict undergraduate students’ outcomes in Introduction to Computing Science at the Federal University of Allied Health Sciences, Enugu, Nigeria. The aim is to develop predictive models capable of early identification of students at risk of academic failure, enabling proactive intervention strategies. A dataset comprising 500 anonymised student records, including demographic, behavioural, and academic features, was preprocessed using normalisation and encoding techniques. Feature selection methods, such as Chi-square tests and Recursive Feature Elimination (RFE), identified midterm test scores, attendance rate, and parental education as key predictors. Five classification algorithms, Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), and Gradient Boosting, were trained and evaluated using 5-fold cross-validation. Results revealed that ensemble models outperformed traditional classifiers, with Gradient Boosting achieving the highest performance (87% accuracy, 0.85 F1-score, and 0.91 ROC-AUC). Feature importance analysis confirmed that early assessments and engagement metrics are strong indicators of final course performance. These findings underscore the potential of machine learning to enhance academic support systems by providing actionable insights for educators and administrators. The study concludes by recommending the integration of predictive analytics into institutional frameworks, the development of academic early warning systems, and future expansion of the model to include behavioural and real-time learning data. This work contributes to the growing field of Educational Data Mining and presents a scalable model for fostering academic excellence in Nigerian higher education.