Scientific Computing is an exciting and growing area that provides an important link between Computer Science and the Engineering and Physical Sciences. Today, computer graphics and geometric modeling are used routinely in science, engineering, business; and entertainment. In this thesis we develop object-oriented techniques and software for computing and visualizing implicitly defined manifolds ("surfaces") that arise a wide range of applications. The software differs from existing software for computing such manifolds in its software architecture. Furthermore, its algorithms are based on numerical continuation methods, rather than on subdivision techniques, which allows its practical application to the computation of two-dimensional manifolds in high-dimensional Euclidean spaces. The overall software provides a graphical user interface, algorithms for computing two-dimensional manifolds in higher-dimensional spaces, and graphics routines to visualize the manifolds.
Youssef Omran Gdura(6-2001)

—Predicting the groundwater level has recently become very important research topic especially with the rise of population density and consequently increasing the water demand. This paper uses the Random Forest and linear regression neural network models to predict the groundwater level of Wadi-Alshaty district in the South West part of Libya. The results are compared with that obtained using the hydrologic long-term forecasting graphical approach. One of the most important findings of this study is the effectiveness of the neural network models to investigate the fluctuation of the groundwater levels over time (20 years)
Amna Elhawil, Alarabi Naji, Malak Nuesry, Almabruk Sanossi(12-2021)

A review of previous array Pascals leads on to a description of the Glasgow Pascal compiler. The compiler is an ISO-Pascal superset with semantic extensions to translate data parallel statements to run on multiple SIMD cores.
Youssef Omran Gdurra, Paul Cockshott, Ciaran Mcreesh, Susanne Oehle(6-2015)