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)

https://theses.gla.ac.uk/3406/Parallelization has moved in recent years into the mainstream compilers, and the demand for parallelizing tools that can do a better job of automatic parallelization is higher than ever. During the last decade considerable attention has been focused on developing programming tools that support both explicit and implicit parallelism to keep up with the power of the new multiple core technology. Yet the success to develop automatic parallelising compilers has been limited mainly due to the complexity of the analytic process required to exploit available parallelism and manage other parallelisation measures such as data partitioning, alignment and synchronization. This dissertation investigates developing a programming tool that automatically parallelises large data structures on a heterogeneous architecture and whether a high-level programming language compiler can use this tool to exploit implicit parallelism and make use of the performance potential of the modern multicore technology. The work involved the development of a fully automatic parallelisation tool, called VSM, that completely hides the underlying details of general purpose heterogeneous architectures. The VSM implementation provides direct and simple access for users to parallelise array operations on the Cell’s accelerators without the need for any annotations or process directives. This work also involved the extension of the Glasgow Vector Pascal compiler to work with the VSM implementation as a one compiler system. The developed compiler system, which is called VP-Cell, takes a single source code and parallelises array expressions automatically. Several experiments were conducted using Vector Pascal benchmarks to show the validity of the VSM approach. The VP-Cell system achieved significant runtime performance on one accelerator as compared to the master processor’s performance and near-linear speedups over code runs on the Cell’s accelerators. Though VSM was mainly designed for developing parallelising compilers it also showed a considerable performance by running C code over the Cell’s accelerators.
Youssef Omran Gdura(5-2012)