COMPUTING CENTER

For Research & Education

LANGSTON CENTER FOR INTERDISCPLINARY RESEARCH AND EDUCATION (LU-CIRE)

Led By Dr. Franklin Fondjo Fotou, Department of Technology & By Dr. Tadesse Abebaw, Department of MAthematics

Project Description


Sub 1: impact of electromagnetic waves (EMW) on skin, muscles, bone, fat, and brain tissue

This project encompasses basic computational research on EMW interaction with biological tissue. EMW is known to have both positive and negative effects on human health. According to the World Health Organization (fact sheet No. 201, July 1998), EMW may cause spontaneous abortion or birth deformed children. With the increased number of mobile devices and mobile phone towers situated in dense population agglomerations, more people are exposed to varying intensities of EMW, putting their lives and health at risk without their knowledge. The fact that an atom place in a magnetic field B acquires a magnetic moment parallel to B therefore changes the momentum of a particle of charge q. Such change if applied to human cells and electrical system could create a considerable disturbance that could lead to health problems  . Our research consists of using computational methods and especially the finite-difference time-domain (FDTD) techniques coupled to the heat transfer equation and the leaky integrate-and-fire model to study the possible impact of EMW absorption on long-term memory loss and other memory degenerative diseases. This work will also study the impact of EMW on skin, muscles, bone, fat and reproductive system using the computation techniques. The project will explore and accumulate a set of data that will help develop new guidelines to prevent excessive human exposure to EWM. The investigation will employ numerous computing approaches to conduct efficient and accurate calculations that will be developed and shared with others in the research community. This project will provide research, educational, and training opportunities at an HBCU for populations under-represented in scientific, engineering, and technological fields.


Sub 2: The Intensity-Modulated Radiation Therapy (IMRT)

The IMRT  project team works to perform basic multidisciplinary scientific computational exploration and testing of the Ensemble- Based Simulated Annealing (EBSA) algorithm on Mathematical Radiation Oncology settings. The use of stochastic ensemble, particularly simulated annealing (SA), as a global optimization solver, has been widely known in biomedical computing sciences. The selection of optimal schedule and its associated multidimensional tuning parameters such as ensemble size, thermodynamic speeds, etc., however, has long been a major challenge in realizing the full potential of this class of global optimization algorithms. The project aims to perform basic multidisciplinary scientific computational exploration and testing of improved adaptive Simulated Annealing algorithms on Mathematical Radiation Oncology settings. The project, thus, paves the way for the development of improved open source radiation therapy planning algorithms which will serve as a foundational platform for development of novel Radiation Therapy Planning Systems which will have a potential impact on the radiation therapy computing research and the biomedical computing research communities at large. Based upon the reported successes of EBSA algorithms on the physical and biomedical computing problems such as Geophysical prospecting applications, protein folding, chemical clustering and the project investigator's recent preliminary results on the applications of EBSA on Intensity Modulated Radiation Therapy planning (IMRT) problems, the project employs open source Simulated Annealing (SA) tools (Frost's concepts) to explore, test and implement EBSA algorithms and hybrids (for selection of optimal schedules, ensemble size, thermodynamic speeds, beam numbers, gantry positions etc.) with Genetic algorithms on 2D/3D IMRT problems under the Computational Environment for Radiotherapy Research (CERR) platform. As the EBSA algorithm is inherently a parallel algorithm, the availability of access to cluster computing resources at Langston University will greatly enhance our research team’s activities.