Hood College Department of Computer Science and Information Technology
Permanent URI for this collectionhttp://hdl.handle.net/11603/12974
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Item Common Envelope Evolution Of Toy Stars Using Smooth Particle Hydrodynamics(2017-04) Pigott, John; Boon, John; Hood College Computer Science, Information Technology, and Mathematics; Hood College Departmental HonorsSpace is both fascinating and mysterious, and there are a staggering number of problems which exist and are being studied. The topic of this paper is on one such problem known as common envelope evolution. This evolution process occurs when two stars in a binary star system are surrounded by a gas cloud. The gas cloud affects the stars in the system in various ways, and the process may be used to explain some observations made on actual binary systems. This process has been studied using various techniques, and one such technique used to study it is the smooth particle hydrodynamic modeling technique. This is a modeling technique which began in the astronomical field but has since expanded into various fields and modeling problems. More information on both this modeling technique as well the astronomical process can be found in the following sections. The purpose of this paper is to describe the methods and results of a year-long research project which blended computer science, mathematics, and physics by using smooth particle hydrodynamics to simulate toy stars in the common envelope pro- cess. These results include simulating multiple stars, improving the runtime of the simulation, and working to better define portions of the simulation. In addition to describing the results of this study, I will also work to explain the topics and math- ematics presented in the paper because the topics are not simple and required a fair amount of study before the work on the project’s simulation could even begin.Item Comparative Evaluation of Access Control Models(2022-04-19) Langmead, Paige; Dimitoglou, George; Hood College Computer Science and Information Technology; Computer ScienceIn cybersecurity, access control models dictate what actions a person can perform, which programs they have permission to execute and overall, the level and type of access of information technology resources. This work compares a number of the most widely used access control models and analyzes their suitability of deployment in different contexts. To perform the analysis, several key background access control mechanisms are described and analyzed. The first result from this analysis is the realization that there is no dominant model that can be suitable across all environments. It is therefore important for access control models to be selected that match the needs of a particular environment. The more in depth analysis focuses on the direct comparison of specific access control models, Bell-Lapdula, Biba, Clark and Wilson and Lampson’s Access Matrix. The result from this analysis is that the Biba model is the most robust and most secure integrity model, especially due to its perfect connection with the Bell Lapadula confidentiality model. These findings are significant as comparing the expressive power of access control models is a fundamental problem in information security.Item Predicting Mortality of Diabetic ICU Patients(2019-04) Wittler, Ian; Liu, Xinlian; Dong, Aijuan; Computer Science; Hood College Departmental HonorsDiabetes mellitus (DM) is a major public health concern that requires continuing medical care. It is also a leading cause of other serious health complications associated with longer hospital stays and increased mortality rates. Fluctuation of blood glucose levels are easy to monitor. Physicians manage patients' blood glucose to prevent or slow the progress of diabetes. In this paper, the MIMIC-III data set is used to develop and train multiple models that aim to predict the mortality of DM patients. Our deep learning model of convolutional neural network produced a 0.885 AUC score, above all baseline models we constructed, which include decision trees, random forests, and fully connected neural networks. The inputs for each model were comprised of admission type, age, Elixhauser comorbidity score, blood glucose measurements, and blood glucose range. The results obtained from these models are valuable for physicians, patients, and insurance companies. By analyzing the features that drive these models, care management for diabetic patients in an ICU setting can be improved resulting in lowered motality rate.