avigate and discover knowledge hidden in life sciences literature. To demonstrate the utility of this system, this thesis also details a prototype enterprise quality search and discovery service that helps researchers with a guided step-by-step query refinement, by suggesting concepts enriched in intermediate results, and thereby facilitating the "discover more as you search" paradigm.
The coffee cup system can be simplified and modeled by a cart-and-pendulum system. Bazzi et al. and Maurice et al. present two different cart-and-pendulum systems to represent the coffee cup system [1],[2]. The purpose of this project was to build upon these systems and to gain a better understanding of the coffee cup system and to determine where chaos existed within the system. The honors thesis team first worked with their senior design group to develop a mathematical model for the cart-and-pendulum system based on the Bazzi and Maurice papers [1],[2]. This system was analyzed and then built upon by the honors thesis team to build a cart-and-two-pendulum model to represent the coffee cup system more accurately.
Analysis of the single pendulum model showed that there exists a low frequency region where the pendulum and the cart remain in phase with each other and a high frequency region where the cart and pendulum have a π phase difference between them. The transition point of the low and high frequency region is determined by the resonant frequency of the pendulum. The analysis of the two-pendulum system also confirmed this result and revealed that differences in length between the pendulum cause the pendulums to transition to the high frequency regions at separate frequency. The pendulums have different resonance frequencies and transition into the high frequency region based on their own resonant frequency. This causes a range of frequencies where the pendulums are out of phase from each other. After both pendulums have transitioned, they remain in phase with each other and out of phase from the cart.
However, if the length of the pendulum is decreased too much, the system starts to exhibit chaotic behavior. The short pendulum starts to act in a chaotic manner and the phase relationship between the pendulums and the carts is no longer maintained. Since the pendulum length represents the distance between the particle of coffee and the top of the cup, this implies that coffee near the top of the cup would cause the system to act chaotically. Further analysis would be needed to determine the reason why the length affects the system in this way.
Dyslexia is a learning disability that negatively affects reading, writing, and spelling development at the word level in 5%-9% of children. The phenotype is variable and complex, involving several potential cognitive and physical concomitants such as sensory dysregulation and immunodeficiencies. The biological pathogenesis is not well-understood. Toward a better understanding of the biological drivers of dyslexia, we conducted the first joint exome and metabolome investigation in a pilot sample of 30 participants with dyslexia and 13 controls. In the metabolite analysis, eight metabolites of interest emerged (pyridoxine, kynurenic acid, citraconic acid, phosphocreatine, hippuric acid, xylitol, 2-deoxyuridine, and acetylcysteine). A metabolite-metabolite interaction analysis identified Krebs cycle intermediates that may be implicated in the development of dyslexia. Gene ontology analysis based on exome variants resulted in several pathways of interest, including the sensory perception of smell (olfactory) and immune system-related responses. In the joint exome and metabolite analysis, the olfactory transduction pathway emerged as the primary pathway of interest. Although the olfactory transduction and Krebs cycle pathways have not previously been described in the dyslexia literature, these pathways have been implicated in other neurodevelopmental disorders including autism spectrum disorder and obsessive-compulsive disorder, suggesting the possibility of these pathways playing a role in dyslexia as well. Immune system response pathways, on the other hand, have been implicated in both dyslexia and other neurodevelopmental disorders.
In this research, I surveyed existing methods of characterizing Epilepsy from Electroencephalogram (EEG) data, including the Random Forest algorithm, which was claimed by many researchers to be the most effective at detecting epileptic seizures [7]. I observed that although many papers claimed a detection of >99% using Random Forest, it was not specified “when” the detection was declared within the 23.6 second interval of the seizure event. In this research, I created a time-series procedure to detect the seizure as early as possible within the 23.6 second epileptic seizure window and found that the detection is effective (> 92%) as early as the first few seconds of the epileptic episode. I intend to use this research as a stepping stone towards my upcoming Masters thesis research where I plan to expand the time-series detection mechanism to the pre-ictal stage, which will require a different dataset.