Rheumatoid Arthritis (RA) is a chronic disease with no known cure. While medications are often effective at managing physical symptoms, RA patients often experience high levels of fatigue. Studies have found that fatigue may be managed through meditation, but little is known about virtual reality meditation’s (VRM) potential to alleviate fatigue. The purpose of this study is to examine the feasibility and acceptability of VRM as an alternative non-pharmacologic intervention for fatigue management in RA patients. This study implements a convergent mixed-methods design to collect patient feedback. Four participants diagnosed with RA were recruited from a local rheumatology clinic. Participants used a VRM headset in their own home over the course of four consecutive weeks. During this time, Patient Reported Outcome Measurement Information System (PROMIS) measures of fatigue, pain, depression, anxiety, physical activity, and mood were taken at baseline and at weekly intervals. Semi-structured interviews occurred at baseline and at the conclusion of the study. Interviews were audio recorded, transcribed, and coded using Atlas.ti (v8). The results are currently pending. Expected results include that participants will find VRM both feasible and acceptable for fatigue management, and that participants will report reduced fatigue levels after using the VR device. While studies have explored the use of VRM in the treatment of anxiety disorder, depression or PTSD, this is the first study to examine VRM’s use for managing fatigue in participants with RA. Results of this study will inform future clinical trials using VRM, implementation of VRM into clinical use, and give a better understanding of the patient’s experience of utilizing VRM for fatigue management. 

The magnitude at which a minimal change in initial conditions can affect data results was first observed by Edward Lorenz in 1963. Through his examination of chaos emerged the discovery that many natural systems are governed by chaotic behavior. The main complication of chaos theory is that nonperiodic unstable systems are unpredictable, and therefore many natural systems have yet to be understood because of its complexity. Our research considers the unstable nature of the Lorenz attractor and its influence on a center of gravity. By examining intervals of data and comparing their centers of gravity, we found that as the amount of data points tends to infinity, a center of gravity never converges to a single point. We also examine what appears to be a stark regularity and group of symmetries under an extension M(n+k)=f(Mn) of Lorenz’ original M(n+1)=f(Mn) study of Poincare sections Z.

In 2007, David Vokoun et al. derived a formula for the force of interaction between magnets. The formula is called the Gilbert's Model. According to the Gilbert’s Model, the force between two ferromagnets is given by a constant factor proportional to the saturation magnetization of each magnet multiplied by a function of the separation distance and geometry of the magnets. We show that the assumed constant is better described as a function of hyperbolic tangent of the separation distance due to the effects of magnetic field interactions on the magnetizations of each magnet, and we demonstrate that the inclusion of a simple toy 1D Ising model acting as a perturbation on the background magnetizations better predicts magnetic coupling of cylindrical magnets over small distances.

The search for renewable energy, electricity-generating wind turbines were first introduced by Charles F. Brush in 1888. Wind turbines use the principles of turning the mechanical energy of the wind into useful electrical energy that is able to produce work while also having no direct emissions towards the atmosphere. Using Betz’s law derived from the principles of conservation of mass and momentum of the air stream flowing, we construct and test a model wind turbine maximizing the power generated due to the varying angular velocities, from which testing data are used to iteratively design blade aerodynamics and assignation angle. For the particular model used thus far, the data indicate that with angular velocity lower than or equal to 4.124 rad/s and 13.359 rad/s a maximum efficiency of 22-23% is achieved. The blade designs are flat and angled blades, which are 3D printed and tested its efficiency on the model with pitches of 15°,30° and 45° to accommodate the motor to generate the optimal use of power input, therefore maximum power output. The result shows that 30° pitch is the most optimal angle for both blades design, with the flat blade generating 29000% more power than the angled blade. Blade pitch of 15° is the least efficient, resulting in no power generated with angled blade design, and significantly lower power in flat blade design compared to the other pitch. This discovery is essential to the future development of renewable energy especially in revolutionizing the wind turbine to be more efficient.