Gravitational waves (GW) are deformations in the curvature of spacetime, a fundamental consequence of Einstein’s general theory of relativity. Due to the vast distances separating Earth from other stellar objects, only the greatest disturbances in the fabric of space can be detected by Earth-based GW observatories. Our research focuses on modeling the gravitational waves emitted by neutron star (NS) binaries and inspiraling black holes and comparing these models to data gathered by LIGO (Laser Interferometer GW Observatory) and the Virgo Interferometer. The results will serve as a test of the accuracy of general relativity and provide an estimate for the frequency and magnitude of GW detections by fourth-generation observatories.

Mathematical models of NS binaries will first be analytically computed with the Newtonian approximations; the results will then be generalized to black hole inspirals by considering time dilation due to general relativity. To consider more complicated interactions of stellar objects, numerical solutions will be generated through the Runge-Kutta approximation method. The generated models will be compared to open-source data from the ATNF Pulsar Database and papers published by the LIGO and Virgo Collaborations.

Blurring of images occurs in many fields, causing significant problems and making it unusable due to inadequate clarity. Blur objects are created from relative motion during exposure between a camera and a scene. While the use of short exposure will minimize blur, this results in an inevitable trade-off with increased noise. Hence, it is desirable to computerically erase blur and obtain as much information as possible, especially in the medical field to accurately detect diseases. Deblurring is a major technique that is developed to restore the lost true image. To tackle this problem, we present blind approaches, which is deblurring by estimating the blur kernel. Through this program, we would find a list of blur removal methods that could be used to extract features from unclear images and accurately diagnose diseases such as cancer.

Multiple deblurring methods such as using GANs (Generative Adversarial Networks), CNNs (Convolutional Neural Network), etc would be worked and then tested on multiple medical applications and analyzed based on their overall effectiveness. Datasets for applications such as these have been used in previous research papers and those would be referred to for the same.

Our research topic will be about how different blood types can trigger different responses to biological processes. This topic delves into the immune system, endocrine system, cardiovascular system, etc. Different blood types can change how a person carries out a certain biological process or how they respond to a biological change. Examples include how blood type can affect the susceptibility of COVID-19 and how the Rh factor can influence pregnancies in mothers. Looking at resources online, we have found that this information is not easily found and there is not as much emphasis for the public eye to notice. Thus, we want to compile information and data from previous research papers and conduct our own data analysis project to create our own conclusions.

Our goal is to educate the public and be aware of the biological science that occurs with blood types. First, we will do background research online and talk with relevant professors and scientists in this field to have a better understanding of our research topic. Secondly, we will read multiple research papers on this topic to have a better grasp on specificity. Thirdly, it is important that we meet with patients who have been affected by certain blood types in order to make a statistical analysis portion for our research. Fourthly, due to the virtual format, we will try our best to come to conclusions on a theoretical stance but try to capture the data accurately. Finally, we will compile all of this information and data into a single paper and have our own conclusions to publish. Obviously, we will add more to this plan in the future but this is the skeleton that we have set forth for the next few months.

One of the biggest causes of lung cancer comes from the different methods of smoking such as vaping, cigarette smoking, etc. When smoked, while substances such as cigarettes release nicotine into the body which stimulates the release of dopamine in the body and creates a feeling of pleasure, the inhalation of nicotine is accompanied by a vast array of toxins that are absorbed by alveoli and bronchioles near the lining of the lung. Not only can this cause chronic inflammation in the lungs, but the toxins may be able to greatly disturb transduction pathways and lead to an increased risk in the development of cancer in the lungs.

The goal of the project is to be able to not only find likely transduction pathways that are disturbed and result in cell cycle malfunctioning, but to also be able to compare different smoking methods and to mathematically model differences in how greatly the risks of lung cancer increase through second hand smoking, cigarette smoking, and vaping.