The goal of the presented research is using Electro Field-assisted Nano Ink Writing(EF-NIW) to deposit poly(3,4-ethylenedioxythiophene) polystyrene sulfonate, or PEDOT, on a substrate to serve as a basis for designing high-efficiency, scalable solar cells. Through the analysis of parameters that affect electrospray deposition, methods to accurately produce a PEDOT film will be determined. With the finished, contingent film, tests for efficacy can be performed. The film will be analyzed for profilometry, determining the thickness of the film. The film will then be put up to a conductivity test.

This project aims to incorporate the aspect of sentiment analysis into traditional stock analysis to enhance stock rating predictions by applying a reliance on the opinion of various stocks from the Internet. Headlines from eight major news publications and conversations from Yahoo! Finance’s “Conversations” feature were parsed through the Valence Aware Dictionary for Sentiment Reasoning (VADER) natural language processing package to determine numerical polarities which represented positivity or negativity for a given stock ticker. These generated polarities were paired with stock metrics typically observed by stock analysts as the feature set for a Logistic Regression machine learning model. The model was trained on roughly 1500 major stocks to determine a binary classification between a “Buy” or “Not Buy” rating for each stock, and the results of the model were inserted into the back-end of the Agora Web UI which emulates search engine behavior specifically for stocks found in NYSE and NASDAQ. The model reported an accuracy of 82.5% and for most major stocks, the model’s prediction correlated with stock analysts’ ratings. Given the volatility of the stock market and the propensity for hive-mind behavior in online forums, the performance of the Logistic Regression model would benefit from incorporating historical stock data and more sources of opinion to balance any subjectivity in the model.

Standardization is sorely lacking in the field of musical machine learning. This thesis project endeavors to contribute to this standardization by training three machine learning models on the same dataset and comparing them using the same metrics. The music-specific metrics utilized provide more relevant information for diagnosing the shortcomings of each model.

I worked with Professor Long and the Long research group in the Biodesign Institute to develop an ultra-violet assisted direct ink write 3D printer to 3D print poly(amic acid) pendant salts for their group. The project included a proof of concept small format 3D printer and then the development of the full scale printer. I wrote custom code to run the printer and create complex models as well as code to automatic dispense the viscous polymer we were using.

I worked with Professor Long and the Long research group in the Biodesign Institute to develop an ultra-violet assisted direct ink write 3D printer to 3D print poly(amic acid) pendant salts for their group. The project included a proof of concept small format 3D printer and then the development of the full scale printer. I wrote custom code to run the printer and create complex models as well as code to automatic dispense the viscous polymer we were using.

I worked with Professor Long and the Long research group in the Biodesign Institute to develop an ultra-violet assisted direct ink write 3D printer to 3D print poly(amic acid) pendant salts for their group. The project included a proof of concept small format 3D printer and then the development of the full scale printer. I wrote custom code to run the printer and create complex models as well as code to automatic dispense the viscous polymer we were using.

Activists seeking to create social change must decide whether to expend more resources trying to change the behavior of individuals or institutions. For example, a climate activist could spend their days urging people to stop flying in airplanes, or they could spend their days urging the government to outlaw excessive flying. Some social change theorists argue that the second tactic is more effective than the first. Are they correct? I use the environmental movement and the animal liberation movement as case studies to examine this question from an empirical perspective. I conclude that while attempts to change individual behavior should not be entirely abandoned, they should be used with caution because of their tendency to distract the public from the need for institutional reform and their tendency to alienate potential allies. Seeing that, for decades, the animal movement’s main strategy has been to urge individuals to change their dietary behavior, this movement would greatly benefit from this knowledge.

The pandemic that hit in 2020 has boosted the growth of online learning that involves the booming of Massive Open Online Course (MOOC). To support this situation, it will be helpful to have tools that can help students in choosing between the different courses and can help instructors to understand what the students need. One of those tools is an online course ratings predictor. Using the predictor, online course instructors can learn the qualities that majority course takers deem as important, and thus they can adjust their lesson plans to fit those qualities. Meanwhile, students will be able to use it to help them in choosing the course to take by comparing the ratings. This research aims to find the best way to predict the rating of online courses using machine learning (ML). To create the ML model, different combinations of the length of the course, the number of materials it contains, the price of the course, the number of students taking the course, the course’s difficulty level, the usage of jargons or technical terms in the course description, the course’s instructors’ rating, the number of reviews the instructors got, and the number of classes the instructors have created on the same platform are used as the inputs. Meanwhile, the output of the model would be the average rating of a course. Data from 350 courses are used for this model, where 280 of them are used for training, 35 for testing, and the last 35 for validation. After trying out different machine learning models, wide neural networks model constantly gives the best training results while the medium tree model gives the best testing results. However, further research needs to be conducted as none of the results are not accurate, with 0.51 R-squared test result for the tree model.

This creative project is a short story in the Gothic genre followed by an explanation of certain literary elements and decisions. The Gothic genre often explores supernatural and uncomfortable topics that can challenge the reader’s perception and understanding of the world. Through this means of storytelling, authors are given the opportunity to connect the supernatural with complex and sensitive topics that may be difficult or even taboo to speak about in certain locations and time periods. In this thesis, I embrace the traditions of the Gothic-genre with a story that focuses on the issues prevalent today. The years 2020 and 2021 have been unprecedented times for humanity. Technology continues to grow at an alarming rate, suicide rates of young people have been on the rise for years, and a global pandemic has people adapting to all new ways of living. During these ever changing times, it is the Gothic that may provide guidance through these uncertainties by shedding light on the problems that will plague humanity both today and tomorrow. The story follows an outcast from society who aids in the creation of a divine monster, and the consequences that follow.

The goal of this experiment was to examine the energy absorption properties of origami-inspired honeycomb and standard honeycomb structures. These structures were 3D printed with two different materials: thermoplastic polyurethane (TPU) and acrylonitrile butadiene styrene (ABS). Quasi-static compression testing was performed on these structures for both types and materials at various wall thicknesses. The energy absorption and other material properties were analyzed for each structure. Overall, the results indicate that origami-inspired structures perform best at energy absorption at a higher wall thickness with a rigid material. The results also indicated that standard honeycomb structures perform better with lower wall thickness, and also perform better with a rigid, rather than a flexible material. Additionally, it was observed that a flexible material, like TPU, better demonstrates the folding and recovery properties of origami-inspired structures. The results of this experiment have applications wherever honeycomb structures are used, mostly on aircraft and spacecraft. In vehicles with structures of a sufficiently high wall thickness with a rigid material, origami-inspired honeycomb structures could be used instead of current honeycomb structures in order to better protect the passengers or payload through improved energy absorption.