This thesis research aims to define, identify, and promote community theatre as a “third space” for disadvantaged youth. A third space is defined by the Oxford dictionary as “...the in-between, or hybrid, spaces, where the first and second spaces work together to generate a new third space. First and second spaces are two different, and possibly conflicting, spatial groupings where people interact physically and socially: such as home (everyday knowledge) and school (academic knowledge)” (Oxford Dictionary, 2021). For disadvantaged youth, the creation of a third space in the theatre can give them a safe environment away from issues they may have at home or at school, it can further their learning about themselves and others, and it can also help those youth feel a sense of belonging to a community larger than themselves. Because of these benefits, it is clear that performing arts programs can offer a great impact on disadvantaged youth; however, many theatre companies struggle to market their programming to said communities. This may be in part, due to low marketing budgets, no specificity in labor resources dedicated to youth programming, or ineffective marketing strategies and tactics.<br/>In order to ideate marketing recommendations for these organizations, primary research was conducted to determine the attitudes and beliefs revolving around youth participation in community theatre, as well as the current marketing strategies and tactics being utilized by programmers. Participants included program managers of youth theatre programs, as well as youth participants from several major cities in the U. S. The secondary research aims to better understand the target demographic (disadvantaged youth), the benefits derived from participation in arts programming, and marketing strategies for the performing arts. Following data analysis are several recommendations for the learning, planning, and implementation of marketing strategies for theatre programmers.

This thesis research aims to define, identify, and promote community theatre as a “third space” for disadvantaged youth. A third space is defined by the Oxford dictionary as “...the in-between, or hybrid, spaces, where the first and second spaces work together to generate a new third space. First and second spaces are two different, and possibly conflicting, spatial groupings where people interact physically and socially: such as home (everyday knowledge) and school (academic knowledge)” (Oxford Dictionary, 2021). For disadvantaged youth, the creation of a third space in the theatre can give them a safe environment away from issues they may have at home or at school, it can further their learning about themselves and others, and it can also help those youth feel a sense of belonging to a community larger than themselves. Because of these benefits, it is clear that performing arts programs can offer a great impact on disadvantaged youth; however, many theatre companies struggle to market their programming to said communities. This may be in part, due to low marketing budgets, no specificity in labor resources dedicated to youth programming, or ineffective marketing strategies and tactics. This research aims to provide tangible recommendations for youth programmers to better involve their target audience.

Music has consistently been documented as a manner to bring people together across cultures throughout the world. In this research, we propose that people use similar musical taste as a strong sign of potential social connection. To investigate this notion, we draw on literature examining how music merges the public/private self, the link to personality, and group identity, as well as how it is linked to romantic relationships. Thus, music can be a tool when wanting to get to know someone else and/or forge a platonic relationship. To test this hypothesis, we designed an experiment comparing music relative to another commonality (sharing a sports team in common) to see which factor is stronger in triggering an online social connection. We argue that people believe they have more in common with someone who shares similar music taste compared to other commonalities. We discuss implications for marketers on music streaming platforms.

The research presented in this Honors Thesis provides development in machine learning models which predict future states of a system with unknown dynamics, based on observations of the system. Two case studies are presented for (1) a non-conservative pendulum and (2) a differential game dictating a two-car uncontrolled intersection scenario. In the paper we investigate how learning architectures can be manipulated for problem specific geometry. The result of this research provides that these problem specific models are valuable for accurate learning and predicting the dynamics of physics systems.<br/><br/>In order to properly model the physics of a real pendulum, modifications were made to a prior architecture which was sufficient in modeling an ideal pendulum. The necessary modifications to the previous network [13] were problem specific and not transferrable to all other non-conservative physics scenarios. The modified architecture successfully models real pendulum dynamics. This case study provides a basis for future research in augmenting the symplectic gradient of a Hamiltonian energy function to provide a generalized, non-conservative physics model.<br/><br/>A problem specific architecture was also utilized to create an accurate model for the two-car intersection case. The Costate Network proved to be an improvement from the previously used Value Network [17]. Note that this comparison is applied lightly due to slight implementation differences. The development of the Costate Network provides a basis for using characteristics to decompose functions and create a simplified learning problem.<br/><br/>This paper is successful in creating new opportunities to develop physics models, in which the sample cases should be used as a guide for modeling other real and pseudo physics. Although the focused models in this paper are not generalizable, it is important to note that these cases provide direction for future research.

High-entropy alloys possessing mechanical, chemical, and electrical properties that far exceed those of conventional alloys have the potential to make a significant impact on many areas of engineering. Identifying element combinations and configurations to form these alloys, however, is a difficult, time-consuming, computationally intensive task. Machine learning has revolutionized many different fields due to its ability to generalize well to different problems and produce computationally efficient, accurate predictions regarding the system of interest. In this thesis, we demonstrate the effectiveness of machine learning models applied to toy cases representative of simplified physics that are relevant to high-entropy alloy simulation. We show these models are effective at learning nonlinear dynamics for single and multi-particle cases and that more work is needed to accurately represent complex cases in which the system dynamics are chaotic. This thesis serves as a demonstration of the potential benefits of machine learning applied to high-entropy alloy simulations to generate fast, accurate predictions of nonlinear dynamics.

Especially during the current COVID-19 pandemic and age of social unrest in the United States, there has been an increasing need for comfort, yet the idea of comfort is quite vague and rarely elaborated upon. To simplify the idea of comfort and communicate the ideas around it effectively, I am defining comfort as a subset of escapism in which a person escapes to reduce or alleviate feelings of grief or distress. As companies rush to comfort their customers in this current state of uncertainty, marketers are pressed to identify people’s insecurities and comfort them without coming off as insensitive or trite. Current comfort marketing focuses on inspiring nostalgia in its customers, having them recall previous positive experiences or feelings to comfort them. Nostalgic marketing techniques may ease mild grief in some cases, but using them to alleviate severe distress probably will not be as effective, and has contributed to several seemingly out-of-touch “COVID-19 era” commercials.<br/>When addressing comfort, marketers should understand the type and hierarchy of comfort that they are catering to. Not all comforts are equal, in that some comforts make us feel better than others and some do not comfort us at all. A better understanding of how and why comforts change among different individuals, and possibly being able to predict the comfort preference based on a product or service, will help marketers market their goods and services more effectively. By diversifying and specializing comfort marketing using this hierarchical method, marketers will be able to more significantly reach their customers during “uncertain times.”

Robots are often used in long-duration scenarios, such as on the surface of Mars,where they may need to adapt to environmental changes. Typically, robots have been built specifically for single tasks, such as moving boxes in a warehouse or surveying construction sites. However, there is a modern trend away from human hand-engineering and toward robot learning. To this end, the ideal robot is not engineered,but automatically designed for a specific task. This thesis focuses on robots which learn path-planning algorithms for specific environments. Learning is accomplished via genetic programming. Path-planners are represented as Python code, which is optimized via Pareto evolution. These planners are encouraged to explore curiously and efficiently. This research asks the questions: “How can robots exhibit life-long learning where they adapt to changing environments in a robust way?”, and “How can robots learn to be curious?”.

Colorimetric assays are an important tool in point-of-care testing that offers several advantages to traditional testing methods such as rapid response times and inexpensive costs. A factor that currently limits the portability and accessibility of these assays are methods that can objectively determine the results of these assays. Current solutions consist of creating a test reader that standardizes the conditions the strip is under before being measured in some way. However, this increases the cost and decreases the portability of these assays. The focus of this study is to create a machine learning algorithm that can objectively determine results of colorimetric assays under varying conditions. To ensure the flexibility of a model to several types of colorimetric assays, three models were trained on the same convolutional neural network with different datasets. The images these models are trained on consist of positive and negative images of ETG, fentanyl, and HPV Antibodies test strips taken under different lighting and background conditions. A fourth model is trained on an image set composed of all three strip types. The results from these models show it is able to predict positive and negative results to a high level of accuracy.