Humans use emotions to communicate social cues to our peers on a daily basis. Are we able to identify context from facial expressions and match them to specific scenarios? This experiment found that people can effectively distinguish negative and positive emotions from each other from a short description. However, further research is needed to find out whether humans can learn to perceive emotions only from contextual explanations.

This thesis proposes hardware and software security enhancements to the robotic explorer of a capstone team, in collaboration with the NASA Psyche Mission Student Collaborations program. The NASA Psyche Mission, launching in 2022 and reaching the metallic asteroid of the same name in 2026, will explore from orbit what is hypothesized to be remnant core material of an early planet, potentially providing key insights to planet formation. Following this initial mission, it is possible there would be scientists and engineers interested in proposing a mission to land an explorer on the surface of Psyche to further document various properties of the asteroid. As a proposal for a second mission, an interdisciplinary engineering and science capstone team at Arizona State University designed and constructed a robotic explorer for the hypothesized surfaces of Psyche, capable of semi-autonomously navigating simulated surfaces to collect scientific data from onboard sensors. A critical component of this explorer is the command and data handling subsystem, and as such, the security of this system, though outside the scope of the capstone project, remains a crucial consideration. This thesis proposes the pairing of Trusted Platform Module (TPM) technology for increased hardware security and the implementation of SELinux (Security Enhanced Linux) for increased software security for Earth-based testing as well as space-ready missions.

"No civil discourse, no cooperation; misinformation, mistruth." These were the words of former Facebook Vice President Chamath Palihapitiya who publicly expressed his regret in a 2017 interview over his role in co-creating Facebook. Palihapitiya shared that social media is ripping apart the social fabric of society and he also sounded the alarm regarding social media’s unavoidable global impact. He is only one of social media’s countless critics. The more disturbing issue resides in the empirical evidence supporting such notions. At least 95% of adolescents own a smartphone and spend an average time of two to four hours a day on social media. Moreover, 91% of 16-24-year-olds use social media, yet youth rate Instagram, Facebook, and Twitter as the worst social media platforms. However, the social, clinical, and neurodevelopment ramifications of using social media regularly are only beginning to emerge in research. Early research findings show that social media platforms trigger anxiety, depression, low self-esteem, and other negative mental health effects. These negative mental health symptoms are commonly reported by individuals from of 18-25-years old, a unique period of human development known as emerging adulthood. Although emerging adulthood is characterized by identity exploration, unbounded optimism, and freedom from most responsibilities, it also serves as a high-risk period for the onset of most psychological disorders. Despite social media’s adverse impacts, it retains its utility as it facilitates identity exploration and virtual socialization for emerging adults. Investigating the “user-centered” design and neuroscience underlying social media platforms can help reveal, and potentially mitigate, the onset of negative mental health consequences among emerging adults. Effectively deconstructing the Facebook, Twitter, and Instagram (i.e., hereafter referred to as “The Big Three”) will require an extensive analysis into common features across platforms. A few examples of these design features include: like and reaction counters, perpetual news feeds, and omnipresent banners and notifications surrounding the user’s viewport. Such social media features are inherently designed to stimulate specific neurotransmitters and hormones such as dopamine, serotonin, and cortisol. Identifying such predacious social media features that unknowingly manipulate and highjack emerging adults’ brain chemistry will serve as a first step in mitigating the negative mental health effects of today’s social media platforms. A second concrete step will involve altering or eliminating said features by creating a social media platform that supports and even enhances mental well-being.

Since the inception of what is now known as the Behavioral Analysis Unit (BAU) at the Federal Bureau of Investigation (FBI) in the 1970s, criminal profiling has become an increasingly prevalent entity in both forensic science and the popular imagination. The fundamental idea of which profiling is premised – behavior as a reflection of personality – has been the subject of a great deal of misunderstanding, with professionals and nonprofessionals alike questioning whether profiling represents an art or a science and what its function in forensic science should be. To provide a more thorough understanding of criminal profiling’s capabilities and its efficacy as a law enforcement tool, this thesis will examine the application of criminal profiling to investigations, various court rulings concerning profiling’s admissibility, and the role that popular media plays in the perception and function of the practice. It will also discuss how future research and regulatory advancements may strengthen criminal profiling’s scientific merit and legitimacy.

This thesis explores the potential for software to act as an educational experience for engineers who are learning system dynamics and controls. The specific focus is a spring-mass-damper system. First, a brief introduction of the spring-mass-damper system is given, followed by a review of the background and prior work concerning this topic. Then, the methodology and main approaches of the system are explained, as well as a more technical overview of the program. Lastly, a conclusion and discussion of potential future work is covered. The project was found to be useful by several engineers who tested it. While there is still plenty of functionality to add, it is a promising first attempt at teaching engineers through software development.

Automated vehicles are becoming more prevalent in the modern world. Using platoons of automated vehicles can have numerous benefits including increasing the safety of drivers as well as streamlining roadway operations. How individual automated vehicles within a platoon react to each other is essential to creating an efficient method of travel. This paper looks at two individual vehicles forming a platoon and tracks the time headway between the two. Several speed profiles are explored for the following vehicle including a triangular and trapezoidal speed profile. It is discovered that a safety violation occurs during platoon formation where the desired time headway between the vehicles is violated. The aim of this research is to explore if this violation can be eliminated or reduced through utilization of different speed profiles.

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.

Self-efficacy in engineering, engineering identity, and coping in engineering have been shown in previous studies to be highly important in the advancement of one’s development in the field of engineering. Through the creation and deployment of a 17 question survey, undergraduate and first year masters students were asked to provide information on their engagement at their university, their demographic information, and to rank their level of agreement with 22 statements relating to the aforementioned ideas. Using the results from the collected data, exploratory factor analysis was completed to identify the factors that existed and any correlations. No statistically significant correlations between the identified three factors and demographic or engagement information were found. There needs to be a significant increase in the data sample size for statistically significant results to be found. Additionally, there is future work needed in the creation of an engagement measure that successfully reflects the level and impact of participation in engineering activities beyond traditional coursework.

Americans today face an age of information overload. With the evolution of Media 3.0, the internet, and the rise of Media 3.5—i.e., social media—relatively new communication technologies present pressing challenges for the First Amendment in American society. Twentieth century law defined freedom of expression, but in an information-limited world. By contrast, the twenty-first century is seeing the emergence of a world that is overloaded with information, largely shaped by an “unintentional press”—social media. Americans today rely on just a small concentration of private technology powerhouses exercising both economic and social influence over American society. This raises questions about censorship, access, and misinformation. While the First Amendment protects speech from government censorship only, First Amendment ideology is largely ingrained across American culture, including on social media. Technological advances arguably have made entry into the marketplace of ideas—a fundamental First Amendment doctrine—more accessible, but also more problematic for the average American, increasing his/her potential exposure to misinformation. <br/><br/>This thesis uses political and judicial frameworks to evaluate modern misinformation trends, social media platforms and current misinformation efforts, against the background of two misinformation accelerants in 2020, the COVID-19 pandemic and U.S. presidential election. Throughout history, times of hardship and intense fear have contributed to the shaping of First Amendment jurisprudence. Thus, this thesis looks at how fear can intensify the spread of misinformation and influence free speech values. Extensive research was conducted to provide the historical context behind relevant modern literature. This thesis then concludes with three solutions to misinformation that are supported by critical American free speech theory.

This research covers the landscape of influencer marketing and combines it with the knowledge of 11 content creators and one social media specialist, ultimately producing an actionable handbook. Participants were asked questions that were intended to discover key strategies, level of difficulty, and overall insight into the content creator world. Best practices and key findings are identified in the research paper, and outlined into four parts in the handbook. The handbook serves as a compilation framework derived from my primary and secondary sources designed to provide anyone interested in becoming a content creator or social media influencer on steps they may take given what their predecessors have done to successfully launch their careers in the space.