One obstacle which children with autism spectrum disorders (ASDs) face when learning in a public-school environment is the lack of feeling included when learning. In this study, the term inclusion refers to time that children with ASDs spend in general education settings, interacting and/or engaging with neurotypical students and teachers. Inclusion can help students with ASDs improve their social skills, as well as academic achievement, mental health, and future success (Camargo et al., 2014). Since children with ASDs often have difficulties with social interaction skills, this can prevent their successful inclusion in general education placements. Music is a type of behaviorally-based intervention, which has proven to be effective in helping students develop the skills necessary to be successfully included, and because it is a type of activity which can serve as a bit of a distraction from the social aspect of the interaction, it can help children practice social skills and interact in a comfortable way. This study examines how music is used in public school settings to help foster the skills necessary for autistic children to be involved in standard school curriculums in order to allow them to receive the full benefits from learning in a general education setting. This study was conducted by reviewing past literature on the benefits of inclusion in special education, the benefits of music for children with ASDs, and the difference in efficacy of music interventions when conducted in an inclusive setting. Interviews with special education teachers, music educators, and music therapists were also conducted to address examples of the impact of music in this research area. The study found that music is beneficial in allowing more students to be included in standard school curriculums, and data showed the trend that inclusion positively affected their social and academic development.

In the past year, considerable misinformation about the COVID-19 pandemic has circulated on social media platforms. Faced with this pervasive issue, it is important to identify the extent to which people are able to spot misinformation on social media and ways to improve people’s accuracy in spotting misinformation. Therefore, the current study aims to investigate people’s accuracy in spotting misinformation, the effectiveness of a game-based intervention, and the role of political affiliation in spotting misinformation. In this study, 235 participants played a misinformation game in which they evaluated COVID-19-related tweets and indicated whether or not they thought each of the tweets contained misinformation. Misinformation accuracy was measured using game scores, which were based on the correct identification of misinformation. Findings revealed that participants’ beliefs about how accurate they are at spotting misinformation about COVID-19 did not predict their actual accuracy. Participants’ accuracy improved after playing the game, but democrats were more likely to improve than republicans.

System and software verification is a vital component in the development and reliability of cyber-physical systems - especially in critical domains where the margin of error is minimal. In the case of autonomous driving systems (ADS), the vision perception subsystem is a necessity to ensure correct maneuvering of the environment and identification of objects. The challenge posed in perception systems involves verifying the accuracy and rigidity of detections. The use of Spatio-Temporal Perception Logic (STPL) enables the user to express requirements for the perception system to verify, validate, and ensure its behavior; however, a drawback to STPL involves its accessibility. It is limited to individuals with an expert or higher-level knowledge of temporal and spatial logics, and the formal-written requirements become quite verbose with more restrictions imposed. In this thesis, I propose a domain-specific language (DSL) catered to Spatio-Temporal Perception Logic to enable non-expert users the ability to capture requirements for perception subsystems while reducing the necessity to have an experienced background in said logic. The domain-specific language for the Spatio-Temporal Perception Logic is built upon the formal language with two abstractions. The main abstraction captures simple programming statements that are translated to a lower-level STPL expression accepted by the testing monitor. The STPL DSL provides a seamless interface to writing formal expressions while maintaining the power and expressiveness of STPL. These translated equivalent expressions are capable of directing a standard for perception systems to ensure the safety and reduce the risks involved in ill-formed detections.

As much as SARS-CoV-2 has altered the way humans live since the beginning of 2020,<br/>this virus's deadly nature has required clinical testing to meet 2020's demands of higher<br/>throughput, higher accuracy and higher efficiency. Information technology has allowed<br/>institutions, like Arizona State University (ASU), to make strategic and operational changes to<br/>combat the SARS-CoV-2 pandemic. At ASU, information technology was one of the six facets<br/>identified in the ongoing review of the ASU Biodesign Clinical Testing Laboratory (ABCTL)<br/>among business, communications, management/training, law, and clinical analysis. The first<br/>chapter of this manuscript covers the background of clinical laboratory automation and details<br/>the automated laboratory workflow to perform ABCTL’s COVID-19 diagnostic testing. The<br/>second chapter discusses the usability and efficiency of key information technology systems of<br/>the ABCTL. The third chapter explains the role of quality control and data management within<br/>ABCTL’s use of information technology. The fourth chapter highlights the importance of data<br/>modeling and 10 best practices when responding to future public health emergencies.

This project seeks to motivate runners by creating an application that selectively plays music based on smartwatch metrics. This is done by analyzing metrics collected through a person’s smartwatch such as heart rate or running power and then selecting the music that best fits their workout’s intensity. This way, as the workout becomes harder for the user, increasingly motivating music is played.