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.

This thesis attempts to explain Everettian quantum mechanics from the ground up, such that those with little to no experience in quantum physics can understand it. First, we introduce the history of quantum theory, and some concepts that make up the framework of quantum physics. Through these concepts, we reveal why interpretations are necessary to map the quantum world onto our classical world. We then introduce the Copenhagen interpretation, and how many-worlds differs from it. From there, we dive into the concepts of entanglement and decoherence, explaining how worlds branch in an Everettian universe, and how an Everettian universe can appear as our classical observed world. From there, we attempt to answer common questions about many-worlds and discuss whether there are philosophical ramifications to believing such a theory. Finally, we look at whether the many-worlds interpretation can be proven, and why one might choose to believe it.

The purpose of this paper is to provide an analysis of entanglement and the particular problems it poses for some physicists. In addition to looking at the history of entanglement and non-locality, this paper will use the Bell Test as a means for demonstrating how entanglement works, which measures the behavior of electrons whose combined internal angular momentum is zero. This paper will go over Dr. Bell's famous inequality, which shows why the process of entanglement cannot be explained by traditional means of local processes. Entanglement will be viewed initially through the Copenhagen Interpretation, but this paper will also look at two particular models of quantum mechanics, de-Broglie Bohm theory and Everett's Many-Worlds Interpretation, and observe how they explain the behavior of spin and entangled particles compared to the Copenhagen Interpretation.

Breast cancer is one of the most common types of cancer worldwide. Early detection and diagnosis are crucial for improving the chances of successful treatment and survival. In this thesis, many different machine learning algorithms were evaluated and compared to predict breast cancer malignancy from diagnostic features extracted from digitized images of breast tissue samples, called fine-needle aspirates. Breast cancer diagnosis typically involves a combination of mammography, ultrasound, and biopsy. However, machine learning algorithms can assist in the detection and diagnosis of breast cancer by analyzing large amounts of data and identifying patterns that may not be discernible to the human eye. By using these algorithms, healthcare professionals can potentially detect breast cancer at an earlier stage, leading to more effective treatment and better patient outcomes. The results showed that the gradient boosting classifier performed the best, achieving an accuracy of 96% on the test set. This indicates that this algorithm can be a useful tool for healthcare professionals in the early detection and diagnosis of breast cancer, potentially leading to improved patient outcomes.