Human-environment interactions in aeolian (windblown) systems has focused research on<br/>human’s role in causing and aiding recovery from natural and anthropogenic disturbance. There<br/>is room for improvement in understanding the best methods and considerations for manual<br/>coastal foredune restoration. Furthermore, the extent to which humans play a role in changing the<br/>shape and surface textures of quartz sand grains is poorly understood. The goal of this thesis is<br/>two-fold: 1) quantify the geomorphic effectiveness of a multi-year manually rebuilt foredune and<br/>2) compare the shapes and microtextures on disturbed and undisturbed quartz sand grains. For<br/>the rebuilt foredune, uncrewed aerial systems (UAS) were used to survey the site, collecting<br/>photos to create digital surface models (DSMs). These DSMs were compared at discrete<br/>moments in time to create a sediment budget. Water levels and cross-shore modeling is also<br/>considered to predict the decadal evolution of the site. In the two years since rebuilding, the<br/>foredune has been stable, but not geomorphically resilient. Modeling shows landward foredune<br/>retreat and beach widening. For the quartz grains, t-testing of shape characteristics showed that<br/>there may be differences in the mean circularity between grains from off-highway vehicle and<br/>non-riding areas. Quartz grains from a variety of coastal and inland dunes were imaged using a<br/>scanning electron microscopy to search for evidence of anthropogenically-induced<br/>microtextures. On grains from Oceano Dunes in California, encouraging textures like parallel<br/>striations, grain fracturing, and linear conchoidal fractures provide exploratory evidence of<br/>anthropogenic microtextures. More focused research is recommended to confirm this exploratory<br/>work.

Motor learning is the process of improving task execution according to some measure of performance. This can be divided into skill learning, a model-free process, and adaptation, a model-based process. Prior studies have indicated that adaptation results from two complementary learning systems with parallel organization. This report attempted to answer the question of whether a similar interaction leads to savings, a model-free process that is described as faster relearning when experiencing something familiar. This was tested in a two-week reaching task conducted on a robotic arm capable of perturbing movements. The task was designed so that the two sessions differed in their history of errors. By measuring the change in the learning rate, the savings was determined at various points. The results showed that the history of errors successfully modulated savings. Thus, this supports the notion that the two complementary systems interact to develop savings. Additionally, this report was part of a larger study that will explore the organizational structure of the complementary systems as well as the neural basis of this motor learning.

Edge computing is a new and growing market that Company X has an opportunity to expand their presence. Within this paper, we compare many external research studies to better quantify the Total Addressable Market of the Edge Computing space. Furthermore, we highlight which Segments within Edge Computing have the most opportunities for growth, along with identify a specific market strategy that Company X could do to capture market share within the most opportunistic segment.

Over the years, advances in research have continued to decrease the size of computers from the size of<br/>a room to a small device that could fit in one’s palm. However, if an application does not require extensive<br/>computation power nor accessories such as a screen, the corresponding machine could be microscopic,<br/>only a few nanometers big. Researchers at MIT have successfully created Syncells, which are micro-<br/>scale robots with limited computation power and memory that can communicate locally to achieve<br/>complex collective tasks. In order to control these Syncells for a desired outcome, they must each run a<br/>simple distributed algorithm. As they are only capable of local communication, Syncells cannot receive<br/>commands from a control center, so their algorithms cannot be centralized. In this work, we created a<br/>distributed algorithm that each Syncell can execute so that the system of Syncells is able to find and<br/>converge to a specific target within the environment. The most direct applications of this problem are in<br/>medicine. Such a system could be used as a safer alternative to invasive surgery or could be used to treat<br/>internal bleeding or tumors. We tested and analyzed our algorithm through simulation and visualization<br/>in Python. Overall, our algorithm successfully caused the system of particles to converge on a specific<br/>target present within the environment.

This thesis project is part of a larger collaboration documenting the history of the ASU Biodesign Clinical Testing Laboratory (ABCTL). There are many different aspects that need to be considered when transforming to a clinical testing laboratory. This includes the different types of tests performed in the laboratory. In addition to the diagnostic polymerase chain reaction (PCR) test that is performed detecting the presence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), antibody testing is also performed in clinical laboratories. Antibody testing is used to detect a previous infection. Antibodies are produced as part of the immune response against SARS-CoV-2. There are many different forms of antibody tests and their sensitives and specificities have been examined and reviewed in the literature. Antibody testing can be used to determine the seroprevalence of the disease which can inform policy decisions regarding public health strategies. The results from antibody testing can also be used for creating new therapeutics like vaccines. The ABCTL recognizes the shifting need of the community to begin testing for previous infections of SARS-CoV-2 and is developing new forms of antibody testing that can meet them.

As the return to normality in the wake of the COVID-19 pandemic enters its early stages, the necessity for accurate, quick, and community-wide surveillance of SARS-CoV-2 has been emphasized. Wastewater-based epidemiology (WBE) has been used across the world as a tool for monitoring the pandemic, but studies of its efficacy in comparison to the best-known method for surveillance, randomly selected COVID-19 testing, has limited research. This study evaluated the trends and correlations present between SARS-CoV-2 in the effluent wastewater of a large university campus and random COVID-19 testing results published by the university. A moderately strong positive correlation was found between the random testing and WBE surveillance methods (r = 0.63), and this correlation was strengthened when accommodating for lost samples during the experiment (r = 0.74).

In this project, I examined the relationship between lockdowns implemented by COVID-19 and the activity of animals in urban areas. I hypothesized that animals became more active in urban areas during COVID-19 quarantine than they were before and I wanted to see if my hypothesis could be researched through Twitter crowdsourcing. I began by collecting tweets using python code, but upon examining all data output from code-based searches, I concluded that it is quicker and more efficient to use the advanced search on Twitter website. Based on my research, I can neither confirm nor deny if the appearance of wild animals is due to the COVID-19 lockdowns. However, I was able to discover a correlational relationship between these two factors in some research cases. Although my findings are mixed with regard to my original hypothesis, the impact that this phenomenon had on society cannot be denied.

As part of Arizona State University’s net-zero carbon initiative, 1000 mesquite trees were planted on a vacant plot of land at West Campus to sequester carbon from the atmosphere. Urban forestry is typically a method of carbon capture in temperate areas, but it is hypothesized that the same principle can be employed in arid regions as well. To test this hypothesis a carbon model was constructed using the pools and fluxes measured at the Carbon sink and learning forest at West Campus. As an ideal, another carbon model was constructed for the mature mesquite forest at the Hassayampa River Preserve to project how the carbon cycle at West Campus could change over time as the forest matures. The results indicate that the West Campus plot currently functions as a carbon source while the site at the Hassayampa river preserve currently functions as a carbon sink. Soil composition at both sites differ with inorganic carbon contributing to the largest percentage at West Campus, and organic carbon at Hassayampa. Predictive modeling using biomass accumulation estimates and photosynthesis rates for the Carbon Sink Forest at West Campus both predict approximately 290 metric tons of carbon sequestration after 30 years. Modeling net ecosystem exchange predicts that the West Campus plot will begin to act as a carbon sink after 33 years.

Cancer rates vary between people, between cultures, and between tissue types, driven by clinically relevant distinctions in the risk factors that lead to different cancer types. Despite the importance of cancer location in human health, little is known about tissue-specific cancers in non-human animals. We can gain significant insight into how evolutionary history has shaped mechanisms of cancer suppression by examining how life history traits impact cancer susceptibility across species. Here, we perform multi-level analysis to test how species-level life history strategies are associated with differences in neoplasia prevalence, and apply this to mammary neoplasia within mammals. We propose that the same patterns of cancer prevalence that have been reported across species will be maintained at the tissue-specific level. We used a combination of factor analysis and phylogenetic regression on 13 life history traits across 90 mammalian species to determine the correlation between a life history trait and how it relates to mammary neoplasia prevalence. The factor analysis presented ways to calculate quantifiable underlying factors that contribute to covariance of entangled life history variables. A greater risk of mammary neoplasia was found to be correlated most significantly with shorter gestation length. With this analysis, a framework is provided for how different life history modalities can influence cancer vulnerability. Additionally, statistical methods developed for this project present a framework for future comparative oncology studies and have the potential for many diverse applications.