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.

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.

Glioblastoma (GB) is one of the deadliest cancers and the most common form of adult primary brain tumors. SGEF (ARHGEF26) has been previously shown to be overexpressed in GB tumors, play a role in cell invasion/migration, and increase temozolomide (TMZ) resistance.[3] It was hypothesized parental LN229 cell lines with SGEF knockdown (LN229-SGEFi) will show decreased metabolism in the MTS assay and decreased colony formation in a colony formation assay compared to parental LN229 cells after challenging the two cell lines with TMZ. For WB and co-immunoprecipitation (co-IP), parental LN229 cells with endogenous SGEF and BRCA were expected to interact and stain in the BRCA1:IP WB. LN229-SGEFi cells were expected to show very little SGEF precipitated due to shRNA targeted knockdown of SGEF. In conditions with mutations in the BRCA1 binding site (LN229-SGEFi + AdBRCAm/AdDM), SGEF expression was expected to decrease compared to parental LN229 or LN229-SGEFi cells reconstituted with WT SGEF (LN229-SGEFi + AdWT). LN229 infected with AdSGEF with a mutated nuclear localization signal (LN229-SGEFi + AdNLS12m) were expected to show BRCA and SGEF interaction since whole cell lysates were used for the co-IP. MTS data showed no significant differences in metabolism between the two cell lines at all three time points (3, 5, and 7 days). Western blot analysis was successful at imaging both SGEF and BRCA1 protein bands from whole cell lysate. The CFA showed no significant difference between cell lines after being challenged with 500uM TMZ. The co-IP immunoblot showed staining for BRCA1 and SGEF for all lysate samples, including unexpected lysates such as LN229-SGEFi, LN229-SGEFi + AdBRCAm, and LN229-SGEFi + AdDM. These results suggested either an indirect protein interaction between BRCA1 and SGEF, an additional BRCA binding site not included in the consensus, or possible detection of the translocated SGEF in knockdown cells lines since shRNA cannot enter the nucleus. Further optimization of CO-IP protocol, MTS assay, and CFA will be needed to characterize the SGEF/BRCA1 interaction and its role in cell survival.

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.

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.

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.

The goal of this project was to design and create a genetic construct that would allow for <br/>tumor growth to be induced in the center of the wing imaginal disc of Drosophila larvae, the <br/>R85E08 domain, using a heat shock. The resulting transgene would be combined with other <br/>transgenes in a single fly that would allow for simultaneous expression of the oncogene and, in <br/>the surrounding cells, other genes of interest. This system would help establish Drosophila as a <br/>more versatile and reliable model organism for cancer research. Furthermore, pilot studies were <br/>performed, using elements of the final proposed system, to determine if tumor growth is possible <br/>in the center of the disc, which oncogene produces the best results, and if oncogene expression <br/>induced later in development causes tumor growth. Three different candidate genes were <br/>investigated: RasV12, PvrACT, and Avli.

The colossal global counterfeit market and advances in cryptography including quantum computing supremacy have led the drive for a class of anti-counterfeit tags that are physically unclonable. Dendrites, previously considered an undesirable side effect of battery operation, have promise as an extremely versatile version of such tags, with their fundamental nature ensuring that no two dendrites are alike and that they can be read at multiple magnification scales. In this work, we first pursue a simulation for electrochemical dendrites that elucidates fundamental information about their growth mechanism. We then translate these results into physical dendrites and demonstrate methods of producing a hash from these dendrites that is damage-tolerant for real-world verification. Finally, we explore theoretical curiosities that arise from the fractal nature of dendrites. We find that uniquely ramified dendrites, which rely on lower ion mobility and conductive deposition, are particularly amenable to wavelet hashing, and demonstrate that these dendrites have strong commercial potential for securing supply chains at the highest level while maintaining a low price point.

Time studies are an effective tool to analyze current production systems and propose improvements. The problem that motivated the project was that conducting time studies and observing the progression of components across the factory floor is a manual process. Four Industrial Engineering students worked with a manufacturing company to develop Computer Vision technology that would automate the data collection process for time studies. The team worked in an Agile environment to complete over 120 classification sets, create 8 strategy documents, and utilize Root Cause Analysis techniques to audit and validate the performance of the trained Computer Vision data models. In the future, there is an opportunity to continue developing this product and expand the team’s work scope to apply more engineering skills on the data collected to drive factory improvements.