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- Genre: Masters Thesis
Description
A novel technique for measuring heavy trace elements in geologic materials with secondary ion mass spectrometry (SIMS) is presented. This technique combines moderate levels of mass resolving power (MRP) with energy filtering in order to remove molecular ion interferences while maintaining enough sensitivity to measure trace elements. The technique was evaluated by measuring a set of heavy chalcophilic elements in two sets of doped glasses similar in composition to rhyolites and basalts, respectively. The normalized count rates of Cu, As, Se, Br, and Te were plotted against concentrations to test that the signal increased linearly with concentration. The signal from any residual molecular ion interferences (e.g. ²⁹Si³⁰Si¹⁶O on ⁷⁵As) represented apparent concentrations ≤ 1 μg/g for most of the chalcophiles in rhyolitic matrices and between 1 and 10 μg/g in basaltic compositions. This technique was then applied to two suites of melt inclusions from the Bandelier Tuff: Ti-rich, primitive and Ti-poor, evolved rhyolitic compositions. The results showed that Ti-rich inclusions contained ~30 μg/g Cu and ~3 μg/g As while the Ti-poor inclusions contained near background Cu and ~6 μg/g As. Additionally, two of the Ti-rich inclusions contained > 5 μg/g of Sb and Te, well above background. Other elements were at or near background. This suggests certain chalcophilic elements may be helpful in unraveling processes relating to diversity of magma sources in large eruptions. Additionally, an unrelated experiment is presented demonstrating changes in the matrix effect on SIMS counts when normalizing against ³⁰Si⁺ versus ²⁸Si²⁺. If one uses doubly charged silicon as a reference, (common when using large-geometry SIMS instruments to study the light elements Li - C) it is important that the standards closely match the major element chemistry of the unknown.
ContributorsCarlson, Eric Norton (Author) / Hervig, Richard L (Thesis advisor) / Roggensack, Kurt (Committee member) / Burt, Donald M (Committee member) / Arizona State University (Publisher)
Created2021
Description
Due to the large scale of power systems, latency uncertainty in communication can cause severe problems in wide-area measurement systems. To resolve the issue, a significant amount of past work focuses on using emerging technologywhich is machine learning methods such as Q-learning to address latency issues in modern controls. Although such a method can deal with the stochastic characteristics of communication latency in the long run, the Q-learning methods tend to overestimate Q-values, leading to high bias. To solve the overestimation bias issue, the learning structure is redesigned with a twin-delayed deep deterministic policy gradient algorithm to handle the damping control issue under unknown latency in the power network. Meanwhile, a new reward function is proposed, taking into account the machine speed deviation, the episode termination prevention, and the feedback from action space. In this way, the system optimally damps down frequency oscillations while maintaining the system’s stability and reliable operation within defined limits. The simulation results verify the proposed algorithm in various perspectives including the latency sensitivity analysis under high renewable energy penetration and the comparison with other machine learning algorithms.
For example, if the proposed twin-delayed deep deterministic policy gradient algorithm is applied, the low-frequency oscillation significantly improved compared to existing algorithms.
Furthermore, under the mentorship of Dr. Yang Weng, the development of a big data analysis software project has been collaborating with the Salt River Project (SRP), a major power utility in Arizona. After a thorough examination of data for
the project, it is examined that SRP is suffering from a lot of smart meters data issues. An important goal of the project is to design big data software to monitor SRP smart meter data and to present indicators of abnormalities and special
events. Currently, the big data software interface has been developed for SRP, which has already been successfully adopted by other utilities, research institutes, and laboratories as well.
ContributorsKim, Gyoungjae (Author) / Weng, Yang (Thesis advisor) / Wu, Meng (Committee member) / Zhao, Yunpeng (Committee member) / Arizona State University (Publisher)
Created2021
Description
Artificial intelligence is one of the leading technologies that mimics the problem solving and decision making capabilities of the human brain. Machine learning algorithms, especially deep learning algorithms, are leading the way in terms of performance and robustness. They are used for various purposes, mainly for computer vision, speech recognition, and object detection. The algorithms are usually tested inaccuracy, and they utilize full floating-point precision (32 bits). The hardware would require a high amount of power and area to accommodate many parameters with full precision. In this exploratory work, the convolution autoencoder is quantized for the working of an event base camera. The model is designed so that the autoencoder can work on-chip, which would sufficiently decrease the latency in processing. Different quantization methods are used to quantize and binarize the weights and activations of this neural network model to be portable and power efficient. The sparsity term is added to make the model as robust and energy-efficient as possible. The network model was able to recoup the lost accuracy due to binarizing the weights and activation's to quantize the layers of the encoder selectively. This method of recouping the accuracy gives enough flexibility to introduce the network on the chip to get real-time processing from systems like event-based cameras. Lately, computer vision, especially object detection have made strides in their object detection accuracy. The algorithms can sufficiently detect and predict the objects in real-time. However, end-to-end detection of the algorithm is challenging due to the large parameter need and processing requirements. A change in the Non Maximum Suppression algorithm in SSD(Single Shot Detector)-Mobilenet-V1 resulted in less computational complexity without change in the quality of output metric. The Mean Average Precision(mAP) calculated suggests that this method can be implemented in the post-processing of other networks.
ContributorsKuzhively, Ajay Balu (Author) / Cao, Yu (Thesis advisor) / Seo, Jae-Sun (Committee member) / Fan, Delian (Committee member) / Arizona State University (Publisher)
Created2021
Description
Automation has become a staple in high volume manufacturing, where the consistency and quality of a product carries as much importance as the quantity produced. The Aerospace Industry has a vested interest in expanding the application of automation beyond simply manufacturing. In this project, the process of systems engineering has been applied to the Conceptual Design Phase of product development; specifically, the Preliminary Structural Design of a Composite wing for an Unmanned Air Vehicle (UAV). Automated structural analysis can be used to develop a composite wing structure that can be directly rendered in Computer Aided Drafting (CAD) and validated using Finite Element Analysis (FEA). This concept provides the user with the ability to quickly iterate designs and demonstrates how different the “optimal light weight” composite structure must look for UAV systems of varied weight, range, and flight maneuverability.
ContributorsBlair, Martin Caceres (Author) / Takahashi, Timothy (Thesis advisor) / Murthy, Raghavendra (Committee member) / Perez, Ruben (Committee member) / Arizona State University (Publisher)
Created2021
Description
The purpose of this research was to investigate the effect of the type of crime (namely, its perceived immorality) a juvenile is suspected of on how juvenile suspects are perceived (in terms of moral character, immaturity, and suggestibility) and, in turn, interrogated. I expected act-person dissociation to influence that effect. To that end, perceptions of crime (i.e., immorality, seriousness) were also investigated. The study was first conducted with law enforcement officers (n = 55), then replicated with laypeople (n = 171). Participants were randomly assigned to one of three crime conditions: robbery, sexual assault, and murder. In each condition, participants read a probable cause statement involving a 15-year-old suspect. There were several key findings: (1) Murder was the most serious crime, whereas robbery and sexual assault were more immoral. (2) Act-person dissociation did not occur. (3) Participants were more likely to endorse the use of psychologically coercive tactics on the juvenile suspected of sexual assault than the juvenile suspected of murder. (4) The more favorably participants perceived a juvenile’s moral character, the less likely they were to endorse the use of psychologically coercive interrogation tactics. (4) Participants who more strongly agreed that juveniles are more immature and suggestible than adults were less likely to endorse the use of psychologically coercive tactics, more likely to endorse the use of tactics that encourage compliance with interrogators, and more likely to adhere to the PEACE model of juvenile interrogations. The implications and limitations of these findings are discussed, along with potential directions for future research.
ContributorsFaison, Lakia (Author) / Mickelson, Kristin (Thesis advisor) / Smalarz, Laura (Committee member) / Salerno, Jessica (Committee member) / Arizona State University (Publisher)
Created2021
Assessing the Response of Macroinvertebrate Communities to River Flow Dynamics in the Sonoran Desert
Description
Climate change is causing hydrologic intensification globally by increasing both the frequency and magnitude of floods and droughts. While environmental variation is a key regulator at all levels of ecological organization, such changes to the hydrological cycle that are beyond the normal range of variability can have strong impacts on stream and riparian ecosystems within sensitive landscapes, such as the American Southwest. The main objective of this study was to investigate how anomalous hydrologic variability influences macroinvertebrate communities in desert streams. I studied seasonal changes in aquatic macroinvertebrate abundances in eleven streams that encompass a hydrologic gradient across Arizona’s Sonoran Desert. This analysis was coupled with the quantification and assessment of stochastic hydrology to determine influences of flow regimes and discrete events on invertebrate community composition. I found high community variability within sites, illustrated by seasonal measures of beta diversity and nonmetric multidimensional scaling (NMDS) plots. I observed notable patterns of NMDS data points when invertebrate abundances were summarized by summer versus winter surveys. These results suggest that there is a difference within the communities between summer and winter seasons, irrespective of differences in site hydroclimate. Estimates of beta diversity were the best metric for summarizing and comparing diversity among sites, compared to richness difference and replacement. Seasonal measures of beta diversity either increased, decreased, or stayed constant across the study period, further demonstrating the high variation within and among study sites. Regime shifts, summarized by regime shift frequency (RSF) and mean net annual anomaly (NAA), and anomalous events, summarized by the power of blue noise (Maximum Blue Noise), were the best predictors of macroinvertebrate diversity, and thus should be more widely applied to ecological data. These results suggest that future studies of community composition in freshwater systems should focus on understanding the cause of variation in biodiversity gradients. This study highlights the importance of considering both flow regimes and discrete anomalous events when studying spatial and temporal variation in stream communities.
ContributorsSainz, Ruby (Author) / Sabo, John L (Thesis advisor) / Grimm, Nancy (Committee member) / Stampoulis, Dimitrios (Committee member) / Arizona State University (Publisher)
Created2021
Description
Human impact alters the natural environment via multiple pathways, including contamination from pollutants. This human activity may adversely impact an organism’s ability to respond to environmental change. Using Bisphenol-A (BPA), a common environmental contaminant, I examined how exposure affected behavioral strategies critical for survival in a changing environment. BPA is used during plastic manufacturing, and it enters aquatic systems from wastewater streams; however, it is an endocrine-disruptor that has broad health effects from metabolism to behavior at a wide exposure range. In this study, I specifically tested whether environmentally relevant concentrations of BPA impact maximum metabolic rate and boldness in zebrafish, Danio rerio. I also examined activity level, optomotor response, body mass, and standard length to see if I can mechanistically explain any underlying changes caused by BPA. I treated groups of adult zebrafish for 7 days and exposed them to either 0.1% dimethyl sulfoxide (DMSO, control), a low environmentally relevant concentration of BPA (0.02 mg/L), or a 1-fold higher BPA concentration (0.2 mg/L). I found that the low exposure group experienced a decrease in maximum metabolic rate and the high exposure group showed a decrease in boldness. In other words, these changes in metabolism were not dosage dependent while the boldness results were dosage dependent. BPA had no effects on optomotor response, body mass, standard length or activity level. These results suggest that no level of BPA is safe, environmentally relevant concentrations are having an effect on adult organisms’ behavior and health that could affect their survival.
ContributorsLopez, Melissa (Author) / Martins, Emilia P (Thesis advisor) / Suriyampola, Piyumika S (Thesis advisor) / Conroy-Ben, Otakuye (Committee member) / Arizona State University (Publisher)
Created2021
Description
Programmed cell death plays an important role in a variety of processes that promote the survival of the host organism. Necroptosis, a form of programmed cell death, occurs through a signaling pathway involving receptor-interacting serine-threonine protein kinase 3 (RIPK3). In response to vaccinia virus infection, necroptosis is induced through DNA-induced activator of interferon (DAI), which activates RIPK3, leading to death of the cell and thereby inhibiting further viral replication in host cells. DAI also localizes into stress granules, accumulations of mRNAs that have stalled in translation due to cellular stress. The toxin arsenite, a canonical inducer of stress granule formation, was used in this project to study necroptosis. By initiating necroptosis with arsenite and vaccinia virus, this research project investigated the roles of necroptosis proteins and their potential localization into stress granules. The two aims of this research project were to determine whether stress granules are important for arsenite- and virus-induced necroptosis, and whether the proteins DAI and RIPK3 localize into stress granules. The first aim was investigated by establishing a DAI and RIPK3 expression system in U2OS cells; arsenite treatment or vaccinia virus infection was then performed on the U2OS cells as well as on U2OSΔΔG3BP1/2 cells, which are not able to form stress granules. The second aim was carried out by designing fluorescent tagging for the necroptosis proteins in order to visualize protein localization with fluorescent microscopy. The results show that arsenite induces DAI-dependent necroptosis in U2OS cells and that this arsenite-induced necroptosis likely requires stress granules. In addition, the results show that vaccinia virus induces DAI-dependent necroptosis that also likely requires stress granules in U2OS cells. Furthermore, a fluorescent RIPK3 construct was created that will allowfor future studies on protein localization during necroptosis and can be used to answer questions regarding localization of necroptosis proteins into stress granules. This project therefore contributes to a greater understanding of the roles of DAI and RIPK3 in necroptosis, as well as the roles of stress granules in necroptosis, both of which are important in research regarding viral infection and cellular stress.
ContributorsGogerty, Carolina (Author) / Jacobs, Bertram (Thesis advisor) / Langland, Jeffrey (Committee member) / Jentarra, Garilyn (Committee member) / Arizona State University (Publisher)
Created2021
Description
In a pursuit-evasion setup where one group of agents tracks down another adversarial group, vision-based algorithms have been known to make use of techniques such as Linear Dynamic Estimation to determine the probable future location of an evader in a given environment. This helps a pursuer attain an edge over the evader that has conventionally benefited from the uncertainty of the pursuit. The pursuer can utilize this knowledge to enable a faster capture of the evader, as opposed to a pursuer that only knows the evader's current location. Inspired by the function of dorsal anterior cingulate cortex (dACC) neurons in natural predators, the use of a predictive model that is built using an encoder-decoder Long Short-Term Memory (LSTM) Network and can produce a more accurate estimate of the evader's future location is proposed. This enables an even quicker capture of a target when compared to previously used filtering-based methods. The effectiveness of the approach is evaluated by setting up these agents in an environment based in the Modular Open Robots Simulation Engine (MORSE). Cross-domain adaptability of the method, without the explicit need to retrain the prediction model is demonstrated by evaluating it in another domain.
ContributorsGodbole, Sumedh (Author) / Yang, Yezhou (Thesis advisor) / Srivastava, Siddharth (Committee member) / Zhang, Wenlong (Committee member) / Arizona State University (Publisher)
Created2021
Description
The design and development process of high-lift systems for commercial transport aircraft has been historically heavily dependent on extensive experimental testing. Whether this testing be in wind tunnels or during aircraft testing, the number and extent of high-lift system variations that can be tested are limited. With technology advancements, analyzing the complex flow around high lift systems using detailed computational fluid dynamics (CFD) has become more common; but, CFD has limitations due to the computational costs for such analysis. An empirical approach can be taken to analyze such systems, but the insight gained from such methods is often limited to a main contributing factor. While these methods often produce reasonable solutions, they fail in showing, and many times overshadow, the important minor effects within complex systems.
This thesis aims to present insight on the need and design of multi-element high-lift systems by using a tool developed which utilizes a legacy vortex lattice potential flow code and methods described in classical aerodynamic literature. With this tool, numerous variations of high lift devices were studied to understand why commercial transport aircraft require a high-lift system. Furthermore, variations of complete high-lift systems were also studied to understand why certain design decisions were made on existing commercial transport aircraft. Ultimately, enough insight was obtained to proceed to design a functioning high-lift system for a commercial transport aircraft capable of meeting all established requirements and exhibit favorable flow separation conditions.
ContributorsMartinez Rodriguez, Gabino (Author) / Takahashi, Timothy (Thesis advisor) / Herrmann, Marcus (Committee member) / Sobester, Andras (Committee member) / Arizona State University (Publisher)
Created2021