Matching Items (104)
Description
Structural Magnetic Resonance Imaging analysis is a vital component in the study of Alzheimer’s Disease pathology and several techniques exist as part of the existing research conducted. In particular, volumetric approaches in this field are known to be beneficial due to the increased capability to express morphological characteristics when compared to manifold methods. To aid in the improvement of the field, this paper aims to propose an intrinsic volumetric conic system that can be applied to bounded volumetric meshes to enable a more effective study of subjects. The computation of the metric involves the use of heat kernel theory and conformal parameterization on genus-0 surfaces extended to a volumetric domain. Additionally, this paper also explores the use of the ’TetCNN’ architecture on the classification of hippocampal tetrahedral meshes to detect features that correspond to Alzheimer’s indicators. The model tested was able to achieve remarkable results with a measured classification accuracy of above 90% in the task of differentiating between subjects diagnosed with Alzheimer’s and normal control subjects.
ContributorsGeorge, John Varghese (Author) / Wang, Yalin (Thesis advisor) / Hansford, Dianne (Committee member) / Gupta, Vikash (Committee member) / Arizona State University (Publisher)
Created2023
Description
Dissolved inorganic carbon (DIC) and dissolved organic carbon (DOC) are crucial nutrients for autotrophic and heterotrophic microbial life, respectively, in hydrothermal systems. Biogeochemical processes that control amounts of DIC and DOC in Yellowstone hot springs can be investigated by measuring carbon abundances and respective isotopic values. A decade and a half of field work in 10 regions within Yellowstone National Park and subsequent geochemical lab analyses reveal that sulfate-dominant acidic regions have high DOC (Up to 57 ppm C) and lower DIC (up to 50 ppm C) compared to neutral-chloride regions with low DOC (< 2 ppm C) and higher DIC (up to 100 ppm C). Abundances and isotopic data suggest that sedimentary rock erosion by acidic hydrothermal fluids, fresh snow-derived meteoric water, and exogenous carbon input allowed by local topography may affect DOC levels. Evaluating the isotopic compositions of DIC and DOC in hydrothermal fluids gives insight on the geology and microbial life in the subsurface between different regions. DIC δ13C values range from -4‰ to +5‰ at pH 5-9 and from -10‰ to +3‰ at pH 2-5 with several springs lower than -10‰. DOC δ13C values parkwide range from -10‰ to -30‰. Within this range, neutral-chloride regions in the Lower Geyser Basin have lighter isotopes than sulfate-dominant acidic regions. In hot springs with elevated levels of DOC, the range only varies between -20‰ and -26‰ which may be caused by local exogenous organic matter runoff. Combining other geochemical measurements, such as differences in chloride and sulfate concentrations, demonstrates that some regions contain mixtures of multiple fluids moving through the complex hydrological system in the subsurface. The mixing of these fluids may account for increased levels of DOC in meteoric sulfate-dominant acidic regions. Ultimately, the foundational values of dissolved carbon and their isotopic composition is provided in a parkwide study, so results can be combined with future studies that apply different sequencing analyses to understand specific biogeochemical cycling and microbial communities that occur in individual hot springs.
ContributorsBarnes, Tanner (Author) / Shock, Everett (Thesis advisor) / Meyer-Dombard, D'Arcy (Committee member) / Hartnett, Hilairy (Committee member) / Arizona State University (Publisher)
Created2023
Description
As air quality standards become more stringent to combat poor air quality, there is a greater need for more effective pollutant control measures and increased air monitoring network coverage. Polluted air, in the form of aerosols and gases, can impact respiratory and cardiovascular health, visibility, the climate, and material weathering. This work demonstrates how traditional networks can be used to study generational events, how these networks can be supplemented with low-cost sensors, and the effectiveness of several control measures. First, an existing network was used to study the effect of COVID-19 travel restrictions on air quality in Maricopa County, Arizona, which would not have been possible without the historical record that a traditional network provides. Although this study determined that decreases in CO and NO2 were not unique to the travel restrictions, it was limited to only three locations due to network sparseness. The second part of this work expanded the traditional NO2 monitoring network using low-cost sensors, that were first collocated with a reference monitor to evaluate their performance and establish a robust calibration. The sensors were then deployed to the field to varying results; their calibration was further improved by cycling the sensors between deployment and reference locations throughout the summer. This calibrated NO2 data, along with volatile organic compound data, were combined to enhance the understanding of ozone formation in Maricopa County, especially during wildfire season. In addition to being in non-attainment for ozone standards, Maricopa County fails to meet particulate matter under 10 μm (PM10) standards. A large portion of PM10 emissions is attributed to fugitive dust that is either windblown or kicked up by vehicles. The third part of this work demonstrated that Enzyme Induced Carbonate Precipitation (EICP) treatments aggregate soil particles and prevent fugitive dust emissions. The final part of the work examined tire wear PM10 emissions, as vehicles are another significant contributor to PM10. Observations showed a decrease in tire wear PM10 during winter with little change when varying the highway surface type.
ContributorsMiech, Jason Andrew (Author) / Herckes, Pierre (Thesis advisor) / Fraser, Matthew P (Committee member) / Shock, Everett (Committee member) / Arizona State University (Publisher)
Created2023
Description
The prevalence and unique properties of airborne nanoparticles have raised concerns regarding their potential adverse health effects. Despite their significance, the understanding of nanoparticle generation, transport, and exposure remains incomplete. This study first aimed to assess nanoparticle exposure in indoor workplace environments, in the semiconductor manufacturing industry. On-site observations during tool preventive maintenance revealed a significant release of particles smaller than 30 nm, which subsequent instrumental analysis confirmed as predominantly composed of transition metals. Although the measured mass concentration levels did not exceed current federal limits, it prompted concerns regarding how well filter-based air sampling methods would capture the particles for exposure assessment and how well common personal protective equipment would protect from exposure. To address these concerns, this study evaluated the capture efficiency of filters and masks. When challenged by aerosolized engineered nanomaterials, common filters used in industrial hygiene sampling exhibited capture efficiencies of over 60%. Filtering Facepiece Respirators, such as the N95 mask, exhibited a capture efficiency of over 98%. In contrast, simple surgical masks showed a capture efficiency of approximately 70%. The experiments showed that face velocity and ambient humidity influence capture performance and mostly identified the critical role of mask and particle surface charge in capturing nanoparticles. Masks with higher surface potential exhibited higher capture efficiency towards nanoparticles. Eliminating their surface charge resulted in a significantly diminished capture efficiency, up to 43%. Finally, this study characterized outdoor nanoparticle concentrations in the Phoenix metropolitan area, revealing typical concentrations on the order of 10^4 #/cm3 consistent with other urban environments. During the North American monsoon season, in dust storms, with elevated number concentrations of large particles, particularly in the size range of 1-10 μm, the number concentration of nanoparticles in the size range of 30-100 nm was substantially lower by approximately 55%. These findings provide valuable insights for future assessments of nanoparticle exposure risks and filter capture mechanisms associated with airborne nanoparticles.
ContributorsZhang, Zhaobo (Author) / Herckes, Pierre (Thesis advisor) / Westerhoff, Paul (Committee member) / Shock, Everett (Committee member) / Fraser, Matthew (Committee member) / Arizona State University (Publisher)
Created2023
Description
Communicating with computers through thought has been a remarkable achievement in recent years. This was made possible by the use of Electroencephalography (EEG). Brain-computer interface (BCI) relies heavily on Electroencephalography (EEG) signals for communication between humans and computers. With the advent ofdeep learning, many studies recently applied these techniques to EEG data to perform various tasks like emotion recognition, motor imagery classification, sleep analysis, and many more.
Despite the rise of interest in EEG signal classification, very few studies have explored the MindBigData dataset, which collects EEG signals recorded at the stimulus of seeing a digit and thinking about it. This dataset takes us closer to realizing the idea of mind-reading or communication via thought. Thus classifying these signals into the respective digit that the user thinks about is a challenging task. This serves as a motivation to study this dataset and apply existing deep learning techniques to study it.
Given the recent success of transformer architecture in different domains like Computer Vision and Natural language processing, this thesis studies transformer architecture for EEG signal classification. Also, it explores other deep learning
techniques for the same. As a result, the proposed classification pipeline achieves comparable performance with the existing methods.
ContributorsMuglikar, Omkar Dushyant (Author) / Wang, Yalin (Thesis advisor) / Liang, Jianming (Committee member) / Venkateswara, Hemanth (Committee member) / Arizona State University (Publisher)
Created2021
Description
Statistical Shape Modeling is widely used to study the morphometrics of deformable objects in computer vision and biomedical studies. There are mainly two viewpoints to understand the shapes. On one hand, the outer surface of the shape can be taken as a two-dimensional embedding in space. On the other hand, the outer surface along with its enclosed internal volume can be taken as a three-dimensional embedding of interests. Most studies focus on the surface-based perspective by leveraging the intrinsic features on the tangent plane. But a two-dimensional model may fail to fully represent the realistic properties of shapes with both intrinsic and extrinsic properties. In this thesis, severalStochastic Partial Differential Equations (SPDEs) are thoroughly investigated and several methods are originated from these SPDEs to try to solve the problem of both two-dimensional and three-dimensional shape analyses. The unique physical meanings of these SPDEs inspired the findings of features, shape descriptors, metrics, and kernels in this series of works. Initially, the data generation of high-dimensional shapes, here, the tetrahedral meshes, is introduced. The cerebral cortex is taken as the study target and an automatic pipeline of generating the gray matter tetrahedral mesh is introduced. Then, a discretized Laplace-Beltrami operator (LBO) and a Hamiltonian operator (HO) in tetrahedral domain with Finite Element Method (FEM) are derived. Two high-dimensional shape descriptors are defined based on the solution of the heat equation and Schrödinger’s equation. Considering the fact that high-dimensional shape models usually contain massive redundancies, and the demands on effective landmarks in many applications, a Gaussian process landmarking on tetrahedral meshes is further studied. A SIWKS-based metric space is used to define a geometry-aware Gaussian process. The study of the periodic potential diffusion process further inspired the idea of a new kernel call the geometry-aware convolutional kernel. A series of Bayesian learning methods are then introduced to tackle the problem of shape retrieval and classification. Experiments of every single item are demonstrated. From the popular SPDE such as the heat equation and Schrödinger’s equation to the general potential diffusion equation and the specific periodic potential diffusion equation, it clearly shows that classical SPDEs play an important role in discovering new features, metrics, shape descriptors and kernels. I hope this thesis could be an example of using interdisciplinary knowledge to solve problems.
ContributorsFan, Yonghui (Author) / Wang, Yalin (Thesis advisor) / Lepore, Natasha (Committee member) / Turaga, Pavan (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2021
Description
Graph matching is a fundamental but notoriously difficult problem due to its NP-hard nature, and serves as a cornerstone for a series of applications in machine learning and computer vision, such as image matching, dynamic routing, drug design, to name a few. Although there has been massive previous investigation on high-performance graph matching solvers, it still remains a challenging task to tackle the matching problem under real-world scenarios with severe graph uncertainty (e.g., noise, outlier, misleading or ambiguous link).In this dissertation, a main focus is to investigate the essence and propose solutions to graph matching with higher reliability under such uncertainty. To this end, the proposed research was conducted taking into account three perspectives related to reliable graph matching: modeling, optimization and learning. For modeling, graph matching is extended from typical quadratic assignment problem to a more generic mathematical model by introducing a specific family of separable function, achieving higher capacity and reliability. In terms of optimization, a novel high gradient-efficient determinant-based regularization technique is proposed in this research, showing high robustness against outliers. Then learning paradigm for graph matching under intrinsic combinatorial characteristics is explored. First, a study is conducted on the way of filling the gap between discrete problem and its continuous approximation under a deep learning framework. Then this dissertation continues to investigate the necessity of more reliable latent topology of graphs for matching, and propose an effective and flexible framework to obtain it. Coherent findings in this dissertation include theoretical study and several novel algorithms, with rich experiments demonstrating the effectiveness.
ContributorsYu, Tianshu (Author) / Li, Baoxin (Thesis advisor) / Wang, Yalin (Committee member) / Yang, Yezhou (Committee member) / Yang, Yingzhen (Committee member) / Arizona State University (Publisher)
Created2021
Description
Graph matching is a fundamental but notoriously difficult problem due to its NP-hard nature, and serves as a cornerstone for a series of applications in machine learning and computer vision, such as image matching, dynamic routing, drug design, to name a few. Although there has been massive previous investigation on high-performance graph matching solvers, it still remains a challenging task to tackle the matching problem under real-world scenarios with severe graph uncertainty (e.g., noise, outlier, misleading or ambiguous link).In this dissertation, a main focus is to investigate the essence and propose solutions to graph matching with higher reliability under such uncertainty. To this end, the proposed research was conducted taking into account three perspectives related to reliable graph matching: modeling, optimization and learning. For modeling, graph matching is extended from typical quadratic assignment problem to a more generic mathematical model by introducing a specific family of separable function, achieving higher capacity and reliability. In terms of optimization, a novel high gradient-efficient determinant-based regularization technique is proposed in this research, showing high robustness against outliers. Then learning paradigm for graph matching under intrinsic combinatorial characteristics is explored. First, a study is conducted on the way of filling the gap between discrete problem and its continuous approximation under a deep learning framework. Then this dissertation continues to investigate the necessity of more reliable latent topology of graphs for matching, and propose an effective and flexible framework to obtain it. Coherent findings in this dissertation include theoretical study and several novel algorithms, with rich experiments demonstrating the effectiveness.
ContributorsYu, Tianshu (Author) / Li, Baoxin (Thesis advisor) / Wang, Yalin (Committee member) / Yang, Yezhou (Committee member) / Yang, Yingzhen (Committee member) / Arizona State University (Publisher)
Created2021
Description
Alzheimer's disease (AD) is a neurodegenerative disease that damages the cognitive abilities of a patient. It is critical to diagnose AD early to begin treatment as soon as possible which can be done through biomarkers. One such biomarker is the beta-amyloid (Aβ) peptide which can be quantified using the centiloid (CL) scale. For identifying the Aβ biomarker, A deep learning model that can model AD progression by predicting the CL value for brain magnetic resonance images (MRIs) is proposed. Brain MRI images can be obtained through the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Open Access Series of Imaging Studies (OASIS) datasets, however a single model cannot perform well on both datasets at once. Thus, A regularization-based continuous learning framework to perform domain adaptation on the previous model is also proposed which captures the latent information about the relationship between Aβ and AD progression within both datasets.
ContributorsTrinh, Matthew Brian (Author) / Wang, Yalin (Thesis advisor) / Liang, Jianming (Committee member) / Su, Yi (Committee member) / Arizona State University (Publisher)
Created2022
Description
Lithium (Li) is a trace element in kerogen, but the content and isotopic distribution (δ7Li) in kerogen has not previously been quantified. Furthermore, kerogen has been overlooked as a potential source of Li to sedimentary porefluids and buried sediments. Thus, knowing the content and isotopic composition of Li derived from kerogen may have implications for research focused on the Li-isotopes of buried sediments (e.g., evaluating paleoclimate variations using marine carbonates).The objective of this work is to better understand the role of kerogen in the Li geochemical cycle. The research approach consisted of 1) developing reference materials and methodologies to measure the Li-contents and δ7Li of kerogen in-situ by Secondary Ion Mass Spectrometry, 2) surveying the Li-contents and δ7Li of kerogen bearing rocks from different depositional and diagenetic environments and 3) quantifying the Li-content and δ7Li variations in kerogen empirically in a field study and 4) experimentally through hydrous pyrolysis.
A survey of δ7Li of coals from depositional basins across the USA showed that thermally immature coals have light δ7Li values (–20 to – 10‰) compared to typical terrestrial materials (> –10‰) and the δ7Li of coal increases with burial temperature suggesting that 6Li is preferentially released from kerogen to porefluids during hydrocarbon generation. A field study was conducted on two Cretaceous coal seams in Colorado (USA) intruded by dikes (mafic and felsic) creating a temperature gradient from the intrusives into the country rock. Results showed that δ7Li values of the unmetamorphosed vitrinite macerals were up to 37‰ lighter than vitrinite macerals and coke within the contact metamorphosed coal.
To understand the significance of Li derived from kerogen during burial diagenesis, hydrous pyrolysis experiments of three coals were conducted. Results showed that Li is released from kerogen during hydrocarbon generation and could increase sedimentary porefluid Li-contents up to ~100 mg/L. The δ7Li of coals becomes heavier with increased temperature except where authigenic silicates may compete for the released Li. These results indicate that kerogen is a significant source of isotopically light Li to diagenetic fluids and is an important contributor to the global geochemical cycle.
ContributorsTeichert, Zebadiah (Author) / Williams, Lynda B. (Thesis advisor) / Bose, Maitrayee (Thesis advisor) / Hervig, Richard (Committee member) / Semken, Steven (Committee member) / Shock, Everett (Committee member) / Arizona State University (Publisher)
Created2022