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Description
In an effort to address the lack of literature in on-campus active travel, this study aims to investigate the following primary questions:<br/>• What are the modes that students use to travel on campus?<br/>• What are the motivations that underlie the mode choice of students on campus?<br/>My first stage of research involved a series of qualitative investigations. I held one-on-one virtual interviews with students in which I asked them questions about the mode they use and why they feel that their chosen mode works best for them. These interviews served two functions. First, they provided me with insight into the various motivations underlying student mode choice. Second, they provided me with an indication of what explanatory variables should be included in a model of mode choice on campus.<br/>The first half of the research project informed a quantitative survey that was released via the Honors Digest to attract student respondents. Data was gathered on travel behavior as well as relevant explanatory variables.<br/>My analysis involved developing a logit model to predict student mode choice on campus and presenting the model estimation in conjunction with a discussion of student travel motivations based on the qualitative interviews. I use this information to make a recommendation on how campus infrastructure could be modified to better support the needs of the student population.
ContributorsMirtich, Laura Christine (Author) / Salon, Deborah (Thesis director) / Fang, Kevin (Committee member) / School of Public Affairs (Contributor) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Current policies subsidizing or accelerating deployment of photovoltaics (PV) are typically motivated by claims of environmental benefit, such as the reduction of CO2 emissions generated by the fossil-fuel fired power plants that PV is intended to displace. Existing practice is to assess these environmental benefits on a net life-cycle basis, where CO2 benefits occurring during use of the PV panels is found to exceed emissions generated during the PV manufacturing phase including materials extraction and manufacture of the PV panels prior to installation. However, this approach neglects to recognize that the environmental costs of CO2 release during manufacture are incurred early, while environmental benefits accrue later. Thus, where specific policy targets suggest meeting CO2 reduction targets established by a certain date, rapid PV deployment may have counter-intuitive, albeit temporary, undesired consequences. Thus, on a cumulative radiative forcing (CRF) basis, the environmental improvements attributable to PV might be realized much later than is currently understood. This phenomenon is particularly acute when PV manufacture occurs in areas using CO2 intensive energy sources (e.g., coal), but deployment occurs in areas with less CO2 intensive electricity sources (e.g., hydro). This thesis builds a dynamic Cumulative Radiative Forcing (CRF) model to examine the inter-temporal warming impacts of PV deployments in three locations: California, Wyoming and Arizona. The model includes the following factors that impact CRF: PV deployment rate, choice of PV technology, pace of PV technology improvements, and CO2 intensity in the electricity mix at manufacturing and deployment locations. Wyoming and California show the highest and lowest CRF benefits as they have the most and least CO2 intensive grids, respectively. CRF payback times are longer than CO2 payback times in all cases. Thin film, CdTe PV technologies have the lowest manufacturing CO2 emissions and therefore the shortest CRF payback times. This model can inform policies intended to fulfill time-sensitive CO2 mitigation goals while minimizing short term radiative forcing.
ContributorsTriplican Ravikumar, Dwarakanath (Author) / Seager, Thomas P (Thesis advisor) / Fraser, Matthew P (Thesis advisor) / Chester, Mikhail V (Committee member) / Sinha, Parikhit (Committee member) / Arizona State University (Publisher)
Created2013
Description
Laboratory automation systems have seen a lot of technological advances in recent times. As a result, the software that is written for them are becoming increasingly sophisticated. Existing software architectures and standards are targeted to a wider domain of software development and need to be customized in order to use them for developing software for laboratory automation systems. This thesis proposes an architecture that is based on existing software architectural paradigms and is specifically tailored to developing software for a laboratory automation system. The architecture is based on fairly autonomous software components that can be distributed across multiple computers. The components in the architecture make use of asynchronous communication methodologies that are facilitated by passing messages between one another. The architecture can be used to develop software that is distributed, responsive and thread-safe. The thesis also proposes a framework that has been developed to implement the ideas proposed by the architecture. The framework is used to develop software that is scalable, distributed, responsive and thread-safe. The framework currently has components to control very commonly used laboratory automation devices such as mechanical stages, cameras, and also to do common laboratory automation functionalities such as imaging.
ContributorsKuppuswamy, Venkataramanan (Author) / Meldrum, Deirdre (Thesis advisor) / Collofello, James (Thesis advisor) / Sarjoughian, Hessam S. (Committee member) / Johnson, Roger (Committee member) / Arizona State University (Publisher)
Created2012
Description
Single cell analysis has become increasingly important in understanding disease onset, progression, treatment and prognosis, especially when applied to cancer where cellular responses are highly heterogeneous. Through the advent of single cell computerized tomography (Cell-CT), researchers and clinicians now have the ability to obtain high resolution three-dimensional (3D) reconstructions of single cells. Yet to date, no live-cell compatible version of the technology exists. In this thesis, a microfluidic chip with the ability to rotate live single cells in hydrodynamic microvortices about an axis parallel to the optical focal plane has been demonstrated. The chip utilizes a novel 3D microchamber design arranged beneath a main channel creating flow detachment into the chamber, producing recirculating flow conditions. Single cells are flowed through the main channel, held in the center of the microvortex by an optical trap, and rotated by the forces induced by the recirculating fluid flow. Computational fluid dynamics (CFD) was employed to optimize the geometry of the microchamber. Two methods for the fabrication of the 3D microchamber were devised: anisotropic etching of silicon and backside diffuser photolithography (BDPL). First, the optimization of the silicon etching conditions was demonstrated through design of experiment (DOE). In addition, a non-conventional method of soft-lithography was demonstrated which incorporates the use of two positive molds, one of the main channel and the other of the microchambers, compressed together during replication to produce a single ultra-thin (<200 µm) negative used for device assembly. Second, methods for using thick negative photoresists such as SU-8 with BDPL have been developed which include a new simple and effective method for promoting the adhesion of SU-8 to glass. An assembly method that bonds two individual ultra-thin (<100 µm) replications of the channel and the microfeatures has also been demonstrated. Finally, a pressure driven pumping system with nanoliter per minute flow rate regulation, sub-second response times, and < 3% flow variability has been designed and characterized. The fabrication and assembly of this device is inexpensive and utilizes simple variants of conventional microfluidic fabrication techniques, making it easily accessible to the single cell analysis community.
ContributorsMyers, Jakrey R (Author) / Meldrum, Deirdre (Thesis advisor) / Johnson, Roger (Committee member) / Frakes, David (Committee member) / Arizona State University (Publisher)
Created2012
Description
Background
Grading schemes for breast cancer diagnosis are predominantly based on pathologists' qualitative assessment of altered nuclear structure from 2D brightfield microscopy images. However, cells are three-dimensional (3D) objects with features that are inherently 3D and thus poorly characterized in 2D. Our goal is to quantitatively characterize nuclear structure in 3D, assess its variation with malignancy, and investigate whether such variation correlates with standard nuclear grading criteria.
Methodology
We applied micro-optical computed tomographic imaging and automated 3D nuclear morphometry to quantify and compare morphological variations between human cell lines derived from normal, benign fibrocystic or malignant breast epithelium. To reproduce the appearance and contrast in clinical cytopathology images, we stained cells with hematoxylin and eosin and obtained 3D images of 150 individual stained cells of each cell type at sub-micron, isotropic resolution. Applying volumetric image analyses, we computed 42 3D morphological and textural descriptors of cellular and nuclear structure.
Principal Findings
We observed four distinct nuclear shape categories, the predominant being a mushroom cap shape. Cell and nuclear volumes increased from normal to fibrocystic to metastatic type, but there was little difference in the volume ratio of nucleus to cytoplasm (N/C ratio) between the lines. Abnormal cell nuclei had more nucleoli, markedly higher density and clumpier chromatin organization compared to normal. Nuclei of non-tumorigenic, fibrocystic cells exhibited larger textural variations than metastatic cell nuclei. At p<0.0025 by ANOVA and Kruskal-Wallis tests, 90% of our computed descriptors statistically differentiated control from abnormal cell populations, but only 69% of these features statistically differentiated the fibrocystic from the metastatic cell populations.
Conclusions
Our results provide a new perspective on nuclear structure variations associated with malignancy and point to the value of automated quantitative 3D nuclear morphometry as an objective tool to enable development of sensitive and specific nuclear grade classification in breast cancer diagnosis.
Grading schemes for breast cancer diagnosis are predominantly based on pathologists' qualitative assessment of altered nuclear structure from 2D brightfield microscopy images. However, cells are three-dimensional (3D) objects with features that are inherently 3D and thus poorly characterized in 2D. Our goal is to quantitatively characterize nuclear structure in 3D, assess its variation with malignancy, and investigate whether such variation correlates with standard nuclear grading criteria.
Methodology
We applied micro-optical computed tomographic imaging and automated 3D nuclear morphometry to quantify and compare morphological variations between human cell lines derived from normal, benign fibrocystic or malignant breast epithelium. To reproduce the appearance and contrast in clinical cytopathology images, we stained cells with hematoxylin and eosin and obtained 3D images of 150 individual stained cells of each cell type at sub-micron, isotropic resolution. Applying volumetric image analyses, we computed 42 3D morphological and textural descriptors of cellular and nuclear structure.
Principal Findings
We observed four distinct nuclear shape categories, the predominant being a mushroom cap shape. Cell and nuclear volumes increased from normal to fibrocystic to metastatic type, but there was little difference in the volume ratio of nucleus to cytoplasm (N/C ratio) between the lines. Abnormal cell nuclei had more nucleoli, markedly higher density and clumpier chromatin organization compared to normal. Nuclei of non-tumorigenic, fibrocystic cells exhibited larger textural variations than metastatic cell nuclei. At p<0.0025 by ANOVA and Kruskal-Wallis tests, 90% of our computed descriptors statistically differentiated control from abnormal cell populations, but only 69% of these features statistically differentiated the fibrocystic from the metastatic cell populations.
Conclusions
Our results provide a new perspective on nuclear structure variations associated with malignancy and point to the value of automated quantitative 3D nuclear morphometry as an objective tool to enable development of sensitive and specific nuclear grade classification in breast cancer diagnosis.
ContributorsNandakumar, Vivek (Author) / Kelbauskas, Laimonas (Author) / Hernandez, Kathryn (Author) / Lintecum, Kelly (Author) / Senechal, Patti (Author) / Bussey, Kimberly (Author) / Davies, Paul (Author) / Johnson, Roger (Author) / Meldrum, Deirdre (Author) / Ira A. Fulton Schools of Engineering (Contributor) / School of Electrical, Computer and Energy Engineering (Contributor) / Biodesign Institute (Contributor) / Center for Biosignatures Discovery Automation (Contributor) / College of Liberal Arts and Sciences (Contributor) / School of Life Sciences (Contributor) / Department of Physics (Contributor)
Created2012-01-05
Description
There has been much work done predicting the effects of climate change on transportation systems, this research parallels that past work and focuses on the effect of changes in precipitation on roadway drainage systems. On a macro level, this work addresses the process that should be taken to make predictions about the vulnerability of this system due to changes in precipitation. This work also addresses the mechanisms of failure of these drainage systems and how they may be affected by changes in precipitation due to climate change. These changes may entail more frequent failure by certain mechanisms, or a shift in the mechanisms for particular infrastructure. A sample water basin in the urban environment of Phoenix, Arizona is given as a case study. This study looks at the mechanisms of failure of the infrastructure therein, as well as provides a process of analyzing the effects of increases in precipitation to the vulnerability of this infrastructure. It was found that drainage structures at roadways being currently designed will see increases from 20-30% in peak discharge, which will lead to increased frequency of failure.
ContributorsHolt, Nathan Thomas (Author) / Chester, Mikhail V (Thesis director) / Mascaro, Giuseppe (Committee member) / Underwood, Benjamin S. (Committee member) / Civil, Environmental and Sustainable Engineering Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Pluvial Flood Risk Modeling, Assessment, and Management under Evolving Urban Climates and Land Cover
Description
Pluvial flooding is a costly, injurious, and even deadly phenomenon with which cities will always contend. However, cities may reduce their risk of flood exposure by changing historically dominant patterns of development that have removed natural landscape features and reduce the damages that flooding causes by identifying and supporting vulnerable populations. Accomplishing either goal requires the development and application of appropriate frameworks for modeling or recording flood exposure. In this dissertation, I used modeling and surveying methods for assessing pluvial flood exposure in two cities, first in Valdivia, Chile, and then in Hermosillo, México. I open with a summary on pluvial flood risk in the present day and the threat it may pose under changing climates. In the second chapter, I explored how a form of urban ecological infrastructure (UEI), the wetland, is being wielded in Valdivia toward pluvial flood mitigation, and found that wetland daily, seasonal, and interannual changes in wetland surface and soil water storage alter pluvial flood risk in the city. In the third chapter, I used a mixed methodology, including projections of future land cover generated by cellular automata models with inputs from visioning workshops conducted by the Urban Resilience to Extremes Sustainability Research Network (UREx SRN), and found that wetland loss in future land configurations may lead to increased pluvial flood risk. In the fourth chapter, I combined these land cover models from the third chapter with downscaled climate data on precipitation, also generated by the UREx SRN, and found that wetland conservation can help to mitigate the pluvial flood risk posed by changing patterns of rainfall. In the fifth chapter, I applied the Arc-Malstrøm method for pluvial flood assessment in Hermosillo, México, and compared it with the more traditional rational method for flood assessment, and through accompanying surveys found that perception of flood risk is significantly affected by flood dimensions and impacts. This dissertation concludes with a synthesis of pluvial flood risk assessment, suggestions for improvements to modeling, as well as suggestions for future research on pluvial flood risk assessment in cities. This dissertation advances the understanding of the utility of inland wetland UEI in cities under present and future land cover and climate conditions. It also qualifies the utility of common and new pluvial flood risk assessments and offers research directions for future pluvial flood assessments.
ContributorsSauer, Jason R (Author) / Grimm, Nancy B (Thesis advisor) / Chester, Mikhail V (Committee member) / Cook, Elizabeth M (Committee member) / Childers, Daniel L (Committee member) / Eakin, Hallie (Committee member) / Arizona State University (Publisher)
Created2022
Description
Traditional Reinforcement Learning (RL) assumes to learn policies with respect to reward available from the environment but sometimes learning in a complex domain requires wisdom which comes from a wide range of experience. In behavior based robotics, it is observed that a complex behavior can be described by a combination of simpler behaviors. It is tempting to apply similar idea such that simpler behaviors can be combined in a meaningful way to tailor the complex combination. Such an approach would enable faster learning and modular design of behaviors. Complex behaviors can be combined with other behaviors to create even more advanced behaviors resulting in a rich set of possibilities. Similar to RL, combined behavior can keep evolving by interacting with the environment. The requirement of this method is to specify a reasonable set of simple behaviors. In this research, I present an algorithm that aims at combining behavior such that the resulting behavior has characteristics of each individual behavior. This approach has been inspired by behavior based robotics, such as the subsumption architecture and motor schema-based design. The combination algorithm outputs n weights to combine behaviors linearly. The weights are state dependent and change dynamically at every step in an episode. This idea is tested on discrete and continuous environments like OpenAI’s “Lunar Lander” and “Biped Walker”. Results are compared with related domains like Multi-objective RL, Hierarchical RL, Transfer learning, and basic RL. It is observed that the combination of behaviors is a novel way of learning which helps the agent achieve required characteristics. A combination is learned for a given state and so the agent is able to learn faster in an efficient manner compared to other similar approaches. Agent beautifully demonstrates characteristics of multiple behaviors which helps the agent to learn and adapt to the environment. Future directions are also suggested as possible extensions to this research.
ContributorsVora, Kevin Jatin (Author) / Zhang, Yu (Thesis advisor) / Yang, Yezhou (Committee member) / Praharaj, Sarbeswar (Committee member) / Arizona State University (Publisher)
Created2021
Description
Traditional infrastructure design approaches were born with industrialization. During this time the relatively stable environments allowed infrastructure systems to reliably provide service with networks designed to precise parameters and organizations fixated on maximizing efficiency. Now, infrastructure systems face the challenge of operating in the Anthropocene, an era of complexity. The environments in which infrastructure systems operate are changing more rapidly than the technologies and governance systems of infrastructure. Infrastructure systems will need to be resilient to navigate stability and instability and avoid obsolescence. This dissertation addresses how infrastructure systems could be designed for the Anthropocene, assessing technologies able to operate with uncertainty, rethinking the principles of technology design, and restructuring infrastructure governance. Resilience, in engineering, has often been defined as resistance to known disturbances with a focus on infrastructure assets. Resilience, more broadly reviewed, includes resistance, adaptation, and transformation across physical and governance domains. This dissertation constructs a foundation for resilient infrastructure through an assessment of resilience paradigms in engineering, complexity and deep uncertainty (Chapter 2), ecology (Chapter 3), and organizational change and leadership (Chapter 4). The second chapter reconciles frameworks of complexity and deep uncertainty to help infrastructure managers navigate the instability infrastructure systems face, with a focus on climate change. The third chapter identifies competencies of resilience in infrastructure theory and practice and compares those competencies with ‘Life’s Principles’ in ecology, presenting opportunities for growth and innovation in infrastructure resilience and highlighting the need for satisficed (to satisfy and suffice) solutions. The fourth chapter navigates pressures of exploitation and exploration that infrastructure institutions face during periods of stability and instability, proposing leadership capabilities to enhance institutional resilience. Finally, the dissertation is concluded with a chapter synthesizing the previous chapters, providing guidance for alternative design approaches for advancing resilient infrastructure. Combined, the work challenges the basic mental models used by engineers when approaching infrastructure design and recommends new ways of doing and thinking for the accelerating and increasingly uncertain conditions of the future.
ContributorsHelmrich, Alysha Marie (Author) / Chester, Mikhail V (Thesis advisor) / Grimm, Nancy B (Committee member) / Garcia, Margaret (Committee member) / Meerow, Sara (Committee member) / Arizona State University (Publisher)
Created2021
Description
Extreme weather events, such as hurricanes, continue to disrupt critical infrastructure like energy grids that provide lifeline services for urban systems, thus making resilience imperative for stakeholders, infrastructure managers, and community leaders to strategize in the face of 21st-century challenges. In Puerto Rico after Hurricane Maria, for example, the energy system took over nine months to recover in parts of the island, thousands of lives were lost, and livelihoods were severely impacted. Urban systems consist of interconnected human networks and physical infrastructure, and the subsequent complexity that is increasingly difficult to make sense of toward resilience enhancing efforts. While the resilience paradigm has continued to progress among and between several disciplinary fields, such as social science and engineering, an ongoing challenge is integrating social and technical approaches for resilience research. Misaligned or siloed perspectives can lead to misinformative and inadequate strategies that undercut inherent capacities or ultimately result in maladaptive infrastructure, social hardship, and sunken investments. This dissertation contributes toward integrating the social and technical resilience domains and transitioning established disaster resilience assessments into complexity perspectives by asking the overarching question: How can a multiplicity of resilience assessments be integrated by geographic and network mapping approaches to better capture the complexity of urban systems, using Hurricane Maria in Puerto Rico as a case study? The first chapter demonstrates how social metrics can be used in a socio-technical network modeling framework for a large-scale electrical system, presents a novel framing of social hardship due to disasters, and proposes a method for developing a social hardship metric using a treatment-effect approach. A second chapter presents a conceptual analysis of disaster resilience indicators from a complexity perspective and links socio-ecological systems resilience principles to tenets of complexity. A third chapter presents a novel methodology for integrating social complexity with performance-based metrics by leveraging distributed ethnographies and a thick mapping approach. Lastly, a concluding chapter synthesizes the previous chapters to discuss a broad framing for socio-technical resilience assessments, the role of space and place as anchors for multiple framings of a complex system, caveats given ongoing developments in Puerto Rico, and implications for collaborative resilience research.
ContributorsCarvalhaes, Thomaz (Author) / Chester, Mikhail V (Thesis advisor) / Reddy, Agami T (Thesis advisor) / Allenby, Braden R (Committee member) / Arizona State University (Publisher)
Created2021