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Description
Despite the arid climate of Maricopa County, Arizona, vector-borne diseases have presented significant health challenges to the residents and public health professionals of Maricopa County in the past, and will continue to do so in the foreseeable future. Currently, West Nile virus is the only mosquitoes-transmitted disease actively, and natively, transmitted throughout the state of Arizona. In an effort to gain a more complete understanding of the transmission dynamics of West Nile virus this thesis examines human, vector, and environment interactions as they exist within Maricopa County. Through ethnographic and geographic information systems research methods this thesis identifies 1) the individual factors that influence residents' knowledge and behaviors regarding mosquitoes, 2) the individual and regional factors that influence residents' knowledge of mosquito ecology and the spatial distribution of local mosquito populations, and 3) the environmental, demographic, and socioeconomic factors that influence mosquito abundance within Maricopa County. By identifying the factors that influence human-vector and vector-environment interactions, the results of this thesis may influence current and future educational and mosquito control efforts throughout Maricopa County.
ContributorsKunzweiler, Colin (Author) / Boone, Christopher (Thesis advisor) / Wutich, Amber (Committee member) / Brewis-Slade, Alexandra (Committee member) / Arizona State University (Publisher)
Created2013
Description
We solve the problem of activity verification in the context of sustainability. Activity verification is the process of proving the user assertions pertaining to a certain activity performed by the user. Our motivation lies in incentivizing the user for engaging in sustainable activities like taking public transport or recycling. Such incentivization schemes require the system to verify the claim made by the user. The system verifies these claims by analyzing the supporting evidence captured by the user while performing the activity. The proliferation of portable smart-phones in the past few years has provided us with a ubiquitous and relatively cheap platform, having multiple sensors like accelerometer, gyroscope, microphone etc. to capture this evidence data in-situ. In this research, we investigate the supervised and semi-supervised learning techniques for activity verification. Both these techniques make use the data set constructed using the evidence submitted by the user. Supervised learning makes use of annotated evidence data to build a function to predict the class labels of the unlabeled data points. The evidence data captured can be either unimodal or multimodal in nature. We use the accelerometer data as evidence for transportation mode verification and image data as evidence for recycling verification. After training the system, we achieve maximum accuracy of 94% when classifying the transport mode and 81% when detecting recycle activity. In the case of recycle verification, we could improve the classification accuracy by asking the user for more evidence. We present some techniques to ask the user for the next best piece of evidence that maximizes the probability of classification. Using these techniques for detecting recycle activity, the accuracy increases to 93%. The major disadvantage of using supervised models is that it requires extensive annotated training data, which expensive to collect. Due to the limited training data, we look at the graph based inductive semi-supervised learning methods to propagate the labels among the unlabeled samples. In the semi-supervised approach, we represent each instance in the data set as a node in the graph. Since it is a complete graph, edges interconnect these nodes, with each edge having some weight representing the similarity between the points. We propagate the labels in this graph, based on the proximity of the data points to the labeled nodes. We estimate the performance of these algorithms by measuring how close the probability distribution of the data after label propagation is to the probability distribution of the ground truth data. Since labeling has a cost associated with it, in this thesis we propose two algorithms that help us in selecting minimum number of labeled points to propagate the labels accurately. Our proposed algorithm achieves a maximum of 73% increase in performance when compared to the baseline algorithm.
ContributorsDesai, Vaishnav (Author) / Sundaram, Hari (Thesis advisor) / Li, Baoxin (Thesis advisor) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2013
Description
Energy is a central concern of sustainability because how we produce and consume energy affects society, economy, and the environment. Sustainability scientists are interested in energy transitions away from fossil fuels because they are nonrenewable, increasingly expensive, have adverse health effects, and may be the main driver of climate change. They see an opportunity for developing countries to avoid the negative consequences fossil-fuel-based energy systems, and also to increase resilience, by leap-frogging-over the centralized energy grid systems that dominate the developed world. Energy transitions pose both challenges and opportunities. Obstacles to transitions include 1) an existing, centralized, complex energy-grid system, whose function is invisible to most users, 2) coordination and collective-action problems that are path dependent, and 3) difficulty in scaling up RE technologies. Because energy transitions rely on technological and social innovations, I am interested in how institutional factors can be leveraged to surmount these obstacles. The overarching question that underlies my research is: What constellation of institutional, biophysical, and social factors are essential for an energy transition? My objective is to derive a set of "design principles," that I term institutional drivers, for energy transitions analogous to Ostrom's institutional design principles. My dissertation research will analyze energy transitions using two approaches: applying the Institutional Analysis and Development Framework and a comparative case study analysis comprised of both primary and secondary sources. This dissertation includes: 1) an analysis of the world's energy portfolio; 2) a case study analysis of five countries; 3) a description of the institutional factors likely to promote a transition to renewable-energy use; and 4) an in-depth case study of Thailand's progress in replacing nonrenewable energy sources with renewable energy sources. My research will contribute to our understanding of how energy transitions at different scales can be accomplished in developing countries and what it takes for innovation to spread in a society.
ContributorsKoster, Auriane Magdalena (Author) / Anderies, John M (Thesis advisor) / Aggarwal, Rimjhim (Committee member) / Van Der Leeuw, Sander (Committee member) / Arizona State University (Publisher)
Created2013
Description
Alzheimer's Disease (AD) is the sixth leading cause of death in the United States and the most common form of dementia. Its cause remains unknown, but it is known to involve two hallmark pathologies: Amyloid Beta plaques and neurofibrillary tangles (NFTs). Several proteins have been implicated in the formation of neurofibrillary tangles, including Tau and S100B. S100B is a dimeric protein that is typically found bound to Ca(II) or Zn(II). These experiments relate to the involvement of S100B in Alzheimer's Disease-related processes and the results suggest that future research of S100B is warranted. Zn(II)-S100B was found to increase the rate at which tau assembled into paired helical filaments, as well as affect the rate at which tubulin polymerized into microtubules and the morphology of SH-SY5Y neuroblastoma cells after 72 hours of incubation. Zn(II)-S100B also increased the firing rate of hippocampal neurons after 36 hours of incubation. Together, these results suggest several possibilities: Zn(II)-S100B may be a key part of the formation of paired helical filaments (PHFs) that subsequently form NFTs. Zn(II)-S100B may also be competing with tau to bind tubulin, which could lead to an instability of microtubules and subsequent cell death. This finding aligns with the neurodegeneration that is commonly seen in AD and which could be a result of this microtubule instability. Ultimately, these results suggest that S100B is likely involved in several AD-related processes, and if the goal is to find an efficient and effective therapeutic target for AD, the relationship between S100B, particularly Zn(II)-S100B, and tau needs to be further studied.
ContributorsNaegele, Hayley (Author) / Mcgregor, Wade C (Thesis advisor) / Baluch, Debra (Committee member) / Francisco, Wilson (Committee member) / Arizona State University (Publisher)
Created2013
Description
Life Cycle Assessment (LCA) quantifies environmental impacts of products in raw material extraction, processing, manufacturing, distribution, use and final disposal. The findings of an LCA can be used to improve industry practices, to aid in product development, and guide public policy. Unfortunately, existing approaches to LCA are unreliable in the cases of emerging technologies, where data is unavailable and rapid technological advances outstrip environmental knowledge. Previous studies have demonstrated several shortcomings to existing practices, including the masking of environmental impacts, the difficulty of selecting appropriate weight sets for multi-stakeholder problems, and difficulties in exploration of variability and uncertainty. In particular, there is an acute need for decision-driven interpretation methods that can guide decision makers towards making balanced, environmentally sound decisions in instances of high uncertainty. We propose the first major methodological innovation in LCA since early establishment of LCA as the analytical perspective of choice in problems of environmental management. We propose to couple stochastic multi-criteria decision analytic tools with existing approaches to inventory building and characterization to create a robust approach to comparative technology assessment in the context of high uncertainty, rapid technological change, and evolving stakeholder values. Namely, this study introduces a novel method known as Stochastic Multi-attribute Analysis for Life Cycle Impact Assessment (SMAA-LCIA) that uses internal normalization by means of outranking and exploration of feasible weight spaces.
ContributorsPrado, Valentina (Author) / Seager, Thomas P (Thesis advisor) / Landis, Amy E. (Committee member) / Chester, Mikhail (Committee member) / White, Philip (Committee member) / Arizona State University (Publisher)
Created2013
Description
This study investigates how well prominent behavioral theories from social psychology explain green purchasing behavior (GPB). I assess three prominent theories in terms of their suitability for GPB research, their attractiveness to GPB empiricists, and the strength of their empirical evidence when applied to GPB. First, a qualitative assessment of the Theory of Planned Behavior (TPB), Norm Activation Theory (NAT), and Value-Belief-Norm Theory (VBN) is conducted to evaluate a) how well the phenomenon and concepts in each theory match the characteristics of pro-environmental behavior and b) how well the assumptions made in each theory match common assumptions made in purchasing theory. Second, a quantitative assessment of these three theories is conducted in which r2 values and methodological parameters (e.g., sample size) are collected from a sample of 21 empirical studies on GPB to evaluate the accuracy and generalize-ability of empirical evidence. In the qualitative assessment, the results show each theory has its advantages and disadvantages. The results also provide a theoretically-grounded roadmap for modifying each theory to be more suitable for GPB research. In the quantitative assessment, the TPB outperforms the other two theories in every aspect taken into consideration. It proves to 1) create the most accurate models 2) be supported by the most generalize-able empirical evidence and 3) be the most attractive theory to empiricists. Although the TPB establishes itself as the best foundational theory for an empiricist to start from, it's clear that a more comprehensive model is needed to achieve consistent results and improve our understanding of GPB. NAT and the Theory of Interpersonal Behavior (TIB) offer pathways to extend the TPB. The TIB seems particularly apt for this endeavor, while VBN does not appear to have much to offer. Overall, the TPB has already proven to hold a relatively high predictive value. But with the state of ecosystem services continuing to decline on a global scale, it's important for models of GPB to become more accurate and reliable. Better models have the capacity to help marketing professionals, product developers, and policy makers develop strategies for encouraging consumers to buy green products.
ContributorsRedd, Thomas Christopher (Author) / Dooley, Kevin (Thesis advisor) / Basile, George (Committee member) / Darnall, Nicole (Committee member) / Arizona State University (Publisher)
Created2012
Description
Urban water systems face sustainability challenges ranging from water quality, leaks, over-use, energy consumption, and long-term supply concerns. Resiliency challenges include the capacity to respond to drought, managing pipe deterioration, responding to natural disasters, and preventing terrorism. One strategy to enhance sustainability and resiliency is the development and adoption of smart water grids. A smart water grid incorporates networked monitoring and control devices into its structure, which provides diverse, real-time information about the system, as well as enhanced control. Data provide input for modeling and analysis, which informs control decisions, allowing for improvement in sustainability and resiliency. While smart water grids hold much potential, there are also potential tradeoffs and adoption challenges. More publicly available cost-benefit analyses are needed, as well as system-level research and application, rather than the current focus on individual technologies. This thesis seeks to fill one of these gaps by analyzing the cost and environmental benefits of smart irrigation controllers. Smart irrigation controllers can save water by adapting watering schedules to climate and soil conditions. The potential benefit of smart irrigation controllers is particularly high in southwestern U.S. states, where the arid climate makes water scarcer and increases watering needs of landscapes. To inform the technology development process, a design for environment (DfE) method was developed, which overlays economic and environmental performance parameters under different operating conditions. This method is applied to characterize design goals for controller price and water savings that smart irrigation controllers must meet to yield life cycle carbon dioxide reductions and economic savings in southwestern U.S. states, accounting for regional variability in electricity and water prices and carbon overhead. Results from applying the model to smart irrigation controllers in the Southwest suggest that some areas are significantly easier to design for.
ContributorsMutchek, Michele (Author) / Allenby, Braden (Thesis advisor) / Williams, Eric (Committee member) / Westerhoff, Paul (Committee member) / Arizona State University (Publisher)
Created2012
Description
Mitochondria produce most of the ATP needed for the cell as an energy source. It is well known that cellular respiration results in oxidative damage to the cell due to the production of reactive oxygen species (ROS). Mitochondrial dysfunction is believed to contribute to a number of degenerative diseases; because of this the mitochondrial respiratory chain is considered as potential drug target. A few series of idebenone analogues with quinone, pyridinol and pyrimidinol redox cores have been synthesized and evaluated as antioxidants able to protect cellular integrity and, more specifically, mitochondrial function. The compounds exhibited a range of activities. The activities observed were used for the design of analogues with enhanced properties as antioxidants. Compounds were identified which provide better protection against oxidative stress than idebenone, and it is thought that they do so catalytically.
ContributorsArce Amezquita, Pablo M (Author) / Hecht, Sidney M. (Thesis advisor) / Moore, Ana (Committee member) / Rose, Seth (Committee member) / Arizona State University (Publisher)
Created2012
Description
Spinal muscular atrophy (SMA) is a neurodegenerative disease that results in the loss of lower body muscle function. SMA is the second leading genetic cause of death in infants and arises from the loss of the Survival of Motor Neuron (SMN) protein. SMN is produced by two genes, smn1 and smn2, that are identical with the exception of a C to T conversion in exon 7 of the smn2 gene. SMA patients lacking the smn1 gene, rely on smn2 for production of SMN. Due to an alternative splicing event, smn2 primarily encodes a non-functional SMN lacking exon 7 (SMN D7) as well as a low amount of functional full-length SMN (SMN WT). SMN WT is ubiquitously expressed in all cell types, and it remains unclear how low levels of SMN WT in motor neurons lead to motor neuron degradation and SMA. SMN and its associated proteins, Gemin2-8 and Unrip, make up a large dynamic complex that functions to assemble ribonucleoproteins. The aim of this project was to characterize the interactions of the core SMN-Gemin2 complex, and to identify differences between SMN WT and SMN D7. SMN and Gemin2 proteins were expressed, purified and characterized via size exclusion chromatography. A stable N-terminal deleted Gemin2 protein (N45-G2) was characterized. The SMN WT expression system was optimized resulting in a 10-fold increase of protein expression. Lastly, the oligomeric states of SMN and SMN bound to Gemin2 were determined. SMN WT formed a mixture of oligomeric states, while SMN D7 did not. Both SMN WT and D7 bound to Gemin2 with a one-to-one ratio forming a heterodimer and several higher-order oligomeric states. The SMN WT-Gemin2 complex favored high molecular weight oligomers whereas the SMN D7-Gemin2 complex formed low molecular weight oligomers. These results indicate that the SMA mutant protein, SMN D7, was still able to associate with Gemin2, but was not able to form higher-order oligomeric complexes. The observed multiple oligomerization states of SMN and SMN bound to Gemin2 may play a crucial role in regulating one or several functions of the SMN protein. The inability of SMN D7 to form higher-order oligomers may inhibit or alter those functions leading to the SMA disease phenotype.
ContributorsNiday, Tracy (Author) / Allen, James P. (Thesis advisor) / Wachter, Rebekka (Committee member) / Ghirlanda, Giovanna (Committee member) / Arizona State University (Publisher)
Created2012
Description
Dietary protein is known to increase postprandial thermogenesis more so than carbohydrates or fats, probably related to the fact that amino acids have no immediate form of storage in the body and can become toxic if not readily incorporated into body tissues or excreted. It is also well documented that subjects report greater satiety on high- versus low-protein diets and that subject compliance tends to be greater on high-protein diets, thus contributing to their popularity. What is not as well known is how a high-protein diet affects resting metabolic rate over time, and what is even less well known is if resting metabolic rate changes significantly when a person consuming an omnivorous diet suddenly adopts a vegetarian one. This pilot study sought to determine whether subjects adopting a vegetarian diet would report decreased satiety or demonstrate a decreased metabolic rate due to a change in protein intake and possible increase in carbohydrates. Further, this study sought to validate a new device called the SenseWear Armband (SWA) to determine if it might be sensitive enough to detect subtle changes in metabolic rate related to diet. Subjects were tested twice on all variables, at baseline and post-test. Independent and related samples tests revealed no significant differences between or within groups for any variable at any time point in the study. The SWA had a strong positive correlation to the Oxycon Mobile metabolic cart but due to a lack of change in metabolic rate, its sensitivity was undetermined. These data do not support the theory that adopting a vegetarian diet results in a long-term change in metabolic rate.
ContributorsMoore, Amy (Author) / Johnston, Carol (Thesis advisor) / Appel, Christy (Thesis advisor) / Gaesser, Glenn (Committee member) / Arizona State University (Publisher)
Created2012