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- All Subjects: Sustainability
- Creators: Arizona State University
Description
At first glance, trends in increased hunger and obesity in the United States (US) would seem to represent the result of different causal mechanisms. The United States Department of Agriculture (USDA) reported that nearly 50 million Americans had experienced hunger in 2009. A year later, the Centers for Disease Control and Prevention published a report showing that 68% of the US population was either overweight or obese. Researchers have found that these contrasting trends are actually interrelated. Being so, it is imperative that communities and individuals experiencing problems with food security are provided better access to healthy food options. In response to the need to increase healthy food access, many farmers markets in the US have received funding from the USDA to accept vouchers from federal food security programs, such as the Supplemental Nutrition Assistance Program (SNAP). In Downtown Phoenix, Arizona, one organization accepting vouchers from several programs is the Phoenix Public Market. However, the mere existence of these programs is not enough to establish food security within a community: characteristics of the population and food environments must also be considered. To examine issues of food security and public health, this thesis utilizes geographical information systems (GIS) technology as a tool to analyze specific environments in order to inform program effectiveness and future funding opportunities. Utilizing methods from community-based participatory research (CBPR) and GIS, a mapping project was conducted in partnership with the Market to answer three questions: (1) what is the demographic makeup of the surrounding community? (2) What retailers around the Market also accept food security vouchers? And (3) where are food security offices (SNAP and WIC) located within the area? Both in terms of demographic characteristics and the surrounding food environment, the project results illustrate that the Market is embedded within a population of need, and an area where it could greatly influence community food security.
ContributorsRawson, Brooke (Author) / Vargas, Perla A (Thesis advisor) / Booze, Randy (Committee member) / Vaughan, Suzanne (Committee member) / Arizona State University (Publisher)
Created2011
Description
Arizona has an abundant solar resource and technologically mature systems are available to capture it, but solar energy systems are still considered to be an innovative technology. Adoption rates for solar and wind energy systems rise and fall with the political tides, and are relatively low in most rural areas in Arizona. This thesis tests the hypothesis that a consumer profile developed to characterize the adopters of renewable energy technology (RET) systems in rural Arizona is the same as the profile of other area residents who performed renovations, upgrades or additions to their homes. Residents of Santa Cruz and Cochise Counties who had obtained building permits to either install a solar or wind energy system or to perform a substantial renovation or upgrade to their home were surveyed to gather demographic, psychographic and behavioristic data. The data from 133 survey responses (76 from RET adopters and 57 from non-adopters) provided insights about their decisions regarding whether or not to adopt a RET system. The results, which are statistically significant at the 99% level of confidence, indicate that RET adopters had smaller households, were older and had higher education levels and greater income levels than the non-adopters. The research also provides answers to three related questions: First, are the energy conservation habits of RET adopters the same as those of non-adopters? Second, what were the sources of information consulted and the most important factors that motivated the decision to purchase a solar or wind energy system? And finally, are any of the factors which influenced the decision to live in a rural area in southeastern Arizona related to the decision to purchase a renewable energy system? The answers are provided, along with a series of recommendations that are designed to inform marketers and other promoters of RETs about how to utilize these results to help achieve their goals.
ContributorsPorter, Wayne Eliot (Author) / Reddy, T. Agami (Thesis advisor) / Pasqualetti, Martin (Committee member) / Larson, Kelli (Committee member) / Kennedy, Linda (Committee member) / Arizona State University (Publisher)
Created2011
Description
This research addresses the ability for neighborhoods to assess resiliency as it applies to their respective local areas. Two demographically and economically contrasting neighborhoods in Glendale, Arizona were studied to understand what residents' value and how those values link to key principles of resiliency. Through this exploratory research, a community-focused process was created to use these values in order to link them to key principles of resiliency and potential measureable indicators. A literature review was conducted to first assess definitions and key principles of resiliency. Second, it explored cases of neighborhoods or communities that faced a pressure or disaster and responded resiliently based on these general principles. Each case study demonstrated that resiliency at the neighborhood level was important to its ability to survive its respective pressure and emerge stronger. The Heart of Glendale and Thunderbird Palms were the two neighborhoods chosen to test the ability to operationalize neighborhood resiliency in the form of indicators. First, an in-depth interview was conducted with a neighborhood expert to understand each area's strengths and weaknesses and get a context for the neighborhood and how it has developed. Second, a visioning session was conducted with each neighborhood consisting of seven participants to discuss its values and how they relate to key principles of resiliency. The values were analyzed and used to shape locally relevant indicators. The results of this study found that the process of identifying participants' values and linking them to key principles of resiliency is a viable methodology for measuring neighborhood resiliency. It also found that indicators and values differed between the Heart of Glendale, a more economically vulnerable yet ethnically diverse area, than Thunderbird Palms, a more racially homogenous, middle income neighborhood. The Heart of Glendale valued the development of social capital more than Thunderbird Palms which placed a higher value on the condition of the built environment as a vehicle for stimulating vibrancy and resiliency in the neighborhood. However, both neighborhoods highly valued public education and providing opportunities for children to be future leaders in their local communities.
ContributorsAcevedo, Shannon (Author) / Pijawka, K. David (Thesis advisor) / Phillips, Rhonda (Committee member) / Lara-Valencia, Francisco (Committee member) / Arizona State University (Publisher)
Created2011
Description
Many studies have shown that access to healthy food in the US is unevenly distributed and that supermarkets and other fresh food retailers are less likely to be located in low-income minority communities, where convenience and dollar stores are more prevalent grocery options. I formed a partnership with Phoenix Revitalization Corporation, a local community development organization engaged in Central City South, Phoenix, to enhance the community's capacity to meet its community health goals by improving access to healthy food. I used a community-based participatory approach that blended qualitative and quantitative elements to accommodate collaboration between both academic and non-academic partners. Utilizing stakeholder interviews, Nutrition Environment Measures Surveys (NEMS), and mapping to analyze the community's food resources, research revealed that the community lacks adequate access to affordable, nutritious food. Community food stores (n=14) scored an average of 10.9 out of a possible 54 points using the NEMS scoring protocol. The community food assessment is an essential step in improving access to healthy food for CCS residents and provides a baseline for tracking progress to improve residents' food access. Recommendations were drafted by the research partnership to equip and empower the community with strategic, community-specific interventions based on the research findings.
ContributorsCrouch, Carolyn (Author) / Harlan, Sharon (Thesis advisor) / Eakin, Hallie (Committee member) / Aftandilian, David (Committee member) / Arizona State University (Publisher)
Created2011
Description
Haiti has witnessed high deforestation rates in recent decades, caused largely by the fuel needs of a growing population. The resulting soil loss is estimated to have contributed towards a decline in agricultural productivity of 0.5% -1.2% per year since 1997. Recent studies show the potential of biochar use through pyrolysis technology to increase crop yields and improve soil health. However, the appropriateness of this technology in the context of Haiti remains unexplored. The three objectives of this research were to identify agricultural- and fuel-use-related needs and gaps in rural Haitian communities; determine the appropriateness of biochar pyrolyzer technology, used to convert agricultural biomass into a carbon-rich charcoal; and develop an action-oriented plan for use by development organizations, communities, and governmental institutions to increase the likelihood of adoption. Data were collected using participatory rural appraisal techniques involving 30 individual interviews and three focus-group discussions in the villages of Cinquantin and La Boule in the La Coupe region of central Haiti. Topics discussed include agricultural practices and assets, fuel use and needs, technology use and adoption, and social management practices. The Sustainable Livelihoods framework was used to examine the assets of households and the livelihood strategies being employed. Individual and focus group interviews were analyzed to identify specific needs and gaps. E.M. Rogers' Diffusion of Innovations theory was used to develop potential strategies for the introduction of pyrolysis technology. Preliminary results indicate biochar pyrolysis has potential to address agricultural and fuel needs in rural Haiti. Probable early adopters of biochar technology include households that have adopted new agricultural techniques in the past, and those with livestock. Education about biochar, and a variety of pyrolysis technology options from which villagers may select, are important factors in successful adoption of biochar use. A grain mill as an example in one of the study villages provides a model of ownership and use of pyrolysis technology that may increase its likelihood of successful adoption. Additionally, women represent a group that may be well suited to control a new local biochar enterprise, potentially benefiting the community.
ContributorsDelaney, Michael Ryan (Author) / Aggarwal, Rimjhim (Thesis advisor) / Chhetri, Nalini (Committee member) / Henderson, Mark (Committee member) / Arizona State University (Publisher)
Created2011
Description
Second-generation biofuel feedstocks are currently grown in land-based systems that use valuable resources like water, electricity and fertilizer. This study investigates the potential of near-shore marine (ocean) seawater filtration as a source of planktonic biomass for biofuel production. Mixed marine organisms in the size range of 20µm to 500µm were isolated from the University of California, Santa Barbara (UCSB) seawater filtration system during weekly backwash events between the months of April and August, 2011. The quantity of organic material produced was determined by sample combustion and calculation of ash-free dry weights. Qualitative investigation required density gradient separation with the heavy liquid sodium metatungstate followed by direct transesterification and gas chromatography with mass spectrometry (GC-MS) of the fatty acid methyl esters (FAME) produced. A maximum of 0.083g/L of dried organic material was produced in a single backwash event and a study average of 0.036g/L was calculated. This equates to an average weekly value of 7,674.75g of dried organic material produced from the filtration of approximately 24,417,792 liters of seawater. Temporal variations were limited. Organic quantities decreased over the course of the study. Bio-fouling effects from mussel overgrowth inexplicably increased production values when compared to un-fouled seawater supply lines. FAMEs (biodiesel) averaged 0.004% of the dried organic material with 0.36ml of biodiesel produced per week, on average. C16:0 and C22:6n3 fatty acids comprised the majority of the fatty acids in the samples. Saturated fatty acids made up 30.71% to 44.09% and unsaturated forms comprised 55.90% to 66.32% of the total chemical composition. Both quantities and qualities of organics and FAMEs were unrealistic for use as biodiesel but sample size limitations, system design, geographic and temporal factors may have impacted study results.
ContributorsPierre, Christophe (Author) / Olson, Larry (Thesis advisor) / Sommerfeld, Milton (Committee member) / Brown, Albert (Committee member) / Arizona State University (Publisher)
Created2011
Description
Multi-label learning, which deals with data associated with multiple labels simultaneously, is ubiquitous in real-world applications. To overcome the curse of dimensionality in multi-label learning, in this thesis I study multi-label dimensionality reduction, which extracts a small number of features by removing the irrelevant, redundant, and noisy information while considering the correlation among different labels in multi-label learning. Specifically, I propose Hypergraph Spectral Learning (HSL) to perform dimensionality reduction for multi-label data by exploiting correlations among different labels using a hypergraph. The regularization effect on the classical dimensionality reduction algorithm known as Canonical Correlation Analysis (CCA) is elucidated in this thesis. The relationship between CCA and Orthonormalized Partial Least Squares (OPLS) is also investigated. To perform dimensionality reduction efficiently for large-scale problems, two efficient implementations are proposed for a class of dimensionality reduction algorithms, including canonical correlation analysis, orthonormalized partial least squares, linear discriminant analysis, and hypergraph spectral learning. The first approach is a direct least squares approach which allows the use of different regularization penalties, but is applicable under a certain assumption; the second one is a two-stage approach which can be applied in the regularization setting without any assumption. Furthermore, an online implementation for the same class of dimensionality reduction algorithms is proposed when the data comes sequentially. A Matlab toolbox for multi-label dimensionality reduction has been developed and released. The proposed algorithms have been applied successfully in the Drosophila gene expression pattern image annotation. The experimental results on some benchmark data sets in multi-label learning also demonstrate the effectiveness and efficiency of the proposed algorithms.
ContributorsSun, Liang (Author) / Ye, Jieping (Thesis advisor) / Li, Baoxin (Committee member) / Liu, Huan (Committee member) / Mittelmann, Hans D. (Committee member) / Arizona State University (Publisher)
Created2011
Description
Real-world environments are characterized by non-stationary and continuously evolving data. Learning a classification model on this data would require a framework that is able to adapt itself to newer circumstances. Under such circumstances, transfer learning has come to be a dependable methodology for improving classification performance with reduced training costs and without the need for explicit relearning from scratch. In this thesis, a novel instance transfer technique that adapts a "Cost-sensitive" variation of AdaBoost is presented. The method capitalizes on the theoretical and functional properties of AdaBoost to selectively reuse outdated training instances obtained from a "source" domain to effectively classify unseen instances occurring in a different, but related "target" domain. The algorithm is evaluated on real-world classification problems namely accelerometer based 3D gesture recognition, smart home activity recognition and text categorization. The performance on these datasets is analyzed and evaluated against popular boosting-based instance transfer techniques. In addition, supporting empirical studies, that investigate some of the less explored bottlenecks of boosting based instance transfer methods, are presented, to understand the suitability and effectiveness of this form of knowledge transfer.
ContributorsVenkatesan, Ashok (Author) / Panchanathan, Sethuraman (Thesis advisor) / Li, Baoxin (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2011
Description
Sparse learning is a technique in machine learning for feature selection and dimensionality reduction, to find a sparse set of the most relevant features. In any machine learning problem, there is a considerable amount of irrelevant information, and separating relevant information from the irrelevant information has been a topic of focus. In supervised learning like regression, the data consists of many features and only a subset of the features may be responsible for the result. Also, the features might require special structural requirements, which introduces additional complexity for feature selection. The sparse learning package, provides a set of algorithms for learning a sparse set of the most relevant features for both regression and classification problems. Structural dependencies among features which introduce additional requirements are also provided as part of the package. The features may be grouped together, and there may exist hierarchies and over- lapping groups among these, and there may be requirements for selecting the most relevant groups among them. In spite of getting sparse solutions, the solutions are not guaranteed to be robust. For the selection to be robust, there are certain techniques which provide theoretical justification of why certain features are selected. The stability selection, is a method for feature selection which allows the use of existing sparse learning methods to select the stable set of features for a given training sample. This is done by assigning probabilities for the features: by sub-sampling the training data and using a specific sparse learning technique to learn the relevant features, and repeating this a large number of times, and counting the probability as the number of times a feature is selected. Cross-validation which is used to determine the best parameter value over a range of values, further allows to select the best parameter value. This is done by selecting the parameter value which gives the maximum accuracy score. With such a combination of algorithms, with good convergence guarantees, stable feature selection properties and the inclusion of various structural dependencies among features, the sparse learning package will be a powerful tool for machine learning research. Modular structure, C implementation, ATLAS integration for fast linear algebraic subroutines, make it one of the best tool for a large sparse setting. The varied collection of algorithms, support for group sparsity, batch algorithms, are a few of the notable functionality of the SLEP package, and these features can be used in a variety of fields to infer relevant elements. The Alzheimer Disease(AD) is a neurodegenerative disease, which gradually leads to dementia. The SLEP package is used for feature selection for getting the most relevant biomarkers from the available AD dataset, and the results show that, indeed, only a subset of the features are required to gain valuable insights.
ContributorsThulasiram, Ramesh (Author) / Ye, Jieping (Thesis advisor) / Xue, Guoliang (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2011
Description
Consumer goods supply chains have gradually incorporated lean manufacturing principles to identify and reduce non-value-added activities. Companies implementing lean practices have experienced improvements in cost, quality, and demand responsiveness. However certain elements of these practices, especially those related to transportation and distribution may have detrimental impact on the environment. This study asks: What impact do current best practices in lean logistics and retailing have on environmental performance? The research hypothesis of this dissertation establishes that lean distribution of durable and consumable goods can result in an increased amount of carbon dioxide emissions, leading to climate change and natural resource depletion impacts, while lean retailing operations can reduce carbon emissions. Distribution and retailing phases of the life cycle are characterized in a two-echelon supply chain discrete-event simulation modeled after current operations from leading organizations based in the U.S. Southwest. By conducting an overview of critical sustainability issues and their relationship with consumer products, it is possible to address the environmental implications of lean logistics and retailing operations. Provided the waste reduction nature from lean manufacturing, four lean best practices are examined in detail in order to formulate specific research propositions. These propositions are integrated into an experimental design linking annual carbon dioxide equivalent emissions to: (1) shipment frequency between supply chain partners, (2) proximity between decoupling point of products and final customers, (3) inventory turns at the warehousing level, and (4) degree of supplier integration. All propositions are tested through the use of the simulation model. Results confirmed the four research propositions. Furthermore, they suggest synergy between product shipment frequency among supply chain partners and product management due to lean retailing practices. In addition, the study confirms prior research speculations about the potential carbon intensity from transportation operations subject to lean principles.
ContributorsUgarte Irizarri, Gustavo Marco Antonio (Author) / Golden, Jay S. (Thesis advisor) / Dooley, Kevin J. (Thesis advisor) / Boone, Christopher G. (Committee member) / Basile, George M. (Committee member) / Arizona State University (Publisher)
Created2011