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- All Subjects: Sustainability
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Description
Water is the defining issue in determining the development and growth of human populations of the Southwest. The cities of Las Vegas, Phoenix, Tucson, Albuquerque, and El Paso have experienced rapid and exponential growth over the past 50 years. The outlook for having access to sustainable sources of water to support this growth is not promising due to water demand and supply deficits. Regional water projects have harnessed the Colorado and Rio Grande rivers to maximize the utility of the water for human consumption and environmental laws have been adopted to regulate the beneficial use of this water, but it still is not enough to create sustainable future for rapidly growing southwest cities. Future growth in these cities will depend on finding new sources of water and creative measures to maximize the utility of existing water resources. The challenge for southwest cities is to establish policies, procedures, and projects that maximizes the use of water and promotes conservation from all areas of municipal users. All cities are faced with the same challenges, but have different options for how they prioritize their water resources. The principal means of sustainable water management include recovery, recharge, reuse, and increasing the efficiency of water delivery. Other strategies that have been adopted include harvesting of rainwater, building codes that promote efficient water use, tiered water rates, turf removal programs, residential water auditing, and native plant promotion. Creating a sustainable future for the southwest will best be achieved by cities that adopt an integrated approach to managing their water resources including discouraging discretionary uses of water, adoption of building and construction codes for master plans, industrial plants, and residential construction. Additionally, a robust plan for education of the public is essential to create a culture of conservation from a very young age.
ContributorsMalloy, Richard (Richard A.) (Author) / Brock, John (Thesis advisor) / Martin, Chris (Thesis advisor) / Thor, Eric (Committee member) / Arizona State University (Publisher)
Created2013
Description
Surgery as a profession requires significant training to improve both clinical decision making and psychomotor proficiency. In the medical knowledge domain, tools have been developed, validated, and accepted for evaluation of surgeons' competencies. However, assessment of the psychomotor skills still relies on the Halstedian model of apprenticeship, wherein surgeons are observed during residency for judgment of their skills. Although the value of this method of skills assessment cannot be ignored, novel methodologies of objective skills assessment need to be designed, developed, and evaluated that augment the traditional approach. Several sensor-based systems have been developed to measure a user's skill quantitatively, but use of sensors could interfere with skill execution and thus limit the potential for evaluating real-life surgery. However, having a method to judge skills automatically in real-life conditions should be the ultimate goal, since only with such features that a system would be widely adopted. This research proposes a novel video-based approach for observing surgeons' hand and surgical tool movements in minimally invasive surgical training exercises as well as during laparoscopic surgery. Because our system does not require surgeons to wear special sensors, it has the distinct advantage over alternatives of offering skills assessment in both learning and real-life environments. The system automatically detects major skill-measuring features from surgical task videos using a computing system composed of a series of computer vision algorithms and provides on-screen real-time performance feedback for more efficient skill learning. Finally, the machine-learning approach is used to develop an observer-independent composite scoring model through objective and quantitative measurement of surgical skills. To increase effectiveness and usability of the developed system, it is integrated with a cloud-based tool, which automatically assesses surgical videos upload to the cloud.
ContributorsIslam, Gazi (Author) / Li, Baoxin (Thesis advisor) / Liang, Jianming (Thesis advisor) / Dinu, Valentin (Committee member) / Greenes, Robert (Committee member) / Smith, Marshall (Committee member) / Kahol, Kanav (Committee member) / Patel, Vimla L. (Committee member) / Arizona State University (Publisher)
Created2013
Description
Transformational sustainability science demands that stakeholders and researchers consider the needs and values of future generations in pursuit of solutions to sustainability problems. This dissertation research focuses on the real-world problem of unsustainable water governance in the Phoenix region of Central Arizona. A sustainability transition is the local water system is necessary to overcome sustainability challenges and scenarios can be used to explore plausible and desirable futures to inform a transition, but this requires some methodological refinements. This dissertation refines scenario methodology to generate water governance scenarios for metropolitan Phoenix that: (i) feature enhanced stakeholder participation; (ii) incorporate normative values and preferences; (iii) focus on governance actors and their activities; and (iv) meet an expanded set of quality criteria. The first study in the dissertation analyzes and evaluates participatory climate change scenarios to provide recommendations for the construction and use of scenarios that advance climate adaptation and mitigation efforts. The second study proposes and tests a set of plausibility indications to substantiate or evaluate claims that scenarios and future projections could become reality, helping to establish the legitimacy of radically different or transformative scenarios among an extended peer community. The case study of water governance begins with the third study, which includes a current state analysis and sustainability appraisal of the Phoenix-area water system. This is followed by a fourth study which surveys Phoenix-area water decision-makers to better understand water-related preferences for use in scenario construction. The fifth and final study applies a multi-method approach to construct future scenarios of water governance in metropolitan Phoenix in 2030 using stakeholder preferences, among other normative frames, and testing systemic impacts with WaterSim 5.0, a dynamic simulation model of water in the region. The scenarios are boundary objects around which stakeholders can weigh tradeoffs, set priorities and reflect on impacts of water-related activities, broadening policy dialogues around water governance in central Arizona. Together the five studies advance transformational sustainability research by refining methods to engage stakeholders in crafting futures that define how individuals and institutions should operate in transformed and sustainable systems.
ContributorsKeeler, Lauren Withycombe (Author) / Wiek, Arnim (Thesis advisor) / White, Dave D (Committee member) / Lang, Daniel J (Committee member) / Arizona State University (Publisher)
Created2014
Description
As the size and scope of valuable datasets has exploded across many industries and fields of research in recent years, an increasingly diverse audience has sought out effective tools for their large-scale data analytics needs. Over this period, machine learning researchers have also been very prolific in designing improved algorithms which are capable of finding the hidden structure within these datasets. As consumers of popular Big Data frameworks have sought to apply and benefit from these improved learning algorithms, the problems encountered with the frameworks have motivated a new generation of Big Data tools to address the shortcomings of the previous generation. One important example of this is the improved performance in the newer tools with the large class of machine learning algorithms which are highly iterative in nature. In this thesis project, I set about to implement a low-rank matrix completion algorithm (as an example of a highly iterative algorithm) within a popular Big Data framework, and to evaluate its performance processing the Netflix Prize dataset. I begin by describing several approaches which I attempted, but which did not perform adequately. These include an implementation of the Singular Value Thresholding (SVT) algorithm within the Apache Mahout framework, which runs on top of the Apache Hadoop MapReduce engine. I then describe an approach which uses the Divide-Factor-Combine (DFC) algorithmic framework to parallelize the state-of-the-art low-rank completion algorithm Orthogoal Rank-One Matrix Pursuit (OR1MP) within the Apache Spark engine. I describe the results of a series of tests running this implementation with the Netflix dataset on clusters of various sizes, with various degrees of parallelism. For these experiments, I utilized the Amazon Elastic Compute Cloud (EC2) web service. In the final analysis, I conclude that the Spark DFC + OR1MP implementation does indeed produce competitive results, in both accuracy and performance. In particular, the Spark implementation performs nearly as well as the MATLAB implementation of OR1MP without any parallelism, and improves performance to a significant degree as the parallelism increases. In addition, the experience demonstrates how Spark's flexible programming model makes it straightforward to implement this parallel and iterative machine learning algorithm.
ContributorsKrouse, Brian (Author) / Ye, Jieping (Thesis advisor) / Liu, Huan (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2014
Description
In response to the rapid rise of emerging markets, shorter product lifecycles, increasing global exchange and worldwide competition, companies are implementing `sustainable development' as a mechanism by which to maintain competitive global advantage. Sustainable product development approaches used in industry focus mainly on environmental issues, and to a certain extent on social and economic aspects. Unfortunately, companies have often ignored or are unsure of how to deal with the cultural dimensions of sustainable product development. Multi-nationals expanding their business across international boundaries are agents of cultural change and should be cognizant of the impact their products have on local markets. Companies need to develop a deeper understanding of local cultures in order to design and deliver products that are not only economically viable but also culturally appropriate. To demonstrate applicability of cultural appropriate design, this research undertakes a case study of food systems in India specifically focusing on the exchange of fresh fruits and vegetables (FFV). This study focuses on understanding the entire supply chain of FFV exchange, which includes consumer experiences, distribution practices and production processes. This study also compares different distribution channels and exchange practices and analyzes the pattern of authority between different players within the distribution network. The ethnographic methods for data collection included a photo-journal assignment, shop-along visits, semi-structured interviews, a participatory design activity and focus group studies. The study revealed that traditional retail formats like pushcart vendors, street retailers and city retail markets are generally preferred over modern retail stores. For consumers, shopping is a non-choreographed activity often resulting in exercising, socializing and accidental purchases. Informal communication, personal relationships and openness to bargaining were important aspects of the consumer-retailer relationship. This study presents cultural insights into interactions, artifacts and contexts relevant to FFV systems in India. It also presents key implications for the field of design, design research, cultural studies, consumer research and sustainability. The insights gained from this study will act as guidelines for designers, researchers and corporations interested in designing products and services that are culturally appropriate to contexts of production, distribution and consumption.
ContributorsDhadphale, Tejas (Author) / Giard, Jacques (Thesis advisor) / Boradkar, Prasad (Thesis advisor) / Broome, Benjamin (Committee member) / Arizona State University (Publisher)
Created2013
Description
Over the past few decades, businesses globally have advanced in incorporating the principles of sustainability as they strive to align economic outcomes with growing and complex social and environmental demands and opportunities. This transition is conditioned by the maturity, scale, and geographical location of a business (among other factors), with particular challenges placed on small enterprises in middle- to low-income communities. Within this context, the overarching research question of this dissertation is why and how business incubation processes may foster sustainable enterprises at the middle and base of the socioeconomic pyramid (MoP/BoP). To explore this question, in this project I used as a case study the experience of a network of social business incubators operated by Tecnologico de Monterrey, a private, non-profit, multi-campus university system in Mexico. Centering on its campus in Guadalajara and in order to understand if and how MoP/BoP businesses address sustainability, I developed a current state assessment of incubator processes, analyzing during two semesters the activities of incubated entrepreneurs and their goals, motivations, and outcomes. The general expectation at the outset of the study was that Tec's social business incubation process, in both its design and implementation, focuses on the economic viability and outcomes of incubated projects and hence does not promote entrepreneur commitment to sustainability goals and practices. The general approach of the research project involved a qualitative, in-depth ethnographic assessment of participants. Data were collected by means of the following research tools: (a) archival and documentary review, (b) participant observation, (c) surveys of participants (entrepreneurs and advisors/mentors), and (d) semi-structured interviews of participants. The overall design of the research was inspired by the transitions management approach and by the intervention research method, while qualitative results were assessed under the grounded theory approach. Results of the research are reported under three general categories: (a) analysis of entrepreneur goals, motivations, and outcomes, (b) identification of social and environmental opportunities, and (c) review of the role of social networks and broader support structures. While results confirmed the general expectation of the study, it was possible to establish (based on the interaction with the entrepreneurs and other actors) that there is both interest and commitment to identify and explore opportunities in social and environmental issues. Thus, the dissertation concludes with a proposal for potential future interventions in this social incubator, exploring a new vision and strategies for a transition to a more sustainability-oriented approach. Finally, key recommendations define the most critical elements of an agenda for transition in the social incubation process at Campus Guadalajara and provide input for other efforts.
ContributorsWood, Mark Williams (Author) / Redman, Charles L. (Thesis advisor) / Wiek, Arnim (Committee member) / Basile, George M (Committee member) / Arizona State University (Publisher)
Created2014
Description
Industrial activities have damaged the natural environment at an unprecedented scale. A number of approaches to environmentally responsible design and sustainability have been developed that are aimed at minimizing negative impacts derived from products on the environment. Environmental assessment methods exist as well to measure these impacts. Major environmentally responsible approaches to design and sustainability were analyzed using content analysis techniques. The results show several recommendations to minimize product impacts through design, and dimensions to which they belong. Two products made by a manufacturing firm with exceptional commitment to environmental responsibility were studied to understand how design approaches and assessment methods were used in their development. The results showed that the company used several strategies for environmentally responsible design as well as assessment methods in product and process machine design, both of which resulted in reduced environmental impacts of their products. Factors that contributed positively to reduce impacts are the use of measurement systems alongside environmentally responsible design, as well as inspiring innovations by observing how natural systems work. From a managerial perspective, positive influencing factors included a commitment to environmental responsibility from the executive level of the company and a clear vision about sustainability that has been instilled from the top through every level of employees. Additionally, a high degree of collaboration between the company and its suppliers and customers was instrumental in making the success possible.
ContributorsHuerta Gajardo, Oscar André (Author) / Giard, Jacques (Thesis advisor) / White, Philip (Committee member) / Dooley, Kevin (Committee member) / Arizona State University (Publisher)
Created2014
Description
The need for alternative energy efficient building heating and cooling technologies has given rise to the development and widespread use of Ground Coupled Heat Pump (GCHP) systems. This dissertation looks at the feasibility of using GCHP systems as a viable economic alternative to traditional air source cooling systems (ASHP) for conditioning buildings in the hot, semi-arid climate of Phoenix, Arizona. Despite high initial costs, GCHPs are gaining a foothold in northern climates where heating dominates, in large part due to government incentives. However, due to issues associated with low ground heat exchanger (GHE) efficiency and thermally-induced soil deformations, GCHPs are typically not considered a viable option in hot climates with deep groundwater and low permeability soil. To evaluate the energy performance and technical feasibility of GCHPs in Phoenix, the DOE 5,500 sq.ft small office, commercial building prototype was simulated in EnergyPlus to determine the cooling and heating loads. Next, a commercial software program, Ground Loop Design (GLD), was used to design and simulate the annual energy performance of both vertical (V-GCHPs) and horizontal GCHPs (H-GCHPs). Life cycle costs (LCC) were evaluated using realistic market costs both under dry, as well as fully saturated soil conditions (meant as an upper performance limit achievable by ground modification techniques). This analysis included performing several sensitivity analyses and also investigating the effect of financial rebates. The range of annual energy savings from the GCHP system for space cooling and heating was around 38-40% compared to ASHPs for dry soil. Saturated soil condition significantly affects the length of the GHE. For V-GCHPs, there was about 26% decrease in the length of GHE, thereby reducing the initial cost by 18-19% and decreasing the payback period by 24-25%. Likewise, for H-GCHPs, the length of GHE was reduced by 25% resulting in 22% and 39-42 % reduction in the initial cost and payback period respectively. With federal incentives, H-GCHPs under saturated soil conditions have the least LCC and a good payback periods of 2.3-4.7 years. V-GCHPs systems were been found to have payback periods of over 25 years, making them unfeasible for Phoenix, AZ, for the type of building investigated.
ContributorsTambe, Vaibhavi Parmanand (Author) / Reddy, T Agami (Thesis advisor) / Kavanzanjian, Edward (Thesis advisor) / Bryan, Harvey (Committee member) / Arizona State University (Publisher)
Created2014
Description
Multidimensional data have various representations. Thanks to their simplicity in modeling multidimensional data and the availability of various mathematical tools (such as tensor decompositions) that support multi-aspect analysis of such data, tensors are increasingly being used in many application domains including scientific data management, sensor data management, and social network data analysis. Relational model, on the other hand, enables semantic manipulation of data using relational operators, such as projection, selection, Cartesian-product, and set operators. For many multidimensional data applications, tensor operations as well as relational operations need to be supported throughout the data life cycle. In this thesis, we introduce a tensor-based relational data model (TRM), which enables both tensor- based data analysis and relational manipulations of multidimensional data, and define tensor-relational operations on this model. Then we introduce a tensor-relational data management system, so called, TensorDB. TensorDB is based on TRM, which brings together relational algebraic operations (for data manipulation and integration) and tensor algebraic operations (for data analysis). We develop optimization strategies for tensor-relational operations in both in-memory and in-database TensorDB. The goal of the TRM and TensorDB is to serve as a single environment that supports the entire life cycle of data; that is, data can be manipulated, integrated, processed, and analyzed.
ContributorsKim, Mijung (Author) / Candan, K. Selcuk (Thesis advisor) / Davulcu, Hasan (Committee member) / Sundaram, Hari (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2014
Description
This thesis is an initial test of the hypothesis that superficial measures suffice for measuring collaboration among pairs of students solving complex math problems, where the degree of collaboration is categorized at a high level. Data were collected
in the form of logs from students' tablets and the vocal interaction between pairs of students. Thousands of different features were defined, and then extracted computationally from the audio and log data. Human coders used richer data (several video streams) and a thorough understand of the tasks to code episodes as
collaborative, cooperative or asymmetric contribution. Machine learning was used to induce a detector, based on random forests, that outputs one of these three codes for an episode given only a characterization of the episode in terms of superficial features. An overall accuracy of 92.00% (kappa = 0.82) was obtained when
comparing the detector's codes to the humans' codes. However, due irregularities in running the study (e.g., the tablet software kept crashing), these results should be viewed as preliminary.
in the form of logs from students' tablets and the vocal interaction between pairs of students. Thousands of different features were defined, and then extracted computationally from the audio and log data. Human coders used richer data (several video streams) and a thorough understand of the tasks to code episodes as
collaborative, cooperative or asymmetric contribution. Machine learning was used to induce a detector, based on random forests, that outputs one of these three codes for an episode given only a characterization of the episode in terms of superficial features. An overall accuracy of 92.00% (kappa = 0.82) was obtained when
comparing the detector's codes to the humans' codes. However, due irregularities in running the study (e.g., the tablet software kept crashing), these results should be viewed as preliminary.
ContributorsViswanathan, Sree Aurovindh (Author) / VanLehn, Kurt (Thesis advisor) / T.H CHI, Michelene (Committee member) / Walker, Erin (Committee member) / Arizona State University (Publisher)
Created2014