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The main objective of this research is to develop an integrated method to study emergent behavior and consequences of evolution and adaptation in engineered complex adaptive systems (ECASs). A multi-layer conceptual framework and modeling approach including behavioral and structural aspects is provided to describe the structure of a class of engineered complex systems and predict their future adaptive patterns. The approach allows the examination of complexity in the structure and the behavior of components as a result of their connections and in relation to their environment. This research describes and uses the major differences of natural complex adaptive systems (CASs) with artificial/engineered CASs to build a framework and platform for ECAS. While this framework focuses on the critical factors of an engineered system, it also enables one to synthetically employ engineering and mathematical models to analyze and measure complexity in such systems. In this way concepts of complex systems science are adapted to management science and system of systems engineering. In particular an integrated consumer-based optimization and agent-based modeling (ABM) platform is presented that enables managers to predict and partially control patterns of behaviors in ECASs. Demonstrated on the U.S. electricity markets, ABM is integrated with normative and subjective decision behavior recommended by the U.S. Department of Energy (DOE) and Federal Energy Regulatory Commission (FERC). The approach integrates social networks, social science, complexity theory, and diffusion theory. Furthermore, it has unique and significant contribution in exploring and representing concrete managerial insights for ECASs and offering new optimized actions and modeling paradigms in agent-based simulation.
ContributorsHaghnevis, Moeed (Author) / Askin, Ronald G. (Thesis advisor) / Armbruster, Dieter (Thesis advisor) / Mirchandani, Pitu (Committee member) / Wu, Tong (Committee member) / Hedman, Kory (Committee member) / Arizona State University (Publisher)
Created2013
Description
Ionizing radiation used in the patient diagnosis or therapy has negative effects on the patient body in short term and long term depending on the amount of exposure. More than 700,000 examinations are everyday performed on Interventional Radiology modalities [1], however; there is no patient-centric information available to the patient or the Quality Assurance for the amount of organ dose received. In this study, we are exploring the methodologies to systematically reduce the absorbed radiation dose in the Fluoroscopically Guided Interventional Radiology procedures. In the first part of this study, we developed a mathematical model which determines a set of geometry settings for the equipment and a level for the energy during a patient exam. The goal is to minimize the amount of absorbed dose in the critical organs while maintaining image quality required for the diagnosis. The model is a large-scale mixed integer program. We performed polyhedral analysis and derived several sets of strong inequalities to improve the computational speed and quality of the solution. Results present the amount of absorbed dose in the critical organ can be reduced up to 99% for a specific set of angles. In the second part, we apply an approximate gradient method to simultaneously optimize angle and table location while minimizing dose in the critical organs with respect to the image quality. In each iteration, we solve a sub-problem as a MIP to determine the radiation field size and corresponding X-ray tube energy. In the computational experiments, results show further reduction (up to 80%) of the absorbed dose in compare with previous method. Last, there are uncertainties in the medical procedures resulting imprecision of the absorbed dose. We propose a robust formulation to hedge from the worst case absorbed dose while ensuring feasibility. In this part, we investigate a robust approach for the organ motions within a radiology procedure. We minimize the absorbed dose for the critical organs across all input data scenarios which are corresponding to the positioning and size of the organs. The computational results indicate up to 26% increase in the absorbed dose calculated for the robust approach which ensures the feasibility across scenarios.
ContributorsKhodadadegan, Yasaman (Author) / Zhang, Muhong (Thesis advisor) / Pavlicek, William (Thesis advisor) / Fowler, John (Committee member) / Wu, Tong (Committee member) / Arizona State University (Publisher)
Created2013
Description
With the increase in computing power and availability of data, there has never been a greater need to understand data and make decisions from it. Traditional statistical techniques may not be adequate to handle the size of today's data or the complexities of the information hidden within the data. Thus knowledge discovery by machine learning techniques is necessary if we want to better understand information from data. In this dissertation, we explore the topics of asymmetric loss and asymmetric data in machine learning and propose new algorithms as solutions to some of the problems in these topics. We also studied variable selection of matched data sets and proposed a solution when there is non-linearity in the matched data. The research is divided into three parts. The first part addresses the problem of asymmetric loss. A proposed asymmetric support vector machine (aSVM) is used to predict specific classes with high accuracy. aSVM was shown to produce higher precision than a regular SVM. The second part addresses asymmetric data sets where variables are only predictive for a subset of the predictor classes. Asymmetric Random Forest (ARF) was proposed to detect these kinds of variables. The third part explores variable selection for matched data sets. Matched Random Forest (MRF) was proposed to find variables that are able to distinguish case and control without the restrictions that exists in linear models. MRF detects variables that are able to distinguish case and control even in the presence of interaction and qualitative variables.
ContributorsKoh, Derek (Author) / Runger, George C. (Thesis advisor) / Wu, Tong (Committee member) / Pan, Rong (Committee member) / Cesta, John (Committee member) / Arizona State University (Publisher)
Created2013
Description
Since its inception in 1973, the Endangered Species Act has been met with both praise and criticism. More than 40 years later, the Act is still polarizing, with proponents applauding its power to protect species and critics arguing against its perceived ineffectiveness and potential mismanagement. Recovery plans, which were required by the 1988 amendments to the Act, play an important role in organizing efforts to protect and recover species under the Act. In 1999, in an effort to evaluate the process, the Society for Conservation Biology commissioned an independent review of endangered species recovery planning. From these findings, the SCB made key recommendations for how management agencies could improve the recovery planning process, after which the Fish and Wildlife Service and the National Marine Fisheries Service redrafted their recovery planning guidelines. One important recommendation called for recovery plans to make threats a primary focus, including organizing and prioritizing recovery tasks for threat abatement. Here, I seek to determine the extent to which SCB recommendations were incorporated into these new guidelines, and if, in turn, the recommendations regarding threats manifested in recovery plans written under the new guidelines. I found that the guidelines successfully incorporated most SCB recommendations, except those that addressed monitoring. As a result, recent recovery plans have improved in their treatment of threats, but still fail to adequately incorporate threat monitoring. This failure suggests that developing clear guidelines for monitoring should be an important priority in future ESA recovery planning.
ContributorsTroyer, Caitlin (Author) / Gerber, Leah (Thesis advisor) / Minteer, Ben (Committee member) / Guston, David (Committee member) / Arizona State University (Publisher)
Created2014
Description
This dissertation examines the nexus of three trends in electricity systems transformations underway worldwide—the scale-up of renewable energy, regionalization, and liberalization. Interdependent electricity systems are being envisioned that require partnership and integration across power disparities. This research explores how actors in the Mediterranean region envisioned a massive scale-up of renewable energy within a single electricity system and market across Europe, North Africa, and the Middle East. It asks: How are regional sociotechnical systems envisioned? What are the anticipated consequences of a system for a region with broad disparities and deep sociopolitical differences? What can be learned about energy justice by examining this vision at multiple scales? A sociotechnical systems framework is used to analyze energy transformations, interweaving the technical aspects with politics, societal effects, and political development issues. This research utilized mixed qualitative methods to analyze Mediterranean electricity transformations at multiple scales, including fieldwork in Morocco and Germany, document analysis, and event ethnography. Each scale—from a global history of concentrating solar power technologies to a small village in Morocco—provides a different lens on the sociotechnical system and its implications for justice. This study updates Thomas Hughes’ Networks of Power, the canonical history of the sociotechnical development of electricity systems, by adding new aspects to sociotechnical electricity systems theory. First, a visioning process now plays a crucial role in guiding innovation and has a lasting influence on the justice outcomes. Second, rather than simply providing people with heat and light, electrical power systems in the 21st century are called upon to address complex integrated solutions. Furthermore, building a sustainable energy system is now a retrofitting agenda, as system builders must graft new infrastructure on top of old systems. Third, the spatial and temporal aspects of sociotechnical energy systems should be amended to account for constructed geography and temporal complexity. Fourth, transnational electricity systems pose new challenges for politics and political development. Finally, this dissertation presents a normative framework for conceptualizing and evaluating energy justice. Multi-scalar, systems-level justice requires collating diverse ideas about energy justice, expanding upon them based on the empirical material, and evaluating them with this framework.
ContributorsMoore, Sharlissa (Author) / Hackett, Ed J. (Thesis advisor) / Minteer, Ben (Committee member) / Parmentier, Mary Jane (Committee member) / Wetmore, Jameson (Committee member) / Arizona State University (Publisher)
Created2015
Description
Over the past two decades there has been much discussion surrounding the potential of zoos as conservation institutions. Although zoos have clearly intensified their rhetorical and programmatic commitment to conservation (both ex situ and in situ), many critics remain skeptical of these efforts. This study was comprised of two parts: 1) an investigation of the general relationship between U.S. zoological institutions and the conservation agenda, and 2) a more specific single case study of conservation engagement and institutional identity at the Phoenix Zoo. Methods included extensive literature review, expert interviews with scholars and zoo professionals, site visits to the Phoenix Zoo and archival research. I found that the Phoenix Zoo is in the process of consciously creating a conservation-centered institutional identity by implementing and publicizing various conservation initiatives. Despite criticism of the embrace of conservation by zoos today, these institutions will be increasingly important agents of biodiversity protection and conservation education in this century.
ContributorsLove, Karen (Author) / Minteer, Ben (Thesis advisor) / Kinzig, Ann (Committee member) / Collins, James (Committee member) / Arizona State University (Publisher)
Created2014
Description
This dissertation analyzes the way in which leaders of certain Taiwanese Buddhist organizations associated with a strand of Buddhist modernism called "humanistic Buddhism" use discourse and rhetoric to make environmentalism meaningful to their members. It begins with an assessment of the field of religion and ecology, situating it in the context of secular environmental ethics. It identifies rhetoric and discourse as important but under acknowledged elements in literature on environmental ethics, both religious and secular, and relates this lack of attention to rhetoric to the presence of a problematic gap between environmental ethics theory and environmentalist practice. This dissertation develops a methodology of rhetorical analysis that seeks to assess how rhetoric contributes to alleviating this gap in religious environmentalism. In particular, this dissertation analyzes the development of environmentalism as a major element of humanistic Buddhist groups in Taiwan and seeks to show that a rhetorical analysis helps demonstrate how these organizations have sought to make environmentalism a meaningful subject of contemporary Buddhist religiosity. This dissertation will present an extended analysis of the concept of "spiritual environmentalism," a term developed and promoted by the late Ven. Shengyan (1930-2009), founder of the Taiwanese Buddhist organization Dharma Drum Mountain. Furthermore, this dissertation suggests that the rhetorical methodology proposed herein offers offers a direction for scholars to more effectively engage with religion and ecology in ways that address both descriptive/analytic approaches and constructive engagements with various forms of religious environmentalism.
ContributorsClippard, Seth (Author) / Chen, Huaiyu (Thesis advisor) / Tirosh-Samuelson, Hava (Committee member) / Bokenkamp, Stephen (Committee member) / Tillman, Hoyt (Committee member) / Minteer, Ben (Committee member) / Arizona State University (Publisher)
Created2012
Description
In recent years, service oriented computing (SOC) has become a widely accepted paradigm for the development of distributed applications such as web services, grid computing and cloud computing systems. In service-based systems (SBS), multiple service requests with specific performance requirements make services compete for system resources. IT service providers need to allocate resources to services so the performance requirements of customers can be satisfied. Workload and performance models are required for efficient resource management and service performance assurance in SBS. This dissertation develops two methods to understand and model the cause-effect relations of service-related activities with resources workload and service performance. Part one presents an empirical method that requires the collection of system dynamics data and the application of statistical analyses. The results show that the method is capable to: 1) uncover the impacts of services on resource workload and service performance, 2) identify interaction effects of multiple services running concurrently, 3) gain insights about resource and performance tradeoffs of services, and 4) build service workload and performance models. In part two, the empirical method is used to investigate the impacts of services, security mechanisms and cyber attacks on resources workload and service performance. The information obtained is used to: 1) uncover interaction effects of services, security mechanisms and cyber attacks, 2) identify tradeoffs within limits of system resources, and 3) develop general/specific strategies for system survivability. Finally, part three presents a framework based on the usage profiles of services competing for resources and the resource-sharing schemes. The framework is used to: 1) uncover the impacts of service parameters (e.g. arrival distribution, execution time distribution, priority, workload intensity, scheduling algorithm) on workload and performance, and 2) build service workload and performance models at individual resources. The estimates obtained from service workload and performance models at individual resources can be aggregated to obtain overall estimates of services through multiple system resources. The workload and performance models of services obtained through both methods can be used for the efficient resource management and service performance assurance in SBS.
ContributorsMartinez Aranda, Billibaldo (Author) / Ye, Nong (Thesis advisor) / Wu, Tong (Committee member) / Sarjoughian, Hessam S. (Committee member) / Pan, Rong (Committee member) / Arizona State University (Publisher)
Created2012
Description
Professional environmental scientists are increasingly under pressure to inform and even shape policy. Scientists engage policy effectively when they act within the bounds of objectivity, credibility, and authority, yet significant portions of the scientific community condemn such acts as advocacy. They argue that it is nonobjective, that it risks damaging the credibility of science, and that it is an abuse of authority. This means objectivity, credibility, and authority deserve direct attention before the policy advocacy quagmire can be reasonably understood. I investigate the meaning of objectivity in science and that necessarily brings the roles of values in science into question. This thesis is a sociological study of the roles environmental values play in the decisions of environmental scientists working in the institution of academia. I argue that the gridlocked nature of the environmental policy advocacy debates can be traced to what seems to be a deep tension and perhaps confusion among these scientists. I provide empirical evidence of this tension and confusion through the use of in depth semi-structured interviews among a sampling of academic environmental scientists (AES). I show that there is a struggle for these AES to reconcile their support for environmentalist values and goals with their commitment to scientific objectivity and their concerns about being credible scientists in the academy. Additionally, I supplemented my data collection with environmental sociology and history, plus philosophy and sociology of science literatures. With this, I developed a system for understanding values in science (of which environmental values are a subset) with respect to the limits of my sample and study. This examination of respondent behavior provides support that it is possible for AES to act on their environmental values without compromising their objectivity, credibility, and authority. These scientists were not likely to practice this in conversations with colleagues and policy-makers, but were likely to behave this way with students. The legitimate extension of this behavior is a viable route for continuing to integrate the human and social dimensions of environmental science into its practice, its training, and its relationship with policy.
ContributorsAppleton, Caroline (Author) / Minteer, Ben (Thesis advisor) / Chew, Matt (Committee member) / Armendt, Brad (Committee member) / Arizona State University (Publisher)
Created2012
Description
Alzheimer's Disease (AD) is the most common form of dementia observed in elderly patients and has significant social-economic impact. There are many initiatives which aim to capture leading causes of AD. Several genetic, imaging, and biochemical markers are being explored to monitor progression of AD and explore treatment and detection options. The primary focus of this thesis is to identify key biomarkers to understand the pathogenesis and prognosis of Alzheimer's Disease. Feature selection is the process of finding a subset of relevant features to develop efficient and robust learning models. It is an active research topic in diverse areas such as computer vision, bioinformatics, information retrieval, chemical informatics, and computational finance. In this work, state of the art feature selection algorithms, such as Student's t-test, Relief-F, Information Gain, Gini Index, Chi-Square, Fisher Kernel Score, Kruskal-Wallis, Minimum Redundancy Maximum Relevance, and Sparse Logistic regression with Stability Selection have been extensively exploited to identify informative features for AD using data from Alzheimer's Disease Neuroimaging Initiative (ADNI). An integrative approach which uses blood plasma protein, Magnetic Resonance Imaging, and psychometric assessment scores biomarkers has been explored. This work also analyzes the techniques to handle unbalanced data and evaluate the efficacy of sampling techniques. Performance of feature selection algorithm is evaluated using the relevance of derived features and the predictive power of the algorithm using Random Forest and Support Vector Machine classifiers. Performance metrics such as Accuracy, Sensitivity and Specificity, and area under the Receiver Operating Characteristic curve (AUC) have been used for evaluation. The feature selection algorithms best suited to analyze AD proteomics data have been proposed. The key biomarkers distinguishing healthy and AD patients, Mild Cognitive Impairment (MCI) converters and non-converters, and healthy and MCI patients have been identified.
ContributorsDubey, Rashmi (Author) / Ye, Jieping (Thesis advisor) / Wang, Yalin (Committee member) / Wu, Tong (Committee member) / Arizona State University (Publisher)
Created2012