ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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- All Subjects: Mathematics
Description
A fundamental motivation for this study was the underrepresentation of women in Science, Technology, Engineering and Mathematics careers. There is no doubt women and men can achieve at the same level in Mathematics, yet it is not clear why women are opting out. Adding race to the equation makes the underrepresentation more dramatic. Considering the important number of Latinos in the United States, especially in school age, it is relevant to find what reasons could be preventing them from participating in the careers mentioned. This study highlight the experiences young successful Latinas have in school Mathematics and how they shape their identities, to uncover potential conflicts that could later affect their participation in the field. In order to do so the author utilizes feminist approaches, Latino Critical Theory and Critical Race Theory to analyze the stories compiled. The participants were five successful Latinas in Mathematics, part of the honors track in a school in the Southwest of the United States. The theoretical lenses chosen allowed women of color to tell their story, highlighting the intersection of race, gender and socio-economical status as a factor shaping different schooling experiences. The author found that the participants distanced themselves from their home culture and from other girls at times to allow themselves to develop and maintain a successful identity as a Mathematics student. When talking about Latinos and their culture, the participants shared a view of themselves as proud Latinas who would prove others what Latinas can do. During other times while discussing the success of Latinos in Mathematics, they manifested Latinos were lazy and distance themselves from that stereotype. Similar examples about gender and Mathematics can be found in the study. The importance of the family as a motivator for their success was clear, despite the participants' concern that parents cannot offer certain types of help they feel they need. This was manifest in a tension regarding who owns the "right" Mathematics at home. Results showed that successful Latinas in the US may undergo a constant negotiation of conflicting discourses that force them to distance themselves from certain aspects of their culture, gender, and even their families, to maintain an identity of success in mathematics.
ContributorsGuerra Lombardi, Paula Patricia (Author) / Middleton, James (Thesis advisor) / Battey, Daniel (Committee member) / Koblitz, Ann (Committee member) / Flores, Alfinio (Committee member) / Arizona State University (Publisher)
Created2011
Description
In 2007, Arizona voters passed House Bill (HB) 2064, a law that fundamentally restructured the Structured English Immersion (SEI) program, putting into place a 4-hour English language development (ELD) block for educating English language learners (ELLs). Under this new language policy, ELL students are segregated from their English-speaking peers to receive a minimum of four hours of instruction in discrete language skills with no contextual or native language support. Furthermore, ELD is separate from content-area instruction, meaning that language and mathematics are taught as two separate entities. While educators and researchers have begun to examine the organizational structure of the 4-hour block curriculum and implications for student learning, there is much to be understood about the extent to which this policy impacts ELLs opportunities to learn mathematics. Using ethnographic methods, this dissertation documents the beliefs and practices of four Arizona teachers in an effort to understand the relationship between language policy and teacher beliefs and practice and how together they coalesce to form learning environments for their ELL students, particularly in mathematics. The findings suggest that the 4-hour block created disparities in opportunities to learn mathematics for students in one Arizona district, depending on teachers' beliefs and the manner in which the policy was enacted, which was, in part, influenced by the State, district, and school. The contrast in cases exemplified the ways in which policy, which was enacted differently in the various classes, restricted teachers' practices, and in some cases resulted in inequitable opportunities to learn mathematics for ELLs.
ContributorsLlamas-Flores, Silvia (Author) / Middleton, James (Thesis advisor) / Battey, Daniel (Committee member) / Sloane, Finbarr (Committee member) / Macswan, Jeffrey (Committee member) / Arizona State University (Publisher)
Created2013
Description
Networks are a versatile modeling tool for the cyber and physical infrastructure that characterize society. They can be used to describe system spatiotemporal dynamics, including distribution of commodities, movement of agents, and data transmission. This flexibility has resulted in the widespread use of network optimization techniques for decision-making in telecommunications, transportation, commerce, among other systems. However, realistic network problems are typically large-scale and require the use of integer variables to incorporate design or logical system constraints. This makes such problems hard to solve and precludes their wide applicability in the solution of applied problems. This dissertation studies four large-scale optimization problems with underlying network structure in different domain applications, including wireless sensor networks, wastewater monitoring, and scheduling. The problems of interest are formulated using mixed-integer optimization formulations. The proposed solution approaches in this dissertation include branch-and-cut and heuristic algorithms, which are enhanced with network-based valid inequalities and network reduction techniques. The first chapter studies a relay node placement problem in wireless sensor networks, with and without the presence of transmission obstacles in the deployment region. The proposed integer linear programming approach leverages the underlying network structure to produce valid inequalities and network reduction heuristics, which are incorporated in the branch-and-bound exploration. The solution approach outperforms the equivalent nonlinear model and solves instances with up to 1000 sensors within reasonable time. The second chapter studies the continuous version of the maximum capacity (widest) path interdiction problem and introduces the first known polynomial time algorithm to solve the problem using a combination of binary search and the discrete version of the Newton’s method. The third chapter explores the service agent transport interdiction problem in autonomous vehicle systems, where an agent schedules service tasks in the presence of an adversary. This chapter proposes a single stage branch-and-cut algorithm to solve the problem, along with several enhancement techniques to improve scalability. The last chapter studies the optimal placement of sensors in a wastewater network to minimize the maximum coverage (load) of placed sensors. This chapter proposes a branch-and-cut algorithm enhanced with network reduction techniques and strengthening constraints.
ContributorsMitra, Ankan (Author) / Sefair, Jorge A (Thesis advisor) / Mirchandani, Pitu (Committee member) / Grubesic, Anthony (Committee member) / Byeon, Geunyeong (Committee member) / Arizona State University (Publisher)
Created2023
Description
This dissertation focuses on three large-scale optimization problems and devising algorithms to solve them. In addition to the societal impact of each problem’s solution, this dissertation contributes to the optimization literature a set of decomposition algorithms for problems whose optimal solution is sparse. These algorithms exploit problem-specific properties and use tailored strategies based on iterative refinement (outer-approximations). The proposed algorithms are not rooted in duality theory, providing an alternative to existing methods based on linear programming relaxations. However, it is possible to embed existing decomposition methods into the proposed framework. These general decomposition principles extend to other combinatorial optimization problems.
The first problem is a route assignment and scheduling problem in which a set of vehicles need to traverse a directed network while maintaining a minimum inter-vehicle distance at any time. This problem is inspired by applications in hazmat logistics and the coordination of autonomous agents. The proposed approach includes realistic features such as continuous-time vehicle scheduling, heterogeneous speeds, minimum and maximum waiting times at any node, among others.
The second problem is a fixed-charge network design, which aims to find a minimum-cost plan to transport a target amount of a commodity between known origins and destinations. In addition to the typical flow decisions, the model chooses the capacity of each arc and selects sources and sinks. The proposed algorithms admit any nondecreasing piecewise linear cost structure. This model is applied to the Carbon Capture and Storage (CCS) problem, which is to design a minimum-cost pipeline network to transport CO2 between industrial sources and geologic reservoirs for long-term storage.
The third problem extends the proposed decomposition framework to a special case of joint chance constraint programming with independent random variables. This model is applied to the probabilistic transportation problem, where demands are assumed stochastic and independent. Using an empirical probability distribution, this problem is formulated as an integer program with the goal of finding a minimum-cost distribution plan that satisfies all the demands with a minimum given probability. The proposed scalable algorithm is based on a concave envelop approximation of the empirical probability function, which is iteratively refined as needed.
The first problem is a route assignment and scheduling problem in which a set of vehicles need to traverse a directed network while maintaining a minimum inter-vehicle distance at any time. This problem is inspired by applications in hazmat logistics and the coordination of autonomous agents. The proposed approach includes realistic features such as continuous-time vehicle scheduling, heterogeneous speeds, minimum and maximum waiting times at any node, among others.
The second problem is a fixed-charge network design, which aims to find a minimum-cost plan to transport a target amount of a commodity between known origins and destinations. In addition to the typical flow decisions, the model chooses the capacity of each arc and selects sources and sinks. The proposed algorithms admit any nondecreasing piecewise linear cost structure. This model is applied to the Carbon Capture and Storage (CCS) problem, which is to design a minimum-cost pipeline network to transport CO2 between industrial sources and geologic reservoirs for long-term storage.
The third problem extends the proposed decomposition framework to a special case of joint chance constraint programming with independent random variables. This model is applied to the probabilistic transportation problem, where demands are assumed stochastic and independent. Using an empirical probability distribution, this problem is formulated as an integer program with the goal of finding a minimum-cost distribution plan that satisfies all the demands with a minimum given probability. The proposed scalable algorithm is based on a concave envelop approximation of the empirical probability function, which is iteratively refined as needed.
ContributorsMatin Moghaddam, Navid (Author) / Sefair, Jorge (Thesis advisor) / Mirchandani, Pitu (Committee member) / Escobedo, Adolfo (Committee member) / Grubesic, Anthony (Committee member) / Arizona State University (Publisher)
Created2020