Matching Items (10)
Filtering by
- Resource Type: Text
Description
This project analyzes the tweets from the 2016 US Presidential Candidates' personal Twitter accounts. The goal is to define distinct patterns and differences between candidates and parties use of social media as a platform. The data spans the period of September 2015 to March 2016, which was during the primary races for the Republicans and Democrats. The overall purpose of this project is to contribute to finding new ways of driving value from social media, in particular Twitter.
ContributorsMortimer, Schuyler Kenneth (Author) / Simon, Alan (Thesis director) / Mousavi, Seyedreza (Committee member) / Department of Information Systems (Contributor) / Department of Supply Chain Management (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
In Arizona, people flock the streets of Tombstone in droves, chatting in period costume while gunshots ring down the street. Others in Bisbee walk in the Queen Mine, listening to the tour guide discuss how the miners extracted ore. Still others drive up the precarious road to Jerome, passing through the famed Grand Hotel. As former Arizona mining towns, Tombstone, Jerome and Bisbee have a shared identity as former mining boomtowns, all of which experienced subsequent economic and population decline. Left with the need to reinvent themselves in order to survive, the past takes on a different role in each city. In Jerome, visitors seem content to "kill a day" against the backdrop of the historic town. In Bisbee, time seems stuck in the 1970s, the focus having shifted from the mining to the "hippies" who are considered to have resuscitated the town from near-extinction. Tombstone seem to inspire devotion, rooted in the influence of the 1993 film titled after the town. By memorializing portions of their past, these three towns have carved out new lives for themselves in the twenty-first century. As visitors are informed by the narrative of the "Old West," as shaped by the Western movie and television genre, they in turn impact how the towns present themselves in order to attract tourists. In all these sites, the past is present and like a kaleidoscope, continually recreated into new formations. While the designation of Jerome, Bisbee and Tombstone as "ghost towns" is disputed by individuals in each site, these stories of visitors and residents reveal the intricate ways in which these towns have acquired new life.
ContributorsLemme, Nicole Lee (Author) / de la Garza, Amira (Thesis director) / Paulesc, Marie Louise (Committee member) / Department of English (Contributor) / School of International Letters and Cultures (Contributor) / Hugh Downs School of Human Communication (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Nitrate (NO3- ) and selenate (SeO42-) are common contaminants found in mining wastewater. Biological treatment has proved successful using bacteria capable of respiring NO3- into nitrogen gas and SeO42- into Se°. The Membrane Biofilm Reactor (MBfR) utilizes biofilm communities on the surface of hollow-fiber membranes to transform oxidized water contaminants into innocuous reduced products. For this project, I set up two MBfRs in a lead and lag configuration to reduce NO3- [input at ~40-45 mg NO3-N/L] and SeO42- [0.62 mg/L], while avoiding sulfate (SO42-) [~1600-1660 mg/L] reduction. Over the course of three experimental phases, I controlled two operating conditions: the applied hydrogen pressure and the total electron acceptor loading. NO3- in the lead MBfR showed average reductions of 50%, 94%, and 91% for phases I, II, and III, respectively. In the lag MBfR, NO3- was reduced by 40%, 96%, and 100% for phases I, II, and III. NO2- was formed in Stage I when NO3- was not reduced completely; nevertheless NO2- accumulation was absent for the remainder of operation. In the lead MBfR, SeO42- was reduced by 65%, 87%, and 50% for phases I, II, and III. In the lag MBfR, SeO42- was reduced 60%, 27%, and 23% for phases I, II, and III. SO42- was not reduced in either MBfR. Biofilm communities were composed of denitrifying bacteria Rhodocyclales and Burkholderiales, Dechloromonas along with the well-known SeO42--reducing Thauera were abundant genera in the biofilm communities. Although SO42- reduction was suppressed, sulfate-reducing bacteria were present in the biofilm. To optimize competition for electron donor and space in the biofilm, optimal operational conditions were hydrogen pressures of 26 and 7 psig and total electron acceptor loading of 3.8 and 3.4 g H2/m2 day for the lead and lag MBfR, respectively.
ContributorsMehta, Sanya Vipul (Author) / Rittmann, Bruce (Thesis director) / Ontiveros-Valencia, Aura (Committee member) / Chemical Engineering Program (Contributor) / School of International Letters and Cultures (Contributor) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Humans are undeniably a part of nature. Without Earth's and her resources, we cease to exist. However, in recent years society has lacked the foresight or possibly care to understand the impact of our actions both on the planet and ourselves. Resources that industrialized societies are based on are dwindling in reserves and the impact of our actions in getting such resources has been largely harmful. In order to change cycles of overexertion both in our selves and the planet, we must change the ways we think. I propose that humans, very much like the Earth, have limited resources and need to be more mindful in our choices. Wellness and sustainability are two branches of sustaining a larger system and our collective future. On an individual scale, wellness is sustaining our individual resources (i.e. time, energy, thoughts), and can be aided through simple practices to encourage healthy patterns and processes. Sustainability in terms of the planet is sustaining our common resources. This requires a change in our individual selves as well as cooperation to change the larger systems that we are parts of. I separated wellness into three components, core values, positivity, and time management. Sustainability is separated into lifestyle, systems thinking, and learning from life. For each of the six components, I briefly describe their importance and benefits.
ContributorsShamas, Ariel Judith (Author) / Sanft, Al (Thesis director) / Heywood, William (Committee member) / The Design School (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
When scientific software is written to specify processes, it takes the form of a workflow, and is often written in an ad-hoc manner in a dynamic programming language. There is a proliferation of legacy workflows implemented by non-expert programmers due to the accessibility of dynamic languages. Unfortunately, ad-hoc workflows lack a structured description as provided by specialized management systems, making ad-hoc workflow maintenance and reuse difficult, and motivating the need for analysis methods. The analysis of ad-hoc workflows using compiler techniques does not address dynamic languages - a program has so few constrains that its behavior cannot be predicted. In contrast, workflow provenance tracking has had success using run-time techniques to record data. The aim of this work is to develop a new analysis method for extracting workflow structure at run-time, thus avoiding issues with dynamics.
The method captures the dataflow of an ad-hoc workflow through its execution and abstracts it with a process for simplifying repetition. An instrumentation system first processes the workflow to produce an instrumented version, capable of logging events, which is then executed on an input to produce a trace. The trace undergoes dataflow construction to produce a provenance graph. The dataflow is examined for equivalent regions, which are collected into a single unit. The workflow is thus characterized in terms of its treatment of an input. Unlike other methods, a run-time approach characterizes the workflow's actual behavior; including elements which static analysis cannot predict (for example, code dynamically evaluated based on input parameters). This also enables the characterization of dataflow through external tools.
The contributions of this work are: a run-time method for recording a provenance graph from an ad-hoc Python workflow, and a method to analyze the structure of a workflow from provenance. Methods are implemented in Python and are demonstrated on real world Python workflows. These contributions enable users to derive graph structure from workflows. Empowered by a graphical view, users can better understand a legacy workflow. This makes the wealth of legacy ad-hoc workflows accessible, enabling workflow reuse instead of investing time and resources into creating a workflow.
The method captures the dataflow of an ad-hoc workflow through its execution and abstracts it with a process for simplifying repetition. An instrumentation system first processes the workflow to produce an instrumented version, capable of logging events, which is then executed on an input to produce a trace. The trace undergoes dataflow construction to produce a provenance graph. The dataflow is examined for equivalent regions, which are collected into a single unit. The workflow is thus characterized in terms of its treatment of an input. Unlike other methods, a run-time approach characterizes the workflow's actual behavior; including elements which static analysis cannot predict (for example, code dynamically evaluated based on input parameters). This also enables the characterization of dataflow through external tools.
The contributions of this work are: a run-time method for recording a provenance graph from an ad-hoc Python workflow, and a method to analyze the structure of a workflow from provenance. Methods are implemented in Python and are demonstrated on real world Python workflows. These contributions enable users to derive graph structure from workflows. Empowered by a graphical view, users can better understand a legacy workflow. This makes the wealth of legacy ad-hoc workflows accessible, enabling workflow reuse instead of investing time and resources into creating a workflow.
ContributorsAcűna, Ruben (Author) / Bazzi, Rida (Thesis advisor) / Lacroix, Zoé (Thesis advisor) / Candan, Kasim (Committee member) / Arizona State University (Publisher)
Created2015
Description
Gallium Nitride (GaN) based microelectronics technology is a fast growing and most exciting semiconductor technology in the fields of high power and high frequency electronics. Excellent electrical properties of GaN such as high carrier concentration and high carrier motility makes GaN based high electron mobility transistors (HEMTs) a preferred choice for RF applications. However, a very high temperature in the active region of the GaN HEMT leads to a significant degradation of the device performance by effecting carrier mobility and concentration. Thus, thermal management in GaN HEMT in an effective manner is key to this technology to reach its full potential.
In this thesis, an electro-thermal model of an AlGaN/GaN HEMT on a SiC substrate is simulated using Silvaco (Atlas) TCAD tools. Output characteristics, current density and heat flow at the GaN-SiC interface are key areas of analysis in this work. The electrical characteristics show a sharp drop in drain currents for higher drain voltages. Temperature profile across the device is observed. At the interface of GaN-SiC, there is a sharp drop in temperature indicating a thermal resistance at this interface. Adding to the existing heat in the device, this difference heat is reflected back into the device, further increasing the temperatures in the active region. Structural changes such as GaN micropits, were introduced at the GaN-SiC interface along the length of the device, to make the heat flow smooth rather than discontinuous. With changing dimensions of these micropits, various combinations were tried to reduce the temperature and enhance the device performance. These GaN micropits gave effective results by reducing heat in active region, by spreading out the heat on to the sides of the device rather than just concentrating right below the hot spot. It also helped by allowing a smooth flow of heat at the GaN-SiC interface. There was an increased peak current density in the active region of the device contributing to improved electrical characteristics. In the end, importance of thermal management in these high temperature devices is discussed along with future prospects and a conclusion of this thesis.
In this thesis, an electro-thermal model of an AlGaN/GaN HEMT on a SiC substrate is simulated using Silvaco (Atlas) TCAD tools. Output characteristics, current density and heat flow at the GaN-SiC interface are key areas of analysis in this work. The electrical characteristics show a sharp drop in drain currents for higher drain voltages. Temperature profile across the device is observed. At the interface of GaN-SiC, there is a sharp drop in temperature indicating a thermal resistance at this interface. Adding to the existing heat in the device, this difference heat is reflected back into the device, further increasing the temperatures in the active region. Structural changes such as GaN micropits, were introduced at the GaN-SiC interface along the length of the device, to make the heat flow smooth rather than discontinuous. With changing dimensions of these micropits, various combinations were tried to reduce the temperature and enhance the device performance. These GaN micropits gave effective results by reducing heat in active region, by spreading out the heat on to the sides of the device rather than just concentrating right below the hot spot. It also helped by allowing a smooth flow of heat at the GaN-SiC interface. There was an increased peak current density in the active region of the device contributing to improved electrical characteristics. In the end, importance of thermal management in these high temperature devices is discussed along with future prospects and a conclusion of this thesis.
ContributorsSuri, Suraj (Author) / Zhao, Yuji (Thesis advisor) / Vasileska, Dragika (Committee member) / Yu, Hongbin (Committee member) / Arizona State University (Publisher)
Created2016
Description
Cryptojacking is a process in which a program utilizes a user’s CPU to mine cryptocurrencies unknown to the user. Since cryptojacking is a relatively new problem and its impact is still limited, very little has been done to combat it. Multiple studies have been conducted where a cryptojacking detection system is implemented, but none of these systems have truly solved the problem. This thesis surveys existing studies and provides a classification and evaluation of each detection system with the aim of determining their pros and cons. The result of the evaluation indicates that it might be possible to bypass detection of existing systems by modifying the cryptojacking code. In addition to this classification, I developed an automatic code instrumentation program that replaces specific instructions with functionally similar sequences as a way to show how easy it is to implement simple obfuscation to bypass detection by existing systems.
ContributorsLarson, Kent Merle (Author) / Bazzi, Rida (Thesis director) / Shoshitaishvili, Yan (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Mining is a key component of both the Brazilian and Chilean economies and accounts for an outsized share of these countries’ exports. Yet, it is a common target for environmental criticism, especially due to its impacts on local populations and ecosystems. Brazil and Chile have adopted markedly different trade strategies over the past three decades, most notably with regards to their involvement in international trade agreements. This paper investigates how these differences in trade policy since 1990 have affected the sustainability of each country’s mining sector by identifying and comparing the channels through which free trade agreements influence the environmental impacts of resource extraction.
ContributorsKopek, Justin (Author) / Sheriff, Glenn (Thesis director) / Goodman, Glen (Committee member) / Barrett, The Honors College (Contributor) / Economics Program in CLAS (Contributor) / School of Politics and Global Studies (Contributor) / Historical, Philosophical & Religious Studies, Sch (Contributor) / School of International Letters and Cultures (Contributor)
Created2023-05
Description
Millions of people around the world daily engage in artisanal and small-scale gold mining (ASGM)––a vital part of total global gold production. For Colombia, this mining accounts for most of the precious metal’s output. It has also made Colombia, per capita, the worst mercury-polluted country in the world. Though cleaner, safer, and more effective methods exist, miners yet opt for mercury-use. Any success with interventions in technology, capacitation, or policy has been limited. This dissertation attends to mercury-use in ASGM in Antioquia, Colombia, via two gaps: a descriptive one (i.e., a failure to pay attention to, and to describe, actual practices in ASGM); and, a theoretical one (i.e., explanations as to why some decisions, including but not limited to policy, succeed or fail). In addition to an ecology of practices, embodiment, and situated knowledges, phenomenological interviews with stakeholders illuminate critical lived experience, as well as whether or how it is possible to reduce mercury-use and contamination. Furthermore, a novel application of speculative sound supplements this work. Finally, key findings complement existing scholarship. The presence of gold drives mining, but an increase in mining comes at a cost. Miners know mercury is hazardous, but mining legally, or formally, has proven too onerous. So, mercury-use persists: it is profitable, and the effects on human health can seem delayed. The state is pivotal to change in mercury-use, but its approach has been punitive. Change will invariably require greater attention to the lived experiences of miners.
ContributorsPimentel, Matthew (Author) / Fonow, Mary Margaret (Thesis advisor) / Parmentier, Mary Jane (Thesis advisor) / Coleman, Grisha (Committee member) / Arizona State University (Publisher)
Created2021
Description
Existing machine learning and data mining techniques have difficulty in handling three characteristics of real-world data sets altogether in a computationally efficient way: (1) different data types with both categorical data and numeric data, (2) different variable relations in different value ranges of variables, and (3) unknown variable dependency.This dissertation developed a Partial-Value Association Discovery (PVAD) algorithm to overcome the above drawbacks in existing techniques. It also enables the discovery of partial-value and full-value variable associations showing both effects
of individual variables and interactive effects of multiple variables. The algorithm is compared with Association rule mining and Decision Tree for validation purposes. The results show that the PVAD algorithm can overcome the shortcomings of existing methods.
The second part of this dissertation focuses on knee point detection on noisy data. This extended research topic was inspired during the investigation into categorization for numeric data, which corresponds to Step 1 of the PVAD algorithm. A new mathematical definition of knee point on discrete data is introduced. Due to the unavailability of ground truth data or benchmark data sets, functions used to generate synthetic data are carefully selected and defined. These functions are subsequently employed to create the data sets for this experiment. These synthetic data sets are useful for systematically evaluating and comparing the performance of existing methods. Additionally, a deep-learning model is devised for this problem. Experiments show that the proposed model surpasses existing methods in all synthetic data sets, regardless of whether the samples have single or multiple knee points.
The third section presents the application results of the PVAD algorithm to real-world
data sets in various domains. These include energy consumption data of an Arizona State University (ASU) building, Computer Network, and ASU Engineering Freshmen Retention. The PVAD algorithm is utilized to create an associative network for energy consumption modeling, analyze univariate and multivariate measures of network flow variables, and identify common and uncommon characteristics related to engineering student retention after their first year at the university. The findings indicate that the PVAD algorithm offers the advantage and capability to uncover variable relationships.
ContributorsFok, Ting Yan (Author) / Ye, Nong (Thesis advisor) / Iquebal, Ashif (Committee member) / Ju, Feng (Committee member) / Collofello, James (Committee member) / Arizona State University (Publisher)
Created2023