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- Member of: ASU Electronic Theses and Dissertations
- Member of: Phoenix Regional Heat and Air Quality Knowledge Repository
Description
The Southwestern Willow Flycatcher (Empidonax traillii extimus) has been studied for over two decades and listed as endangered for most of that time. Though the flycatcher has been granted protected status since 1995, critical habitat designation for the flycatcher has not shared the same history. Critical habitat designation is essential for achieving the long-term goals defined in the flycatcher recovery plan where emphasis is on both the protection of this species and "the habitats supporting these flycatchers [that] must be protected from threats and loss" (U.S. Fish and Wildlife Service 2002). I used a long-term data set of habitat characteristics collected at three study areas along the Lower Colorado River to develop a method for quantifying habitat quality for flycatcher. The data set contained flycatcher nest observations (use) and habitat availability (random location) from 2003-2010 that I statistically analyzed for flycatcher selection preferences. Using both Pearson's Chi-square test and SPSS Principal Component Analysis (PCA) I determined that flycatchers were selecting 30 habitat traits significantly different among an initial list of 127 habitat characteristics. Using PCA, I calculated a weighted value of influence for each significant trait per study area and used those values to develop a habitat classification system to build predictive models for flycatcher habitat quality. I used ArcGIS® Model Builder to develop three habitat suitability models for each of the habitat types occurring in western riparian systems, native, mixed exotic and exotic dominated that are frequented by breeding flycatchers. I designed a fourth model, Topock Marsh, to test model accuracy on habitat quality for flycatchers using reserved accuracy assessment points of previous nest locations. The results of the fourth model accurately predicted a decline in habitat at Topock Marsh that was confirmed by SWCA survey reports released in 2011 and 2012 documenting a significant decline in flycatcher productivity in the Topock Marsh study area.
ContributorsChenevert-Steffler, Ann (Author) / Miller, William (Thesis advisor) / Bateman, Heather (Committee member) / Alford, Eddie (Committee member) / Arizona State University (Publisher)
Created2013
Description
The history of outdoor water use in the Phoenix, Arizona metropolitan area has given rise to a general landscape aesthetic and pattern of residential irrigation that seem in discord with the natural desert environment. While xeric landscaping that incorporates native desert ecology has potential for reducing urban irrigation demand, there are societal and environmental factors that make mesic landscaping, including shade trees and grass lawns, a common choice for residential yards. In either case, there is potential for water savings through irrigation schedules based on fluxes affecting soil moisture in the active plant rooting zone. In this thesis, a point-scale model of soil moisture dynamics was applied to two urban sites in the Phoenix area: one with xeric landscaping, and one with mesic. The model was calibrated to observed soil moisture data from irrigated and non-irrigated sensors, with local daily precipitation and potential evapotranspiration records as model forcing. Simulations were then conducted to investigate effects of irrigation scheduling, plant stress parameters, and precipitation variability on soil moisture dynamics, water balance partitioning, and plant water stress. Results indicated a substantial difference in soil water storage capacity at the two sites, which affected sensitivity to irrigation scenarios. Seasonal variation was critical in avoiding unproductive water losses at the xeric site, and allowed for small water savings at the mesic site by maintaining mild levels of plant stress. The model was also used to determine minimum annual irrigation required to achieve specified levels of plant stress at each site using long-term meteorological records. While the xeric site showed greater potential for water savings, a bimodal schedule consisting of low winter and summer irrigation was identified as a means to conserve water at both sites, with moderate levels of plant water stress. For lower stress levels, potential water savings were found by fixing irrigation depth and seasonally varying the irrigation interval, consistent with municipal recommendations in the Phoenix metropolitan area. These results provide a deeper understanding of the ecohydrologic differences between the two types of landscape treatments, and can assist water and landscape managers in identifying opportunities for water savings in desert urban areas.
ContributorsVolo, Thomas J (Author) / Vivoni, Enrique R (Thesis advisor) / Ruddell, Benjamin L (Committee member) / Wang, Zhihua (Committee member) / Arizona State University (Publisher)
Created2013
Description
Traditional approaches to modeling microgrids include the behavior of each inverter operating in a particular network configuration and at a particular operating point. Such models quickly become computationally intensive for large systems. Similarly, traditional approaches to control do not use advanced methodologies and suffer from poor performance and limited operating range. In this document a linear model is derived for an inverter connected to the Thevenin equivalent of a microgrid. This model is then compared to a nonlinear simulation model and analyzed using the open and closed loop systems in both the time and frequency domains. The modeling error is quantified with emphasis on its use for controller design purposes. Control design examples are given using a Glover McFarlane controller, gain sched- uled Glover McFarlane controller, and bumpless transfer controller which are compared to the standard droop control approach. These examples serve as a guide to illustrate the use of multi-variable modeling techniques in the context of robust controller design and show that gain scheduled MIMO control techniques can extend the operating range of a microgrid. A hardware implementation is used to compare constant gain droop controllers with Glover McFarlane controllers and shows a clear advantage of the Glover McFarlane approach.
ContributorsSteenis, Joel (Author) / Ayyanar, Raja (Thesis advisor) / Mittelmann, Hans (Committee member) / Tsakalis, Konstantinos (Committee member) / Tylavsky, Daniel (Committee member) / Arizona State University (Publisher)
Created2013
Description
Adaptive processing and classification of electrocardiogram (ECG) signals are important in eliminating the strenuous process of manually annotating ECG recordings for clinical use. Such algorithms require robust models whose parameters can adequately describe the ECG signals. Although different dynamic statistical models describing ECG signals currently exist, they depend considerably on a priori information and user-specified model parameters. Also, ECG beat morphologies, which vary greatly across patients and disease states, cannot be uniquely characterized by a single model. In this work, sequential Bayesian based methods are used to appropriately model and adaptively select the corresponding model parameters of ECG signals. An adaptive framework based on a sequential Bayesian tracking method is proposed to adaptively select the cardiac parameters that minimize the estimation error, thus precluding the need for pre-processing. Simulations using real ECG data from the online Physionet database demonstrate the improvement in performance of the proposed algorithm in accurately estimating critical heart disease parameters. In addition, two new approaches to ECG modeling are presented using the interacting multiple model and the sequential Markov chain Monte Carlo technique with adaptive model selection. Both these methods can adaptively choose between different models for various ECG beat morphologies without requiring prior ECG information, as demonstrated by using real ECG signals. A supervised Bayesian maximum-likelihood (ML) based classifier uses the estimated model parameters to classify different types of cardiac arrhythmias. However, the non-availability of sufficient amounts of representative training data and the large inter-patient variability pose a challenge to the existing supervised learning algorithms, resulting in a poor classification performance. In addition, recently developed unsupervised learning methods require a priori knowledge on the number of diseases to cluster the ECG data, which often evolves over time. In order to address these issues, an adaptive learning ECG classification method that uses Dirichlet process Gaussian mixture models is proposed. This approach does not place any restriction on the number of disease classes, nor does it require any training data. This algorithm is adapted to be patient-specific by labeling or identifying the generated mixtures using the Bayesian ML method, assuming the availability of labeled training data.
ContributorsEdla, Shwetha Reddy (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Kovvali, Narayan (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Arizona State University (Publisher)
Created2012
Description
This study was designed to influence consumer habits, specifically those relating to purchases of fruits, vegetables, and junk food. Previous studies have clearly shown the ineffectiveness of simply describing the health benefits of eating more fruits and vegetables (F/V). In contrast, this study aimed to change the result by changing the message: providing participants with insight into the hidden agendas of food companies and grocery stores, provide useful tips on how to include children when selecting F/V, and emphasizing the importance of parental modeling in regard to food purchases. Participants of this study were separated into two groups, the tour group and the education group. The tour group was guided through a grocery store where they learned about sales tactics and manipulations used by grocery stores and food companies to influence purchases. Education group participants were provided with an education session focusing on USDA and FDA handouts displaying current educational suggestions for increasing F/V consumption. Grocery store receipts were collected and analyzed to track the progress of both groups. The goal of the study was to identify a method of informing consumers that will produce a significant change in behavior. Increasing F/V consumption, even in relatively small amounts, would be an important step forward in improving the diet and overall health of Americans. This study was the first of its kind to measure purchasing patterns objectively (through analysis of purchase receipts, rather than personal opinion/evaluation surveys) and in a wide-scope retail environment that includes all grocery store purchases by participants. Significant increases or decreases in the amount of money spent on F/V, or the amount (pounds) of F/V purchased were not seen, however a small correlation (r = 0.133) exists when comparing the weight of F/V purchased pre/post intervention. Data from Food Frequency Questionnaires shows participants consuming significantly higher amounts of F/V post intervention (p=0.043). The tour group and education group experienced an average increase of 0.7 servings per day. Future interventions might benefit by extending their scope to include cooking demonstrations, in-home interventions, and education on healthy eating outside of the home.
ContributorsKinsfather, Diana (Author) / Johnston, Carol (Thesis advisor) / Hekler, Eric (Committee member) / Tetreault, Colin (Committee member) / Arizona State University (Publisher)
Created2013
Description
New technologies enable the exploration of space, high-fidelity defense systems, lighting fast intercontinental communication systems as well as medical technologies that extend and improve patient lives. The basis for these technologies is high reliability electronics devised to meet stringent design goals and to operate consistently for many years deployed in the field. An on-going concern for engineers is the consequences of ionizing radiation exposure, specifically total dose effects. For many of the different applications, there is a likelihood of exposure to radiation, which can result in device degradation and potentially failure. While the total dose effects and the resulting degradation are a well-studied field and methodologies to help mitigate degradation have been developed, there is still a need for simulation techniques to help designers understand total dose effects within their design. To that end, the work presented here details simulation techniques to analyze as well as predict the total dose response of a circuit. In this dissertation the total dose effects are broken into two sub-categories, intra-device and inter-device effects in CMOS technology. Intra-device effects degrade the performance of both n-channel and p-channel transistors, while inter-device effects result in loss of device isolation. In this work, multiple case studies are presented for which total dose degradation is of concern. Through the simulation techniques, the individual device and circuit responses are modeled post-irradiation. The use of these simulation techniques by circuit designers allow predictive simulation of total dose effects, allowing focused design changes to be implemented to increase radiation tolerance of high reliability electronics.
ContributorsSchlenvogt, Garrett (Author) / Barnaby, Hugh (Thesis advisor) / Goodnick, Stephen (Committee member) / Vasileska, Dragica (Committee member) / Holbert, Keith E. (Committee member) / Arizona State University (Publisher)
Created2014
Description
Nonvolatile memory (NVM) technologies have been an integral part of electronic systems for the past 30 years. The ideal non-volatile memory have minimal physical size, energy usage, and cost while having maximal speed, capacity, retention time, and radiation hardness. A promising candidate for next-generation memory is ion-conducting bridging RAM which is referred to as programmable metallization cell (PMC), conductive bridge RAM (CBRAM), or electrochemical metallization memory (ECM), which is likely to surpass flash memory in all the ideal memory characteristics. A comprehensive physics-based model is needed to completely understand PMC operation and assist in design optimization.
To advance the PMC modeling effort, this thesis presents a precise physical model parameterizing materials associated with both ion-rich and ion-poor layers of the PMC's solid electrolyte, so that captures the static electrical behavior of the PMC in both its low-resistance on-state (LRS) and high resistance off-state (HRS). The experimental data is measured from a chalcogenide glass PMC designed and manufactured at ASU. The static on- and off-state resistance of a PMC device composed of a layered (Ag-rich/Ag-poor) Ge30Se70 ChG film is characterized and modeled using three dimensional simulation code written in Silvaco Atlas finite element analysis software. Calibrating the model to experimental data enables the extraction of device parameters such as material bandgaps, workfunctions, density of states, carrier mobilities, dielectric constants, and affinities.
The sensitivity of our modeled PMC to the variation of its prominent achieved material parameters is examined on the HRS and LRS impedance behavior.
The obtained accurate set of material parameters for both Ag-rich and Ag-poor ChG systems and process variation verification on electrical characteristics enables greater fidelity in PMC device simulation, which significantly enhances our ability to understand the underlying physics of ChG-based resistive switching memory.
To advance the PMC modeling effort, this thesis presents a precise physical model parameterizing materials associated with both ion-rich and ion-poor layers of the PMC's solid electrolyte, so that captures the static electrical behavior of the PMC in both its low-resistance on-state (LRS) and high resistance off-state (HRS). The experimental data is measured from a chalcogenide glass PMC designed and manufactured at ASU. The static on- and off-state resistance of a PMC device composed of a layered (Ag-rich/Ag-poor) Ge30Se70 ChG film is characterized and modeled using three dimensional simulation code written in Silvaco Atlas finite element analysis software. Calibrating the model to experimental data enables the extraction of device parameters such as material bandgaps, workfunctions, density of states, carrier mobilities, dielectric constants, and affinities.
The sensitivity of our modeled PMC to the variation of its prominent achieved material parameters is examined on the HRS and LRS impedance behavior.
The obtained accurate set of material parameters for both Ag-rich and Ag-poor ChG systems and process variation verification on electrical characteristics enables greater fidelity in PMC device simulation, which significantly enhances our ability to understand the underlying physics of ChG-based resistive switching memory.
ContributorsRajabi, Saba (Author) / Barnaby, Hugh (Thesis advisor) / Kozicki, Michael (Committee member) / Vasileska, Dragica (Committee member) / Arizona State University (Publisher)
Created2014
Description
Nicotine is thought to underlie the reinforcing and dependence-producing effects of tobacco-containing products. Nicotine supports self-administration in rodents, although measures of its reinforcing effects are often confounded by procedures that are used to facilitate acquisition, such as food restriction, prior reinforcement training, or response-contingent co-delivery of a naturally reinforcing light. This study examined whether rats acquire nicotine self-administration in the absence of these facilitators. A new mathematical modeling procedure was used to define the criterion for acquisition and to determine dose-dependent differences in rate and asymptote levels of intake. Rats were trained across 20 daily 2-h sessions occurring 6 days/week in chambers equipped with active and inactive levers. Each active lever press resulted in nicotine reinforcement (0, 0.015, 0.03, 0.06 mg/kg, IV) and retraction of both levers for a 20-s time out, whereas inactive lever presses had no consequences. Acquisition was defined by the best fit of a logistic function (i.e., S-shaped) versus a constant function (i.e., flat line) for reinforcers obtained across sessions using a corrected Akaike information criterion (AICc) as a model selection tool. The results showed an inverted-U shaped function for dose in relation to the percentage of animals that acquired nicotine self-administration, with 46% acquiring at 0.015 mg/kg, 73% at 0.03 mg/kg, and 58% at 0.06 mg/kg. All saline rats failed to acquire as expected. For rats that acquired nicotine self-administration, multiple model comparisons demonstrated that the asymptote (highest number of reinforcers/session) and half learning point (h; session during which half the assymptote had been achieved) were justified as free parameters of the reinforcers/session function, indicating that these parameters vary with nicotine dose. Asymptote exhibited an inverted U-shaped function across doses and half learning point exhibited a negative relationship to dose (i.e., the higher the dose the fewer sessions to reach h). These findings suggest that some rats acquire nicotine self-administration without using procedures that confound measures of acquisition rate. Furthermore, the modeling approach provides a new way of defining acquisition of drug self-administration that takes advantage of using all data generated from individual subjects and is less arbitrary than some criteria that are currently used.
ContributorsCole, Natalie (Author) / Neisewander, Janet L (Thesis advisor) / Sanabria, Federico (Thesis advisor) / Bimonte-Nelson, Heather A. (Committee member) / Olive, Michael F (Committee member) / Arizona State University (Publisher)
Created2011
Description
Many species e.g. sea urchin form amorphous calcium carbonate (ACC) precursor phases that subsequently transform into crystalline CaCO3. It is certainly possible that the biogenic ACC might have more than 10 wt% Mg and ∼ 3 wt% of water. The structure of ACC and the mechanisms by which it transforms to crystalline phase are still poorly understood. In this dissertation our goal is to determine an atomic structure model that is consistent with diffraction and IR measurements of ACC. For this purpose a calcite supercell with 24 formula units, containing 120 atoms, was constructed. Various configurations with substitution of Ca by 6 Mg ions (6 wt.%) and insertion of 3-5 H2O molecules (2.25-3.75 wt.%) in the interstitial positions of the supercell, were relaxed using a robust density function code VASP. The most noticeable effects were the tilts of CO3 groups and the distortion of Ca sub-lattice, especially in the hydrated case. The distributions of Ca-Ca nearest neighbor distance and CO3 tilts were extracted from various configurations. The same methods were also applied to aragonite. Sampling from the calculated distortion distributions, we built models for amorphous calcite/aragonite of size ∼ 1700 nm3 based on a multi-scale modeling scheme. We used these models to generate diffraction patterns and profiles with our diffraction code. We found that the induced distortions were not enough to generate a diffraction profile typical of an amorphous material. We then studied the diffraction profiles from several nano-crystallites as recent studies suggest that ACC might be a random array of nanocryatallites. It was found that the generated diffraction profile from a nano-crystallite of size ∼ 2 nm3 is similar to that from the ACC.
ContributorsSinha, Sourabh (Author) / Rez, Peter (Thesis advisor) / Bearat, Hamdallah A. (Committee member) / Bennett, Peter A. (Committee member) / McCartney, Martha R. (Committee member) / Peng, Xihong (Committee member) / Arizona State University (Publisher)
Created2012
Description
A sequence of models is developed to describe urban population growth in the context of the embedded physical, social and economic environments and an urban disease are developed. This set of models is focused on urban growth and the relationship between the desire to move and the utility derived from city life. This utility is measured in terms of the economic opportunities in the city, the level of human constructed amenity, and the level of amenity caused by the natural environment. The set of urban disease models is focused on examining prospects of eliminating a disease for which a vaccine does not exist. It is inspired by an outbreak of the vector-borne disease dengue fever in Peru, during 2000-2001.
ContributorsMurillo, D (Author) / Castillo-Chavez, Carlos (Thesis advisor) / Anderies, John M (Thesis advisor) / Boone, Christopher (Committee member) / Arizona State University (Publisher)
Created2012