Matching Items (43,917)
Description
The current study analyzed existing data, collected under a previous U.S. Department of Education Reading First grant, to investigate the strength of the relationship between scores on the first- through third-grade Dynamic Indicators of Basic Early Literacy Skills - Oral Reading Fluency (DIBELS-ORF) test and scores on a reading comprehension test (TerraNova-Reading) administered at the conclusion of second- and third-grade. Participants were sixty-five English Language Learners (ELLs) learning to read in a school district adjacent to the U.S.-Mexico border. DIBELS-ORF and TerraNova-Reading scores were provided by the school district, which administers the assessments in accordance with state and federal mandates to monitor early literacy skill development. Bivariate correlation results indicate moderate-to-strong positive correlations between DIBELS-ORF scores and TerraNova-Reading performance that strengthened between grades one and three. Results suggest that the concurrent relationship between oral reading fluency scores and performance on standardized and high-stakes measures of reading comprehension may be different among ELLs as compared to non-ELLs during first- and second-grade. However, by third-grade the correlations approximate those reported in previous non-ELL studies. This study also examined whether the Peabody Picture Vocabulary Test (PPVT), a receptive vocabulary measure, could explain any additional variance on second- and third-grade TerraNova-Reading performance beyond that explained by the DIBELS-ORF. The PPVT was individually administered by researchers collecting data under a Reading First research grant prior to the current study. Receptive vocabulary was found to be a strong predictor of reading comprehension among ELLs, and largely overshadowed the predictive ability of the DIBELS-ORF during first-grade. Results suggest that receptive vocabulary scores, used in conjunction with the DIBELS-ORF, may be useful for identifying beginning ELL readers who are at risk for third-grade reading failure as early as first-grade.
Contributors
Millett, Joseph Ridge (Author) / Atkinson, Robert (Thesis advisor) / Blanchard, Jay (Committee member) / Thompson, Marilyn (Committee member) / Christie, James (Committee member) / Arizona State University (Publisher)
Created
2011
Description
Genomic and proteomic sequences, which are in the form of deoxyribonucleic acid (DNA) and amino acids respectively, play a vital role in the structure, function and diversity of every living cell. As a result, various genomic and proteomic sequence processing methods have been proposed from diverse disciplines, including biology, chemistry, physics, computer science and electrical engineering. In particular, signal processing techniques were applied to the problems of sequence querying and alignment, that compare and classify regions of similarity in the sequences based on their composition. However, although current approaches obtain results that can be attributed to key biological properties, they require pre-processing and lack robustness to sequence repetitions. In addition, these approaches do not provide much support for efficiently querying sub-sequences, a process that is essential for tracking localized database matches. In this work, a query-based alignment method for biological sequences that maps sequences to time-domain waveforms before processing the waveforms for alignment in the time-frequency plane is first proposed. The mapping uses waveforms, such as time-domain Gaussian functions, with unique sequence representations in the time-frequency plane. The proposed alignment method employs a robust querying algorithm that utilizes a time-frequency signal expansion whose basis function is matched to the basic waveform in the mapped sequences. The resulting WAVEQuery approach is demonstrated for both DNA and protein sequences using the matching pursuit decomposition as the signal basis expansion. The alignment localization of WAVEQuery is specifically evaluated over repetitive database segments, and operable in real-time without pre-processing. It is demonstrated that WAVEQuery significantly outperforms the biological sequence alignment method BLAST for queries with repetitive segments for DNA sequences. A generalized version of the WAVEQuery approach with the metaplectic transform is also described for protein sequence structure prediction. For protein alignment, it is often necessary to not only compare the one-dimensional (1-D) primary sequence structure but also the secondary and tertiary three-dimensional (3-D) space structures. This is done after considering the conformations in the 3-D space due to the degrees of freedom of these structures. As a result, a novel directionality based 3-D waveform mapping for the 3-D protein structures is also proposed and it is used to compare protein structures using a matched filter approach. By incorporating a 3-D time axis, a highly-localized Gaussian-windowed chirp waveform is defined, and the amino acid information is mapped to the chirp parameters that are then directly used to obtain directionality in the 3-D space. This mapping is unique in that additional characteristic protein information such as hydrophobicity, that relates the sequence with the structure, can be added as another representation parameter. The additional parameter helps tracking similarities over local segments of the structure, this enabling classification of distantly related proteins which have partial structural similarities. This approach is successfully tested for pairwise alignments over full length structures, alignments over multiple structures to form a phylogenetic trees, and also alignments over local segments. Also, basic classification over protein structural classes using directional descriptors for the protein structure is performed.
Contributors
Ravichandran, Lakshminarayan (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Spanias, Andreas S (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Lacroix, Zoé (Committee member) / Arizona State University (Publisher)
Created
2011
Description
Meter-resolution topography gathered by LiDAR (Light Detection and Ranging) has become an indispensable tool for better understanding of many surface processes including those sculpting landscapes that record information about earthquake hazards for example. For this reason, and because of the spectacular representation of the phenomena that these data provide, it is appropriate to integrate these data into Earth science educational materials. I seek to answer the following research question: "will using the LiDAR topography data instead of, or alongside, traditional visualizations and teaching methods enhance a student's ability to understand geologic concepts such as plate tectonics, the earthquake cycle, strike-slip faults, and geomorphology?" In order to answer this question, a ten-minute introductory video on LiDAR and its uses for the study of earthquakes entitled "LiDAR: Illuminating Earthquake Hazards" was produced. Additionally, LiDAR topography was integrated into the development of an undergraduate-level educational activity, the San Andreas fault (SAF) earthquake cycle activity, designed to teach introductory Earth science students about the earthquake cycle. Both the LiDAR video and the SAF activity were tested in undergraduate classrooms in order to determine their effectiveness. A pretest and posttest were administered to introductory geology lab students. The results of these tests show a notable increase in understanding LiDAR topography and its uses for studying earthquakes from pretest to posttest after watching the video on LiDAR, and a notable increase in understanding the earthquake cycle from pretest to posttest using the San Andreas Fault earthquake cycle exercise. These results suggest that the use of LiDAR topography within these educational tools is beneficial for students when learning about the earthquake cycle and earthquake hazards.
Contributors
Robinson, Sarah Elizabeth (Author) / Arrowsmith, Ramon (Thesis advisor) / Reynolds, Stephen J. (Committee member) / Semken, Steven (Committee member) / Arizona State University (Publisher)
Created
2011
Description
This thesis research attempts to observe, measure and visualize the communication patterns among developers of an open source community and analyze how this can be inferred in terms of progress of that open source project. Here I attempted to analyze the Ubuntu open source project's email data (9 subproject log archives over a period of five years) and focused on drawing more precise metrics from different perspectives of the communication data. Also, I attempted to overcome the scalability issue by using Apache Pig libraries, which run on a MapReduce framework based Hadoop Cluster. I described four metrics based on which I observed and analyzed the data and also presented the results which show the required patterns and anomalies to better understand and infer the communication. Also described the usage experience with Pig Latin (scripting language of Apache Pig Libraries) for this research and how they brought the feature of scalability, simplicity, and visibility in this data intensive research work. These approaches are useful in project monitoring, to augment human observation and reporting, in social network analysis, to track individual contributions.
Contributors
Motamarri, Lakshminarayana (Author) / Santanam, Raghu (Thesis advisor) / Ye, Jieping (Thesis advisor) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created
2011
Description
Sparse learning is a technique in machine learning for feature selection and dimensionality reduction, to find a sparse set of the most relevant features. In any machine learning problem, there is a considerable amount of irrelevant information, and separating relevant information from the irrelevant information has been a topic of focus. In supervised learning like regression, the data consists of many features and only a subset of the features may be responsible for the result. Also, the features might require special structural requirements, which introduces additional complexity for feature selection. The sparse learning package, provides a set of algorithms for learning a sparse set of the most relevant features for both regression and classification problems. Structural dependencies among features which introduce additional requirements are also provided as part of the package. The features may be grouped together, and there may exist hierarchies and over- lapping groups among these, and there may be requirements for selecting the most relevant groups among them. In spite of getting sparse solutions, the solutions are not guaranteed to be robust. For the selection to be robust, there are certain techniques which provide theoretical justification of why certain features are selected. The stability selection, is a method for feature selection which allows the use of existing sparse learning methods to select the stable set of features for a given training sample. This is done by assigning probabilities for the features: by sub-sampling the training data and using a specific sparse learning technique to learn the relevant features, and repeating this a large number of times, and counting the probability as the number of times a feature is selected. Cross-validation which is used to determine the best parameter value over a range of values, further allows to select the best parameter value. This is done by selecting the parameter value which gives the maximum accuracy score. With such a combination of algorithms, with good convergence guarantees, stable feature selection properties and the inclusion of various structural dependencies among features, the sparse learning package will be a powerful tool for machine learning research. Modular structure, C implementation, ATLAS integration for fast linear algebraic subroutines, make it one of the best tool for a large sparse setting. The varied collection of algorithms, support for group sparsity, batch algorithms, are a few of the notable functionality of the SLEP package, and these features can be used in a variety of fields to infer relevant elements. The Alzheimer Disease(AD) is a neurodegenerative disease, which gradually leads to dementia. The SLEP package is used for feature selection for getting the most relevant biomarkers from the available AD dataset, and the results show that, indeed, only a subset of the features are required to gain valuable insights.
Contributors
Thulasiram, Ramesh (Author) / Ye, Jieping (Thesis advisor) / Xue, Guoliang (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created
2011
Description
The antioxidant, antihistamine, and chemotactic properties of vitamin C provide the theoretical basis linking vitamin C supplementation to combating the common cold; yet, the clinical evidence is mixed. To date, vitamin C intervention trials have not systematically recorded cold symptoms daily or looked at fluctuations in plasma histamine over an extended period. Also, trials have not been conducted in individuals with marginal vitamin C status. This study examined the impact of vitamin C supplementation during cold season on specific cold symptoms in a population with low plasma vitamin C concentrations. Healthy young males who were not regular smokers or training for competitive sports between the ages of 18 and 35 with below average plasma vitamin C concentrations were stratified by age, body mass index, and vitamin C status into two groups: VTC (500 mg vitamin C capsule ingested twice daily) or CON (placebo capsule ingested twice daily). Participants were instructed to fill out the validated Wisconsin Upper Respiratory Symptom Survey-21 daily for 8 weeks. Blood was sampled at trial weeks 0, 4, and 8. Plasma vitamin C concentrations were significantly different by groups at study week 4 and 8. Plasma histamine decreased 4.2% in the VTC group and increased 17.4% in the CON group between study weeks 0 and 8, but these differences were not statistically significant (p>0.05). Total cold symptom scores averaged 43±15 for the VTC group compared to 148±36 for the CON group, a 244% increase in symptoms for CON participants versus VTC participants (p=0.014). Additionally, recorded symptom severity and functional impairment scores were lower in the VCT group than the CON group (p=0.031 and 0.058, respectively). Global perception of sickness was 65% lower in the VTC group compared to the CON group (p=0.022). These results suggest that 1000 mg of vitamin C in a divided dose daily may lower common cold symptoms, cold symptom severity, and the perception of sickness. More research is needed to corroborate these findings.
Contributors
Osterday, Gillean (Author) / Johnston, Carol (Thesis advisor) / Beezhold, Bonnie (Committee member) / Vaughan, Linda (Committee member) / Arizona State University (Publisher)
Created
2012
Description
In recent years environmental life-cycle assessments (LCA) have been increasingly used to support planning and development of sustainable infrastructure. This study demonstrates the application of LCA to estimate embedded energy use and greenhouse gas (GHG) emissions related to materials manufacturing and construction processes for low and high density single-family neighborhoods typically found in the Southwest. The LCA analysis presented in this study includes the assessment of more than 8,500 single family detached units, and 130 miles of related roadway infrastructure. The study estimates embedded and GHG emissions as a function of building size (1,500 - 3000 square feet), number of stories (1 or 2), and exterior wall material composition (stucco, brick, block, wood), roof material composition (clay tile, cement tile, asphalt shingles, built up), and as a function of roadway typology per mile (asphalt local residential roads, collectors, arterials). While a hybrid economic input-out life-cycle assessment is applied to estimate the energy and GHG emissions impacts of the residential units, the PaLATE tool is applied to determine the environmental effects of pavements and roads. The results indicate that low density single family neighborhoods are 2 - 2.5 X more energy and GHG intensive, per residential dwelling (unit) built, than high density residential neighborhoods. This relationship holds regardless of whether the functional unit is per acre or per capita. The results also indicate that a typical low density neighborhood (less than 2 dwellings per acre) requires 78 percent more energy and resource in roadway infrastructure per residential unit than a traditional small lot high density (more than 6 dwelling per acre). Also, this study shows that new master planned communities tend to be more energy intensive than traditional non master planned residential developments.
Contributors
Frijia, Stephane (Author) / Guhathakurta, Subhrajit (Committee member) / Williams, Eric D. (Committee member) / Pijawka, David K (Committee member) / Arizona State University (Publisher)
Created
2011
Description
As the information available to lay users through autonomous data sources continues to increase, mediators become important to ensure that the wealth of information available is tapped effectively. A key challenge that these information mediators need to handle is the varying levels of incompleteness in the underlying databases in terms of missing attribute values. Existing approaches such as Query Processing over Incomplete Autonomous Databases (QPIAD) aim to mine and use Approximate Functional Dependencies (AFDs) to predict and retrieve relevant incomplete tuples. These approaches make independence assumptions about missing values--which critically hobbles their performance when there are tuples containing missing values for multiple correlated attributes. In this thesis, I present a principled probabilis- tic alternative that views an incomplete tuple as defining a distribution over the complete tuples that it stands for. I learn this distribution in terms of Bayes networks. My approach involves min- ing/"learning" Bayes networks from a sample of the database, and using it do both imputation (predict a missing value) and query rewriting (retrieve relevant results with incompleteness on the query-constrained attributes, when the data sources are autonomous). I present empirical studies to demonstrate that (i) at higher levels of incompleteness, when multiple attribute values are missing, Bayes networks do provide a significantly higher classification accuracy and (ii) the relevant possible answers retrieved by the queries reformulated using Bayes networks provide higher precision and recall than AFDs while keeping query processing costs manageable.
Contributors
Raghunathan, Rohit (Author) / Kambhampati, Subbarao (Thesis advisor) / Liu, Huan (Committee member) / Lee, Joohyung (Committee member) / Arizona State University (Publisher)
Created
2011
Description
The San Gabriel Mountains (SGM) of southern California provide the opportunity to study the topographic controls on erosion rate in a mountain range where climate and lithology are relatively constant. I use a combination of digital elevation model data, detailed channel survey data, decadal climate records, and catchment-averaged erosion rates quantified from 10Be concentrations in stream sands to investigate the style and rates of hillslope and channel processes across the transition from soil-mantled to rocky landscapes in the SGM. Specifically, I investigate (1) the interrelations among different topographic metrics and their variation with erosion rate, (2) how hillslopes respond to tectonic forcing in "threshold" landscapes, (3) the role of discharge variability and erosion thresholds in controlling the relationship between relief and erosion rate, and (4) the style and pace of transient adjustment in the western SGM to a recent increase in uplift rate. Millennial erosion rates in the SGM range from 0.03-1.1 mm/a, generally increasing from west to east. For low erosion rates (< 0.3 mm/a), hillslopes tend to be soil-mantled, and catchment-averaged erosion rates are positively correlated with catchment-averaged slope, channel steepness, and local relief. For erosion rates greater than 0.3 mm/a, hillslopes become increasingly rocky, catchment-mean hillslope angle becomes much less sensitive to erosion rate, and channels continue to steepen. I find that a non-linear relationship observed between channel steepness and erosion rate can be explained by a simple bedrock incision model that combines a threshold for erosion with a probability distribution of discharge events where large floods follow an inverse power-law. I also find that the timing of a two-staged increase in uplift rate in the western SGM based on stream profile analysis agrees with independent estimates. Field observations in the same region suggest that the relict topography that allows for this calculation has persisted for more than 7 Ma due to the stalling of migrating knickpoints by locally stronger bedrock and a lack of coarse sediment cover.
Contributors
Dibiase, Roman Alexander (Author) / Whipple, Kelin X (Thesis advisor) / Heimsath, Arjun M. (Thesis advisor) / Arrowsmith, J Ramon (Committee member) / Garnero, Edward J. (Committee member) / Hodges, Kip V. (Committee member) / Arizona State University (Publisher)
Created
2011
Description
The repression of reproductive competition and the enforcement of altruism are key components to the success of animal societies. Eusocial insects are defined by having a reproductive division of labor, in which reproduction is relegated to one or few individuals while the rest of the group members maintain the colony and help raise offspring. However, workers have retained the ability to reproduce in most insect societies. In the social Hymenoptera, due to haplodiploidy, workers can lay unfertilized male destined eggs without mating. Potential conflict between workers and queens can arise over male production, and policing behaviors performed by nestmate workers and queens are a means of repressing worker reproduction. This work describes the means and results of the regulation of worker reproduction in the ant species Aphaenogaster cockerelli. Through manipulative laboratory studies on mature colonies, the lack of egg policing and the presence of physical policing by both workers and queens of this species are described. Through chemical analysis and artificial chemical treatments, the role of cuticular hydrocarbons as indicators of fertility status and the informational basis of policing in this species is demonstrated. An additional queen-specific chemical signal in the Dufour's gland is discovered to be used to direct nestmate aggression towards reproductive competitors. Finally, the level of actual worker-derived males in field colonies is measured. Together, these studies demonstrate the effectiveness of policing behaviors on the suppression of worker reproduction in a social insect species, and provide an example of how punishment and the threat of punishment is a powerful force in maintaining cooperative societies.
Contributors
Smith, Adrian A. (Author) / Liebig, Juergen (Thesis advisor) / Hoelldobler, Bert (Thesis advisor) / Gadau, Juergen (Committee member) / Johnson, Robert A. (Committee member) / Pratt, Stephen (Committee member) / Arizona State University (Publisher)
Created
2011