"The purpose of this research is the development and testing of an instrument to be used in prediction of success in college freshman mathematics courses"--Introduction. / Typescript. / "April, 1967." / "Submitted to the Graduate School of Florida State University in partial fulfillment of the requirements for the degree of Doctor of Education." / Advisor: R. Heimer, Professor Directing Dissertation. / Vita. / Includes bibliographical references (leaves 118-120).

In this thesis, we perform analysis and prediction for community structures in graphs using unsupervised learning. The methods we use require the data matrices to be of low rank, and such matrices appear quite often in real world problems across a broad range of domains. Such a modelling assumption is widely considered by classical algorithms such as principal component analysis (PCA), and the same assumption is often used to achieve dimensionality reduction. Dimension reduction, which is a classic method in unsupervised learning, can be leveraged in a wide array of problems, including prediction of strength of connection between communities from unlabeled or partially labeled data. Accordingly, a low rank assumption addresses many real world problems, and a low rank assumption has been used in this thesis to predict the strength of connection between communities in Amazon product data. In particular, we have analyzed real world data across retail and cyber domains, with the focus being on the retail domain. Herein, our focus is on analyzing the strength of connection between the communities in Amazon product data, where each community represents a group of products, and we are given the strength of connection between the individual products but not between the product communities. We call the strength of connection between individual products first order data and the strength of connection between communities second order data. This usage is inspired by [1] where first order time series are used to compute second order covariance matrices where such covariance matrices encode the strength of connection between the time series. In order to find the strength of connection between the communities, we define various metrics to measure this strength, and one of the goals of this thesis is to choose a good metric, which supports effective predictions. However, the main objective is to predict the strength of connection between most of the communities, given measurements of the strength of connection between only a few communities. To address this challenge, we use modern extensions of PCA such as eRPCA that can provide better predictions and can be computationally efficient for large problems. However, the current theory of eRPCA algorithms is not designed to treat problems where the initial data (such as the second order matrix of communities strength) is both low rank and sparse. Therefore, we analyze the performance of eRPCA algorithm on such data and modify our approaches for the particular structure of Amazon product communities to perform the necessary predictions.

Typescript (photocopy). / Digitized by Kansas Correctional Industries

Preeclampsia (PE) is a major cause of perinatal and maternal morbidity and mortality. In the United Kingdom, the National Institute for Clinical Excellence (NICE) has issued guidelines on routine antenatal care recommending that at the booking visit a woman’s level of risk for PE should be determined and the subsequent intensity of antenatal care should be based on this risk assessment. This method relies on a risk scoring system derived from maternal characteristics and medical history; the performance of screening by this method is poor with detection of less than 50% of cases of preterm-PE and term-PE. The objective of this thesis is to develop a method for the estimation of the patient-specific risk for PE by combining the a priori risk based on maternal characteristics and medical history with the results of biophysical and biochemical markers obtained at 11-13 weeks’ gestation. Such early identification of high-risk pregnancies could lead to the use of pharmacological interventions, such as low-dose aspirin, which could prevent the development of the disease. The data for the thesis were derived from two types of studies: First, prospective screening in 65,771 singleton pregnancies, which provided data for maternal factors and serum pregnancy associated plasma protein-A (PAPP-A). In an unselected sequential process we also measured uterine artery pulsatility index (PI) in 45,885 of these pregnancies, mean arterial pressure (MAP) in 35,215 cases and placental growth factor (PLGF) in 14,252 cases. Second, cases-control studies for evaluating the ten most promising biochemical markers identified from search of the literature; for these studies we used stored serum or plasma samples obtained during screening and measured PLGF, Activin-A, Inhibin-A, placental protein-13 (PP13), P-selectin, Pentraxin-3 (PTX-3), soluble Endoglin (sEng), Plasminogen activator inhibitor-2 (PAI-2), Angiopoietin-2 (Ang-2) and soluble fms-like tyrosine kinase-1 (s-Flt-1). A competing risk model was developed which is based on Bayes theorem and combines the prior risk from maternal factors with the distribution of biomarkers to derive patient-specific risk for PE at different stages in pregnancy. The prior risk was derived by multiple regression analysis of maternal factors in the screening study. The distribution of biophysical and biochemical markers was derived from both the screening study and the case-control studies. The prior risk increased with advancing maternal age, increasing weight, was higher in women of Afro-Caribbean and South-Asian racial origin, those with a previous pregnancy with PE, conception by in vitro fertilization and medical history of chronic hypertension, type 1 diabetes mellitus and systemic lupus erythematosus (SLE) or antiphospholipid syndrome (APS). The estimated detection rate (DR) of PE requiring delivery at < 34, < 37 weeks’ gestation and all PE, at false positive rate (FPR) of 10%, in screening by maternal factors were 51, 43 and 40%, respectively. The addition of biochemical markers to maternal factors, including maternal serum PLGF and PAPPA, improved the performance of screening with respective DRs of 74, 56 and 41%. Similarly, addition of biophysical markers to maternal factors, including uterine artery PI and MAP, improved the performance of screening with respective DRs of 90, 72 and 57%. The combination of maternal factors with all the above biophysical and biochemical markers improved the respective DRs to 96, 77 and 54%. The findings of these studies demonstrate that a combination of maternal factors, biophysical and biochemical markers can effectively identify women at high-risk of developing PE.

In the health-informatics and bio-medical domains, clinicians produce an enormous amount of data which can be complex and high in dimensionality. This scenario includes visual field data, which are used for managing the second leading cause of blindness in the world: glaucoma. Visual field data are the most common type of data collected to diagnose glaucoma in patients, and usually the data consist of 54 or 76 variables (which are referred to as visual field locations). Due to the large number of variables, the six nerve fiber bundles (6NFB), which is a collection of visual field locations in groups, are the standard clusters used in visual field data to represent the physiological traits of the retina. However, with regard to classification accuracy of the data, this research proposes a technique to find other significant spatial clusters of visual field with higher classification accuracy than the 6NFB. This thesis presents a novel clustering technique, namely, Simultaneous Modelling and Clustering (SMC). SMC performs clustering on data based on classification accuracy using heuristic search techniques. The method searches a collection of significant clusters of visual field locations that indicate visual field loss progression. The aim of this research is two-fold. Firstly, SMC algorithms are developed and tested on data to investigate the effectiveness and efficiency of the method using optimisation and classification methods. Secondly, a significant clustering arrangement of visual field, which highly interrelated visual field locations to represent progression of visual field loss with high classification accuracy, is searched to complement the 6NFB in diagnosis of glaucoma. A new clustering arrangement of visual field locations can be used by medical practitioners together with the 6NFB to complement each other in diagnosis of glaucoma in patients. This research conducts extensive experiment work on both visual field and simulated data to evaluate the proposed method. The results obtained suggest the proposed method appears to be an effective and efficient method in clustering visual field data and 3 improving classification accuracy. The key contributions of this work are the novel model-based clustering of visual field data, effective and efficient algorithms for SMC, practical knowledge of visual field data in the diagnosis of glaucoma and the presentation a generic framework for modelling and clustering which is highly applicable to many other dataset/model combinations.

Doctor of Philosophy / Department of Agricultural Economics / Nathan P. Hendricks / This dissertation focuses on the prediction of agricultural land values and the effects of water rights on land values using machine learning algorithms and hedonic pricing methods. I predict agricultural land values with different machine learning algorithms, including ridge regression, least absolute shrinkage and selection operator, random forests, and extreme gradient boosting methods. To analyze the causal effects of water right seniority on agricultural land values, I use the double-selection LASSO technique. The second chapter presents the data used in the dissertation. A unique set of parcel sales from Property Valuation Division of Kansas constitute the backbone of the data used in the estimation. Along with parcel sales data, I collected detailed basis, water, tax, soil, weather, and urban influence data. This chapter provides detailed explanation of various data sources and variable construction processes. The third chapter presents different machine learning models for irrigated agricultural land price predictions in Kansas. Researchers, and policymakers use different models and data sets for price prediction. Recently developed machine learning methods have the power to improve the predictive ability of the models estimated. In this chapter I estimate several machine learning models for predicting the agricultural land values in Kansas. Results indicate that the predictive power of the machine learning methods are stronger compared to standard econometric methods. Median absolute error in extreme gradient boosting estimation is 0.1312 whereas it is 0.6528 in simple OLS model. The fourth chapter examines whether water right seniority is capitalized into irrigated agricultural land values in Kansas. Using a unique data set of irrigated agricultural land sales, I analyze the causal effect of water right seniority on agricultural land values. A possible concern during the estimation of hedonic models is the omitted variable bias so we use double-selection LASSO regression and its variable selection properties to overcome the omitted variable bias. I also estimate generalized additive models to analyze the nonlinearities that may exist. Results show that water rights have a positive impact on irrigated land prices in Kansas. An additional year of water right seniority causes irrigated land value to increase nearly $17 per acre. Further analysis also suggest a nonlinear relationship between seniority and agricultural land prices.

Low-dimensional systems are an important and intensely studied area of condensed matter physics. When a material is forced to adopt a low-dimensional structure, its behaviour is often dramatically different to that of the bulk phase. It is vital to predict the structures of low-dimensional systems in order to reliably predict their properties. To this end, the ab initio random structure searching (AIRSS) method, which has previously been used to identify the structures of bulk materials, has been extended to deal with the case of nanowires encapsulated inside carbon nanotubes. Such systems are a rapidly developing area of research with important nanotechnological applications, including information storage, energy storage and chemical sensing. The extended AIRSS method for encapsulated nanowires (ENWs) was implemented and used to identify the structures formed by germanium telluride, silver chloride, and molybdenum diselenide ENWs. In each of these cases, a number of novel nanowire structures were identified, and a phase diagram predicting the ground state nanowire structure as a function of the radius of the encapsulating nanotube was calculated. In the case of germanium telluride, which is a technologically important phase-change material, the potential use of GeTe ENWs as switchable nanoscale memory devices was investigated. The vibrational properties of silver chloride ENWs were also considered, and a novel scheme was developed to predict the Raman spectra of systems which can be decomposed into multiple weakly interacting subsystems. This scheme was used to obtain a close approximation to the Raman spectra of AgCl ENWs at a fraction of the computational cost that would otherwise be necessary. The encapsulation of AgCl was shown to produce substantial shifts in the Raman spectra of nanotubes, providing an important link with experiment. A method was developed to predict the stress-strain response of an ENW based on a polygonal representation of its surface, and was used to investigate the elastic response of molybdenum diselenide ENWs. This was used to predict stress-radius phase diagrams for MoSe_2 ENWs, and hence to investigate stress-induced phase change within such systems. The X-ray diffraction of ENWs was also considered. A program was written to simulate X-ray diffraction in low-dimensional systems, and was used to predict the diffraction patterns of some of the encapsulated GeTe nanowire structures predicted by AIRSS. By modelling the interactions within a bundle of nanotubes, diffraction patterns for bundles of ENWs were obtained.

Chan, Kam Fung. / "September 2010." / Thesis (M.Phil.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 61-62). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.6 / Chapter 1.1 --- Main result --- p.6 / Chapter 1.2 --- Useful definition --- p.7 / Chapter 1.3 --- Outline of proof of Theoreml.l --- p.11 / Chapter 2 --- Proof of Theorem1.2 and 13 --- p.12 / Chapter 2.1 --- The spaces \hV\ and \hW\ --- p.13 / Chapter 2.2 --- The space \hWloc\ --- p.19 / Chapter 2.3 --- The space \Wloc\ --- p.23 / Chapter 3 --- Proof of Theoreml4 --- p.26 / Chapter 3.1 --- Sheaves with category structure --- p.26 / Chapter 3.2 --- W° and hW° --- p.29 / Chapter 3.3 --- Armlets --- p.29 / Chapter 4 --- Proof of Theorem15 --- p.36 / Chapter 4.1 --- Homotopy colimit decompositions --- p.36 / Chapter 4.2 --- Introducing boundaries --- p.50 / Chapter 4.2.1 --- Proof of Theorem4.21 --- p.53 / Chapter 4.2.2 --- Proof of Lemma4.20 --- p.56 / Chapter 4.3 --- Using the Harer-Ivanov stabilization theorem --- p.58 / Bibliography --- p.61

Permeable pavement structures (PPSs) are one of the significant LID systems that have potential positive effect on the ecosystem. Yet, the performance of permeable pavements is still questionable. Further studies on the hydrological performance of the system need to be addressed for better design criteria and maintenance during the operation. The infiltration through the pavement is a crucial parameter that projects the system performance. Several factors affect its deterioration. The entrapment of suspended materials associated with the infiltrated stormwater through the system is one of the major factors that affect its performance. Factors that promote the entrapment of particles were discussed thoroughly through the literature and are explained in this study. Many previous studies were focused on performing experimental work and developing empirical models to study the hydraulic performance of the system. Yet, prediction models on the infiltration deterioration need to be addressed and theoretical analysis needs to be performed in order to determine the empirical coefficients with defined parameters that were introduced in the previous literature. Furthermore, the sensitivity of the pore and particle size distribution and mass loading rate of the suspended materials on the infiltration rate need to be addressed. The study focuses on investigating performance of PPSs with examining the variation effect of pore and particle size distribution on it. A prediction model was made and simulated using Matlab software, in which pore and particle size means and standard deviations are taken as inputs. Further, the variation in these parameters on infiltration is examined. Critical levels, that infiltration decline would reach, were defined based on the introduced mechanisms from the previous literature. Based on the variation of pore and particle size means and standard deviations, these critical levels were studied through the analysis of the obtained results from the simulated model. / Graduate / 2019-12-10

The purpose of this study was to determine if data routinely collected during the kindergarten year and at entry into first grade could be used to predict whether a child would be perceived as successful or not successful by the end of first grade. The need for immediate continued research on this topic was established through the review of literature, which highlighted the extent of the at-risk problem both locally and nationally. The growing number of at-risk students combined with the minimal impact of the educational programs mandates the need to identify these students in time to prevent school failure. However, clear identification procedures are not currently available and previous studies have raised substantial questions regarding the accuracy of early identification procedures. The presenting problem of this study was to determine the sensitivity and specificity of a set of predictor variables, and then to analyze these findings as to whether or not they were accurate enough for use as an initial identification process for subsequent classes. The primary research approach of this study was a longitudinal data collection and correlational analysis, with discriminant analysis techniques used to determine predictive accuracy. The study was limited to data on the class of 2001 from two elementary schools within the Washougal School District. The data collected and the subsequent analysis were used to answer six exploratory research questions. No hypothesis was proposed. This study used ratings and scores obtained from the administration of the Preschool Screening system, kindergarten teacher ratings, the School Success Rating Scale, and the Gates-MacGinitie Reading Readiness Tests as predictor variables. Criterion measures of school success/failure were: placement into special programs or grade retention, and end-of-first-grade evaluations of individual student success (report cards, teacher ratings, Gates-MacGinitie Reading Achievement, and the School Success Ratings Scale). The demographic variables of gender, age, parent marital status, and eligibility for free or reduced lunch were analyzed for their potential to exceed or enhance the accuracy of the predictor variables. Three types of measurement were defined and required in order for a predictor or predictor combination to be considered adequate for use in an identification process. These were overall accuracy, criterion sensitivity and specificity accuracy, and prediction sensitivity and specificity accuracy. An 80 percent accuracy level was desired on all three types of measurement. Findings of this study indicated that no single or combination of predictor, and/or demographic variables produced all three desired levels of accuracy. Various combinations of the predictor and demographic variables produced overall accuracy rates exceeding 80 percent for each of the criterion variables. Criterion measured sensitivity and specificity were found to be adequate for use in the prediction of at-risk students. Prediction measured specificity was also found to be highly accurate. Prediction sensitivity, however, was below the desired 80 percent level, indicating that the predictor variables over identify at-risk students. It was concluded that the predictor variables could be used in an identification process if mild over-identification of at-risk students was acceptable to the district. Any use of these identification procedures is assumed to be in connection with ethical intervention practices. Recommendations from this study included cross validation of the results and continuation of the study regarding the predictive accuracy of the identified variables as the students move through higher grade levels. The study also encouraged the Washougal School District to develop a formal collection and processing procedure for their routinely collected data.