Topic models are a new research field within the computer sciences information retrieval and text mining. They are generative probabilistic models of text corpora inferred by machine learning and they can be used for retrieval and text mining tasks. The most prominent topic model is latent Dirichlet allocation (LDA), which was introduced in 2003 by Blei et al. and has since then sparked off the development of other topic models for domain-specific purposes. This thesis focuses on LDA's practical application. Its main goal is the replication of the data analyses from the 2004 LDA paper ``Finding scientific topics'' by Thomas Griffiths and Mark Steyvers within the framework of the R statistical programming language and the R~package topicmodels by Bettina Grün and Kurt Hornik. The complete process, including extraction of a text corpus from the PNAS journal's website, data preprocessing, transformation into a document-term matrix, model selection, model estimation, as well as presentation of the results, is fully documented and commented. The outcome closely matches the analyses of the original paper, therefore the research by Griffiths/Steyvers can be reproduced. Furthermore, this thesis proves the suitability of the R environment for text mining with LDA. (author's abstract) / Series: Theses / Institute for Statistics and Mathematics

The purpose of this paper is to generalize regression models for repeated categorical data based on maximizing a conditional likelihood. Some existing methods, such as those proposed by Duncan (1985), Fischer (1989), and Agresti (1993, and 1997) are special cases of this latent variable approach, used to account for dependencies in clustered observations. The generalization concerns the incorporation of rather general data structures such as subject-specific time-dependent covariates, a variable number of observations per subject and time periods of arbitrary length in order to evaluate treatment effects on a categorical response variable via a linear parameterization. The response may be polytomous, ordinal or dichotomous. The main tool is the log-linear representation of appropriately parameterized Rasch-type models, which can be fitted using standard software, e.g., R. The proposed method is applied to data from a psychiatric study on the evaluation of psychobiological variables in the therapy of depression. The effects of plasma levels of the antidepressant drug Clomipramine and neuroendocrinological variables on the presence or absence of anxiety symptoms in 45 female patients are analyzed. The individual measurements of the time dependent variables were recorded on 2 to 11 occasions. The findings show that certain combinations of the variables investigated are favorable for the treatment outcome. (author´s abstract) / Series: Research Report Series / Department of Statistics and Mathematics

Preventative interventions often demand that resources be consumed in the present in exchange for future benefits. Understanding these trade-offs, in a context of resource constraints, is essential for policy makers. Cost-effectiveness analysis is one tool to inform decision-making. Targeted testing and treatment (TTT) for latent tuberculosis infection (LTBI) consists in identifying people at high risk for LTBI for preventive treatment to decrease the risk that they will develop active tuberculosis disease (ATBD). The state of Tennessee began conducting TTT statewide in 2001. This study uses a decision tree to evaluate the cost and outcomes of TTT for LTBI in Tennessee, compared to passive ATBD case finding (PACF). Key event probabilities were obtained from the Tennessee TTT program and from the literature. Outcomes are measured in terms of Quality Adjusted Life Years (QALY). The cost-effectiveness threshold was set at $100,000/QALY saved. One-way sensitivity analyses around factors related to study design (exclusion of patient costs, secondary transmission, discount rate and analytical horizon), the program’s environment (prevalence of LTBI and drug resistance, ATBD treatment costs) and program performance (program maturity, treatment initiation and completion rate, testing in low-risk group, test characteristics, screening costs) were conducted, as was probabilistic sensitivity analysis (PSA) which takes into account the uncertainty in multiple parameters simultaneously. The base case, with a 25-year time horizon and 3% discount rate, shows that TTT prevents 47 ATBD cases, and saves 31 QALYs per 100,000 patients screened for LTBI at a societal cost of $12,579 (2011 US$) per QALY saved. Sensitivity analyses identified value thresholds that would trigger a change in preferred policy. PSA shows that the likelihood that TTT would be cost-effective is low. Decision makers interested in implementing TTT should carefully assess the characteristics of the local TB epidemic and expected program performance to determine whether TTT is preferable over PACF from a cost-effectiveness viewpoint.

The use of a latent heat storage system using phase change materials (PCMs) is an effective way of storing thermal energy. Latent heat storage enables high-energy storage density which reduces the footprint of the system and the cost. However, PCMs have very low thermal conductivities making them unsuitable for large-scale use without enhancing the effective thermal conductivity. In order to address, the low thermal conductivity of the PCMs, macroencapsulation of PCMs has been adopted as an effective technique. The macroencapsulation not only provides a self-supporting structure of PCM and separates the PCM from thermal fluids but also enhances the heat transfer rate. The current work involves study of various concepts of encapsulation of low cost inorganic PCMs. Sodium nitrate (NaNO3), a low cost PCM, was selected for thermal storage in a temperature range of 300 - 500˚C. Various techniques like electroless coatings, coatings using silicates, coatings with metal oxide (SiO2) and sand encapsulation are discussed. A novel technique of metal oxide coating was developed where firstly a high temperature polymer, such as, polymer (stable > 500˚C) was coated over PCM pellets, and cured, so that the pellet becomes insoluble in water as well as several organic solvents and later the metal oxide is coated over the pellet using self-assembly, hydrolysis, and simultaneous chemical oxidation at various temperatures. The coated PCM pellets were characterized.

Thermal energy storage (TES) is one of the most attractive and cost effective solutions to the intermittent generation systems like solar, wind and other renewable sources, compared to alternatives. It creates a bridge between the power supply and demand during peak hours or at times of emergency to ensure the continuous supply of energy. Among all the TES systems, latent heat thermal energy storage (LHTES) draws lots of interests as it has high energy density and can store or retrieve energy isothermally. Two major technical challenges associated with the LHTES are low thermal conductivity of the phase change materials (PCMs), and corrosion tendency of the containment vessel with the PCMs. Macro-encapsulation of the PCM is one of the techniques to encounter the low thermal conductivity issue as it will maximize the heat transfer area for the given volume of the PCM and restrict the PCMs to get in contact with the containment vessel. However, finding a suitable encapsulation technique that can address the volumetric expansion problem and compatible shell material are significant barriers of this approach. In the present work, an innovative technique to encapsulate PCMs that melt in the 100-350 oC temperature range was developed for industrial and private applications. This technique did not require a sacrificial layer to accommodate the volumetric expansion of the PCMs on melting. The encapsulation consisted of coating a non-reactive polymer over the PCM pellet followed by deposition of a metal layer by a novel non-vacuum metal deposition technique. The fabricated spherical capsules were tested in different heat transfer fluid (HTF) environments like air, oil and molten salt (solar salt). Thermophysical properties of the PCMs were investigated by DSC/TGA, IR and weight change analysis before and after the thermal cycling. Also, the constrained melting and solidification of sodium nitrate PCM inside the spherical capsules of different sizes were compared to explore the charging and discharging time. To accomplish this, three thermocouples were installed vertically inside the capsule at three equidistant positions. Low-density graphene was dispersed in the PCM to increase its conductivity and compared with pure PCM capsules. A laboratory scale packed-bed LHTES system was designed and built to investigate the performance of the capsules. Sodium nitrate (m.p. 306oC) was used as the PCM and air was used as the heat transfer fluid (HTF). The storage system was operated between 286oC and 326oC and the volumetric flow rate of the HTF was varied from 110 m3/h to 151 m3/h. The temperature distribution along the bed (radially and axially) and inside the capsules was monitored continuously during charging and discharging of the system. The effect of the HTF mass flow rate on the charging and discharging time and on the pressure drop across the bed was evaluated. Also, the energy and exergy efficiencies were calculated for three different flow rates. Finally, a step-by-step trial manufacturing process was proposed to produce large number of spherical capsules.

There has been a significant increase in private vehicle ownership in the last decade leading to substantial increase in air pollution, depleting fuel reserves, etc. One of the alternatives known as battery operated electric vehicles (BEVs) has the potential to reduce carbon footprints due to lesser or no emissions and thus the focus on shifting people from gasoline operated vehicles (GVs) to BEVs has increased considerably recently. However, BEVs have a limited ‘range’ and takes considerable time to completely recharge its battery. In addition, charging stations are not as pervasive as gasoline stations. As a result a new fear of getting stranded is observed in BEV drivers, known as range anxiety. Range anxiety has the potential to substantially affect the route choice of a BEV user. It has also been a major cause of lower market shares of BEVs. Range anxiety is a latent feeling which cannot be measured directly. It is not homogenous either and varies among different socio-economic groups. Thus, a better understanding of BEV users’ behavior may shed light on some potential solutions that can then be used to improve their market shares and help in developing new network models which can realistically capture effects of varying EV adoptions. Thus, in this study, we analyze the factors that may impact BEV users’ range anxiety in addition to their route choice behavior using the integrated choice latent variable model (ICLV) proposed by Bhat and Dubey (2014). Our results indicate that an individual’s range anxiety is significantly affected by their age, gender, income, awareness of charging stations, BEV ownership and other category vehicle ownership. Further, it also highlights the importance of including disutility caused by distance while considering network flow models with combined GV and BEV assignment. Finally, a more concentrated effort can be directed towards increasing the awareness of charging station locations in the neighborhood to help reduce the psychological barrier associated with range anxiety. Overcoming this barrier may help increase consumer confidence, resulting in increased BEV adoption and ultimately will lead towards a potentially pollution-free environment. / text

Obesity, as defined as BMI at or above the 95th percentile on the Centers for Disease Control and Prevention’s growth charts, has increased almost 3-fold among children in the United States since 1980. Overweight in adolescence has been associated with increased fat retention and high blood pressure in adulthood, among other symptoms of metabolic syndrome. However, normative patterns of weight change in childhood have not been developed. Groups of children may follow different trajectory patterns of BMI change over time. If common trajectory patterns could be identified, and their risk factors and outcomes understood, more nuanced intervention with families and children at risk for obesity could be developed. This study used a national dataset of 1,364 children whose weight and length was measured 12 times from birth through 15 ½ years. Testing both latent class growth analysis and growth mixture modeling identified four distinct subgroups, or classes, of BMI growth trajectory from 24 months – 8th grade. These classes were compared on numerous demographic, biological, and psychosocial risk factors identified in previous research as related to obesity. Classes were differentiated primarily on the child’s BMI at 15 months, the mother’s BMI at 15 months, birth weight for age, and percent increase in birth weight. Being male, Black, and lower SES were also related to membership in the higher-BMI trajectory classes. Of the psychosocial factors, maternal sensitivity, maternal depression, and attachment classification were also related to BMI class. Membership in these trajectories strongly predicted weight-related and blood-pressure outcomes at 15 ½ years over and above individual risk factors, demonstrating that patterns of change themselves are highly influential. The best-fitting models of weight-related outcomes at 15 ½ years included change trajectory in combination with biological, psychosocial, and SES risk factors from 0-24 months, with R² ranging from .31 = .50. Characteristics predicting adolescent overweight can be identified in the first years of life and should trigger the development and implementation of early intervention protocols in obstetrics and pediatrics. / text

This research analyzes welfare conditions of Afro-Colombians vis-à-vis non Afro-Colombians using Amartya Sen's Capability Approach as the theoretical framework, and the latent variable modeling as the empirical method. Multiple Indicators Multiple Causes (MIMIC) models are estimated using data from the Colombian Quality of Life Survey, 2003. Two latent constructs, namely, 'knowledge' and 'being adequately sheltered', represent the two 'Capability' dimensions to be analyzed. Ethnic background appears to have a consistently negative influence after controlling statistically by a set of relevant variables (e.g. being poor, area, marital status, age and gender, among others) included in the models as exogenous "causes" or "determinants" of each capability dimension. This implies that the capability set or the freedom an Afro-descendant enjoys in achieving the life he or she wants in terms of 'knowledge' and 'shelter' is consistently lower than that of a non Afro-descendant (whites and mestizos). As a consequence, achieved welfare or functioning achievement as expressed in terms of aspects such as years of education or dwelling conditions in the household is and would be consistently lower for individual members of this ethnic group. This evidence points toward the proposition that embedded patterns of racial discrimination are limiting Afro-Colombian capabilities and individual agency, beyond income levels or even access to educational resources. Hence, from a capability perspective removing racial discrimination must be an explicit objective of developmental policy. Accordingly, national policy must be directed not only to improving access for Afro-Colombians to resources and economic wellbeing, as traditional analysis of class disparity argues, but also toward the nurturing and expansion of the real freedom they have to pursue the goals they value. Thus, development policy in Colombia must altogether work toward the improvement of resource access for Afro-descendants and toward the creation of specific mechanisms to enforce the judicial instruments to fight against racial discrimination. These laws and judicial mechanisms were created to open spaces for political, social and economic participation for this population group on an equal basis, as their fellow citizens of non African descent, and are yet to be fulfilled.

The present research reports the first comprehensive map of brain networks underlying latent inhibition learning, the first application of structural equation modeling to cytochrome oxidase data, and the first effects of methylene blue, a known metabolic enhancer, on latent inhibition. In latent inhibition, repeated exposure to a stimulus results in a latent form of learning that inhibits subsequent associations with that stimulus. As neuronal energy demand to form learned associations changes, so does the induction of the respiratory enzyme cytochrome oxidase. Therefore, cytochrome oxidase can be used as an endpoint metabolic marker of the effects of experience on regional brain metabolic capacity. Quantitative cytochrome oxidase histochemistry was used to map brain regions in mice trained on a tone-footshock fear conditioning paradigm with either tone preexposure (latent inhibition), conditioning only (acquisition), conditioning followed by tone alone (extinction), or no handling or conditioning (naïve). In normal latent inhibition, the ventral cochlear nucleus, medial geniculate, CA1 hippocampus, and perirhinal cortex showed modified metabolic capacity due to latent inhibition. Structural equation modeling was used to determine the causal influences in an anatomical network of these regions and others thought to mediate latent inhibition, including the accumbens and entorhinal cortex. An uncoupling of ascending influences between auditory regions was observed in latent inhibition. There was also a reduced influence on the accumbens from the perirhinal cortex in both latent inhibition and extinction. These results suggest a specific network with a neural mechanism of latent inhibition that involves sensory gating, as evidenced by modifications in metabolic capacity, effective connectivity between auditory regions, and reduced hippocampal influence on the accumbens. The effects of methylene blue on disrupted latent inhibition were also investigated. Reduced tone-alone presentations disrupted the latent inhibition effect and led to an increase in freezing behavior. Repeated low-dose administration of methylene blue decreased freezing levels and facilitated the disrupted latent inhibition effect. Methylene blue administration also resulted in changes in metabolic capacity in limbic and cortical regions. A unique functional neural network was found in methylene blue-restored latent inhibition that emphasized sensory gating of auditory information, attention processing, and cortical inhibition of behavior. / text

published_or_final_version / Community Medicine / Doctoral / Doctor of Philosophy