Leishmania of the subgenus Viannia are protozoan parasites transmitted by phlebotomine sandflies (Diptera: Phlebotominae). They occur in tropical and subtropical areas in South America, where they cause cutaneous and mucocutaneous leishmaniasis. In this thesis, we studied developmental pattern of Viannia group and factors affecting its development within the sand fly gut. First, we investigated Leishmania braziliensis development within the Lutzomyia longipalpis digestive tract. Using GFP-labeled strain we demonstrated peripylar development: promastigotes escaped from the endoperitrophic space, colonized the hindgut and then migrated anteriorly. Four morphological forms were found within the Lu. longipalpis digestive tract: elongated nectomonads, short nectomonads, metacyclic promastigotes and paramastigotes. Furthermore, using the histological methods we demonstrated parasite attachment in pylorus region, while there were only free promastigotes in the midgut; neither form was found attached to the midgut epithelium. The next part was devoted to the effect of temperature on Viannia in Lu. longipalpis. We compared development of two closely related species L. peruviana and L. braziliensis at 20 řC and 26 řC. Leishmania braziliensis developed well in both temperatures tested, L. peruviana developed...

Indiana University-Purdue University Indianapolis (IUPUI) / Efficient topic modeling is needed to support applications that aim at identifying main themes from a collection of documents. In this thesis, a reduced vector embedding representation and particle swarm optimization (PSO) are combined to develop a topic modeling strategy that is able to identify representative themes from a large collection of documents. Documents are encoded using a reduced, contextual vector embedding from a general-purpose pre-trained language model (sBERT). A modified PSO algorithm (pPSO) that tracks particle fitness on a dimension-by-dimension basis is then applied to these embeddings to create clusters of related documents. The proposed methodology is demonstrated on three datasets across different domains. The first dataset consists of posts from the online health forum r/Cancer. The second dataset is a collection of NY Times abstracts and is used to compare

Wireless networks are an iconic technology of today’s modern era, theyare present in our daily activities as can be exemplified by cellular communications,wi-fi, bluetooth, and others. Vector measurements play an importantrole in the design, simulation, and testing of wireless networks and are usedto characterize key devices operating in the radio interface, such as amplifiers,filters, and mixers.Accurate characterization is the key for improving the capacity and efficiencyof wireless networks. As the demand for network capacity continuouslyincreases, the accuracy of vector measurements must also improve. Further,it is anticipated that such trends will continue in the years to come. Consequently,the wireless industry needs to include nonlinear behavior in theircharacterization and analysis, to assess and guaranty the operation of the devices,and to comply to the specifications from governmental regulations. Incontrast to linear behavior, nonlinear behavior presents an additional bandwidthrequirement because the signal bandwidth grows when it passes throughnonlinear devices. In this thesis, vector measurements for devices operatingin wireless networks are studied, emphasizing a synthetic approach for theinstrumentation. This approach enables the use of digital post-processing algorithms,which enhances the measurement accuracy and/or speed and canovercome hardware impairments. This thesis presents the design of a vectorialmeasurement system for wireless devices considering the aforementionedtrends and requirements. It also explores the advantages of the proposedapproach, describes its limitations, and discusses the digital signal processingalgorithms used to reach its final functionality. Finally, measurement resultsof the proposed setup are presented, analyzed and compared to those of modernindustrial instruments. / <p>QC 20130204</p>

Studying past landscapes existing before the modern era (pre-1945) carries great difficulty. Historical maps can offer insight to researchers, but the two-dimensional cartographic features on these maps remain largely inaccessible for geospatial analyses. This study investigates the idea of unlocking the data within historical maps to be utilized by Geographic Information Systems (GIS). To realize this goal, the cartographic features must be extracted and converted into digital vector (line) and raster (grid) data. For the purposes of this study, we focus on the extraction of elevation contour lines in United States Geological Survey (USGS) historical topographic maps. These lines are converted into Digital Elevation Models (DEMs), thus creating historically accurate digital landscapes. To ensure a high-quality result, the topographically derived DEMs (TOPO-DEMs) are compared to modern satellite-derived DEMs. The implications of this study can be directly applied to historical, archeological, and environmental research.

Deep learning techniques have achieved profound success in many challenging real-world applications, including image recognition, speech recognition, and machine translation. This success has increased the demand for developing deep neural networks and more effective learning approaches. The aim of this thesis is to consider the problem of learning a neural network classifier and to propose a novel approach to solve this problem under the Information Bottleneck (IB) principle. Based on the IB principle, we associate with the classification problem a representation learning problem, which we call ``IB learning". A careful investigation shows there is an unconventional quantization problem that is closely related to IB learning. We formulate this problem and call it ``IB quantization". We show that IB learning is, in fact, equivalent to the IB quantization problem. The classical results in rate-distortion theory then suggest that IB learning can benefit from a vector quantization approach, namely, simultaneously learning the representations of multiple input objects. Such an approach assisted with some variational techniques, result in a novel learning framework that we call ``Aggregated Learning (AgrLearn)", for classification with neural network models. In this framework, several objects are jointly classified by a single neural network. In other words, AgrLearn can simultaneously optimize against multiple data samples which is different from standard neural networks. In this learning framework, two classes are introduced, ``deterministic AgrLearn (dAgrLearn)" and ``probabilistic AgrLearn (pAgrLearn)". We verify the effectiveness of this framework through extensive experiments on standard image recognition tasks. We show the performance of this framework over a real world natural language processing (NLP) task, sentiment analysis. We also compare the effectiveness of this framework with other available frameworks for the IB learning problem.

Mathematical modeling is a powerful tool to study and analyze the disease dynamics prevalent in the community. This thesis studies the dynamics of two time since infection structured vector borne models with direct transmission. We have included disease induced death rate in the first model to form the second model. The aim of this thesis is to analyze whether these two models have same or different disease dynamics. An explicit expression for the reproduction number denoted by R0 is derived. Dynamical analysis reveals the forward bifurcation in the first model. That is when the threshold value R0 < 1, disease free-equilibrium is stable locally implying that if there is small perturbation of the system, then after some time, the system will return to the disease free equilibrium. When R0 > 1 the unique endemic equilibrium is locally asymptotically stable. For the second model, analysis of the existence and stability of equilibria reveals the existence of backward bifurcation i.e. where the disease free equilibrium coexists with the endemic equilibrium when the reproduction number R02 is less than unity. This aspect shows that in order to control vector borne disease, it is not sufficient to have reproduction number less than unity although necessary. Thus, the infection can persist in the population even if the reproduction number is less than unity. Numerical simulation is presented to see the bifurcation behaviour in the model. By taking the reproduction number as the bifurcation parameter, we find the system undergoes backward bifurcation at R02 = 1. Thus, the model has backward bifurcation and have two positive endemic equilibrium when R02 < 1 and unique positive endemic equilibrium whenever R02 > 1. Stability analysis shows that disease free equilibrium is locally asymptotically stable when R02 < 1 and unstable when R02 > 1. When R02 < 1, lower endemic equilibrium in backward bifurcation is locally unstable. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2020. / FAU Electronic Theses and Dissertations Collection

Since Schwarzschild found the first solution of the Einstein’s equations, more than 800 solutions were found. Solutions of Einstein’s equations are classified according to their Lie algebras of isometries and their isotropy subalgebras. Solutions were taken from the USU electronic library of solutions of Einstein’s field equations and the classification used Maple code developed at USU. This classification adds to the data contained in the library of solutions and provides additional tools for addressing the equivalence problem for solutions to the Einstein field equations. In this thesis, homogeneous spacetimes, hypersurface-homogeneous spacetimes, Robinson-Trautman solutions, and some famous black hole solutions have been classified.

Purpose: This article provides an empirical identification of groups of marketing scholars who share common beliefs about the role of science and the logic of scientific discovery. Design: We use Topology Representing Network quantization to empirically identify classes of marketing researchers within a representative sample of marketing professors. Findings: We find six distinct classes of marketing scholars. They differ with regard to popularity (size) and productivity (levels of publication output). Comparing the sub-samples of German-speaking and US respondents shows cross-cultural differences. Value: The study enhances our understanding of the current scientific orientation(s) of marketing. It may help to motivate marketing scholars to ponder on their own positions and assist them in judging where they may belong. Future comparisons over time would give us indication about the future of the academic discipline of marketing.(author's abstract)

Indiana University-Purdue University Indianapolis (IUPUI) / Emotions are essential for our existence, as they exert great influence on the mental health of people. Speech is the most powerful mode to communicate. It controls our intentions and emotions. Over the past years many researchers worked hard to recognize emotion from speech samples. Many systems have been proposed to make the Speech Emotion Recognition (SER) process more correct and accurate. This thesis research discusses the design of speech emotion recognition system implementing a comparatively new method, i-vector model. I-vector model has found much success in the areas of speaker identification, speech recognition, and language identification. But it has not been much explored in recognition of emotion. In this research, i-vector model was implemented in processing extracted features for speech representation. Two different classification schemes were designed using two different classifiers - Gaussian Mixture Model (GMM) and Support Vector Machine (SVM), along with i-vector algorithm. Performance of these two systems was evaluated using the same emotional speech database to identify four emotional speech signals: Angry, Happy, Sad and Neutral. Results were analyzed, and more than 75% of accuracy was obtained by both systems, which proved that our proposed i-vector algorithm can identify speech emotions with less error and with more accuracy.

Indiana University-Purdue University Indianapolis (IUPUI) / Geospatial analysis is a generic term describing several technologies or methods of computational analysis using the Earth as a living laboratory. These methods can be implemented to assess risk and study preventative mitigation practices for Public Health. Through the incorporation Geographic Information Science and Remote Sensing tools, data collection can be conducted at a larger scale, more frequent, and less expensive that traditional in situ methods. These techniques can be extrapolated to be used to study a variety of topics. Application of these tools and techniques were demonstrated through Public Health research. Although it is understand resolution, or scale, of a research project can impact a study’s results; further research is needed to understand the extent of the result’s bias. Extreme heat vulnerability analysis was studied to validate previously identified socioeconomic and environmental variables influential for mitigation studies, and how the variability of resolution impacts the results of the methodology. Heat was also investigated for the implication of spatial and temporal resolution, or aggregation, influence on results. Methods studying the physical and socioeconomic environments of Dengue Fever outbreaks were also studied to identify patters of vector emergence.