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- Title
- Cerebrovascular Burden and Depression: Examining a Process Model of Geriatric Developmental Psychopathology.
- Creator
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Scott, Rosanna, Paulson, Daniel, Cassisi, Jeffrey, Jentsch, Florian, University of Central Florida
- Abstract / Description
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Depression is the second leading cause of disability worldwide, and is associated with substantial functional impairment and poor health implications in older adults. These adverse outcomes are exacerbated in older adults who exhibit comorbid depression and cerebrovascular burden (CVB). Given that the population of older adults is projected to double by year 2050, a process model of the development of depression in later-life and a subsequent clear delineation of the relationship between CVB...
Show moreDepression is the second leading cause of disability worldwide, and is associated with substantial functional impairment and poor health implications in older adults. These adverse outcomes are exacerbated in older adults who exhibit comorbid depression and cerebrovascular burden (CVB). Given that the population of older adults is projected to double by year 2050, a process model of the development of depression in later-life and a subsequent clear delineation of the relationship between CVB and depression is paramount. One explanation of this process of disease development is the vascular depression theory, however alternative hypotheses have not been exhaustively falsified and the literature consists of methodological barriers that produce potentially unreliable results. The goals of this thesis are (1) to examine the interrelationship between CVB and depressive symptomatology from mid-life to later-life, and (2) to assess a potential genetic modifier of the CVB/depressive symptomatology relationship. Participants were drawn from the Wisconsin Longitudinal Study, which represents the 1957 graduating class from Wisconsin high schools. Data was drawn from three waves (1993, 2004, and 2011), spanning 18 years. Study 1 utilized a dual-change model to evaluate the relationship between CVB and depressive symptomatology from mid-life to later-life. Results indicated that depressive symptomatology at both follow-up waves was predicted by earlier depressive symptomatology. Prior CVB significantly predicted future depressive symptomatology in both 2004 and 2011. Depressive symptomatology in 2004 significantly predicted CVB in 2011. Thus, CVB significantly predicted future depressive symptomatology even after accounting for prior depressive symptomatology. Study 2 utilized a repeated-measures ANOVA and a moderated path structural model to evaluate the moderating effect of ApoE carriage on the relationship between CVB and depressive symptomatology. Results indicated that ApoE carriage has no significant main effect on depressive symptomatology, nor is it a significant moderator of the relationship between CVB and depressive symptomatology. Overall findings strongly support the vascular depression theory, and do not implicate ApoE carriage in the manifestation of depressive symptomatology. Future research should longitudinally evaluate the relationship between CVB and depressive symptomatology across a greater number of defined time points and with a more diverse sample. Lastly, future research should continue to identify genetic risk factors that influence the development of detrimental disease processes.
Show less - Date Issued
- 2016
- Identifier
- CFE0006178, ucf:51121
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0006178
- Title
- GENETIC INTERVENTION AS A LIFESTYLE APPROACH: AN ANALYSIS OF DISEASE AND TREATMENT.
- Creator
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Dempton, Jennifer, D'Amato-Kubiet, Leslee, University of Central Florida
- Abstract / Description
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Purpose: The scientific knowledge of how genes affect disease expression and evolution can facilitate more effective environmental and drug therapy interventions delivered by health care professionals. The purpose of this paper is to a) describe the role of genetic science in healthcare; b) explore genotype determinants for environmental and pharmacological interventions; c) and analyze ethical dilemmas, barriers to access, and allocation of resources based on genotype. Methods: A review of...
Show morePurpose: The scientific knowledge of how genes affect disease expression and evolution can facilitate more effective environmental and drug therapy interventions delivered by health care professionals. The purpose of this paper is to a) describe the role of genetic science in healthcare; b) explore genotype determinants for environmental and pharmacological interventions; c) and analyze ethical dilemmas, barriers to access, and allocation of resources based on genotype. Methods: A review of literature was conducted from the disciplines of nursing, medicine, psychology, and sociology using the CINAHL, Ebsco Host, Medline, and PsychINFO databases. The search was limited to peer reviewed, full text article in English that dated from 1987 to 2011. Inclusion criteria were articles describing environmental, pharmacologic, and nutritional influence on genetic expression. Forty-five articles on genetic intervention were chosen for further review, in addition to five book publications which met inclusion criteria. Many of the sources retrieved were obtained from the biomedical sciences and published in the last decade, owing to more recent innovations in genetic discovery. Results: Disease and treatment must be approached according to genetic profiles for effectiveness and to increase health outcomes. Several variations were found regarding response to pharmaceuticals, as well as environmental exposures, based on genotype. Conclusions: Health care has been practiced using a 'universal protocol' approach; however, as the literature reveals, each individual genotype must be taken into account to provide optimal care.
Show less - Date Issued
- 2011
- Identifier
- CFH0004060, ucf:44792
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFH0004060
- Title
- AN ADAPTIVE MULTIOBJECTIVE EVOLUTIONARY APPROACH TO OPTIMIZE ARTMAP NEURAL NETWORKS.
- Creator
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Kaylani, Assem, Georgiopoulos, Michael, University of Central Florida
- Abstract / Description
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This dissertation deals with the evolutionary optimization of ART neural network architectures. ART (adaptive resonance theory) was introduced by a Grossberg in 1976. In the last 20 years (1987-2007) a number of ART neural network architectures were introduced into the literature (Fuzzy ARTMAP (1992), Gaussian ARTMAP (1996 and 1997) and Ellipsoidal ARTMAP (2001)). In this dissertation, we focus on the evolutionary optimization of ART neural network architectures with the intent of optimizing...
Show moreThis dissertation deals with the evolutionary optimization of ART neural network architectures. ART (adaptive resonance theory) was introduced by a Grossberg in 1976. In the last 20 years (1987-2007) a number of ART neural network architectures were introduced into the literature (Fuzzy ARTMAP (1992), Gaussian ARTMAP (1996 and 1997) and Ellipsoidal ARTMAP (2001)). In this dissertation, we focus on the evolutionary optimization of ART neural network architectures with the intent of optimizing the size and the generalization performance of the ART neural network. A number of researchers have focused on the evolutionary optimization of neural networks, but no research has been performed on the evolutionary optimization of ART neural networks, prior to 2006, when Daraiseh has used evolutionary techniques for the optimization of ART structures. This dissertation extends in many ways and expands in different directions the evolution of ART architectures, such as: (a) uses a multi-objective optimization of ART structures, thus providing to the user multiple solutions (ART networks) with varying degrees of merit, instead of a single solution (b) uses GA parameters that are adaptively determined throughout the ART evolution, (c) identifies a proper size of the validation set used to calculate the fitness function needed for ART's evolution, thus speeding up the evolutionary process, (d) produces experimental results that demonstrate the evolved ART's effectiveness (good accuracy and small size) and efficiency (speed) compared with other competitive ART structures, as well as other classifiers (CART (Classification and Regression Trees) and SVM (Support Vector Machines)). The overall methodology to evolve ART using a multi-objective approach, the chromosome representation of an ART neural network, the genetic operators used in ART's evolution, and the automatic adaptation of some of the GA parameters in ART's evolution could also be applied in the evolution of other exemplar based neural network classifiers such as the probabilistic neural network and the radial basis function neural network.
Show less - Date Issued
- 2008
- Identifier
- CFE0002212, ucf:47907
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0002212
- Title
- Assessment of Staphylococcus aureus Genetics: Clinical versus Community Epidemiology.
- Creator
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Lawrance, Matthew, Parkinson, Christopher, Savage, Anna, Cole, Alexander, University of Central Florida
- Abstract / Description
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Staphylococcus aureus has an historical relationship with anthropogenic environments, particularly hospitals, where infection characteristics differ from community-acquired disease. This has promoted a designation of strains as healthcare or community associated. Despite this affiliation, genetic approaches have failed to support these groupings. In order to establish the genetic relationship between S. aureus from differing anthropogenic environments, I have analyzed the relatedness between...
Show moreStaphylococcus aureus has an historical relationship with anthropogenic environments, particularly hospitals, where infection characteristics differ from community-acquired disease. This has promoted a designation of strains as healthcare or community associated. Despite this affiliation, genetic approaches have failed to support these groupings. In order to establish the genetic relationship between S. aureus from differing anthropogenic environments, I have analyzed the relatedness between three cohorts of S. aureus: nasal carriage isolates from community participants, infectious isolates from hospitals, and a cohort from an uninvestigated environment, an ambulatory clinic. Multilocus Sequence Typing (MLST) and Staphylococcus aureus protein a (spa) repeat regions were analyzed and the genetic relationships between cohorts at these sites were determined. I found high similarity in recovered sequences within and between all cohorts, with cohorts sharing 100% sequence identity across some samples. Phylogenetic reconstruction of the combined datasets indicate panmixia, with samples of all origins belonging to shared genetic lineages. Additional clustering algorithms supported this pattern. The findings of this study indicate that there is strong genetic similarity between both infectious strains and nasal carriage strains and between isolates from all cohorts. This research has implications for healthcare, as it demonstrates that S. aureus from differing environments are genetically similar (often identical), cautioning against delineating strains into nasal carriage or infectious based on origin. This research also informs the study of S. aureus evolution (-) strengthening the conclusion that differentiation at stably selected markers in lineages within differing 'healthcare habitats' is insufficient to explain observed phenotypic differences, and alternative explanations must be explored.
Show less - Date Issued
- 2016
- Identifier
- CFE0006534, ucf:51369
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0006534
- Title
- Unearthing the past and present of a semi-fossorial lizard: conservation genetics, phylogeography, and taxonomy of Plestiodon egregius.
- Creator
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Mercier, Kathryn, Savage, Anna, Parkinson, Christopher, Jenkins, David, University of Central Florida
- Abstract / Description
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Characterizing an organism's evolutionary history and population structure as well as understanding the forces shaping that divergence is crucial to conservation biology. A clear understanding of the patterns of diversity and divergence are imperative for the best management ofthe organism, while an awareness of what drives these patterns can lead to better predictions of how organisms will respond to future climate change. Historical climate changes and associated sea levelchange are among...
Show moreCharacterizing an organism's evolutionary history and population structure as well as understanding the forces shaping that divergence is crucial to conservation biology. A clear understanding of the patterns of diversity and divergence are imperative for the best management ofthe organism, while an awareness of what drives these patterns can lead to better predictions of how organisms will respond to future climate change. Historical climate changes and associated sea levelchange are among the main forces driving divergence in many species. To examine how effects of climate changes may have driven patterns of intraspecific divergence, I examined Mole Skinks,Plestiodon egregius, a semi-fossorial lizard of conservation concern. First, I characterized P. egregius evolutionary history and population structure using multiple data sources: morphological characters,mitochondrial sequences (mtDNA), and genome-wide single nucleotide polymorphisms (SNPs). I determined that SNP data distinguished population structure at a finer resolution than morphologyor mtDNA. From these data, I defined six conservation units within P. egregius, three of which are consistent with current subspecific taxonomy. Next, I used statistical phylogeography to examinehow the effects of historical climate change in the southeastern United States (US) may have driven patterns of intraspecific divergence in P. egregius. I devised a set of alternative hypotheses regardingthe historical distribution and dispersal of P. egregius to test using genome-wide SNP markers. I found support for a historical refugia within the southern scrub ridges in Florida followed byexpansion into the Florida peninsula and mainland US. Synthesizing the results from both studies, I evaluate the current subspecific taxonomy and discuss the conservation of P. egregius. Overall, Iconclude that P. egregius evolutionary history has been driven by historical sea level changes in the southeastern US, and that insular populations should be the focus of conservation efforts.
Show less - Date Issued
- 2018
- Identifier
- CFE0007225, ucf:52228
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007225
- Title
- Indirect estimates of gene flow and conservation implications in the striped newt (Notophthalmus perstriatus).
- Creator
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May, Sarah, Hoffman, Eric, Parkinson, Christopher, Johnson, Steve, University of Central Florida
- Abstract / Description
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This study used indirect methods to estimate patterns of gene flow in a rare salamander species, the striped newt (Notophthalmus perstriatus). First, we used combined genetic and ecological methods to determine whether populations that appear to exist in two regions separated by 125 km, exhibited genetic and ecological distinctness such that the regions demarcate separate conservation units. Using mtDNA (cyt-b), we found that haplotypes were shared between localities within each region but...
Show moreThis study used indirect methods to estimate patterns of gene flow in a rare salamander species, the striped newt (Notophthalmus perstriatus). First, we used combined genetic and ecological methods to determine whether populations that appear to exist in two regions separated by 125 km, exhibited genetic and ecological distinctness such that the regions demarcate separate conservation units. Using mtDNA (cyt-b), we found that haplotypes were shared between localities within each region but none were shared between regions. Niche-based distribution modeling revealed significant differences in the ecological setting between the two regions. In combination, the absence of evidence for recent genetic exchange and model-based support for differing ecological conditions utilized by newts between regions provides evidence that eastern and western populations are both distinct and significant. This study suggests a framework to evaluate discreteness and significance among populations for assessment of distinct population segments (DPSs which can be used as a conservation tool for many species. Second, we used microsatellites to characterize patterns of population connectivity, genetic differentiation, and effective population size in N. perstriatus. We assessed these patterns by testing several a priori hypotheses regarding the influence of gene flow and genetic drift on the distribution of genetic variation among and within populations. Interestingly, several of our results did not conform to our hypotheses. For example, our assessment did not reveal a significant pattern of isolation by distance among populations in this study. Additionally, we found that effective population sizes and genetic diversity of isolated populations were higher than expected. We discuss our results relate to our a priori hypotheses and we address the general question of why this species exhibited patterns contrary to what we expected given previous data on this taxon and other studies of similar taxa
Show less - Date Issued
- 2011
- Identifier
- CFE0004481, ucf:49311
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0004481
- Title
- BEHAVIOR OF VARIABLE-LENGTH GENETIC ALGORITHMS UNDER RANDOM SELECTION.
- Creator
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Stringer, Harold, Wu, Annie, University of Central Florida
- Abstract / Description
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In this work, we show how a variable-length genetic algorithm naturally evolves populations whose mean chromosome length grows shorter over time. A reduction in chromosome length occurs when selection is absent from the GA. Specifically, we divide the mating space into five distinct areas and provide a probabilistic and empirical analysis of the ability of matings in each area to produce children whose size is shorter than the parent generation's average size. Diversity of size within a...
Show moreIn this work, we show how a variable-length genetic algorithm naturally evolves populations whose mean chromosome length grows shorter over time. A reduction in chromosome length occurs when selection is absent from the GA. Specifically, we divide the mating space into five distinct areas and provide a probabilistic and empirical analysis of the ability of matings in each area to produce children whose size is shorter than the parent generation's average size. Diversity of size within a GA's population is shown to be a necessary condition for a reduction in mean chromosome length to take place. We show how a finite variable-length GA under random selection pressure uses 1) diversity of size within the population, 2) over-production of shorter than average individuals, and 3) the imperfect nature of random sampling during selection to naturally reduce the average size of individuals within a population from one generation to the next. In addition to our findings, this work provides GA researchers and practitioners with 1) a number of mathematical tools for analyzing possible size reductions for various matings and 2) new ideas to explore in the area of bloat control.
Show less - Date Issued
- 2007
- Identifier
- CFE0001652, ucf:47249
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0001652
- Title
- A NEAT APPROACH TO GENETIC PROGRAMMING.
- Creator
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Rodriguez, Adelein, Wu, Annie, University of Central Florida
- Abstract / Description
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The evolution of explicitly represented topologies such as graphs involves devising methods for mutating, comparing and combining structures in meaningful ways and identifying and maintaining the necessary topological diversity. Research has been conducted in the area of the evolution of trees in genetic programming and of neural networks and some of these problems have been addressed independently by the different research communities. In the domain of neural networks, NEAT (Neuroevolution...
Show moreThe evolution of explicitly represented topologies such as graphs involves devising methods for mutating, comparing and combining structures in meaningful ways and identifying and maintaining the necessary topological diversity. Research has been conducted in the area of the evolution of trees in genetic programming and of neural networks and some of these problems have been addressed independently by the different research communities. In the domain of neural networks, NEAT (Neuroevolution of Augmenting Topologies) has shown to be a successful method for evolving increasingly complex networks. This system's success is based on three interrelated elements: speciation, marking of historical information in topologies, and initializing search in a small structures search space. This provides the dynamics necessary for the exploration of diverse solution spaces at once and a way to discriminate between different structures. Although different representations have emerged in the area of genetic programming, the study of the tree representation has remained of interest in great part because of its mapping to programming languages and also because of the observed phenomenon of unnecessary code growth or bloat which hinders performance. The structural similarity between trees and neural networks poses an interesting question: Is it possible to apply the techniques from NEAT to the evolution of trees and if so, how does it affect performance and the dynamics of code growth? In this work we address these questions and present analogous techniques to those in NEAT for genetic programming.
Show less - Date Issued
- 2007
- Identifier
- CFE0001971, ucf:47451
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0001971
- Title
- A METHODOLOGY FOR MINIMIZING THE OSCILLATIONS IN SUPPLY CHAINS USING SYSTEM DYNAMICS AND GENETIC ALGORITHMS.
- Creator
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LAKKOJU, RAMAMOORTHY, RABELO, LUIS, University of Central Florida
- Abstract / Description
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Supply Chain Management (SCM) is a critically significant strategy that enterprises depend on to meet challenges that they face because of highly competitive and dynamic business environments of today. Supply chain management involves the entire network of processes from procurement of raw materials/services/technologies to manufacturing or servicing intermediate products/services to converting them into final products or services and then distributing and retailing them till they reach final...
Show moreSupply Chain Management (SCM) is a critically significant strategy that enterprises depend on to meet challenges that they face because of highly competitive and dynamic business environments of today. Supply chain management involves the entire network of processes from procurement of raw materials/services/technologies to manufacturing or servicing intermediate products/services to converting them into final products or services and then distributing and retailing them till they reach final customers. A supply chain network by nature is a large and complex, engineering and management system. Oscillations occurring in a supply chain because of internal and/or external influences and measures to be taken to mitigate/minimize those oscillations are a core concern in managing the supply chain and driving an organization towards a competitive advantage. The objective of this thesis is to develop a methodology to minimize the oscillations occurring in a supply chain by making use of the techniques of System Dynamics (SD) and Genetic Algorithms (GAs). System dynamics is a very efficient tool to model large and complex systems in order to understand their complex, non-linear dynamic behavior. GAs are stochastic search algorithms, based on the mechanics of natural selection and natural genetics, used to search complex and non-linear search spaces where traditional techniques may be unsuitable.
Show less - Date Issued
- 2005
- Identifier
- CFE0000683, ucf:46489
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0000683
- Title
- GENETIC DIFFERENTIATION AMONG FLORIDA POPULATIONS OF DIADEMA ANTILLARUM.
- Creator
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Chandler, Luke M, Hoffman, Eric, University of Central Florida
- Abstract / Description
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This project used molecular genetic markers (microsatellites) to determine the amount of genetic diversity within populations and whether significant differentiation exists among Florida populations of the long-spined sea urchin, Diadema antillarum. Specifically, this project aimed to (1) compare genetic diversity of D. antillarum from six populations in south Florida ranging from Biscayne Bay, the Florida Keys, and Dry Tortugas, and (2) determine whether two broodstock populations of D....
Show moreThis project used molecular genetic markers (microsatellites) to determine the amount of genetic diversity within populations and whether significant differentiation exists among Florida populations of the long-spined sea urchin, Diadema antillarum. Specifically, this project aimed to (1) compare genetic diversity of D. antillarum from six populations in south Florida ranging from Biscayne Bay, the Florida Keys, and Dry Tortugas, and (2) determine whether two broodstock populations of D. antillarum contain variation indicative of native Florida populations. Together, these questions can address whether broodstock populations contain the genetic variation necessary to meet the Florida Fish and Wildlife Conservation Commission�s (FWC�s) genetic policies for reintroduction throughout south Florida. Global FST among native populations was 0.0004 with a highest pairwise FST of 0.0025 between the Upper Keys and the area west of Key West, showing an overall trend of little natural differentiation between populations. Global FST for all populations inclusive of the broodstock samples was 0.0019 with a highest pairwise FST between a native population and broodstock of 0.0066 between Dry Tortuga and Mote�s broodstock, indicating little differentiation resulting from captive breeding. Average allelic richness and heterozygosity ranged from 22.6�24.4 and 0.937�0.956, respectively, for each population. Two-way ANOVAs comparing genetic diversity between native and broodstock populations showed no statistical difference in allelic richness (F= 3.892, p= 0.0535) or heterozygosity (F=1.43, p=0.237). The computer program STRUCTURE estimated the most likely number of genetic clusters to be k=1, inclusive of broodstock populations, further indicating a lack of differentiation either among native populations or between native and broodstock populations. These data suggest that captive-bred individuals of D. antillarum could be used for reintroduction as part of a plan to re-establish healthy urchin populations throughout the Florida Keys.
Show less - Date Issued
- 2016
- Identifier
- CFH2000044, ucf:45558
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFH2000044
- Title
- PHARMACOGENOMIC MANAGEMENT OF FAMILIAL HYPERCHOLESTEROLEMIA: AN INTEGRATIVE REVIEW OF THE LITERATURE.
- Creator
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Skibo, Brian V., Bushy, Angeline, Kubiet, Leslee, University of Central Florida
- Abstract / Description
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The purpose of this thesis is to examine familial hypercholesterolemia (FH) and emerging pharmacogenomics therapies that propose to lower serum low density lipid (LDL) levels. The search of various data bases resulted in nine research articles being selected for review. Syntheses of the articles suggest emerging phamacogenomic drug therapy can improve treatment outcomes for individuals with a diagnosis of FH. The Human Genome Project (HGP) has had far reaching applications for genomic...
Show moreThe purpose of this thesis is to examine familial hypercholesterolemia (FH) and emerging pharmacogenomics therapies that propose to lower serum low density lipid (LDL) levels. The search of various data bases resulted in nine research articles being selected for review. Syntheses of the articles suggest emerging phamacogenomic drug therapy can improve treatment outcomes for individuals with a diagnosis of FH. The Human Genome Project (HGP) has had far reaching applications for genomic technologies and pharmacagenomic interventions, tailored to human conditions associated with select genomic traits. Synthesis of nine research articles demonstrate that little is known on the topic and reveals extensive gaps in the evidence. This thesis concludes with implications for nursing education, practice, policy and research along with limitations are noted.
Show less - Date Issued
- 2016
- Identifier
- CFH2000076, ucf:45544
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFH2000076
- Title
- A COMPETITIVE RECONFIGURATION APPROACH TO AUTONOMOUS FAULT HANDLING USING GENETIC ALGORITHMS.
- Creator
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Zhang, Kening, DeMara, Ronald F, University of Central Florida
- Abstract / Description
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In this dissertation, a novel self-repair approach based on Consensus Based Evaluation (CBE) for autonomous repair of SRAM-based Field Programmable Gate Arrays (FPGAs) is developed, evaluated, and refined. An initial population of functionally identical (same input-output behavior), yet physically distinct (alternative design or place-and-route realization) FPGA configurations is produced at design time. During run-time, the CBE approach ranks these alternative configurations after evaluating...
Show moreIn this dissertation, a novel self-repair approach based on Consensus Based Evaluation (CBE) for autonomous repair of SRAM-based Field Programmable Gate Arrays (FPGAs) is developed, evaluated, and refined. An initial population of functionally identical (same input-output behavior), yet physically distinct (alternative design or place-and-route realization) FPGA configurations is produced at design time. During run-time, the CBE approach ranks these alternative configurations after evaluating their discrepancy relative to the consensus formed by the population. Through runtime competition, faults in the logical resources become occluded from the visibility of subsequent FPGA operations. Meanwhile, offspring formed through crossover and mutation of faulty and viable configurations are selected at a controlled re-introduction rate for evaluation and refurbishment. Refurbishments are evolved in-situ, with online real-time input-based performance evaluation, enhancing system availability and sustainability, creating an Organic Embedded System (OES). A fault tolerance model called N Modular Redundancy with Standby (NMRSB) is developed which combines the two popular fault tolerance techniques of NMR and Standby fault tolerance in order to facilitate the CBE approach. This dissertation develops two of instances of the NMRSB system – Triple Modular Redundancy with Standby (TMRSB) and Duplex with Standby (DSB). A hypothetical Xilinx Virtex-II Pro FPGA model demonstrates their viability for various applications including a 3-bit x 3-bit multiplier, and the MCNC91 benchmark circuits. Experiments conducted on the model iii evaluate the performance of three new genetic operators and demonstrate progress towards a completely self-contained single-chip implementation so that the FPGA can refurbish itself without requiring a PC host to execute the Genetic Algorithm. This dissertation presents results from the simulations of multiple applications with a CBE model implemented in the C++ programming language. Starting with an initial population of 20 and 30 viable configurations for TMRSB and DSB respectively, a single stuck-at fault is introduced in the logic resources. Fault refurbishment experiments are conducted under supervision of CBE using a fitness state evaluation function based on competing outputs, fitness adjustment, and different level threshold. The device remains online throughout the process by which a complete repair is realized with Hamming Distance and Bitweight voting schemes. The results indicate a Hamming Distance TMRSB approach can prevent the most pervasive fault impacts and realize complete refurbishment. Experimental results also show that the Autonomic Layer demonstrates 100% faulty component isolation for both Functional Elements (FEs) and Autonomous Elements (AEs) with randomly injected single and multiple faults. Using logic circuits from the MCNC-91 benchmark set, availability during repair phases averaged 75.05%, 82.21%, and 65.21% for the z4ml, cm85a, and cm138a circuits respectively under stated conditions. In addition to simulation, the proposed OES architecture synthesized from HDL was prototyped on a Xilinx Virtex II Pro FPGA device supporting partial reconfiguration to demonstrate the feasibility for intrinsic regeneration of the selected circuit.
Show less - Date Issued
- 2008
- Identifier
- CFE0002280, ucf:47849
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0002280
- Title
- LEARNING FROM GEOMETRY IN LEARNING FOR TACTICAL AND STRATEGIC DECISION DOMAINS.
- Creator
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Gauci, Jason, Stanley, Kenneth, University of Central Florida
- Abstract / Description
-
Artificial neural networks (ANNs) are an abstraction of the low-level architecture of biological brains that are often applied in general problem solving and function approximation. Neuroevolution (NE), i.e. the evolution of ANNs, has proven effective at solving problems in a variety of domains. Information from the domain is input to the ANN, which outputs its desired actions. This dissertation presents a new NE algorithm called Hypercube-based NeuroEvolution of Augmenting Topologies ...
Show moreArtificial neural networks (ANNs) are an abstraction of the low-level architecture of biological brains that are often applied in general problem solving and function approximation. Neuroevolution (NE), i.e. the evolution of ANNs, has proven effective at solving problems in a variety of domains. Information from the domain is input to the ANN, which outputs its desired actions. This dissertation presents a new NE algorithm called Hypercube-based NeuroEvolution of Augmenting Topologies (HyperNEAT), based on a novel indirect encoding of ANNs. The key insight in HyperNEAT is to make the algorithm aware of the geometry in which the ANNs are embedded and thereby exploit such domain geometry to evolve ANNs more effectively. The dissertation focuses on applying HyperNEAT to tactical and strategic decision domains. These domains involve simultaneously considering short-term tactics while also balancing long-term strategies. Board games such as checkers and Go are canonical examples of such domains; however, they also include real-time strategy games and military scenarios. The dissertation details three proposed extensions to HyperNEAT designed to work in tactical and strategic decision domains. The first is an action selector ANN architecture that allows the ANN to indicate its judgements on every possible action all at once. The second technique is called substrate extrapolation. It allows learning basic concepts at a low resolution, and then increasing the resolution to learn more advanced concepts. The final extension is geometric game-tree pruning, whereby HyperNEAT can endow the ANN the ability to focus on specific areas of a domain (such as a checkers board) that deserve more inspection. The culminating contribution is to demonstrate the ability of HyperNEAT with these extensions to play Go, a most challenging game for artificial intelligence, by combining HyperNEAT with UCT.
Show less - Date Issued
- 2010
- Identifier
- CFE0003464, ucf:48962
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0003464
- Title
- ENGINEERING A NEW FORM OF ENCLOSURE: INTERNATIONAL CONVERGENCE IN GMO REGULATION.
- Creator
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Altif, Jessica, Jacques, Peter, University of Central Florida
- Abstract / Description
-
As society begins to recognize its impact on ecological systems, the belief that modern political institutions can offer a sense of control and certainty, as well as protect the health of its citizens, is increasingly questioned. In an era of uncertainty, faith in science and technology to alleviate industrial impacts on the environment is often embraced by policymakers yet questioned by the public who see the authoritative role of the sciences in the political sphere as contributing to...
Show moreAs society begins to recognize its impact on ecological systems, the belief that modern political institutions can offer a sense of control and certainty, as well as protect the health of its citizens, is increasingly questioned. In an era of uncertainty, faith in science and technology to alleviate industrial impacts on the environment is often embraced by policymakers yet questioned by the public who see the authoritative role of the sciences in the political sphere as contributing to global risk. The development of biotechnology, specifically genetically modified food, places an anthropocentric focus on resolving and/or adapting to environmental degradation, further reflecting an adherence to the dominant social paradigm to address the consequences of modernization. In order to explicate the dualism of human/nature relations inherent in biotechnology, the focus of this research provides an exploration into two competing paradigms of genetically modified organism (GMO) regulatory policy: scientific rationality and social rationality. Through a careful examination of the evolution of GMO regulation in the United States and the European Union, the precarious relationships between science and politics and progress and precaution reveal an actual convergence instead of divergence between these two actors in the international system. Although existing literature proclaims a division between the values and ethics of U.S. and EU environmental policy, the end result of this comparison in GMO regulation illustrates that in both the risk assessment and precautionary approaches, nature is still viewed as an instrument for advancing enclosure of the commons.
Show less - Date Issued
- 2010
- Identifier
- CFE0003021, ucf:48371
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0003021
- Title
- Self-Scaling Evolution of Analog Computation Circuits.
- Creator
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Pyle, Steven, DeMara, Ronald, Vosoughi, Azadeh, Chanda, Debashis, University of Central Florida
- Abstract / Description
-
Energy and performance improvements of continuous-time analog-based computation for selected applications offer an avenue to continue improving the computational ability of tomorrow's electronic devices at current technology scaling limits. However, analog computation is plagued by the difficulty of designing complex computational circuits, programmability, as well as the inherent lack of accuracy and precision when compared to digital implementations. In this thesis, evolutionary algorithm...
Show moreEnergy and performance improvements of continuous-time analog-based computation for selected applications offer an avenue to continue improving the computational ability of tomorrow's electronic devices at current technology scaling limits. However, analog computation is plagued by the difficulty of designing complex computational circuits, programmability, as well as the inherent lack of accuracy and precision when compared to digital implementations. In this thesis, evolutionary algorithm-based techniques are utilized within a reconfigurable analog fabric to realize an automated method of designing analog-based computational circuits while adapting the functional range to improve performance. A Self-Scaling Genetic Algorithm is proposed to adapt solutions to computationally-tractable ranges in hardware-constrained analog reconfigurable fabrics. It operates by utilizing a Particle Swarm Optimization (PSO) algorithm that operates synergistically with a Genetic Algorithm (GA) to adaptively scale and translate the functional range of computational circuits composed of high-level or low-level Computational Analog Elements to improve performance and realize functionality otherwise unobtainable on the intrinsic platform. The technique is demonstrated by evolving square, square-root, cube, and cube-root analog computational circuits on the Cypress PSoC-5LP System-on-Chip. Results indicate that the Self-Scaling Genetic Algorithm improves our error metric on average 7.18-fold, up to 12.92-fold for computational circuits that produce outputs beyond device range. Results were also favorable compared to previous works, which utilized extrinsic evolution of circuits with much greater complexity than was possible on the PSoC-5LP.
Show less - Date Issued
- 2015
- Identifier
- CFE0005866, ucf:50873
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0005866
- Title
- Using Molecular Genetic and Demographic Tools to Improve Management of Ex Situ Avian Populations.
- Creator
-
Ferrie, Gina, Hoffman, Eric, Parkinson, Christopher, Quintana-Ascencio, Pedro, Bettinger, Tamara, University of Central Florida
- Abstract / Description
-
Small populations, specifically those that are isolated from others, are more prone to extinction than larger inter-connected populations. The risks that these small isolated populations face include loss of genetic diversity due to founder effects and inbreeding due to population bottlenecks, as well as demographic uncertainty due to fluctuating fecundity and mortality rates and impacts of external environmental factors. Ex situ populations, including those managed as conservation breeding...
Show moreSmall populations, specifically those that are isolated from others, are more prone to extinction than larger inter-connected populations. The risks that these small isolated populations face include loss of genetic diversity due to founder effects and inbreeding due to population bottlenecks, as well as demographic uncertainty due to fluctuating fecundity and mortality rates and impacts of external environmental factors. Ex situ populations, including those managed as conservation breeding programs with species recovery aims, as well as those that do not have reintroduction goals but are managed for long term population sustainability, suffer from the same extinction risks as small and isolated natural populations. Using three separate avian species which have different life histories and population structures, I investigated impacts of multiple genetic and demographic management strategies on these ex situ populations. I examined the use of molecular genetic datasets including microsatellites and single nucleotide polymorphisms (SNPs) to determine their utility for reconstructing pedigrees, examining individual relatedness within populations, and compared results of measuring genetic diversity through theoretical methods verses those obtained from a molecular dataset. These methods can then ultimately be applied to improve future management including improving studbook datasets and to measure actual loss of genetic diversity. I also used analytical strategies including population viability analysis to determine how management practices influence demographic parameters and determine the future probability of population extinction. The genetic and demographic analyses of both the historic management of an ex situ population, and its current status, are a first step in hypothesizing the potential directions for future management and understanding the likelihood of survival of an ex situ population.
Show less - Date Issued
- 2017
- Identifier
- CFE0006940, ucf:51670
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0006940
- Title
- GENETICALLY ENGINEERED ADAPTIVE RESONANCE THEORY (ART) NEURAL NETWORK ARCHITECTURES.
- Creator
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Al-Daraiseh, Ahmad, Georgiopoulos, Michael, University of Central Florida
- Abstract / Description
-
Fuzzy ARTMAP (FAM) is currently considered to be one of the premier neural network architectures in solving classification problems. One of the limitations of Fuzzy ARTMAP that has been extensively reported in the literature is the category proliferation problem. That is Fuzzy ARTMAP has the tendency of increasing its network size, as it is confronted with more and more data, especially if the data is of noisy and/or overlapping nature. To remedy this problem a number of researchers have...
Show moreFuzzy ARTMAP (FAM) is currently considered to be one of the premier neural network architectures in solving classification problems. One of the limitations of Fuzzy ARTMAP that has been extensively reported in the literature is the category proliferation problem. That is Fuzzy ARTMAP has the tendency of increasing its network size, as it is confronted with more and more data, especially if the data is of noisy and/or overlapping nature. To remedy this problem a number of researchers have designed modifications to the training phase of Fuzzy ARTMAP that had the beneficial effect of reducing this phenomenon. In this thesis we propose a new approach to handle the category proliferation problem in Fuzzy ARTMAP by evolving trained FAM architectures. We refer to the resulting FAM architectures as GFAM. We demonstrate through extensive experimentation that an evolved FAM (GFAM) exhibits good (sometimes optimal) generalization, small size (sometimes optimal size), and requires reasonable computational effort to produce an optimal or sub-optimal network. Furthermore, comparisons of the GFAM with other approaches, proposed in the literature, which address the FAM category proliferation problem, illustrate that the GFAM has a number of advantages (i.e. produces smaller or equal size architectures, of better or as good generalization, with reduced computational complexity). Furthermore, in this dissertation we have extended the approach used with Fuzzy ARTMAP to other ART architectures, such as Ellipsoidal ARTMAP (EAM) and Gaussian ARTMAP (GAM) that also suffer from the ART category proliferation problem. Thus, we have designed and experimented with genetically engineered EAM and GAM architectures, named GEAM and GGAM. Comparisons of GEAM and GGAM with other ART architectures that were introduced in the ART literature, addressing the category proliferation problem, illustrate similar advantages observed by GFAM (i.e, GEAM and GGAM produce smaller size ART architectures, of better or improved generalization, with reduced computational complexity). Moverover, to optimally cover the input space of a problem, we proposed a genetically engineered ART architecture that combines the category structures of two different ART networks, FAM and EAM. We named this architecture UART (Universal ART). We analyzed the order of search in UART, that is the order according to which a FAM category or an EAM category is accessed in UART. This analysis allowed us to better understand UART's functionality. Experiments were also conducted to compare UART with other ART architectures, in a similar fashion as GFAM and GEAM were compared. Similar conclusions were drawn from this comparison, as in the comparison of GFAM and GEAM with other ART architectures. Finally, we analyzed the computational complexity of the genetically engineered ART architectures and we compared it with the computational complexity of other ART architectures, introduced into the literature. This analytical comparison verified our claim that the genetically engineered ART architectures produce better generalization and smaller sizes ART structures, at reduced computational complexity, compared to other ART approaches. In review, a methodology was introduced of how to combine the answers (categories) of ART architectures, using genetic algorithms. This methodology was successfully applied to FAM, EAM and FAM and EAM ART architectures, with success, resulting in ART neural networks which outperformed other ART architectures, previously introduced into the literature, and quite often produced ART architectures that attained optimal classification results, at reduced computational complexity.
Show less - Date Issued
- 2006
- Identifier
- CFE0000977, ucf:46696
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0000977
- Title
- USING LANDSCAPE GENETICS TO ASSESS POPULATION CONNECTIVITY IN A HABITAT GENERALIST.
- Creator
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Hether, Tyler, Hoffman, Eric, University of Central Florida
- Abstract / Description
-
Understanding the nature of genetic variation in natural populations is an underlying theme of population genetics. In recent years population genetics has benefited from the incorporation of landscape and environmental data into pre-existing models of isolation by distance (IBD) to elucidate features influencing spatial genetic variation. Many of these landscape genetics studies have focused on populations separated by discrete barriers (e.g., mountain ridges) or species with specific...
Show moreUnderstanding the nature of genetic variation in natural populations is an underlying theme of population genetics. In recent years population genetics has benefited from the incorporation of landscape and environmental data into pre-existing models of isolation by distance (IBD) to elucidate features influencing spatial genetic variation. Many of these landscape genetics studies have focused on populations separated by discrete barriers (e.g., mountain ridges) or species with specific habitat requirements (i.e., habitat specialists). One difficulty in using a landscape genetics approach for taxa with less stringent habitat requirements (i.e., generalists) is the lack of obvious barriers to gene flow and preference for specific habitats. My study attempts to fill this information gap to understand mechanisms underlying population subdivision in generalists, using the squirrel treefrog (Hyla squirella) and a system for classifying 'terrestrial ecological systems' (i.e. habitat types). I evaluate this dataset with microsatellite markers and a recently introduced method based on ensemble learning (Random Forest) to identify whether spatial distance, habitat types, or both have influenced genetic connectivity among 20 H. squirella populations. Next, I hierarchically subset the populations included in the analysis based on (1) genetic assignment tests and (2) Mantel correlograms to determine the relative role of spatial distance in shaping landscape genetic patterns. Assignment tests show evidence of two genetic clusters that separate populations in Florida's panhandle (Western cluster) from those in peninsular Florida and southern Georgia (Eastern cluster). Mantel correlograms suggest a patch size of approximately 150 km. Landscape genetic analyses at all three spatial scales yielded improved model fit relative to isolation by distance when including habitat types. A hierarchical effect was identified whereby the importance of spatial distance (km) was the strongest predictor of patterns of genetic differentiation above the scale of the genetic patch. Below the genetic patch, spatial distance was still an explanatory variable but was only approximately 30% as relevant as mesic flatwoods or upland oak hammocks. Thus, it appears that habitat types largely influence patterns of population genetic connectivity at local scales but the signal of IBD becomes the dominant driver of regional connectivity. My results highlight some habitats as highly relevant to increased genetic connectivity at all spatial scales (e.g., upland oak hammocks) while others show no association (e.g., silviculture) or scale specific associations (e.g., pastures only at global scales). Given these results it appears that treating habitat as a binary metric (suitable/non-suitable) may be overly simplistic for generalist species in which gene flow probably occurs in a spectrum of habitat suitability. The overall pattern of spatial genetic and landscape genetic structure identified here provides insight into the evolutionary history and patterns of population connectivity for H. squirella and improves our understanding of the role of matrix composition for habitat generalists.
Show less - Date Issued
- 2010
- Identifier
- CFE0003204, ucf:48580
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0003204
- Title
- GENETIC AND PHENOTYPIC EVOLUTION IN THE ORNATE CHORUS FROG (PSEUDACRIS ORNATA): TESTING THE RELATIVE ROLES OF NATURAL SELECTION, MIGRATION, AND GENETIC DRIFT.
- Creator
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Degner, Jacob, Hoffman, Eric, University of Central Florida
- Abstract / Description
-
Understanding how migration, genetic drift, and natural selection interact to maintain the genetic and phenotypic variation we observe in natural populations is a central goal of population genetics. Amphibians provide excellent model organisms for investigating the interplay between these evolutionary forces because amphibians are generally characterized by limited dispersal abilities, high philopatry, and are obligately associated with the areas around suitable habitats (e.g. breeding ponds...
Show moreUnderstanding how migration, genetic drift, and natural selection interact to maintain the genetic and phenotypic variation we observe in natural populations is a central goal of population genetics. Amphibians provide excellent model organisms for investigating the interplay between these evolutionary forces because amphibians are generally characterized by limited dispersal abilities, high philopatry, and are obligately associated with the areas around suitable habitats (e.g. breeding ponds). Thus, on relatively small geographic scales, the relative effects of all of these evolutionary forces can be studied together. Here, we study the interaction of migration, genetic drift, natural selection, and historical process in the ornate chorus frog (Pseudacris ornata). We report the development and characterization of 10 polymorphic microsatellite genetic markers. Number of alleles per locus ranged from 2 to 21 averaging 9.2 and expected heterozygosities ranged from 0.10 to 0.97 averaging 0.52. However, in an analysis of two populations, three locus-by-population comparisons exhibited significant heterozygote deficiencies and indicated that null alleles may be present some loci. Furthermore, we characterized genetic structure and historical biogeographic patterns in P. ornata using these microsatellite markers along with mitochondrial DNA sequence data. Our data indicate that in these frogs, migration may play a large role in determining population structure as pairwise estimates of FST were relatively small ranging from 0.04 to 0.12 (global FST = 0.083). Additionally, we observed an overall pattern of isolation-by-distance in neutral genetic markers across the species range. Moreover, our data suggest that the Apalachicola River basin does not impede gene flow in P. ornata as it does in many vertebrate taxa. Interestingly, we identified significant genetic structure between populations separated by only 6 km. However, this fine scale genetic structure was only present in the more urbanized of two widespread sampling localities. Finally, in this study, we demonstrated that there was a significant correlation between the frequency of green frogs and latitude. There was a higher frequency of green frogs in southern samples and a lower frequency of green frogs in northern samples. However, when we interpreted this phenotypic cline in light of the overall pattern of isolation-by-distance, it was apparent that the neutral evolutionary forces of genetic drift and migration could explain the cline, and the invocation of natural selection was not necessary.
Show less - Date Issued
- 2007
- Identifier
- CFE0001721, ucf:47319
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0001721
- Title
- EVOLUTIONARY OPTIMIZATION OF SUPPORT VECTOR MACHINES.
- Creator
-
Gruber, Fred, Rabelo, Luis, University of Central Florida
- Abstract / Description
-
Support vector machines are a relatively new approach for creating classifiers that have become increasingly popular in the machine learning community. They present several advantages over other methods like neural networks in areas like training speed, convergence, complexity control of the classifier, as well as a stronger mathematical background based on optimization and statistical learning theory. This thesis deals with the problem of model selection with support vector machines, that is...
Show moreSupport vector machines are a relatively new approach for creating classifiers that have become increasingly popular in the machine learning community. They present several advantages over other methods like neural networks in areas like training speed, convergence, complexity control of the classifier, as well as a stronger mathematical background based on optimization and statistical learning theory. This thesis deals with the problem of model selection with support vector machines, that is, the problem of finding the optimal parameters that will improve the performance of the algorithm. It is shown that genetic algorithms provide an effective way to find the optimal parameters for support vector machines. The proposed algorithm is compared with a backpropagation Neural Network in a dataset that represents individual models for electronic commerce.
Show less - Date Issued
- 2004
- Identifier
- CFE0000244, ucf:46251
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0000244
