9,006 research outputs found
UMSL Bulletin 2023-2024
The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp
The AddACO: A bio-inspired modified version of the ant colony optimization algorithm to solve travel salesman problems
The Travel Salesman Problem (TSP) consists in finding the minimal-length closed tour that connects the entire group of nodes of a given graph. We propose to solve such a combinatorial optimization problem with the AddACO algorithm: it is a version of the Ant Colony Optimization method that is characterized by a modified probabilistic law at the basis of the exploratory movement of the artificial insects. In particular, the ant decisional rule is here set to amount in a linear convex combination of competing behavioral stimuli and has therefore an additive form (hence the name of our algorithm), rather than the canonical multiplicative one. The AddACO intends to address two conceptual shortcomings that characterize classical ACO methods: (i) the population of artificial insects is in principle allowed to simultaneously minimize/maximize all migratory guidance cues (which is in implausible from a biological/ecological point of view) and (ii) a given edge of the graph has a null probability to be explored if at least one of the movement trait is therein equal to zero, i.e., regardless the intensity of the others (this in principle reduces the exploratory potential of the ant colony). Three possible variants of our method are then specified: the AddACO-V1, which includes pheromone trail and visibility as insect decisional variables, and the AddACO-V2 and the AddACO-V3, which in turn add random effects and inertia, respectively, to the two classical migratory stimuli. The three versions of our algorithm are tested on benchmark middle-scale TPS instances, in order to assess their performance and to find their optimal parameter setting. The best performing variant is finally applied to large-scale TSPs, compared to the naive Ant-Cycle Ant System, proposed by Dorigo and colleagues, and evaluated in terms of quality of the solutions, computational time, and convergence speed. The aim is in fact to show that the proposed transition probability, as long as its conceptual advantages, is competitive from a performance perspective, i.e., if it does not reduce the exploratory capacity of the ant population w.r.t. the canonical one (at least in the case of selected TSPs). A theoretical study of the asymptotic behavior of the AddACO is given in the appendix of the work, whose conclusive section contains some hints for further improvements of our algorithm, also in the perspective of its application to other optimization problems
Novel 129Xe Magnetic Resonance Imaging and Spectroscopy Measurements of Pulmonary Gas-Exchange
Gas-exchange is the primary function of the lungs and involves removing carbon dioxide from the body and exchanging it within the alveoli for inhaled oxygen. Several different pulmonary, cardiac and cardiovascular abnormalities have negative effects on pulmonary gas-exchange. Unfortunately, clinical tests do not always pinpoint the problem; sensitive and specific measurements are needed to probe the individual components participating in gas-exchange for a better understanding of pathophysiology, disease progression and response to therapy.
In vivo Xenon-129 gas-exchange magnetic resonance imaging (129Xe gas-exchange MRI) has the potential to overcome these challenges. When participants inhale hyperpolarized 129Xe gas, it has different MR spectral properties as a gas, as it diffuses through the alveolar membrane and as it binds to red-blood-cells. 129Xe MR spectroscopy and imaging provides a way to tease out the different anatomic components of gas-exchange simultaneously and provides spatial information about where abnormalities may occur.
In this thesis, I developed and applied 129Xe MR spectroscopy and imaging to measure gas-exchange in the lungs alongside other clinical and imaging measurements. I measured 129Xe gas-exchange in asymptomatic congenital heart disease and in prospective, controlled studies of long-COVID. I also developed mathematical tools to model 129Xe MR signals during acquisition and reconstruction. The insights gained from my work underscore the potential for 129Xe gas-exchange MRI biomarkers towards a better understanding of cardiopulmonary disease. My work also provides a way to generate a deeper imaging and physiologic understanding of gas-exchange in vivo in healthy participants and patients with chronic lung and heart disease
Optimising water quality outcomes for complex water resource systems and water grids
As the world progresses, water resources are likely to be subjected to much greater pressures than in the past. Even though the principal water problem revolves around inadequate and uncertain water supplies, water quality management plays an equally important role. Availability of good quality water is paramount to sustainability of human population as well as the environment. Achieving water quality and quantity objectives can be conflicting and becomes more complicated with challenges like, climate change, growing populations and changed land uses. Managing adequate water quality in a reservoir gets complicated by multiple inflows with different water quality levels often resulting in poor water quality. Hence, it is fundamental to approach this issue in a more systematic, comprehensive, and coordinated fashion. Most previous studies related to water resources management focused on water quantity and considered water quality separately. However, this research study focused on considering water quantity and quality objectives simultaneously in a single model to explore and understand the relationship between them in a reservoir system. A case study area was identified in Western Victoria, Australia with water quantity and quality challenges. Taylors Lake of Grampians System in Victoria, Australia receives water from multiple sources of differing quality and quantity and has the abovesaid problems. A combined simulation and optimisation approach was adopted to carry out the analysis. A multi-objective optimisation approach was applied to achieve optimal water availability and quality in the storage. The multi-objective optimisation model included three objective functions which were: water volume and two water quality parameters: salinity and turbidity. Results showed competing nature of water quantity and quality objectives and established the trade-offs. It further showed that it was possible to generate a range of optimal solutions to effectively manage those trade-offs. The trade-off analysis explored and informed that selective harvesting of inflows is effective to improve water quality in storage. However, with strict water quality restriction there is a considerable loss in water volume. The robustness of the optimisation approach used in this study was confirmed through sensitivity and uncertainty analysis. The research work also incorporated various spatio-temporal scenario analyses to systematically articulate long-term and short-term operational planning strategies. Operational decisions around possible harvesting regimes while achieving optimal water quantity and quality and meeting all water demands were established. The climate change analysis revealed that optimal management of water quantity and quality in storage became extremely challenging under future climate projections. The high reduction in storage volume in the future will lead to several challenges such as water supply shortfall and inability to undertake selective harvesting due to reduced water quality levels. In this context, selective harvesting of inflows based on water quality will no longer be an option to manage water quantity and quality optimally in storage. Some significant conclusions of this research work included the establishment of trade-offs between water quality and quantity objectives particular to this configuration of water supply system. The work demonstrated that selective harvesting of inflows will improve the stored water quality, and this finding along with the approach used is a significant contribution to decision makers working within the water sector. The simulation-optimisation approach is very effective in providing a range of optimal solutions, which can be used to make more informed decisions around achieving optimal water quality and quantity in storage. It was further demonstrated that there are range of planning periods, both long-term (>10 years) and short-term (<1 year), all of which offer distinct advantages and provides useful insights, making this an additional key contribution of the work. Importantly, climate change was also considered where it was found that diminishing water resources, particularly to this geographic location, makes it increasingly difficult to optimise both quality and quantity in storage providing further useful insights from this work.Doctor of Philosoph
Assessing Atmospheric Pollution and Its Impacts on the Human Health
This reprint contains articles published in the Special Issue entitled "Assessing Atmospheric Pollution and Its Impacts on the Human Health" in the journal Atmosphere. The research focuses on the evaluation of atmospheric pollution by statistical methods on the one hand, and on the other hand, on the evaluation of the relationship between the level of pollution and the extent of its effect on the population's health, especially on pulmonary diseases
Ciguatoxins
Ciguatoxins (CTXs), which are responsible for Ciguatera fish poisoning (CFP), are liposoluble toxins produced by microalgae of the genera Gambierdiscus and Fukuyoa. This book presents 18 scientific papers that offer new information and scientific evidence on: (i) CTX occurrence in aquatic environments, with an emphasis on edible aquatic organisms; (ii) analysis methods for the determination of CTXs; (iii) advances in research on CTX-producing organisms; (iv) environmental factors involved in the presence of CTXs; and (v) the assessment of public health risks related to the presence of CTXs, as well as risk management and mitigation strategies
Evolving Decision Rules with Geometric Semantic Genetic Programming
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceDue to the ever increasing amount of data available in today’s world, a variety of
methods to harness this information are continuously being created, refined and
utilized, drawing inspiration from a multitude of sources. Relevant to this work are
Supervised Learning techniques, that attempt to discover the relationship between the
characteristics of data and a certain feature, to uncover the function that maps input
to output. Among these, Genetic Programming (GP) attempts to replicate the concept
of evolution as defined by Charles Darwin, mimicking natural selection and genetic
operators to generate and improve a population of solutions for a given prediction
problem.
Among the possible variants of GP, Geometric Semantic Genetic Programming
(GSGP) stands out, due to its focus on the meaning of each individual it creates, rather
than their structure. It achieves by imagining an hypothetical and perfect model, and
evaluating the performance of others by measuring how much their behaviour differ
from it, and uses a set of genetic operators that have a specific effect on the individual’s
semantics (i.e., its predictions for training data), with the goal of reaching ever closer
to the so called perfect specimen.
This thesis conceptualizes and evaluates the performance of aGSGPimplementation
made specifically to deal with multi-class classification problems, using tree-based
individuals that are composed by a set of rules to allow the categorization of data. This
is achieved through the careful translation of GSGP’s theoretical foundation, first into
algorithms and then into an actual code library, able to tackle problems of this domain.
The results demonstrate that the implementation works successfully and respects the
properties of the the original technique, allowing us to obtain excellent results on
training data, although performance on unseen data is a slightly worse than that of
other state-of-the-art algorithms.Devido à crescente quantidade de dados do mundo de hoje, uma variedade de métodos
para utilizar esta informação é continuamente criada, melhorada e utilizado, com
inspiração de diversas fontes. Com particular relevância para este trabalho são técnicas
de Supervised Learning, que visam descobrir a relação entre as características dos
dados e um traço específico destes, de modo a encontrar uma função que consiga
mapear os inputs aos outputs. Entre estas, Programação Genética (PG) tenta recriar o
conceito de evolução como definido por Charles Darwin, imitando a seleção natural e
operadores genéticos para gerar e melhorar uma população de soluções para um dado
problema preditivo.
Entre as possíveis variantes de PG, Programação Genética em Geometria Semântica
(PGGS) é notável, pois coloca o seu foco no significado de cada indivíduo que cria,
em vez da sua estrutura. Realiza isto ao imaginar um modelo hipotético e perfeito,
e avaliar as capacidades dos outros medindo o quão diferente o seu comportamento
difere deste, e utiliza um conjunto de operadores genéticos com um efeito específico
na semântica de um indíviduo (i.e., as suas previsões para dados de treino), visando
chegar cada vez mais perto ao tão chamado espécime perfeito.
Esta tese conceptualiza e avalia o desempenho de uma implementação de PGGS
feita especificamente para lidar com problemas de classificação multi-classe, utilizando
indivíduos baseados em árvores compostos por uma série de regras que permitem a
categorização de dados. Isto é feito através de uma tradução cuidadosa da base teórica
de PGGS, primeiro para algoritmos e depois para uma biblioteca de código, capaz de
enfrentar problemas deste domínio. Os resultados demonstram que a implementação
funciona corretamente e respeita as propriedades da técnica original, permitindo que
obtivéssemos resultados excelentes nos dados de treino, embora o desempenho em
dados não vistos seja ligeiramente abaixo de outros algoritmos de última geração
Analysing Equilibrium States for Population Diversity
Population diversity is crucial in evolutionary algorithms as it helps with
global exploration and facilitates the use of crossover. Despite many runtime
analyses showing advantages of population diversity, we have no clear picture
of how diversity evolves over time. We study how population diversity of
algorithms, measured by the sum of pairwise Hamming distances,
evolves in a fitness-neutral environment. We give an exact formula for the
drift of population diversity and show that it is driven towards an equilibrium
state. Moreover, we bound the expected time for getting close to the
equilibrium state. We find that these dynamics, including the location of the
equilibrium, are unaffected by surprisingly many algorithmic choices. All
unbiased mutation operators with the same expected number of bit flips have the
same effect on the expected diversity. Many crossover operators have no effect
at all, including all binary unbiased, respectful operators. We review
crossover operators from the literature and identify crossovers that are
neutral towards the evolution of diversity and crossovers that are not.Comment: To appear at GECCO 202
Capacitor Optimization in Power Distribution Networks Using Numerical Computation Techniques
This paper presents a power distribution network (PDN) decoupling capacitor
optimization application with three primary goals: reduction of solution times
for large networks, development of flexible network scoring routines, and a
concentration strictly on achieving the best network performance. Example
optimizations are performed using broadband models of a printed circuit board
(PCB), a chip-package, on-die networks, and candidate capacitors. A novel
worst-case time-domain optimization technique is presented as an alternative to
the traditional frequency-domain approach. The trade-offs and criteria for
scoring the computed network are presented. The output is a recommended set of
capacitors which can then be applied to the product design.Comment: 24 pages, 13 figures, DesignCon 202
CHARACTERISTICS OF REFRACTIVITY AND SEA STATE IN THE MARINE ATMOSPHERIC SURFACE LAYER AND THEIR INFLUENCE ON X-BAND PROPAGATION
Predictions of environmental conditions within the marine atmospheric surface layer (MASL) are important to X-band radar system performance. Anomalous propagation occurs in conditions of non-standard atmospheric refractivity, driven by the virtually permanent presence of evaporation ducts (ED) in marine environments. Evaporation ducts are commonly characterized by the evaporation duct height (EDH), evaporation duct strength, and the gradients below the EDH, known as the evaporation duct curvature. Refractivity, and subsequent features, are estimated in the MASL primarily using four methods: in-situ measurements, numerical weather and surface layer modeling, boundary layer theory, and inversion methods.
The existing refractivity estimation techniques often assume steady homogeneous conditions, and discrepancies between measured and simulated propagation predictions exist. These discrepancies could be attributed to the exclusion of turbulent fluctuations of the refractive index, exclusion of spatially heterogeneous refractive environments, and inaccurate characterization of the sea surface in propagation simulations. Due to the associated complexity and modeling challenges, unsteady inhomogeneous refractivity and rough sea surfaces are often omitted from simulations.
This dissertation first investigates techniques for steady homogeneous refractivity and characterizes refractivity predictions using EDH and profile curvature, examining their effects on X-band propagation. Observed differences between techniques are explored with respect to prevailing meteorological conditions. Significant characteristics are then utilized in refractivity inversions for mean refractivity based-on point-to-point EM measurements. The inversions are compared to the other previously examined techniques. Differences between refractivity estimation methods are generally observed in relation to EDH, resulting in the largest variations in propagation, where most significant EDH discrepancies occur in stable conditions. Further, discrepancies among the refractivity estimation methods (in-situ, numerical models, theory, and inversion) when conditions are unstable and the mean EDH are similar, could be attributed to the neglect of spatial heterogeneity of EDH and turbulent fluctuations in the refractive index. To address this, a spectral-based turbulent refractive index fluctuation model (TRIF) is applied to emulate refractive index fluctuations. TRIF is verified against in-situ meteorological measurements and integrated with a heterogenous EDH model to estimate a comprehensive propagation environment. Lastly, a global sensitivity analysis is applied to evaluate the leading-order effects and non-linear interactions between the parameters of the comprehensive refractivity model and the sea surface in a parabolic wave equation propagation simulation under different atmospheric stability regimes (stable, neutral, and unstable). In neutral and stable regimes, mean evaporation duct characteristics (EDH and refractive gradients below the EDH) have the greatest impact on propagation, particularly beyond the geometric horizon. In unstable conditions, turbulence also plays a significant role. Regardless of atmospheric stability, forward scattering from the rough sea surface has a substantial effect on propagation predictions, especially within the lowest 10 m of the atmosphere
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