Combined Vertical And Lateral Channel Evolution Numerical Modeling

Vertical stream bed erosion has been studied routinely and its modeling is getting widespread acceptance. The same cannot be said with lateral stream bank erosion since its measurement or numerical modeling is very challenging. Bank erosion, however, can be important to channel morphology. It may contribute significantly to the overall sediment budget of a stream, is a leading cause of channel migration, and is the cause of major channel maintenance. However, combined vertical and lateral channel evolution is seldom addressed. In this study, a new geofluival numerical model is developed to simulate combined vertical and lateral channel evolution. Vertical erosion is predicted with a 2D depth-averaged model SRH-2D, while lateral erosion is simulated with a linear retreat bank erosion model developed in this study. SRH-2D and the bank erosion model are coupled together both spatially and temporally through a common mesh and the same time advancement. The new geofluvial model is first tested and verified using laboratory meander channels; good agreement are obtained between predicted bank retreat and measured data. The model is then applied to a 16-kilometer reach of Chosui River, Taiwan. Vertical and lateral channel evolution during a three-year period (2004 to 2007) is simulated and results are compared with the field data. It is shown that the geofluvial model correctly captures all major erosion and deposition patterns. The new model is shown to be useful for identifying potential erosion sites and providing information for river maintenance planning.

Post-breakup tectonics in southeast Brazil from thermochronological data and combined inverse-forward thermal history modeling

The continental margin of southeast Brazil is elevated. Onshore Tertiary basins and Late Cretaceous/Paleogene intrusions are good evidence for post breakup tectono-magmatic activity. To constrain the impact of post-rift reactivation on the geological history of the area, we carried out a new thermochronological study. Apatite fission track ages range from 60.7 +/- 1.9 Ma to 129.3 +/- 4.3 Ma, mean track lengths from 11.41 +/- 0.23 mu m to 14.31 +/- 0.24 mu m and a subset of the (U-Th)/He ages range from 45.1 +/- 1.5 to 122.4 +/- 2.5 Ma. Results of inverse thermal history modeling generally support the conclusions from an earlier study for a Late Cretaceous phase of cooling. Around the onshore Taubate Basin, for a limited number of samples, the first detectable period of cooling occurred during the Early Tertiary. The inferred thermal histories for many samples also imply subsequent reheating followed by Neogene cooling. Given the uncertainty of the inversion results, we did deterministic forward modeling to assess the range of possibilities of this Tertiary part of the thermal history. The evidence for reheating seems to be robust around the Taubate Basin, but elsewhere the data cannot discriminate between this and a less complex thermal history. However, forward modeling results and geological information support the conclusion that the whole area underwent cooling during the Neogene. The synchronicity of the cooling phases with Andean tectonics and those in NE Brazil leads us to assume a plate-wide compressional stress that reactivated inherited structures. The present-day topographic relief of the margin reflects a contribution from post-breakup reactivation and uplift.

Modeling random corrosion processes via polynomial chaos expansion

Polynomial Chaos Expansion (PCE) is widely recognized as a flexible tool to represent different types of random variables/processes. However, applications to real, experimental data are still limited. In this article, PCE is used to represent the random time-evolution of metal corrosion growth in marine environments. The PCE coefficients are determined in order to represent data of 45 corrosion coupons tested by Jeffrey and Melchers (2001) at Taylors Beach, Australia. Accuracy of the representation and possibilities for model extrapolation are considered in the study. Results show that reasonably accurate smooth representations of the corrosion process can be obtained. The representation is not better because a smooth model is used to represent non-smooth corrosion data. Random corrosion leads to time-variant reliability problems, due to resistance degradation over time. Time variant reliability problems are not trivial to solve, especially under random process loading. Two example problems are solved herein, showing how the developed PCE representations can be employed in reliability analysis of structures subject to marine corrosion. Monte Carlo Simulation is used to solve the resulting time-variant reliability problems. However, an accurate and more computationally efficient solution is also presented.

Imaging and modeling of pore scale processes in porous media using X-ray computed tomography and lattice Boltzmann solver

In der Erdöl– und Gasindustrie sind bildgebende Verfahren und Simulationen auf der Porenskala im Begriff Routineanwendungen zu werden. Ihr weiteres Potential lässt sich im Umweltbereich anwenden, wie z.B. für den Transport und Verbleib von Schadstoffen im Untergrund, die Speicherung von Kohlendioxid und dem natürlichen Abbau von Schadstoffen in Böden. Mit der Röntgen-Computertomografie (XCT) steht ein zerstörungsfreies 3D bildgebendes Verfahren zur Verfügung, das auch häufig für die Untersuchung der internen Struktur geologischer Proben herangezogen wird. Das erste Ziel dieser Dissertation war die Implementierung einer Bildverarbeitungstechnik, die die Strahlenaufhärtung der Röntgen-Computertomografie beseitigt und den Segmentierungsprozess dessen Daten vereinfacht. Das zweite Ziel dieser Arbeit untersuchte die kombinierten Effekte von Porenraumcharakteristika, Porentortuosität, sowie die Strömungssimulation und Transportmodellierung in Porenräumen mit der Gitter-Boltzmann-Methode. In einer zylindrischen geologischen Probe war die Position jeder Phase auf Grundlage der Beobachtung durch das Vorhandensein der Strahlenaufhärtung in den rekonstruierten Bildern, das eine radiale Funktion vom Probenrand zum Zentrum darstellt, extrahierbar und die unterschiedlichen Phasen ließen sich automatisch segmentieren. Weiterhin wurden Strahlungsaufhärtungeffekte von beliebig geformten Objekten durch einen Oberflächenanpassungsalgorithmus korrigiert. Die Methode der „least square support vector machine” (LSSVM) ist durch einen modularen Aufbau charakterisiert und ist sehr gut für die Erkennung und Klassifizierung von Mustern geeignet. Aus diesem Grund wurde die Methode der LSSVM als pixelbasierte Klassifikationsmethode implementiert. Dieser Algorithmus ist in der Lage komplexe geologische Proben korrekt zu klassifizieren, benötigt für den Fall aber längere Rechenzeiten, so dass mehrdimensionale Trainingsdatensätze verwendet werden müssen. Die Dynamik von den unmischbaren Phasen Luft und Wasser wird durch eine Kombination von Porenmorphologie und Gitter Boltzmann Methode für Drainage und Imbibition Prozessen in 3D Datensätzen von Böden, die durch synchrotron-basierte XCT gewonnen wurden, untersucht. Obwohl die Porenmorphologie eine einfache Methode ist Kugeln in den verfügbaren Porenraum einzupassen, kann sie dennoch die komplexe kapillare Hysterese als eine Funktion der Wassersättigung erklären. Eine Hysterese ist für den Kapillardruck und die hydraulische Leitfähigkeit beobachtet worden, welche durch die hauptsächlich verbundenen Porennetzwerke und der verfügbaren Porenraumgrößenverteilung verursacht sind. Die hydraulische Konduktivität ist eine Funktion des Wassersättigungslevels und wird mit einer makroskopischen Berechnung empirischer Modelle verglichen. Die Daten stimmen vor allem für hohe Wassersättigungen gut überein. Um die Gegenwart von Krankheitserregern im Grundwasser und Abwässern vorhersagen zu können, wurde in einem Bodenaggregat der Einfluss von Korngröße, Porengeometrie und Fluidflussgeschwindigkeit z.B. mit dem Mikroorganismus Escherichia coli studiert. Die asymmetrischen und langschweifigen Durchbruchskurven, besonders bei höheren Wassersättigungen, wurden durch dispersiven Transport aufgrund des verbundenen Porennetzwerks und durch die Heterogenität des Strömungsfeldes verursacht. Es wurde beobachtet, dass die biokolloidale Verweilzeit eine Funktion des Druckgradienten als auch der Kolloidgröße ist. Unsere Modellierungsergebnisse stimmen sehr gut mit den bereits veröffentlichten Daten überein.

A fixed-links modeling approach to assess individual differences in the attentional blink: Analysis of behavioral and psychophysiological data

The attentional blink (AB) is a fundamental limitation of the ability to select relevant information from irrelevant information. It can be observed with the detection rate in an AB task as well as with the corresponding P300 amplitude of the event-related potential. In previous research, however, correlations between these two levels of observation were weak and rather inconsistent. A possible explanation of this finding might be that multiple processes underlie the AB and, thus, obscure a possible relationship between AB-related detection rate and the corresponding P300 amplitude. The present study investigated this assumption by applying a fixed-links modeling approach to represent behavioral individual differences in the AB as a latent variable. Concurrently, this approach enabled us to control for additional sources of variance in AB performance by deriving two additional latent variables. The correlation between the latent variable reflecting behavioral individual differences in AB magnitude and a corresponding latent variable derived from the P300 amplitude was high (r=.70). Furthermore, this correlation was considerably stronger than the correlations of other behavioral measures of the AB magnitude with their psychophysiological counterparts (all rs<.40). Our findings clearly indicate that the systematic disentangling of various sources of variance by utilizing the fixed-links modeling approach is a promising tool to investigate behavioral individual differences in the AB and possible psychophysiological correlates of these individual differences.

Bayesian joint modeling of longitudinal and survival data

The joint modeling of longitudinal and survival data is a new approach to many applications such as HIV, cancer vaccine trials and quality of life studies. There are recent developments of the methodologies with respect to each of the components of the joint model as well as statistical processes that link them together. Among these, second order polynomial random effect models and linear mixed effects models are the most commonly used for the longitudinal trajectory function. In this study, we first relax the parametric constraints for polynomial random effect models by using Dirichlet process priors, then three longitudinal markers rather than only one marker are considered in one joint model. Second, we use a linear mixed effect model for the longitudinal process in a joint model analyzing the three markers. In this research these methods were applied to the Primary Biliary Cirrhosis sequential data, which were collected from a clinical trial of primary biliary cirrhosis (PBC) of the liver. This trial was conducted between 1974 and 1984 at the Mayo Clinic. The effects of three longitudinal markers (1) Total Serum Bilirubin, (2) Serum Albumin and (3) Serum Glutamic-Oxaloacetic transaminase (SGOT) on patients' survival were investigated. Proportion of treatment effect will also be studied using the proposed joint modeling approaches. ^ Based on the results, we conclude that the proposed modeling approaches yield better fit to the data and give less biased parameter estimates for these trajectory functions than previous methods. Model fit is also improved after considering three longitudinal markers instead of one marker only. The results from analysis of proportion of treatment effects from these joint models indicate same conclusion as that from the final model of Fleming and Harrington (1991), which is Bilirubin and Albumin together has stronger impact in predicting patients' survival and as a surrogate endpoints for treatment. ^

Modeling the survival of T-cell lymphocytes using compound branching processes

Radiotherapy has been a method of choice in cancer treatment for a number of years. Mathematical modeling is an important tool in studying the survival behavior of any cell as well as its radiosensitivity. One particular cell under investigation is the normal T-cell, the radiosensitivity of which may be indicative to the patient's tolerance to radiation doses.^ The model derived is a compound branching process with a random initial population of T-cells that is assumed to have compound distribution. T-cells in any generation are assumed to double or die at random lengths of time. This population is assumed to undergo a random number of generations within a period of time. The model is then used to obtain an estimate for the survival probability of T-cells for the data under investigation. This estimate is derived iteratively by applying the likelihood principle. Further assessment of the validity of the model is performed by simulating a number of subjects under this model.^ This study shows that there is a great deal of variation in T-cells survival from one individual to another. These variations can be observed under normal conditions as well as under radiotherapy. The findings are in agreement with a recent study and show that genetic diversity plays a role in determining the survival of T-cells. ^

Environmental variables modeling based on remote sensing data using time series analysis: forestry and agricultural applications

En la actualidad, el seguimiento de la dinámica de los procesos medio ambientales está considerado como un punto de gran interés en el campo medioambiental. La cobertura espacio temporal de los datos de teledetección proporciona información continua con una alta frecuencia temporal, permitiendo el análisis de la evolución de los ecosistemas desde diferentes escalas espacio-temporales. Aunque el valor de la teledetección ha sido ampliamente probado, en la actualidad solo existe un número reducido de metodologías que permiten su análisis de una forma cuantitativa. En la presente tesis se propone un esquema de trabajo para explotar las series temporales de datos de teledetección, basado en la combinación del análisis estadístico de series de tiempo y la fenometría. El objetivo principal es demostrar el uso de las series temporales de datos de teledetección para analizar la dinámica de variables medio ambientales de una forma cuantitativa. Los objetivos específicos son: (1) evaluar dichas variables medio ambientales y (2) desarrollar modelos empíricos para predecir su comportamiento futuro. Estos objetivos se materializan en cuatro aplicaciones cuyos objetivos específicos son: (1) evaluar y cartografiar estados fenológicos del cultivo del algodón mediante análisis espectral y fenometría, (2) evaluar y modelizar la estacionalidad de incendios forestales en dos regiones bioclimáticas mediante modelos dinámicos, (3) predecir el riesgo de incendios forestales a nivel pixel utilizando modelos dinámicos y (4) evaluar el funcionamiento de la vegetación en base a la autocorrelación temporal y la fenometría. Los resultados de esta tesis muestran la utilidad del ajuste de funciones para modelizar los índices espectrales AS1 y AS2. Los parámetros fenológicos derivados del ajuste de funciones permiten la identificación de distintos estados fenológicos del cultivo del algodón. El análisis espectral ha demostrado, de una forma cuantitativa, la presencia de un ciclo en el índice AS2 y de dos ciclos en el AS1 así como el comportamiento unimodal y bimodal de la estacionalidad de incendios en las regiones mediterránea y templada respectivamente. Modelos autorregresivos han sido utilizados para caracterizar la dinámica de la estacionalidad de incendios y para predecir de una forma muy precisa el riesgo de incendios forestales a nivel pixel. Ha sido demostrada la utilidad de la autocorrelación temporal para definir y caracterizar el funcionamiento de la vegetación a nivel pixel. Finalmente el concepto “Optical Functional Type” ha sido definido, donde se propone que los pixeles deberían ser considerados como unidades temporales y analizados en función de su dinámica temporal. ix SUMMARY A good understanding of land surface processes is considered as a key subject in environmental sciences. The spatial-temporal coverage of remote sensing data provides continuous observations with a high temporal frequency allowing the assessment of ecosystem evolution at different temporal and spatial scales. Although the value of remote sensing time series has been firmly proved, only few time series methods have been developed for analyzing this data in a quantitative and continuous manner. In the present dissertation a working framework to exploit Remote Sensing time series is proposed based on the combination of Time Series Analysis and phenometric approach. The main goal is to demonstrate the use of remote sensing time series to analyze quantitatively environmental variable dynamics. The specific objectives are (1) to assess environmental variables based on remote sensing time series and (2) to develop empirical models to forecast environmental variables. These objectives have been achieved in four applications which specific objectives are (1) assessing and mapping cotton crop phenological stages using spectral and phenometric analyses, (2) assessing and modeling fire seasonality in two different ecoregions by dynamic models, (3) forecasting forest fire risk on a pixel basis by dynamic models, and (4) assessing vegetation functioning based on temporal autocorrelation and phenometric analysis. The results of this dissertation show the usefulness of function fitting procedures to model AS1 and AS2. Phenometrics derived from function fitting procedure makes it possible to identify cotton crop phenological stages. Spectral analysis has demonstrated quantitatively the presence of one cycle in AS2 and two in AS1 and the unimodal and bimodal behaviour of fire seasonality in the Mediterranean and temperate ecoregions respectively. Autoregressive models has been used to characterize the dynamics of fire seasonality in two ecoregions and to forecasts accurately fire risk on a pixel basis. The usefulness of temporal autocorrelation to define and characterized land surface functioning has been demonstrated. And finally the “Optical Functional Types” concept has been proposed, in this approach pixels could be as temporal unities based on its temporal dynamics or functioning.

Data Processing Modeling in Decision Support Systems.

Due to the advancement of both, information technology in general, and databases in particular; data storage devices are becoming cheaper and data processing speed is increasing. As result of this, organizations tend to store large volumes of data holding great potential information. Decision Support Systems, DSS try to use the stored data to obtain valuable information for organizations. In this paper, we use both data models and use cases to represent the functionality of data processing in DSS following Software Engineering processes. We propose a methodology to develop DSS in the Analysis phase, respective of data processing modeling. We have used, as a starting point, a data model adapted to the semantics involved in multidimensional databases or data warehouses, DW. Also, we have taken an algorithm that provides us with all the possible ways to automatically cross check multidimensional model data. Using the aforementioned, we propose diagrams and descriptions of use cases, which can be considered as patterns representing the DSS functionality, in regard to DW data processing, DW on which DSS are based. We highlight the reusability and automation benefits that this can be achieved, and we think this study can serve as a guide in the development of DSS.

Aggregation and caking processes of granular materials:continuum model and simulation with application to sugar

Aggregation and caking of particles are common severe problems in many operations and processing of granular materials, where granulated sugar is an important example. Prevention of aggregation and caking of granular materials requires a good understanding of moisture migration and caking mechanisms. In this paper, the modeling of solid bridge formation between particles is introduced, based on moisture migration of atmospheric moisture into containers packed with granular materials through vapor evaporation and condensation. A model for the caking process is then developed, based on the growth of liquid bridges (during condensation), and their hardening and subsequent creation of solid bridges (during evaporation). The predicted caking strengths agree well with some available experimental data on granulated sugar under storage conditions.

Modeling of Effective Process of Network Maintaining Based on Statistical Data

This paper is dedicated to modelling of network maintaining based on live example – maintaining ATM banking network, where any problems are mean money loss. A full analysis is made in order to estimate valuable and not-valuable parameters based on complex analysis of available data. Correlation analysis helps to estimate provided data and to produce a complex solution of increasing network maintaining effectiveness.

Influence of hydrobiogeochemistry on transport of chromium through manganese -containing sediments: Experimental and modeling approaches

Chromium (Cr) is a metal of particular environmental concern, owing to its toxicity and widespread occurrence in groundwater, soil, and soil solution. A combination of hydrological, geochemical, and microbiological processes governs the subsurface migration of Cr. Little effort has been devoted to examining how these biogeochemical reactions combine with hydrologic processes influence Cr migration. This study has focused on the complex problem of predicting the Cr transport in laboratory column experiments. A 1-D reactive transport model was developed and evaluated against data obtained from laboratory column experiments. ^ A series of dynamic laboratory column experiments were conducted under abiotic and biotic conditions. Cr(III) was injected into columns packed with β-MnO 2-coated sand at different initial concentrations, variable flow rates, and at two different pore water pH (3.0 and 4.0). In biotic anaerobic column experiments Cr(VI) along with lactate was injected into columns packed with quartz sand or β-MnO2-coated sand and bacteria, Shewanella alga Simidu (BrY-MT). A mathematical model was developed which included advection-dispersion equations for the movement of Cr(III), Cr(VI), dissolved oxygen, lactate, and biomass. The model included first-order rate laws governing the adsorption of each Cr species and lactate. The equations for transport and adsorption were coupled with nonlinear equations for rate-limited oxidation-reduction reactions along with dual-monod kinetic equations. Kinetic batch experiments were conducted to determine the reduction of Cr(VI) by BrY-MT in three different substrates. Results of the column experiments with Cr(III)-containing influent solutions demonstrate that β-MnO2 effectively catalyzes the oxidation of Cr(III) to Cr(VI). For a given influent concentration and pore water velocity, oxidation rates are higher, and hence effluent concentrations of Cr(VI) are greater, at pH 4 relative to pH 3. Reduction of Cr(VI) by BrY-MT was rapid (within one hour) in columns packed with quartz sand, whereas Cr(VI) reduction by BrY-MT was delayed (57 hours) in presence of β-MnO 2-coated sand. BrY-MT grown in BHIB (brain heart infusion broth) reduced maximum amount of Cr(VI) to Cr(III) followed by TSB (tryptic soy broth) and M9 (minimum media). The comparisons of data and model results from the column experiments show that the depths associated with Cr(III) oxidation and transport within sediments of shallow aquatic systems can strongly influence trends in surface water quality. The results of this study suggests that carefully performed, laboratory column experiments is a useful tool in determining the biotransformation of redox-sensitive metals even in the presence of strong oxidant, like β-MnO2. ^

Advances in Dynamic Modeling and Computation for Count Data

A class of multi-process models is developed for collections of time indexed count data. Autocorrelation in counts is achieved with dynamic models for the natural parameter of the binomial distribution. In addition to modeling binomial time series, the framework includes dynamic models for multinomial and Poisson time series. Markov chain Monte Carlo (MCMC) and Po ́lya-Gamma data augmentation (Polson et al., 2013) are critical for fitting multi-process models of counts. To facilitate computation when the counts are high, a Gaussian approximation to the P ́olya- Gamma random variable is developed.

Three applied analyses are presented to explore the utility and versatility of the framework. The first analysis develops a model for complex dynamic behavior of themes in collections of text documents. Documents are modeled as a “bag of words”, and the multinomial distribution is used to characterize uncertainty in the vocabulary terms appearing in each document. State-space models for the natural parameters of the multinomial distribution induce autocorrelation in themes and their proportional representation in the corpus over time.

The second analysis develops a dynamic mixed membership model for Poisson counts. The model is applied to a collection of time series which record neuron level firing patterns in rhesus monkeys. The monkey is exposed to two sounds simultaneously, and Gaussian processes are used to smoothly model the time-varying rate at which the neuron’s firing pattern fluctuates between features associated with each sound in isolation.

The third analysis presents a switching dynamic generalized linear model for the time-varying home run totals of professional baseball players. The model endows each player with an age specific latent natural ability class and a performance enhancing drug (PED) use indicator. As players age, they randomly transition through a sequence of ability classes in a manner consistent with traditional aging patterns. When the performance of the player significantly deviates from the expected aging pattern, he is identified as a player whose performance is consistent with PED use.

All three models provide a mechanism for sharing information across related series locally in time. The models are fit with variations on the P ́olya-Gamma Gibbs sampler, MCMC convergence diagnostics are developed, and reproducible inference is emphasized throughout the dissertation.

Modeling the distribution of crossovers and interference with mice DNA data

Chiasma and crossover are two related biological processes of great importance in the understanding genetic variation. The study of these processes is straightforward in organisms where all products of meiosis are recovered and can be observed. This is not the case in mammals. Our understanding of these processes depends on our ability to model them. In this study I describe the biological processes that underline chiasma and crossover as well as the two main inference problems associated with these processes: i) in mammals we only recover one of the four products of meiosis and, ii) in general, we do not observe where the crossovers actually happen, but we find an interval containing type-2 censored information. NPML estimate was proposed and used in this work and used to compare chromosome length and chromosome expansion through the crosses.

Study of the development and evolution of seasonal thermocline and turbulent processes in the northern parts of Persian Gulf using numerical modeling

The Persian Gulf (PG) is a semi-enclosed shallow sea which is connected to open ocean through the Strait of Hormuz. Thermocline as a suddenly decrease of temperature in subsurface layer in water column leading to stratification happens in the PG seasonally. The forcing comprise tide, river inflow, solar radiation, evaporation, northwesterly wind and water exchange with the Oman Sea that influence on this process. In this research, analysis of the field data and a numerical (Princeton Ocean Model, POM) study on the summer thermocline development in the PG are presented. The Mt. Mitchell cruise 1992 salinity and temperature observations show that the thermocline is effectively removed due to strong wind mixing and lower solar radiation in winter but is gradually formed and developed during spring and summer; in fact as a result of an increase in vertical convection through the water in winter, vertical gradient of temperature is decreased and thermocline is effectively removed. Thermocline development that evolves from east to west is studied using numerical simulation and some existing observations. Results show that as the northwesterly wind in winter, at summer transition period, weakens the fresher inflow from Oman Sea, solar radiation increases in this time interval; such these factors have been caused the thermocline to be formed and developed from winter to summer even over the northwestern part of the PG. The model results show that for the more realistic monthly averaged wind experiments the thermocline develops as is indicated by summer observations. The formation of thermocline also seems to decrease the dissolved oxygen in water column due to lack of mixing as a result of induced stratification. Over most of PG the temperature difference between surface and subsurface increases exponentially from March until May. Similar variations for salinity differences are also predicted, although with smaller values than observed. Indeed thermocline development happens more rapidly in the Persian Gulf from spring to summer. Vertical difference of temperature increases to 9 centigrade degrees in some parts of the case study zone from surface to bottom in summer. Correlation coefficients of temperature and salinity between the model results and measurements have been obtained 0.85 and 0.8 respectively. The rate of thermcline development was found to be between 0.1 to 0.2 meter per day in the Persian Gulf during the 6 months from winter to early summer. Also it is resulted from the used model that turbulence kinetic energy increases in the northwestern part of the PG from winter to early summer that could be due to increase in internal waves activities and stability intensified through water column during this time.