Although deep saline aquifers are found in all sedimentary basins and provide very large storage capacities, a little is known about them because they are rarely a target for the exploration. Furthermore, nearly all the experiments and simulations made for CO2 sequestration in deep saline aquifers are related to the sandstone formations. The aim of this study is to create a predictive model to estimate the CO2 storage capacity of the deep saline carbonate aquifers since a little is known about them. To create a predictive model, the variables which affect the CO2 storage capacity and their ranges are determined from published literature data. They are rock properties (porosity, permeability, vertical to horizontal permeability ratio), fluid properties (irreducible water saturation, gas permeability end point, Corey water and gas coefficients), reaction properties (forward and backward reaction rates) and reservoir properties (depth, pressure gradient, temperature gradient, formation dip angle, salinity), diffusion coefficient and Kozeny-Carman Coefficient. Other parameters such as pore volume compressibility and density of brine are calculated from correlations found in literature. To cover all possibilities, Latin Hypercube Space Filling Design is used to construct 100 simulation cases and CMG STARS is used for simulation runs. By using least squares method, a linear correlation is found to calculate CO2 storage capacity of the deep saline carbonate aquifers with a correlation coefficient 0.81 by using variables found from literature and simulation results. Numerical dispersion effects have been considered by increasing the grid dimensions. It has been found that correlation coefficient decreased to 0.77 when the grid size was increased from 250 ft to 750 ft. The sensitivity analysis shows that the most important parameter that affects CO2 storage capacity is depth since the pressure difference between formation pressure and fracture pressure increases with depth. Also, CO2 storage mechanisms are investigated at the end of 300 years of simulation. Most of the gas (up to 90%) injected into formation dissolves into the formation water and negligible amount of CO2 reacts with carbonate. This result is consistent with sensitivity analysis results since the variables affecting the solubility of CO2 in brine have greater affect on storage capacity of aquifers. Dimensionless linear and nonlinear predictive models are constructed to estimate the CO2 storage capacity of all deep saline carbonate aquifers and it is found that the best dimensionless predictive model is linear one independent of bulk volume of the aquifer.

No description available.

The development of predictive appearance models for organic tissues is a challenging task due to the inherent complexity of these materials. In this thesis, we closely examine the biophysical processes responsible for the appearance attributes of whole blood, one the most fundamental of these materials. We describe a new appearance model that simulates the mechanisms of light propagation and absorption within the cellular and fluid portions of this specialized tissue. The proposed model employs a comprehensive, and yet flexible first principles approach based on the morphological, optical and biochemical properties of blood cells. This approach allows for environment driven changes in the cells' anatomy and orientation to be appropriately included into the light transport simulations. The correctness and predictive capabilities of the proposed model are quantitatively and qualitatively evaluated through comparisons of modeled results with actual measured data and experimental observations reported in the scientific literature. Its incorporation into rendering systems is illustrated through images of blood samples depicting appearance variations controlled by physiologically meaningful parameters. Besides the contributions to the modeling of material appearance, the research presented in this thesis is also expected to have applications in a wide range of biomedical areas, from optical diagnostics to the visualization and noninvasive imaging of blood-perfused tissues.

Studies focused on developing a semiochemical-based monitoring system for Plutella xylostella (L.) using sex pheromone and Z3-hexenyl acetate. A commercially available pheromone trapping system was used to capture male moths at sites in Alberta in 2007 and 2008. Larval sampling occurred every two weeks after the first males were captured. Male moth capture was predictive of larval density on individual sample dates during the growing season. The predictive capability of pheromone-baited trap capture was not in direct proportion to population density and was inconsistent. Modifications to the trapping system were tested to improve attractiveness. Adding Z3-hexenyl acetate at various doses to pheromone did not improve the attractiveness to males over pheromone alone and did not attract significant numbers of females when tested at various times during the flight season. Trap height and colour did not influence male capture. Pheromone dose and lure type did influence male moth capture in traps. / Plant Science

This research project was a step forward in developing an efficient data mining method for estimating the service life of metallic components in Queensland school buildings. The developed method links together the different data sources of service life information and builds the model for a real situation when the users have information on limited inputs only. A practical lifetime prediction system was developed for the industry partners of this project including Queensland Department of Public Works and Queensland Department of Main Roads. The system provides high accuracy in practice where not all inputs are available for querying to the system.

Optimization and control theory are well developed techniques to quantize, model, understand and optimize real world systems and they have been widely used in engineering, economics, and science. In this thesis, we focus on applications in biomedical systems ranging from cells to microbial communities, and to something as complex as the human body. The first problem we consider is that of medication dosage control for drugs delivered intravenously to the patient. We focus specifically on a blood thinner (called bivalirudin) used in the post cardiac surgery Intensive Care Unit (ICU). We develop two approaches (a model-free and a model-based one) that predict the effect of bivalirudin. After obtaining the model and its best fit parameters by solving a non-linear optimization problem, we develop automatic dosage controllers that adaptively regulate its effect to desired levels. Our algorithms are validated using actual data from a large hospital in the Boston area. In the second problem, we introduce a cellular objective function inference mechanism in metabolic networks. We develop an inverse optimization method, called InvFBA (Inverse Flux Balance Analysis), to infer the objective functions of growing cells by using their reaction fluxes. InvFBA can be seen as an inverse version of FBA (Flux Balance Analysis) which predicts the distribution of the cell's reaction fluxes by using a hypothetical objective function. The objective functions can be linear, quadratic and non-parametric. The efficiency of the InvFBA approach matches the structure of the FBA and ensures scalability to large networks and optimality of the solution. After testing our algorithm on simulated E. coli data and time-dependent S. oneidensis fluxes inferred from gene expression data, we apply our inverse approach to flux measurements in long-term evolved E. coli strains, revealing objective functions that provide insight into metabolic adaptation trajectories. In the final problem in this thesis, we formulate a novel resource allocation problem in microbial ecosystems. We consider a given number of microbial species living symbiotically in a community and a list of all metabolic reactions present in the community, expressed in terms of the metabolite proportions involved in each reaction. We are interested in allocating reactions to organisms so that each organism maintains a minimal level of growth and the community optimizes certain objectives, such as maximizing growth and/or the uptake of specific compounds from the common environment. We leverage tools from Flux Balance Analysis (FBA) and formulate the problem as a mixed integer linear programming problem. We test our method in a toy model involving two organisms that can only survive through cross-feeding, demonstrating that the method can recover this interaction. We also test the method in a community of two simplified bacteria described in terms of their core, simplified metabolic network. We demonstrate that the method can obtain syntrophic cross-feeding species that would be very difficult to design manually.

abstract: Dimensionality assessment is an important component of evaluating item response data. Existing approaches to evaluating common assumptions of unidimensionality, such as DIMTEST (Nandakumar & Stout, 1993; Stout, 1987; Stout, Froelich, & Gao, 2001), have been shown to work well under large-scale assessment conditions (e.g., large sample sizes and item pools; see e.g., Froelich & Habing, 2007). It remains to be seen how such procedures perform in the context of small-scale assessments characterized by relatively small sample sizes and/or short tests. The fact that some procedures come with minimum allowable values for characteristics of the data, such as the number of items, may even render them unusable for some small-scale assessments. Other measures designed to assess dimensionality do not come with such limitations and, as such, may perform better under conditions that do not lend themselves to evaluation via statistics that rely on asymptotic theory. The current work aimed to evaluate the performance of one such metric, the standardized generalized dimensionality discrepancy measure (SGDDM; Levy & Svetina, 2011; Levy, Xu, Yel, & Svetina, 2012), under both large- and small-scale testing conditions. A Monte Carlo study was conducted to compare the performance of DIMTEST and the SGDDM statistic in terms of evaluating assumptions of unidimensionality in item response data under a variety of conditions, with an emphasis on the examination of these procedures in small-scale assessments. Similar to previous research, increases in either test length or sample size resulted in increased power. The DIMTEST procedure appeared to be a conservative test of the null hypothesis of unidimensionality. The SGDDM statistic exhibited rejection rates near the nominal rate of .05 under unidimensional conditions, though the reliability of these results may have been less than optimal due to high sampling variability resulting from a relatively limited number of replications. Power values were at or near 1.0 for many of the multidimensional conditions. It was only when the sample size was reduced to N = 100 that the two approaches diverged in performance. Results suggested that both procedures may be appropriate for sample sizes as low as N = 250 and tests as short as J = 12 (SGDDM) or J = 19 (DIMTEST). When used as a diagnostic tool, SGDDM may be appropriate with as few as N = 100 cases combined with J = 12 items. The study was somewhat limited in that it did not include any complex factorial designs, nor were the strength of item discrimination parameters or correlation between factors manipulated. It is recommended that further research be conducted with the inclusion of these factors, as well as an increase in the number of replications when using the SGDDM procedure. / Dissertation/Thesis / M.A. Educational Psychology 2013

La deserción universitaria se ha convertido en un problema importante a ser investigado. La tasa de deserción ha llegado a constituir uno de los principales indicadores de eficiencia interna dentro de cualquier institución de educación. Investigar las causas de la deserción con metodologías adecuadas que permitan predecir esta, contribuye a la toma de decisiones dentro de la gestión de la unidad académica. El objetivo del presente proyecto consiste en investigar y proponer una metodología que permita identificar en forma automática a los estudiantes con mayor riesgo de deserción de las carreras de Ingeniería Informática en la Universidad Ricardo Palma. Para la implementación de este proyecto se adoptó la metodología CRISP-DM que estructura el proceso de minería de datos en seis fases, que interactúan entre ellas de forma iterativa. Se evaluaron los modelos de Árboles de decisión y Clustering para analizar el comportamiento de los estudiantes, evaluando factores como el rendimiento del alumno, condición social y aspectos socioeconómicos. La exactitud de los modelos es calculada a partir de la información que brindó la Oficina Central de Informática y Cómputo de la Universidad Ricardo Palma, en la cual se realizó una transformación y simulación de algunas variables para mayor efectividad del modelo. The college desertion has become an important problem to be investigated. The desertion rate has come to be one of the main indicators of internal efficiency in any educational institution. The research of the causes of desertion with appropriate methodologies to predict, contributes to decision making in the academic management The objective of this project is to investigate and propose a methodology to automatically identify students at high risk of dropping out of the Computer Engineering career at the Ricardo Palma University. To implement this project, we used the CRISP-DM methodology to structure the data mining process into six phases, which interact with each other iteratively. It was applied the models of decision trees and clustering to analyze the behavior of the students, evaluating factors such as student performance, social status and socio-economic aspects. The accuracy of the models is calculated from the information provided the Central Office of Information and Computation at the Ricardo Palma University, in which it was applied a transformation and simulation of some variables for greater effectiveness and performance of the model.

Introdução: O impacto da doença renal na saúde é alto para os pacientes e para os serviços de saúde em todo o mundo, e a triagem para doença renal crônica (DRC) tem sido cada vez mais defendida. Estudos de base populacional referentes à prevalência da DRC na comunidade são limitados. Objetivos: Estudamos prospectivamente se a estratificação pelos valores do SCORED registrados poderá ser útil para identificar indivíduos que estão em alto risco de ter doença renal crônica em uma amostra da população geral e comparamos com os métodos de diagnósticos convencionais para DRC. Casuística e métodos: A freqüência de indivíduos com alto risco para a DRC foi determinada utilizando um estudo transversal de 873 indivíduos adultos em Palmas, Tocantins, Brasil. Os indivíduos entrevistados foram selecionados aleatoriamente através de um método estratificado por conglomerados. Idade, sexo e raça foram semelhantes à população urbana de Palmas. DRC foi definida através do ritmo de filtração glomerular estimado (RFGe) <60 ml/min/1.73 m2. Resultados: Um RFGe <60 ml/min/ 1.73 m2 estava presente em 46 (5,3%) dos participantes estudados. O risco de ter doença renal crônica foi maior em mulheres que em homens, e aumentou com a idade de 2,7% no grupo de 18-44 anos de idade para 19,0% naqueles com 65 anos de idade ou mais. As freqüências da DRC nos estágios 3, 4 e 5 foram de 4,8%, 0,5% e 0%, respectivamente. Os valores do SCORED incluíram 224 (25,7%) indivíduos com altos valores ( 4), e 649 (74,3%) indivíduos com baixos valores. Indivíduos com maiores valores na pontuação do SCORED tiveram um risco significativamente maior de ter doença renal crônica em comparação com aqueles que tinham menores valores pontuados (12,9% vs 2,6%, 2 = 35,58, p <0,001). A sensibilidade para prever DRC por esse modelo foi de 63% e a especificidade foi de 76%, o valor preditivo positivo foi de 13%, enquanto o valor preditivo negativo foi de 76%. Conclusão: Valores elevados do SCORED foram associados a um risco maior de ter doença renal crônica em uma amostra da população geral. Esta ferramenta simples de triagem foi uma ferramenta útil para identificar indivíduos de alto risco para DRC / Background and objective: The health burden of renal disease is high for patients and health services worldwide, and screening for chronic kidney disease (CKD) has been increasingly advocated. Population-based studies relating to the prevalence of CKD in the community are limited. We prospective studied whether stratification by SCORED values could be useful to identify subjects who are at high-risk for having CKD in a general population-based sampling. Design, participants & methods: The frequency of individuals at high-risk for CKD was determined using a cross-sectional study of 873 adult households in Palmas, Brazil, randomly selected using a stratified, cluster method. Age, gender, and race were similar to the entire Palmas´ urban population. Results: An estimated GFR <60 ml/min/1.73 m2 was present in 46 (5.3%) of participants studied, and the risk for having CKD was greater in women than in men, and it increased with age from 2.7% in the 18 to 44 yr age group to 19.0% in those 65 yr of age older. The frequencies of CKD Stage 3, 4 and 5 were 4.8%, 0.5% and 0%, respectively. SCORED values included 224 (25.7%) patients with high SCORED values (4), and 649 (74.3%) subjects with low SCORED values. Subjects with higher SCORED values were at a significantly higher risk of having CKD compared with those who had lower SCORED values (12.9% vs 2.6%, 2 = 35.58; p <0.001). The sensivity for predicting CKD by SCORED model was 63% and the specificity was 76%; the positive predictive value was 13%, whereas the negative predictive value was 76%. Conclusion: High SCORED values were associated with a higher risk for having CKD in a general population-based sampling. This simple screening tool was a useful tool to identify individuals at high-risk for CKD

Physiotherapist-supervised pelvic floor muscle (PFM) training is the recommended first-line treatment for women with stress urinary incontinence (SUI); however, only up to 50% of women are cured with this treatment. The primary objective of this study was to develop a predictive model of successful physiotherapy intervention outcomes among women with SUI using predictors that are accessible to clinicians. The secondary objective of this study was to run a preliminary investigation of morphologic differences between women with SUI who were cured with a physiotherapy intervention and those who were not cured, using a subset of ultrasound imaging data. Seventy-nine women with SUI were assessed at baseline on measures of demographic data (i.e. age, body mass index, etc.), 3-day bladder diary, 30-minute standardized pad test, clinical assessments of PFM strength and tone, and transperineal ultrasound assessments of PFM morphology. Women then attended a 12-week physiotherapy intervention and returned for a follow-up assessment. The multivariate logistic regression model was significant (p < .001) with two predictors: baseline ICIQ-FLUTS UI subscale (SUI severity; p = .01) and parity (p = .06). A significant ROC curve for the ICIQ-FLUTS UI subscale (p ˂ .01) predicts physiotherapy intervention outcomes with 55.6% sensitivity and 80.8% specificity at a cut-off score of 7.50. Women most likely to be cured with a physiotherapy intervention were those with lower scores on the ICIQ-FLUTS UI subscale and those who have given birth to fewer children. Significant differences were found in a subset of data between women with SUI who are cured with the physiotherapy intervention and women who are not cured on morphologic measures in standing of bladder neck height at peak cough (p = .03), descent of the bladder neck during maximal Valsalva maneuver (MVM; p = .04), levator hiatus circumference at rest (p = .03) and at maximal voluntary contraction in both standing and supine (MVC; p = .01; p = .03). Variables that were trending towards significance included bladder neck height in standing at rest, levator plate length (LPL) at maximal excursion during a cough and MVM in standing, and mid-urethral wall cross-sectional area. These significant differences indicate potential value in using ultrasound imaging outcomes as predictors of a cure with physiotherapy intervention in future models, and a combination of demographic, clinical, and morphologic variables may build a more robust predictive model.