Tsunami is one of the most dangerous natural hazards in the coastal zone worldwide. Large tsunamis are relatively infrequent. Deposits are the only concrete evidence in the geological record with which we can determine both tsunami frequency and magnitude. Numerical modeling of sediment transport during a tsunami is important interdisciplinary research to estimate the frequency and magnitude of past events and quantitative prediction of future events. The goal of this dissertation is to develop robust, accurate, and computationally efficient models for sediment transport during a tsunami. There are two different modeling approaches (forward and inverse) to investigate sediment transport. A forward model consists of tsunami source, hydrodynamics, and sediment transport model. In this dissertation, we present one state-of-the-art forward model for Sediment TRansport In Coastal Hazard Events (STRICHE), which couples with GeoClaw and is referred to as GeoClaw-STRICHE. In an inverse model, deposit characteristics, such as grain-size distribution and thickness, are inputs to the model, and flow characteristics are outputs. We also depict one trial-and-error inverse model (TSUFLIND) and one data assimilation inverse model (TSUFLIND-EnKF) in this dissertation. All three models were validated and verified against several theoretical, experimental, and field cases. / Ph. D.

El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / This article describes how using logistics management models in collaboration with a process traceability system improves storage management processes in the coffee supply chain by reducing losses and high storage-related logistics costs, with support from a digital transformation process. For the purposes of this study, data on times and costs incurred as per the corresponding criteria and purchasing power, errors in order specifications, and delivery delays that result in losses were used, as these cause coffee to lose market value within an organization in a cooperative setting (business associations).

In this work, we apply a nonlinear mixture autoregressive (MixAR) model to supplant the Gaussian mixture model for speaker verification. MixAR is a statistical model that is a probabilistically weighted combination of components, each of which is an autoregressive filter in addition to a mean. The probabilistic mixing and the datadependent weights are responsible for the nonlinear nature of the model. Our experiments with synthetic as well as real speech data from standard speech corpora show that MixAR model outperforms GMM, especially under unseen noisy conditions. Moreover, MixAR did not require delta features and used 2.5x fewer parameters to achieve comparable or better performance as that of GMM using static as well as delta features. Also, MixAR suffered less from overitting issues than GMM when training data was sparse. However, MixAR performance deteriorated more quickly than that of GMM when evaluation data duration was reduced. This could pose limitations on the required minimum amount of evaluation data when using MixAR model for speaker verification.

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The Gaussian mixture model has been used for model-based clustering analysis for decades. Most model-based clustering analyses are based on the Gaussian mixture model. Model averaging approaches for Gaussian mixture models are proposed by Wei and McNicholas, based on a family of 14 Gaussian parsimonious clustering models. In this thesis, we use non-Gaussian mixture models, namely the tEigen family, for our averaging approaches. This paper studies fitting in an averaged model from a set of multivariate t-mixture models instead of fitting a best model. / Thesis / Master of Science (MSc)

The detrimental influence of humans on the environment is of increasing concern. Humans, their livestock, and their pets have caused fecal contamination of waterways throughout the United States. Understanding the sources of fecal indicator bacteria (FIB) and the environmental processes that affect them can be crucial to reducing the number of impaired streams and limiting the negative impacts on the environment. Antibiotic resistance is an emerging issue facing human health in the United States and across the world. Antibiotic resistant bacteria (ARB) have antibiotic resistance genes (ARGs) that prevent antibiotics from killing them. Limited research has been done on the role of the environment in the propagation of antibiotic resistance. As the use of antibiotics increases, it is critical to examine how this impacts human health through the environment. Models of watersheds in Patillas, Puerto Rico and Christiansburg, Virginia were created using the Soil and Water Assessment Tool (SWAT) to compare how the differences in spatial and temporal sampling of FIB, climate, and population affect FIB movement. The performances of the calibrated bacteria models were comparable to other published studies. A primary challenge faced in this study was the use of grab samples taken months apart as monthly averages of FIB. The high precipitation and constant warm climate made the model for Patillas more difficult to fit because of the high variability in the observed data. While the Patillas watershed had a lower population of people and livestock, the Christiansburg watershed had more available data on wildlife. The lack of spatial variance of data and the use of data from 1993-2018, hindered the ability for the model for Patillas to model FIB. Additionally, the model's performance was limited due to the strong hurricanes that affect land use, soils, and populations of humans and animals in the watershed. Using open-source data needs to be explored further as a faster and more cost-effective way of developing SWAT FIB models. The feasibility to use data collected in the Christiansburg and Patillas watershed to calibrate a SWAT-ARB model was determined based on available ARG data. The results indicate that the bacteria models need to be improved before an effective SWAT-ARB model can be calibrated. One limitation in the available ARG data for the two watersheds was that they were only sampled once. Out of the ARGs sampled, sul1 was the best modeled in both watersheds because it has the highest normalized values and correlated with the amount of developed land. / Master of Science / Humans negatively impact the environment. Humans and animals contribute to the bacteria contamination of waterways. Investigation into where the contamination sources are and environmental processes that contribute can help researchers limit the impact on the environment. Bacteria can build resistance to antibiotics, which can be especially dangerous to humans and livestock when exposed. Little research has been done on how the environment has contributed to the spread of antibiotic resistance in bacteria. The Soil and Water Assessment Tool (SWAT) was used to investigate bacteria in the Patillas, Puerto Rico and Christiansburg, Virginia watershed. These models used data published by the United States Geological Survey (USGS) and Environmental Protection Agency (EPA) to improve performance. When comparing simulated data to observed data, the performances of the models were comparable to other published studies. The Patillas watershed was particularly difficult to model because of the warm climate and high precipitation that caused high variability in bacteria concentrations. Strong weather events including hurricanes and a lack of available data on wildlife were other hinderances to the Patillas model. In comparison, more published data on wildlife was available in the Christiansburg watershed and it had a more temperate climate. The SWAT-ARB model was reviewed and recommendations were made to improve the model. Using the previously collected antibiotic resistance bacteria data in the Christiansburg and Patillas watersheds, it would be impossible to create accurate models. More antibiotic resistance data needs to be taken across as a greater time period before the performance of the models can be assessed.

One form of model robust regression (MRR) predicts mean response as a convex combination of a parametric and a nonparametric prediction. MRR is a semiparametric method by which an incompletely or an incorrectly specified parametric model can be improved through adding an appropriate amount of a nonparametric fit. The combined predictor can have less bias than the parametric model estimate alone and less variance than the nonparametric estimate alone. Additionally, as shown in previous work for uncorrelated data with linear mean function, MRR can converge faster than the nonparametric predictor alone. We extend the MRR technique to the problem of predicting mean response for clustered non-normal data. We combine a nonparametric method based on local estimation with a global, parametric generalized estimating equations (GEE) estimate through a mixing parameter on both the mean scale and the linear predictor scale. As a special case, when data are uncorrelated, this amounts to mixing a local likelihood estimate with predictions from a global generalized linear model. Cross-validation bandwidth and optimal mixing parameter selectors are developed. The global fits and the optimal and data-driven local and mixed fits are studied under no/some/substantial model misspecification via simulation. The methods are then illustrated through application to data from a longitudinal study. / Ph. D.

There has been confusion in choosing a proper survival model between two popular survival models of discrete and continuous survival analysis. This study aimed to provide empirical outcomes of two survival models in educational contexts and suggest a guideline for researchers who should adopt a suitable survival model. For the model specification, the study paid attention to three factors of time metrics, censoring proportions, and sample sizes. To arrive at comprehensive understanding of the three factors, the study investigated the separate and combined effect of these factors. Furthermore, to understand the interaction mechanism of those factors, this study examined the role of the factors to determine hazard rates which have been known to cause the discrepancies between discrete and continuous survival models. To provide empirical evidence from different combinations of the factors in the use of survival analysis, this study built a series of discrete and continuous survival models using secondary data and simulated data. In the first study, using empirical data from the National Longitudinal Survey of Youth 1997 (NLSY97), this study compared analyses results from the two models having different sizes of time metrics. In the second study, by having various specifications with combination of two other factors of censoring proportions and sample sizes, this study simulated datasets to build two models and compared the analysis results. The major finding of the study is that discrete models are recommended in the conditions of large units of time metrics, low censoring proportion, or small sample sizes. Particularly, discrete model produced better outcomes for conditions with low censoring proportion (20%) and small number (i.e., four) of large time metrics (i.e., year) regardless of sample sizes. Close examination of those conditions of time metrics, censoring proportion, and sample sizes showed that the conditions resulted into high hazards (i.e., 0.20). In conclusion, to determine a proper model, it is recommended to examine hazards of each of the time units with the specific factors of time metrics, censoring proportion and sample sizes. / Ph. D.

Published Article / The medical biological and ecosystemic models are two paradigms which are currently making a huge impact on education support services on an international level. The medical biological model has been dominating the way in which multidisciplinary support has been delivered within 20th-century special education. However, with the advent of inclusive education, the ecosystemic model has initially been pushed to the fore as the preferred metatheory of support services. This article specifically interrogates these two conflicting paradigms in education support services within the South African schooling and higher education bands, as well as Bronfenbrenner's integration of these models with regard to the bio-ecological model. Finally, this article proposes the bio-ecosystemic framework according to which the training of multidisciplinary education support services staff should proceed in order to ensure a sound and less conflicting theoretical framework.