This thesis proposes a functional framework for content management. This framework provides concepts and vocabulary for analysis and description of content management systems. The framework is derived from an analysis of eight content management systems. It describes forty-five conceptual functions organized into five functional groups. The functionality derived from the analysis of the content management systems is described using the vocabulary provided by the functional framework. Coverage of the concepts in the existing systems is verified. The utility of the framework is validated through the creation of a prototype that implements sufficient functionality to support a set of specific use cases.

This paper gives an overview of the mathematical and practical tools that can be used in turbo-machinery RANS simulation to realize unstructured mesh adaptation. It first presents the concept of metric and recalls that the hessian of the physical flow properties can become, thanks to small modifications, both a metric and a upper bound of the P1 projection error. The resulting metric is then studied on a simple 2D case. In a second part, the industrial application of this concept is addressed and the tools used to overcome the turbo-machinery specificities are explained. Finally, some 2D and 3D results are presented.

Though exploring one’s family lineage through genealogical family trees can be insightful to developing one’s identity, this knowledge is typically held behind closed doors by private companies or require expensive technologies, such as DNA testing, to uncover. With the ever-booming explosion of data on the world wide web, many unstructured text documents, both old and new, are being discovered, written, and processed which contain rich genealogical information. With access to this immense amount of data, however, entails a costly process whereby people, typically volunteers, have to read large amounts of text to find relationships between people. This delays having genealogical information be open and accessible to all. This thesis explores state-of-the-art methods for relation extraction across the genealogical and biomedical domains and bridges new and old research by proposing an updated three-tier system for parsing unstructured documents. This system makes use of recently developed and massively pretrained transformers and fine-tuning techniques to take advantage of these deep neural models’ inherent understanding of English syntax and semantics for classification. With only a fraction of labeled data typically needed to train large models, fine-tuning a LUKE relation classification model with minimal added features can identify genealogical relationships with macro precision, recall, and F1 scores of 0.880, 0.867, and 0.871, respectively, in data sets with scarce (∼10%) positive relations. Further- more, with the advent of a modern coreference resolution system utilizing SpanBERT embeddings and a modern named entity parser, our end-to-end pipeline can extract and correctly classify relationships within unstructured documents with macro precision, recall, and F1 scores of 0.794, 0.616, and 0.676, respectively. This thesis also evaluates individual components of the system and discusses future improvements to be made.

Difficulties exist with designing and testing on a model scale. The purpose of this study is to examine variations in the flow field of a submarine due to hull/propulsor interaction and Reynolds scaling. The scope of this study includes the simulation of the flow past a 1) five-bladed marine propeller with 0° skew, 2) unappended submarine hull, 3) forward propelled submarine with asymmetrical stern appendages, and 4) submarine in crashback with asymmetrical stern appendages. The bare hull simulations are conducted for three different length scales: small model scale, large model scale, and full scale. The isolated propeller and appended submarine simulations are conducted on the large model scale. It is of interest how sensitive the various flow characteristics are to Reynolds number and the turbulence model. All simulations are at 0° angle of attack, and validated with experimental data where available.

Recent research shows that liquid crystals can be used to report the presence of different types of substances through optical amplication of ligand-receptor binding. In this work, simulations based on a coarse-grained method have been performed to study a class of liquid-crystal-based sensors. A tensor order parameter was used to model the liquid crystalline system and the Beris-Edwards formulation was employed to obtain the time evolution of a liquid crystal medium containing particles. The simulation cases are built using three-dimensional unstructured meshes and the simulation geometries studied include simple models involving spheres as well as detailed modeling for a protein. The dynamics of a liquid crystal medium confined between two solid walls has been studied in the presence of spherical particles and a representative biological macromolecule. Comparisons of steady state and transient solutions from the present study with corresponding results from molecular dynamics based simulations in the literature yield good agreements.

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This research provides an integrated approach of applying innovative ensemble learning techniques that has the potential to increase the overall accuracy of classification models. Actual structured and unstructured data sets from industry are utilized during the research process, analysis and subsequent model evaluations. The first research section addresses the consumer demand forecasting and daily capacity management requirements of a nationally recognized alpine ski resort in the state of Utah, in the United States of America. A basic econometric model is developed and three classic predictive models evaluated the effectiveness. These predictive models were subsequently used as input for four ensemble modeling techniques. Ensemble learning techniques are shown to be effective. The second research section discusses the opportunities and challenges faced by a leading firm providing sponsored search marketing services. The goal for sponsored search marketing campaigns is to create advertising campaigns that better attract and motivate a target market to purchase. This research develops a method for classifying profitable campaigns and maximizing overall campaign portfolio profits. Four traditional classifiers are utilized, along with four ensemble learning techniques, to build classifier models to identify profitable pay-per-click campaigns. A MetaCost ensemble configuration, having the ability to integrate unequal classification cost, produced the highest campaign portfolio profit. The third research section addresses the management challenges of online consumer reviews encountered by service industries and addresses how these textual reviews can be used for service improvements. A service improvement framework is introduced that integrates traditional text mining techniques and second order feature derivation with ensemble learning techniques. The concept of GLOW and SMOKE words is introduced and is shown to be an objective text analytic source of service defects or service accolades. / Ph. D.

The availability of high performance computing clusters has allowed scientists and engineers to study more challenging problems. However, new algorithms need to be developed to take advantage of the new computer architecture (in particular, distributed memory clusters). Since the solution of linear systems still demands most of the computational effort in many problems (such as the approximation of partial differential equation models) iterative methods and, in particular, efficient preconditioners need to be developed. In this study, we consider application of incomplete LU (ILU) preconditioners for finite element models to partial differential equations. Since finite elements lead to large, sparse systems, reordering the node numbers can have a substantial influence on the effectiveness of these preconditioners. We study two implementations of the ILU preconditioner: a stucturebased method and a threshold-based method. The main emphasis of the thesis is to test a variety of breadth-first ordering strategies on the convergence properties of the preconditioned systems. These include conventional Cuthill-McKee (CM) and Reverse Cuthill-McKee (RCM) orderings as well as strategies related to the physical distance between nodes and post-processing methods based on relative sizes of associated matrix entries. Although the success of these methods were problem dependent, a number of tendencies emerged from which we could make recommendations. Finally, we perform a preliminary study of the multi-processor case and observe the importance of partitioning quality and the parallel ILU reordering strategy. / Master of Science

A finite-volume procedure, comprising a gradient-reconstruction technique and a multidimensional limiter, has been proposed for upwind algorithms on unstructured grids. The high-resolution strategy, with its inherent dependence on a wide computational stencil, does not suffer from a catastrophic loss of accuracy on a grid with poor connectivity as reported recently is the case with many unstructured-grid limiting procedures. The continuously-differentiable limiter is shown to be effective for strong discontinuities, even on a grid which is composed of highly-distorted triangles, without adversely affecting convergence to steady state. Numerical experiments involving transient computations of two-dimensional scalar convection to steady-state solutions of Euler and Navier-Stokes equations demonstrate the capabilities of the new procedure.