The current well planning practice is usually done section by section with limited help of some knowledge-based tools. This thesis presents an integrated approach and a software prototype developed for well planning. It considers the geological input, i.e. pore pressure, over burden etc., to generate a step by step interactive drilling plan. The implemented well planning stages include the casing setting depth, casing and hole size determination, casing selection and then drill string design and modeling. The system is integrated with a Computer Aided Design (CAD) system for generating three-dimensional parametric model. The conceptual design and CAD modeling system are integrated in such a way that any changes in the design will be reflected to the CAD model. Such intelligent CAD design practice is new in the drilling industry. An Operational Parameters module is also attached with the system to predict the drilling coefficients by using offset well data and determine the optimum weight on bit, and the drill string rotation that minimizes drilling cost per foot for a single bit run. Based on this approach, an integrated well planning system can be fully developed and it will be very useful for the decision making of drilling companies.

This study was part of the development of an ice jam flood forecasting system for Hay River, NWT. 2-D numerical models were used to simulate ice processes in an effort to predict ice jam formation. A summer survey was conducted to finalize the bathymetry of the Hay River Delta. Observations were undertaken during freeze-up and the winter to better understand Hay River ice conditions. Ice jam events were surveyed during breakup in 2008 and 2009 for model testing. The data collected was used to develop CRISSP2D and River2D models to simulate observed conditions. Simple tests were conducted with CRISSP2D to better understand the model inputs. CRISSP2D modeling of Hay River was unsuccessful and it limitations were discussed. River2D was able to match observed ice jam profiles. The results were used to create an Ice Jam Profile Generator to assist the Town of Hay River with evacuation planning. / Water Resources Engineering

Prostate Cancer (PC) is associated with disease- and treatment-related side effects that can compromise quality of life (QoL). Psychosocial interventions designed to improve adjustment and quality of life (QoL) for post-treatment PC patients have been conducted with mixed results. Intervention effects are typically analyzed using either mean difference scores or a single estimate of growth parameters (e.g., intercept and slope factors) across groups. These methods assume homogeneity within groups. Evidence suggests, however, considerable variability both in the experience of disease-specific outcomes and in the long-term adjustment and emotional well-being of men with PC. The present study used growth mixture modeling (GMM) to explore the effects of a cognitive behavioral stress management (CBSM) intervention on emotional well-being among men recently treated for localized PC. This methodology allowed examination of intervention effects across unobserved subgroups characterized by different trajectories of emotional well-being and identified factors associated with intervention efficacy.

Responding to Joint Attention (RJA) involves an infant's ability to follow a gaze or point by a partner. Prenatal cocaine exposure (PCE), which places a child in danger of numerous risks, has been accepted as having subtle effects on developmental outcomes such as social competence and associated socio-emotional outcomes. The current study looked at a sample of 166 children prenatally exposed to cocaine who were attending an early intervention program. The study established group and individual trajectories of responding to joint attention from 12, 15, and 18 months of age. Hierarchical modeling identified two groups, a delay group and an average group, while individual trajectories identified a linear pattern of growth of RJA. Both individual and group trajectories indicated that children with higher RJA from 12 to 18 months demonstrated better social competence at three years of age and first grade. The delay and average group showed significant differences on later social competence measures, but not problem behaviors, such that RJA, a positive behavior, may connect more closely with later positive behaviors than with behavior problems. RJA may therefore be useful in a preventative intervention targeted at enhancing positive social behaviors and as an important and simple screening tool for possible delay early in a child's life, helping to deliver early intervention services in a targeted and effective manner.

Understanding coral disease dynamics within the heterogeneous populations in which they act is critical for predicting how the structure of reefs may change as a result of enzootic or epizootic levels of these important sources of mortality. This work focused on combining field studies and the development and testing of a spatially-explicit, individual-based epizootiological computer model with the aim of gaining a greater understanding of the dynamics and impact of white plague, a significant source of mortality on reef-building corals in the Caribbean region. Field studies focused on the incidence and distribution of all sources of coral mortality, including suspect white plague in situ, at two locations; the Florida Keys (United States of America) and Little Cayman Island (Cayman Islands, British West Indies). Results indicated that in both regions disease was the most significant source of mortality during the monitoring time periods, and that suspect white plague type II in Cayman is likely contributing to major structural changes. In Florida, observations made during a mass bleaching event indicated that a significant relationship exists between bleaching severity and disease incidence, and that mortality during the event was largely the result of disease and not bleaching. The simulation model was developed using a long-term data set from Little Cayman, and results of calibration indicated that suspect white plague type II on these reefs is transmissible between colonies within a limited field and require a yearly input from an outside source, and that host susceptibility to infection is low and likely not variable among species. Parameters describing the distribution and composition of the coral population were varied, and results indicated a significant effect of colony density, aggregation, and mean size on the impact of disease. Scenario testing of various disease management strategies indicated that should local prevention measures be developed in the future, it is they, and not treatment, that will likely be the most effective in limiting the impact of disease.

This study integrated variable- and child-centered techniques to investigate trajectories of four learning behaviors (initiative, persistence, planning, and problem-solving flexibility) and their influence on Head Start preschoolers' academic school readiness. Variable-centered findings revealed differential, quadratic growth trajectories for each of the four learning behaviors. However, where children began the year (intercept), how much they changed across the year (slope), and how much their rate of change changed across the year (quadratic) differed depending on the learning behavior. Initiative and problem-solving flexibility emerged as significant predictors of end-of-year academic school readiness skills, controlling for persistence and planning. There was no evidence of moderation of the relations between learning behaviors and academic skills by child demographic characteristics. Child-centered results provided a more nuanced description of the development of these four learning behaviors. Analyses suggested there may be subgroups of children with different developmental trajectories for each of the four learning behaviors and that these subgroups have significantly different school readiness skills at the end of the year. These findings help extend our current understanding of learning behaviors and, if replicated, may inform the content and timing of early childhood teaching practices and interventions.

If an estuarine system is to be investigated using an oceanographic modeling approach, a decision must be made whether to use a simple and robust framework based on e.g. mass-balance considerations, or if a more advanced process-resolving three-dimensional (3-D) numerical model are necessary. Although the former are straightforward to apply, certain fundamental constraints must be fulfilled. 3-D modeling, even though requiring significant efforts to implement, generates an abundance of highly resolved data in time and space, which may lead to problems when attempting to specify the "representative state" of the system, a common goal in estuarine studies. In this thesis, different types of models suitable for investigating estuarine systems have been utilized in various settings. A mass-balance model was applied to investigate potential changes of water fluxes and salinities due to the restoration of a mangrove estuary in northern Colombia. Seiches, i.e. standing waves, in the Baltic Sea were simulated using a 2-D shallow-water model which showed that the dominating harmonic oscillation originates from a fjord seiche in the Gulf of Finland rather than being global. Another study pertaining to the Gulf of Finland used velocity-fields from a 3-D numerical model together with Lagrangian-trajectory analyses to investigate the mixing dynamics. The results showed that water from the Baltic proper is mixed with that from the river Neva over a limited zone in the inner parts of the Gulf. Lagrangian-trajectory analysis was finally also used as a tool to compare mass-balance and 3-D model results from the Gulf of Riga and the Bay of Gdansk, highlighting when and where each method is applicable. From the present thesis it can be concluded that the above described estuarine-modeling approaches not only require different levels of effort for their implementation, but also yield results of varying quality. If oceanographic aspects are to be taken into account within Integrated Coastal Zone Managment, which most likely should be the case, it is therefore important to decide as early as possible in the planning process which model to use, since this choice ultimately determines how much information about the physical processes characterizing the system the model can be expected to provide.

The in-stream water quality model, QUAL2Kw, can provide guidance in watershed management decisions by linking changes in nutrient loads to responses in water quality. This model is particularly useful for determining wasteload allocations, aiding in total maximum daily load analyses, and developing numeric nutrient criteria. Unfortunately, states struggle to balance the data collection and modeling requirements to accomplish many of these water quality management tasks due to limited resources. This commonly results in routine data collection and monitoring efforts that do not satisfy the data requirements for modeling. To address this disconnect, this study presents a data collection and parameter calibration methodology suited to meet general QUAL2Kw modeling requirements. Then, with the goal of identifying a range of numeric nitrogen and phosphorus criteria, this general data collection and modeling strategy was applied to sites throughout Utah. To help automate and test scenarios targeted at tracking effects of loading and response combinations, a nutrient criteria tool was also developed to interface with these QUAL2Kw models. By implementing the tool on these models, input concentrations of ammonium (NH4+) ranging from 10 to 101 µg/L and inorganic phosphorus (PO4-) ranging from 1 to 14 µg/L were found to exceed thresholds of bottom algae, gross primary productivity, and ecosystem respiration. Conversely, NH4+ concentrations above 3,500 µg/L and PO4- above 490 µg/L exceeded dissolved oxygen thresholds of 5-6 mg/L in some applications. Some limitations of using mechanistic models in this manner were identified, including model capabilities (e.g., steady-state versus dynamic), inclusion of appropriate processes, uncertainty in calibrated parameters, and site-specific conditions. Although many problems will require more complex modeling efforts with significantly larger data collection efforts, this approach provides an initial framework that aids in the judicial use of resources to aid in watershed management decisions.

In recent years, extensive studies of many interesting aspects of online community dynamics promoted a better understanding of this area. One of the most challenging problems facing builders of online communities is the design of incentive mechanisms that can ensure user participation. However, running online community experiments in the real world is expensive, and requires a great deal of motivation from users. In this thesis two major approaches are explored: system dynamics modeling and agent-based modeling, to simulate the overall behaviours of participants in online communities. Although these models are developed by using two different methodologies, both of them can provide insights into the user motivation process, incentive mechanism evaluation and community development. The target online community for my study is called Comtella, which is used in several senior Computer Science classes in the Department of Computer Science, University of Saskatchewan. Simulation models for the Comtella online community have been developed and the simulation results are useful to provide future directions for incentive mechanism improvement.

Molecular docking is a computational tool commonly applied in drug discovery projects and fundamental biological studies of protein-ligand interactions. Traditionally, molecular docking is used to address one of three following questions: (i) given a ligand molecule and a protein receptor, predict the binding mode (pose) of the ligand within the context of a receptor, (ii) screen a collection of small-molecules against a receptor and rank ligands by their likelihood of being active, and (iii) given a ligand molecule and a target receptor, predict the binding affinity of the two. Here, we focus on the first two questions, namely ranking and pose prediction. Currently, state-of-the-art docking algorithms predict poses within 2A of the native pose in a rate lower than ∼60% and in many cases, below 40%. In ranking, their ability to identify active ligands is inconsistent and generally suffers from high false-positive rate. In this thesis we present novel algorithms to enhance the ability of molecular docking to address these two questions. These algorithms do not substitute traditional docking but rather being applied on top of them to provide synergistic effect. Our algorithms improve pose predictions by 0.5-1.0A and ranking order for 23% of the targets in gold-standard benchmarks. As importantly, the algorithms improve the consistence of the posing and ranking predictions over diverse sets of targets and screening libraries. In addition to the posing and ranking, we present the pharmacophore concept. A pharmacophore is an ensemble of physiochemical descriptors associated with a biological target that elucidates common interaction patterns of ligands with that target. We introduce a novel pharmacophore inference algorithm and demonstrate its utilization in molecular docking. This thesis is outlined as follow. First we introduce the molecular docking approach for pose prediction and ranking. Second, we discuss the pharmacophore concept and present algorithms for pharmacophore inference. Third, we demonstrate the utilization of pharmacophores for pose prediction by re-scoring candidate poses generated by docking algorithms. Finally, we present algorithms to improve ranking by reducing bias in scoring functions employed by docking algorithms.