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D.Ing. / Conventional traffic models used for the analysis of packet-switched data are Markovian in nature and are based on assumptions, such as Poissonian arrivals. The introduction of packet oriented networks has resulted in an influx of information highlighting numerous discrepancies from these assumptions. Several studies have shown that traffic patterns from diverse packet-switched networks and services exhibit the presence of properties such as self-similarity, long-range dependencies, slowly decaying variances, "heavy tailed" or power law distributions, and fractal structures. Heavy Tailed distributions decay slower than predicted by conventional exponential assumptions and lead to significant underestimation of network traffic variables. Furthermore, it was shown that the statistical multiplexing of multiple packet-switched sources do not give rise to a more homogenous aggregate, but that properties such as burstiness are conserved. The results of the above mentioned studies have shown that none of the commonly used traffic models and assumptions are able to completely capture the bursty behaviour of packet- and cellbased networks. Artificial Intelligent methods provide the capability to extract the inherent characteristics of a system and include soft decision-making approaches such as Fuzzy Logic. Adaptive methods such as Fuzzy Logic Self-learning algorithms have the potential to solve some of the most pressing problems of traffic Modelling and Management in modern packet-switched networks. This dissertation is concerned with providing alternative solutions to the mentioned problems, in the following three sub-sections; the Description of Heavy Tailed Arrival Distributions, Timeseries Forecasting of bursty Traffic Intensities, and Management related Soft Decision-Making. Although several alternative methods, such as Kalman Filters, Bayesian Distributions, Fractal Analysis and Neural Networks are considered, the main emphasis of this work is on Fuzzy Logic applications.

The wet weather pattern since the early 1990's has created two problems for the people living in the Red River Valley (RRV): (1) wet field conditions for farmers and (2) more frequent major spring floods in the Red River system. Farmers in the region are increasingly adopting subsurface drainage practice to remove excess water from their fields to mitigate the first problem. However, it is not clear whether subsurface drainage will deteriorate or mitigate the spring flood situation, the second problem. The Soil and Water Assessment Tool (SWAT) model was applied to evaluate the impacts of tile drainage on the Red River's streamflows. The model was calibrated and validated against monthly streamflows at the watershed scale and against daily tile flows at the field scale. The locations and areas of the existing and potential tile drained (PTD) areas were identified using a GIS based decision tree classification method. The existing and maximum PTD areas were found to be about 0.75 and 17.40% of the basin area, respectively. At the field scale, the range of Nash-Sutcliffe efficiency (NSE) for model calibration and validation was 0.34-0.63. At the watershed scale, the model showed satisfactory performance in simulating monthly streamflows with NSE ranging from 0.69 to 0.99, except that the model under-predicted the highest spring flood peak flows in three years. The results of modeling a 100% tiled experimental field showed that about 30-40% of water yield was produced as tile flow. Surface runoff and soil water content decreased about 34% and 19%, respectively, due to tile drainage. However, the impact of subsurface drainage on evapotranspiration (ET) and water yield was mixed. ET slightly decreased in a wet year and slightly increased in a dry year, while the pattern for water yield was opposite to that of ET. The watershed-scaled modeling results showed that a tiling rate of 0.75-5.70% would not have significant effects on the monthly average streamflows in the Red River at Fargo. For the 17.40% tiling rate, the streamflow in the Red River at Fargo might increase up to 1% in April and about 2% in Fall (September to November), while decreasing up to 5% in the remaining months. This SWAT modeling study helped to better understand the impact of subsurface drainage on the water balance and streamflows in the Red River of the North basin. The findings will also help watershed managers in making decisions for the purpose of managing agricultural drainage development in the RRV and other snow dominated watersheds around the world.

Simulation in nursing education involves an instructor observing students performing a specific task using an anatomical model, standardized patients, or high-fidelity simulation using a simulator. This education method allows creation of realistic clinical scenarios to allow nurses to perform tasks in a safe learning environment. However, accurate performance in the simulation lab must be replicated in the clinical environment to impact patient outcomes. Chapter 2 is a systematic literature review resulting in 21 articles addressing dependent variables, conceptual frameworks, study designs and outcomes measured in new nurses. Knowledge, simulation satisfaction, confidence, and clinical performance were common simulation outcomes studied. Only 7 research articles reported using a theoretical framework. Even though most of the studies used self-reported measures to demonstrate improvement in the studied variables after simulation, none of the studies used objective observation in the actual clinical setting after simulation. Assessing performance in the actual clinical environment after simulation is an opportunity to link simulation education to patient outcomes. Researchers should explore if learned skills in simulation are transferred to the real clinical setting resulting in safe and competent care. This gap in the literature was the impetus for the study described in Chapter 3. Chapter 3 focuses on a quasi-experimental study on new nurses hired at an oncology institution to determine if skills learned in the simulation lab transferred to the clinical setting. The study’s aim addressed if a difference exists in clinical observation scores between the intervention group (involved in simulation) and control group (not involved in simulation) when performing a central line dressing change. Fifty-six participants were consented with 19 nurses completing all data collection points. No significant differences were identified between groups. Chapter 4 is a narrative account applying the concept of resilience to the doctoral dissertation experience during a pandemic. Using Richardson’s Resiliency Model, the challenges of performing research on staff development during a pandemic are presented. The presence of a stressor (pandemic) and the resulting stages of disruption and reintegration are described resulting in resilient reintegration during the doctoral research process. Simulation is an educational methodology in academia and staff development. However, outcomes such as confidence, knowledge and simulation satisfaction are over-studied and there is a need for studies to focus on if skills performed during simulation are replicated accurately in the clinical setting. In addition, simulations should be designed to ensure they are addressing the identified construct (e.g. clinical judgement). In addition, the logistics involved in observing practice at the point of care is challenging due to unpredictable factors such as staffing, patient acuity, and operational decisions that may impact study design present unique challenges. Keywords: Simulation, new nurses, resilience

The effects of interphase coupling in a multiphase power system become important in the presence of network imbalances and unbalanced phase loadings. In grounded-wye systems, currents that flow in the earth can have significant effects on the system's behavior. Both these effects must be considered in an accurate multiphase power system model. A new treatment of multiphase power system modeling is presented. The treatment relies upon linear graph theory and produces a system multiport model. Mutual coupling effects, the effects of neutral and static conductors, the finite conductivity of earth, and various component models are considered. A reduction in the order of the multiport model also is presented. Multiphase load flow analysis is introduced. Special considerations that arise in multiphase analyses are discussed. Example solutions are presented. A convenient method of representing multiple slack ports is described which results from an application of the principle of superposition. Circulating power flow in multiphase loops is discussed. A procedure is proposed for conveniently representing common shunt and series faults that occur in power systems. The procedure is constructed for efficient computer modeling of multiple cases of various fault combinations in a particular system. / Doctor of Philosophy

The coal fields of Appalachia of many contiguously mineable coal seams. Common practice in mining multiple seams is to extract the seams in a descending order. However, the mining sequence may still be based on seam ownership, availability of the seam and the general economic situation, net on ground control considerations. One of the major ground control mechanisms that must be considered in the design of a mine is the arching of stresses around a previously mined lower seam. This investigation deals with the extent and magnitude of the stresses above an underground opening. The finite element approach was utilized to determine the extent and magnitude of stresses for various widths of mine opening, depths of cover and overburden material. This information can then be used as an approximation of the stresses that may be encountered in an actual mining situation. / Master of Science

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In a new approach used to model quench tests conducted with long cylindrical specimens in fluids, heat transfer within the fluid is described by the heat conduction equations while heat convection is neglected. Analytical solutions for the temperatures and stresses as functions of the time and of the radial coordinate in the cylindrical specimen are presented and the maximum tensile stresses in the specimen are shown to depend on the ratio of thermal conductivities and on the ratio of volumetric heat capacities in the specimens and the quench bath. Plots of maximum tensile stresses are given for a wide range of these ratios and plots of temperatures, stresses and heat fluxes are included for five conductivity ratios. It was found that the maximum stresses predicted by this analysis are in satisfactory agreement with experimental results in the literature for high conductivity fluids, while a larger discrepancy was noted for low conductivity fluids. / M.S.

Statistical extrapolation is required to predict extreme loads, associated with a target return period, for offshore wind turbines. In statistical extrapolation, “short-term" distributions of the load random variable(s) conditional on the environment are integrated with the joint probability distribution of environmental random variables (from wind, waves, current etc.) to obtain the so-called “long-term" distribution, from which long-term loads may be obtained for any return period. The accurate prediction of long-term extreme loads for offshore wind turbines, using efficient extrapolation procedures, is our main goal. While loads data, needed for extrapolation, are obtained by simulations in a design scenario, field data can be valuable for understanding the offshore environment and the resulting turbine response. We use limited field data from a 2MW turbine at the Blyth site in the United Kingdom, and study the influence of contrasting environmental (wind) regimes and associated waves at this site on long-term loads, derived using extrapolation. This study also highlights the need for efficient extrapolation procedures and for modeling nonlinear waves at sites with shallow water depths. An important first step in extrapolation is to establish robust short-term distributions of load extremes. Using data from simulations of a 5MW onshore turbine model, we compare empirical short-term load distributions when two alternative models for extremes--global and block maxima--are used. We develop a convergence criterion, based on controlling the uncertainty in rare load fractiles, which serves to assess whether or not an adequate number of simulations has been performed. To establish long-term loads for a 5MW offshore wind turbine, we employ an inverse reliability approach, which is shown to predict reasonably accurate long-term loads, compared to a more expensive direct integration approach. We show that blade pitching control actions can be a major source of response variability, due to which a large number of simulations may be required to obtain stable tails of short-term load distributions, and to predict accurate ultimate loads. We address model uncertainty as it pertains to wave models. We investigate the effect of using irregular nonlinear (second-order) waves, compared to irregular linear waves, on loads for an offshore wind turbine. We incorporate this nonlinear irregular wave model into a procedure for integrated wind-wave-response analysis of offshore wind turbines. We show that computed loads are generally somewhat larger with nonlinear waves and, hence, that modeling nonlinear waves is important is response simulations of offshore wind turbines and prediction of long-term loads. / text

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