An interface-preserving level set method is incorporated into the Reynolds- Averaged Navier-Stokes (RANS) numerical method for the time-domain simulation of green water effects. This generalized method can be used to evaluate two- and three-dimensional, laminar and turbulent, free surface flows in moving non-orthogonal grids. In the method, free surface flows are modeled as immiscible two-phase (air and water) flows. A level set function is used to mark the individual fluids and the free surface itself is represented by the zero level set function. The level set evolution equation is coupled with the conservation equations for mass and momentum, and solved in the transformed plane. Chimera domain decomposition technique is employed to handle embedding, overlapping, or matching grids. To demonstrate the feasibility of the method, calculations are performed in several bench mark free surface flows including dam break flows, free jets, solitary wave propagations and the impingement of dam break flow on a fixed structure. The comparisons between the simulations and the experimental data provide a thorough validation of the present method. The results also show the potential capability of level-set RANS method in much more complicated free surface flow simulations. After validations, the method is applied to simulate sloshing flows in LNG tank and green water over the platform. In sloshing flows, the level-set RANS method captures the large impact pressure accurately on LNG tank walls. It also generates a plunging breaker successfully in front of a platform in the numerical wave tank. The good agreements between numerical and experimental results prove the level set RANS method is a powerful and accurate CFD methodology in free surface flow simulations.

The purpose of this study was to determine if applying set induction to the beginning of a lesson would have an effect on student knowledge, attitude, and/or engagement levels throughout the lesson. Researchers addressed specific objectives in this report to determine if using a set induction at the beginning of a lesson will have a significant effect on students? attained knowledge levels, to determine if using a set induction at the beginning of a lesson will have a significant effect on students? attitudinal perspectives towards the lesson, to determine if using a set induction at the beginning of a lesson will have a significant effect on student?s engagement levels throughout the lesson. To address these objectives, a quasi?experimental study using four separate agricultural education classrooms was conducted. Descriptive and comparative analyses were then performed on all participating groups. The results of this study illustrate the following conclusions: (a) there is no significant difference in student knowledge between the treatment and control groups; (b) there is no significant difference in student attitude between the treatment and control groups; (c) the group that received the set induction was more engaged than the group that did not. These findings have implications to agricultural educators in designing lesson plans that can better engage students from the start. The results are also significant to researchers in this field in that they may choose to further their studies in this area of focus. Additional collected data could be to be extremely useful to those studying the use and effects of set inductions in classroom methodologies.

Over the past few years, a considerable number of studies have been made on Support Vector Machines (SVMs) in many domains to improve classification or prediction. However, SVMs request high computational time and memory when the datasets are large. Although incremental learning techniques are viewed as one possible solution developed to reduce the computation complexity of the scalability problem, few studies have considered that some examples close to the decision hyperplane other than support vectors (SVs) might contribute to the learning process. Consequently, we propose three novel algorithms, named Mixed Incremental learning (MIL), Half-Mixed Incremental learning (HMIL), and Partition Incremental learning (PIL), by improving Syed¡¦s incremental learning method based on fuzzy set theory. We expect to achieve better accuracy than other methods. In the experiments, the proposed algorithms are investigated on five standard machine learning benchmark datasets to demonstrate the effectiveness of the method. Experimental results show that HIL have superior classification accuracy than the other incremental or active learning algorithms. Especially, for the datasets that might have high accuracy in other research reports, HMIL and PIL could even improve the performance.

Thesis (Ph. D.)--York University, 1999. Graduate Programme in Mathematics and Statistics. / Typescript. Includes bibliographical references (leaves 87-93). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL: http://wwwlib.umi.com/cr/yorku/fullcit?pNQ43427.

Thesis (M.S.)--University of Alabama at Birmingham, 2009. / Title from PDF title page (viewed Jan. 21, 2010). Additional advisors: Yulia Karpeshina, Ian Knowles, Rudi Weikard. Includes bibliographical references (p. 19).

Thesis (Ph. D.)--State University of New York at Buffalo, 1998. / "April 1998." Includes bibliographical references (leaves 144-153). Also available in print.

Proefschrift--Utrecht. / "Stellingen" (2 L.) laid in.

Thesis (Ph. D.)--University of Missouri-Columbia, 2003. / Typescript. Vita. Includes bibliographical references (leaves 99-101). Also available on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 2003. / Typescript. Vita. Includes bibliographical references (leaves 99-101). Also available on the Internet.

Thesis (Ph.D)--Mathematics, Georgia Institute of Technology, 2009. / Committee Chair: Croot, Ernie; Committee Member: Costello, Kevin; Committee Member: Lyall, Neil; Committee Member: Tetali, Prasad; Committee Member: Yu, XingXing. Part of the SMARTech Electronic Thesis and Dissertation Collection.