The purpose of this paper is to examine the interactions between two plant species endemic to Florida and develop a model for the growth of one of the plant species. An equation for the growth of Hypericum cumulicola is developed through analyzing how the distance to and the height of the nearest Ceratiola ericoides (Florida rosemary) affects the growth of Hypericum cumulicola. The hypericums were separated into five separate regions according to the distance to the nearest rosemary plant. The parameters for a basic growth equation were obtained in each of the five regions and compared to each other along with the average deviations in each of the five regions. Analysis of the five separate regions aided in the creation of different growth equations that each encompassed all of the regions together. Four different growth equations are developed and then compared and analyzed for their accuracy.

<p> Two methods of approach are presented for the analysis of digitized data containing instrument response effects. The first method corrects the data vector by means of multiplication by the inverse response matrix. In order to accomodate large size data fields, a special effort is made to obtain expressions for the inverse. matrix elements in closed form. Reduction of statistical uncertainties is accomplished by application of non-negativity conditions. The second approach is based on the method of least squares. Applications to two-dimensional coincidence spectra and nonlinear model functions are discussed in some detail. Although the main emphasis is placed on analysis of nuclear spectra, the techniques presented need· not be limited to this application alone. </p> / Thesis / Doctor of Philosophy (PhD)

One of the major challenges of implementing additive manufacturing (AM) processes for the purpose of production is the lack of understanding of its underlying process-structure-property relationship. Parts manufactured using AM technologies may be too inconsistent and unreliable to meet the stringent requirements for many industrial applications. The first objective of the present research is to characterize the underlying thermo-physical dynamics of AM process, captured by melt pool signals, and predict porosity during the build. Herein, we propose a novel porosity prediction method based on the temperature distribution of the top surface of the melt pool as the AM part is being built. Advance data analytic and machine learning methods are then used to further analyze the 2D melt pool image streams to identify the patterns of melt pool images and its relationship to porosity. Furthermore, the lack of geometric accuracy of AM parts is a major barrier preventing its use in mission-critical applications. Hence, the second objective of this work is to quantify the geometric deviations of additively manufactured parts from a large data set of laser-scanned coordinates using an unsupervised machine learning approach. The outcomes of this research are: 1) quantifying the link between process conditions and geometric accuracy; and 2) significantly reducing the amount of point cloud data required for characterizing of geometric accuracy.

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From the case studies presented, one could conclude that for large values of n the standard deviation of r, the usual estimator of the correlation coefficient, and its transform z are only negligibly affected by variation in skewness or variation in kurtosis, the effect being slightly greater for variation in kurtosis. When the variations are in both skewness and kurtosis, the standard deviation of r and of Z are more affected by non-normality, a few significantly so. In small samples (n=10, n=5) the standard deviations of r and,z are quite visibly larger for variations in skewness and variations in kurtosis. The effect is greater for the simultaneous variation of the two. However, all of the values fall within a 95% confidence interval. It would appear then that the increase in the standard deviation of rand z is due more to the natural rise of the standard deviation in small samples rather than to non-normality. Viewing the studies made in totality we may in final conclusion state that the effect of non-normality on the standard deviation of r for samples of any size is not significant enough for concern; i.e., from this Monte Carlo study we will state that the standard deviation of the sample correlation coefficient is robust to the assumption of normality. / Master of Science

by Ma Yiguang. / "April 2000." / Thesis (Ph.D.)--Chinese University of Hong Kong, 2000. / Includes bibliographical references (p. 117-[124]). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese.

La théorie des grandes déviations traite des comportements asymptotiques d'évènements rares. C'est le langage moderne de la physique statistique d'équilibre, qui semble offrir un cadre naturel pour une extension hors équilibre. Nous présentons dans cette thèse plusieurs applications, analytiques et numériques, de cette théorie dans différents contextes. D'abord, nous montrons comment localiser numériquement des trajectoires de chaoticité atypique de systèmes dynamiques complexes. Nous étendons ensuite l'algorithme présenté à une classe de systèmes et d'observables plus large. La deuxième partie de cette thèse montre sur un exemple comment le calcul de fonctions de grandes déviations d'un système hors équilibre peut parfois être ramené à un calcul d'équilibre. La dernière partie traite des chemins de réactions en chimie et de leur détermination numérique. Le formalisme introduit repose sur la supersymétrie de l'équation de Fokker-Planck et redonne naturellement la théorie de Morse.

Thesis--New York University. / Photocopy of typescript. Ann Arbor, Mich. : University Microfilms International, 1977. -- 21 cm. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 73-80).

While most western hydropower sites are already developed or protected by legislation, the aging hydropower park requires refurbishment actions. Especially to tackle the challenges of an increased fluctuation at the grid coming from the expansion of other renewable energy sources such as wind power. The company Fortum is carrying out a number of major refurbishment projects every year and want to enhance their time performance during the outage. Delayed projects are resulting in unexpected costs and production losses. By investigating 20 historical refurbishment outages from a project manager perspective, the delays are related to different sections of the outage time. These sections are referred to as work packages, meaning a set of activities related to a functional part of the plant. The material is based on interviews and project documentation. The outages are divided into three groups depending on the amount of delay and some additional set of factors are used in a comparison. The results are discussed from a time management and a multi-project perspective. The study can be viewed as an initial study to address time management challenges in a company. The methodology proves to be an efficient way to get time management challenges at a company to the surface. The results displays late manufacturing deliveries and overruns of assembly and erection durations as the most common reasons of delay. A number of potential success/failure factors are suggested. It is also pointed out that small projects are at risk being more delayed compared to larger ones.