International trade is one of the leading discussions taken not only in South Africa but worldwide on daily basis. The importance of international trade is that one country can be able to assist the other country to meet its needs. The level of economic growth is important in any country not only in South Africa. The purpose of this study is to examine the impact of foreign trade on economic growth in South Africa. The findings of this study will demonstrate the light about positive and negative effects of international trade on economic growth. The empirical analysis is conducted by using a time series data from 199001 - 201302 quarterly obtained from South African Reserve Bank (SARB) and Organisation of Economic Co-operation Development (OECD). The study follows a Cointegrated vector autoregression (CVAR) which contains the following: Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests for stationarity. The model is also taken through the Johansen cointegration test and Vector error correction model (VECM). VECM approach will be followed if cointegration amongst the variables has been established. The findings of the study are that all variables have unit root. The cointegration model emphasizes the long run equilibrium relationship between dependent and independent variables. The empirical results for the Johansen cointegration test reject the null hypothesis of no cointegration and suggest the presence of a long term relationship among all the variables. Empirical investigation reveals that three variables such as inflation rate , export and exchange rates are positively related to GOP while other one variable such as import is negatively related to GOP. The conclusion drawn from this work is that there is a correlation amongst GOP and its independent variable. This dissertation recommends that The South African government must start strengthening the competiveness of export by making sure that it is always balanced with the import. / Thesis (M.Sc. (Economics) North-West University, Mafikeng Campus, 2013

Detection and localization of sound sources in an ocean environment can be achieved with a distributed array of passive acoustic sensors. Utilizing an array of autonomous littoral gliders, which offer long-term and quiet operation, and vector sensors, which measure both acoustic pressure and particle velocity, the array's localization performance can be improved. However, vector sensors are susceptible to errors induced by acoustic noise, and autonomous gliders as a sensor platform introduce positional errors. Through both simulations and at-sea data, the localization performance of four processing methods are evaluated under various noisy conditions. In both simulated and at-sea data results, a new cross-coherent method outperforms traditional methods by mitigating the effects of acoustic noise, provided sufficient positional accuracy of the array elements.

Master of Science / Department of Computing and Information Sciences / William H. Hsu / Alternative splicing is a mechanism for generating different gene transcripts (called iso- forms) from the same genomic sequence. Finding alternative splicing events experimentally is both expensive and time consuming. Computational methods in general, and EST analy- sis and machine learning algorithms in particular, can be used to complement experimental methods in the process of identifying alternative splicing events. In this thesis, I first iden- tify alternative splicing exons by analyzing EST-genome alignment. Next, I explore the predictive power of a rich set of features that have been experimentally shown to affect al- ternative splicing. I use these features to build support vector machine (SVM) classifiers for distinguishing between alternatively spliced exons and constitutive exons. My results show that simple, linear SVM classifiers built from a rich set of features give results comparable to those of more sophisticated SVM classifiers that use more basic sequence features. Finally, I use feature selection methods to identify computationally the most informative features for the prediction problem considered.

Forecasting is used for decision making and unreliable predictions can instill a false sense of condence. Traditional time series modelling is astatistical art form rather than a science and errors can occur due to lim-itations of human judgment. In minimizing the risk of falsely specifyinga process the practitioner can make use of machine learning models. Inan eort to nd out if there's a benet in using models that require lesshuman judgment, the machine learning models Random Forest and Neural Network have been used to model a VAR(1) time series. In addition,the classical time series models AR(1), AR(2), VAR(1) and VAR(2) havebeen used as comparative foundation. The Random Forest and NeuralNetwork are trained and ultimately the models are used to make pre-dictions evaluated by RMSE. All models yield scattered forecast resultsexcept for the Random Forest that steadily yields comparatively precisepredictions. The study shows that there is denitive benet in using Random Forests to eliminate the risk of falsely specifying a process and do infact provide better results than a correctly specied model.

BACKGROUND:Tomato leaf curl Sudan virus (ToLCSDV) is a single-stranded DNA begomovirus of tomato that causes downward leaf curl, yellowing, and stunting. Leaf curl disease results in significant yield reduction in tomato crops in the Nile Basin. ToLCSDV symptoms resemble those caused by Tomato yellow leaf curl virus, a distinct and widespread begomovirus originating in the Middle East. In this study, tomato samples exhibiting leaf curl symptoms were collected from Gezira, Sudan. The associated viral genome was molecularly characterized, analyzed phylogenetically, and an infectious clone for one isolate was constructed.FINDINGS:The complete genomes for five newly discovered variants of ToLCSDV, ranging in size from 2765 to 2767-bp, were cloned and sequenced, and subjected to pairwise and phylogenetic analyses. Pairwise analysis indicated that the five Gezira isolates shared 97-100% nucleotide identity with each other. Further, these variants of ToLCSDV shared their highest nucleotide identity at 96-98%, 91-95%, 91-92%, and 91-92% with the Shambat, Gezira, Oman and Yemen strains of ToLCSDV, respectively. Based on the high maximum nucleotide identities shared between these ToLCSDV variants from Gezira and other previously recognized members of this taxonomic group, they are considered isolates of the Shambat strain of ToLCSDV. Analysis of the complete genome sequence for these new variants revealed that they were naturally occurring recombinants between two previously reported strains of ToLCSDV. Finally, a dimeric clone constructed from one representative ToLCSV genome from Gezira was shown to be infectious following inoculation to tomato and N. benthamiana plants.CONCLUSION:Five new, naturally occurring recombinant begomovirus variants (>96% shared nt identity) were identified in tomato plants from Gezira in Sudan, and shown to be isolates of the Shambat strain of ToLCSDV. The cloned viral genome was infectious in N. benthamiana and tomato plants, and symptoms in tomato closely resembled those observed in field infected tomato plants, indicating the virus is the causal agent of the leaf curl disease. The symptoms that developed in tomato seedlings closely resembled those observed in field infected tomato plants, indicating that ToLCSDV is the causal agent of the leaf curl disease in Gezira.

No description available.

published_or_final_version / Surgery / Doctoral / Doctor of Philosophy

Three distinct plant viruses, transmitted by the tobacco whitefly Bemisia tabaci Genn., were associated with diseased food or fiber crops grown in the southwestern deserts of Arizona. The cotton leaf crumple virus (CLCV), thought to affect only cotton Gossypium (L.) spp., is now known to infect other malvaceous plants and members of the Convolvulaceae and Leguminosae. Results of an experimental host range study suggest that potential virus-vector reservoirs may exist in cotton growing regions which include both weeds and cultivated plants. Geminivirus-like (GVL) particles of ∼18 x 30 nm were isolated for the first time from CLCV-infected bean, Phaseolus vulgaris (L.), 'Red Kidney', a plant which was a better purification host than cotton. Studies of CLCV-vector relationships indicated that the acquisition- and inoculation-access times, latent period and length of retention by whitefly vectors were similar to those of the original isolate reported in California in 1954. When growth chamber temperatures of 26, 32, and 37C were used in virus-vector studies, optimal acquisition and transmission occurred at 32C while temperatures of 37C were lethal to whitefly adults. Two additional virus-like agents were isolated from single and mixed infections of lettuce or melons, respectively. The virus-like agent from lettuce infected primarily members of the Chenopodiaceae, Compositae and Cucurbitaceae, and was whitefly but not mechanically transmissible. Long flexuous closterovirus-like rods of ∼10 x 1400-2000 nm were visualized in extracts prepared from plants inoculated with the lettuce isolate. The isolate was similar to the lettuce infectious yellow virus (LIYV) based upon host range, transmission characteristics and unique particle morphology. Both long flexuous rods like those associated with the lettuce isolate and GVL particles of 18 x 30 nm were associated with diseased melons. The host range of the GVL agent was confined to the Cucurbitaceae and Leguminosae and the agent was separated from the mixed infection by mechanical transmission to a non-LIYV host. The GVL-agent was distinct from previously described cucurbit viruses including the squash leaf curl virus, based upon host range and transmission characteristics and was tentatively designated as the watermelon curly mottle virus (WCMV).

This thesis paper is meant to serve as a supporting document for a thesis exhibition that was held the University of Waterloo Art Gallery. The show consisted of paintings on Plexiglas and sculptural installations with fluorescent lights. <br /><br /> The aesthetic style of my paintings makes a strong reference to the visual vocabulary of computer software. More specifically, it mimics architectural computer vector graphics from the 1980s. There is a visual metaphor created in my paintings where it blueprint drawing has 'evolved' into computer vector graphics, ultimately though, nothing has changed. The images are still hand drafted with pencils and then hand painted. The lexicon of digital software is appropriated, but by transferring the images from the virtual space of the screen to a literal three-dimensional space, the meaning is discarded. They become generalized abstract signs that retain their connotations, but not their meaning and function. The work thus makes a simple point in its refusal to 'get digital. ' There is a fetishization of technology, yet simultaneously a refusal of it. <br /><br /> Other concerns that I deal with in my work and thesis paper, include notions of good and bad taste, kitsch and the Camp aesthetic, science-fiction, nostalgia, representations of the 'future,' Suprematist painting, Minimalism, Design, and the utopian ideals of Modernism.

This thesis is composed of three chapters, which can be read independently. The first chapter investigates how oil price volatility affects the investment decisions for a panel of Japanese firms. The model is estimated using a system generalized method of moments technique for panel data. The results are presented to show that there is a U-shaped relationship between oil price volatility and Japanese firm investment. The results from subsamples of these data indicate that this U-shaped relationship is more significant for oil-intensive firms and small firms. The second chapter aims to examine the underlying causes of changes in real oil price and their transmission mechanisms in the Japanese stock market. I decompose real oil price changes into three components; namely, oil supply shock, aggregate demand shock and oil-specific demand shock, and then estimate the dynamic effects of each component on stock returns using a structural vector autoregressive (SVAR) model. I find that the responses of aggregate Japanese real stock returns differ substantially with different underlying causes of oil price changes. In the long run, oil shocks account for 43% of the variation in the Japanese real stock returns. The response of Japanese real stock returns to oil price shocks can be attributed in its entirety to the cash flow variations. The third chapter tests the robustness of SVAR and investigates the impact of oil price shocks on the different U.S. stock indices. I find that the responses of real stock returns of alternate stock indices differ substantially depending on the underlying causes of the oil price increase. However, the magnitude and length of the effect depends on the firm size. The response of U.S. stock returns to oil price shocks can be attributed to the variations of expected discount rates and expected cash flows.