In this thesis, I undertake an empirical search for the existence of price and exchange rate bubbles during the inter-war European hyperinflations of Germany, Hungary and Poland. Since the choice of an appropriate policy to control inflation depends upon the true nature of the underlying process generating the inflation, the existence or non-existence of inflationary bubbles has important policy implications. If bubbles do exist, positive action will be required to counter the public's self-fulfilling expectation of a price surge. Hyperinflationary episodes have been chosen as my case study because of the dominant role that such expectations play in price determination. In the literature, there are frequently expressed concerns about empirical research into bubbles. The existence of model misspecification and the nonlinear dynamics in the fundamentals under conditions of regime switching may lead to spurious conclusions concerning the existence of bubbles. Furthermore, some stochastic bubbles may display different collapsing properties and consequently appear to be linearly stationary. Thus, the evidence against the existence of bubbles may not be reliable. In my thesis, I attempt to tackle the above empirical problems of testing for the existence of bubbles using advances in testing procedures and methodologies. Since the number of bubble solutions is infinite in the rational expectations framework, I adopt indirect tests, rather than direct tests, for the empirical study. From the findings of my empirical research, the evidence for stationary specification errors and the nonlinearity of the data series cannot be rejected, but the evidence for the existence of price and exchange rate bubbles is rejected for all the countries under study. It leads to the conclusion that the control of the inter-war European hyperinflations was attributable to control of the fundamental processes, since the dynamics of prices and exchange rates for these countries might not be driven by self-fulfilling expectations.

Early avoidable 30-day post discharge readmission among patients diagnosed with chronic obstructive pulmonary disease (COPD) is associated with poor transition care processes. The purpose of this project was to analyze organizational system processes for admission and discharge transition care of patients diagnosed with COPD to identify key intervention strategies that could decrease the rate of 30-day post-discharge readmission by 1%. The project used the transitional care model as the framework to target specific care transition needs and create patient-centered, supportive, evidence-based relationships among the patient, the providers, the community, and the health care system to identify key intervention strategies for implementation. A retrospective chart review was conducted of transitional care management and care coordination practices of providers of patients diagnosed with COPD. Analysis of the data revealed that the local regional organization used a single, generic, computerized discharge planning and care transition process for patients diagnosed with COPD. As a result, missed opportunities to target a patient's specific care needs led to higher rates of readmission. The implications of the findings of this project for social change include identification of evidence-based recommendations and practices that could influence clinician practices and improve patient outcomes and the quality of health care delivery.

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The foreign exchange market is one of the largest and most active financial markets with enormous daily trading volumes. Exchange rates are influenced by the interactions of a large number of agents, each operating with different intentions and on different time scales. This gives rise to nonlinear and non-stationary behaviour which complicates modelling. This research proposes a neural network based model trained on data filtered with a novel Empirical Mode Decomposition (EMD) filtering method for the forecasting of exchange rates. One minor and two major exchange rates are evaluated in this study. Firstly the ideal prediction horizons for trading are calculated for each of the exchange rates. The data is filtered according to this ideal prediction horizon using the EMD-filter. This EMD-filter dynamically filters the data based on the apparent number of intrinsic modes in the signal that can contribute towards prediction over the selected horizon. The filter is employed to filter out high frequency noise and components that would not contribute to the prediction of the exchange rate at the chosen timescale. This results in a clearer signal that still includes nonlinear behaviour. An artificial neural network predictor is trained on the filtered data using different sampling rates that are compatible with the cut-off frequency. The neural network is able to capture the nonlinear relationships between historic and future filtered data with greater certainty compared to a neural network trained on unfiltered data. Results show that the neural network trained on EMD-filtered data is significantly more accurate at prediction of exchange rates compared to the benchmark models of a neural network trained on unfiltered data and a random walk model for all the exchange rates. The EMD-filtered neural network’s predicted returns for the higher sample rates show higher correlations with the actual returns, and significant profits can be made when applying a trading strategy based on the predictions. Lower sample rates that just marginally satisfy the Nyquist criterion perform comparably with the neural network trained on unfiltered data; this may indicate that some aliasing occurs for these sampling rates as the EMD low-pass filter has a gradual cut-off, leaving some high frequency noise within the signal. The proposed model of the neural network trained on EMD-filtered data was able to uncover systematic relationships between the filtered inputs and actual outputs. The model is able to deliver profitable average monthly returns for most of the tested sampling rates and forecast horizons of the different exchange rates. This provides evidence that systematic predictable behaviour is present within exchange rates, and that this systematic behaviour can be modelled if it is properly separated from high frequency noise. / MIng (Computer and Electronic Engineering), North-West University, Potchefstroom Campus, 2015

The foreign exchange market is one of the largest and most active financial markets with enormous daily trading volumes. Exchange rates are influenced by the interactions of a large number of agents, each operating with different intentions and on different time scales. This gives rise to nonlinear and non-stationary behaviour which complicates modelling. This research proposes a neural network based model trained on data filtered with a novel Empirical Mode Decomposition (EMD) filtering method for the forecasting of exchange rates. One minor and two major exchange rates are evaluated in this study. Firstly the ideal prediction horizons for trading are calculated for each of the exchange rates. The data is filtered according to this ideal prediction horizon using the EMD-filter. This EMD-filter dynamically filters the data based on the apparent number of intrinsic modes in the signal that can contribute towards prediction over the selected horizon. The filter is employed to filter out high frequency noise and components that would not contribute to the prediction of the exchange rate at the chosen timescale. This results in a clearer signal that still includes nonlinear behaviour. An artificial neural network predictor is trained on the filtered data using different sampling rates that are compatible with the cut-off frequency. The neural network is able to capture the nonlinear relationships between historic and future filtered data with greater certainty compared to a neural network trained on unfiltered data. Results show that the neural network trained on EMD-filtered data is significantly more accurate at prediction of exchange rates compared to the benchmark models of a neural network trained on unfiltered data and a random walk model for all the exchange rates. The EMD-filtered neural network’s predicted returns for the higher sample rates show higher correlations with the actual returns, and significant profits can be made when applying a trading strategy based on the predictions. Lower sample rates that just marginally satisfy the Nyquist criterion perform comparably with the neural network trained on unfiltered data; this may indicate that some aliasing occurs for these sampling rates as the EMD low-pass filter has a gradual cut-off, leaving some high frequency noise within the signal. The proposed model of the neural network trained on EMD-filtered data was able to uncover systematic relationships between the filtered inputs and actual outputs. The model is able to deliver profitable average monthly returns for most of the tested sampling rates and forecast horizons of the different exchange rates. This provides evidence that systematic predictable behaviour is present within exchange rates, and that this systematic behaviour can be modelled if it is properly separated from high frequency noise. / MIng (Computer and Electronic Engineering), North-West University, Potchefstroom Campus, 2015

Master of Science / Department of Animal Sciences and Industry / Jeffrey S. Stevenson / We hypothesized that administration of human chorionic gonadotropin (hCG) to recipients at embryo transfer (ET) would induce accessory corpora lutea (CL), increase circulating progesterone concentrations, and reduce early embryonic loss. At three locations, purebred and crossbred Angus, Simmental, and Hereford recipients (n = 719) were assigned alternately to receive i.m. 1,000 IU hCG or 1 ml saline (control) at ET. Fresh or frozen-thawed embryos were transferred on d 5.5 to 8.5 (median = d 7) of the estrous cycle to recipients having a palpable CL. Recipients received a body condition score (BCS) at ET. Pregnancy diagnoses occurred by transrectal ultrasonography 28 to 39 d (median = d 35) and reconfirmed 58 to 77 d (median = d 67) post-estrus. At one location (n = 108), ovaries were examined to count the number of CL at pregnancy diagnosis. More (P < 0.001) pregnant hCG-treated cows (69.0%) had multiple CL than pregnant controls (0%). Serum progesterone (ng/mL) determined at two locations (n=471) at both pregnancy diagnoses in pregnant cows was greater (P ≤ 0.05) after hCG treatment than in controls (first: 8.1 ± 0.9 vs. 6.1 ± 0.8; second: 8.8 ± 0.9 vs. 6.6 ± 0.7), respectively. Transfer pregnancy rates were analyzed using logistic regression. Unadjusted pregnancy rates at the first diagnosis was 61.8 vs. 53.9% for hCG vs. controls. At the second diagnosis, pregnancy rates were 59.0 vs. 51.4%, respectively. Factors affecting pregnancy rates were treatment (P = 0.03), embryo type (P = 0.02), and BCS (P = 0.08). Odds ratios indicated that greater pregnancy rates occurred in recipients receiving hCG treatment, receiving a fresh embryo (66.3 vs.55.5%), and when BCS >5 vs. ≤5 (62.3 vs. 55.3%). We concluded that hCG at ET increased incidence of accessory CL, increased progesterone in pregnant recipients, and increased transfer pregnancy rates.

ITC/USA 2007 Conference Proceedings / The Forty-Third Annual International Telemetering Conference and Technical Exhibition / October 22-25, 2007 / Riviera Hotel & Convention Center, Las Vegas, Nevada / Modern telemetry systems require flexible bit rate telemetry encoders in order to optimize mission formats for varying data rate requirements and/or signal to noise conditions given a fixed transmitter power. Implementing a variable bit rate telemetry encoder requires consideration of several possible architectural topologies that place different system requirements on data acquisition modules within the encoder in order to maintain adequate signal fidelity of sensor information. This paper focuses on the requirements, design considerations and tradeoffs associated with differing architectural topologies for implementing a variable bit rate encoder and the resulting implications on the encoder systems data acquisition units.

Previous research suggests as more scores are interpreted, there is a coinciding increase in the chance significant scores will be obtained. Interpretation of the MMPI-2 can involve the analysis of as many as 98 or more separate scores, suggesting the measure has a strong proclivity for producing a high frequency of seemingly abnormal scores amongst normal healthy adults. In the current study the incidence of elevated MMPI-2 scores was simulated for the normal population using Monte Carlo methodology. Interscale correlations from the MMPI-2 restandardization sample were obtained to determine the percentage of the population with N or more seemingly abnormal scores. Simulations were conducted for all scales combined, and for the Clinical, Harris-Lingoes, Content, Content-Component, and Supplementary scales separately at varying T-score cutoffs. 36.8% of normal adults are expected exhibit at least one elevated score on the Clinical scales at 65T. The normal incidence of at least one seemingly abnormal score was 38.3% on the Content, and 55.1% on the Supplementary scales. When all scale groups are considered together, approximately 50% of the normal population has three or more significant scores, and at least seven seemingly meaningful scores are found for one out of five normal persons. These results imply that consideration of a large number of MMPI-2 scales should be conducted with caution, and that high T-score cut-points may optimally increase confidence in the absence of corroborative test scores and extra test data.

Due to the interesting financial moment we are living, my motivations to write this Master thesis has mostly been the behavior of interest rates and models that can be used predict them. Thus, in this dissertation I have presented theHull-White model and the way to calibrate it against market data so it can be used to price interest rate derivatives. The reader can find both theoretical and practical presentations and examples along with the code to program them byhim/herself.