The aim of this work was to design a set of rules for levodopa infusion dose adjustment in Parkinson’s disease based on a simulation experiments. Using this simulator, optimal infusions dose in different conditions were calculated. There are seven conditions (-3 to +3)appearing in a rating scale for Parkinson’s disease patients. By finding mean of the differences between conditions and optimal dose, two sets of rules were designed. The set of rules was optimized by several testing. Usefulness for optimizing the titration procedure of new infusion patients based on rule-based reasoning was investigated. Results show that both of the number of the steps and the errors for finding optimal dose was shorten by new rules. At last, the dose predicted with new rules well on each single occasion of majority of patients in simulation experiments.

Current research suggests that a high level of confidence and a low level of anxiety are predictive of higher math achievement. Compared to students from other provinces, previous research has found that Saskatchewan students have a higher level of confidence and a lower level of anxiety for learning math, but still tend to achieve lower math scores compared to students in other provinces. The data suggest that there may be unique factors effecting math learning for students in Saskatchewan. The purpose of the study is to determine the factors that may affect Saskatchewan students math achievement. Exploratory factor analyses and regression methods were employed to investigate possible traits that aid students in achieving higher math scores. Results from a 2007 math assessment administered to grade 5 students in Saskatchewan were used for the current study. The goal of the study was to provide a better understanding of the factors and trends unique to students for mathematic achievements in Saskatchewan.<p> Using results from a province-wide math assessment and an accompanying questionnaire administered to students in grade five across public school in Saskatchewan (n=11,279), the present study found statistical significance in three factors that have been supported by previous studies to influence math achievement differences, specifically in (1) confidence in math, (2) parental involvement in math and (3) extracurricular participation in math. The three aforementioned factors were found to be related to math achievement as predicted by the Assessment for Learning (AFL) program in Saskatchewan, although there were reservations to the findings due to a weak amount of variances accounted for in the regression model (r2 =.084). Furthermore, a multivariate analysis of variance indicated gender and locations of schools to have effects on students math achievement scores. Although a high amount of measurement errors in the questionnaire (and subsequently a low variance accounted for by the regression model) limited the scope and implications of the model, future implications and improvements are discussed

GDP and GDP per capita are widely used to gauge for living standards across countries. However, they have originally not been constructed for this purpose and are therefore subject to significant limitations. This paper aims at developing a better and non-monetary development index with which cross-country living standards can be assessed. This index, the BDI, can then be utilized for policy making. When constructing the BDI, this study utilizes time series analysis and panel unit root tests. A major finding of this study is that the BDI does indeed produce statistically significantly different results/ rankings for a special set of countries, compared to GDP and GDP per capita.

In this thesis, we have proposed a novel microarray gene selection algorithm consisting of five processes for solving gene expression classification problem. A normalization process is first used to remove the differences among different scales of genes. Second, an efficient gene ranking process is proposed to filter out the unrelated genes. Then, the genetic algorithm is adopted to find the informative gene subsets for each class. For each class, these informative gene subsets are adopted to classify the testing dataset separately. Finally, the separated classification results are fused to one final classification result. In the first experiment, 4 microarray datasets are used to verify the performance of the proposed algorithm. The experiment is conducted using the leave-one-out-cross-validation (LOOCV) resampling method. We compared the proposed algorithm with twenty one existing methods. The proposed algorithm obtains three wins in four datasets, and the accuracies of three datasets all reach 100%. In the second experiment, 9 microarray datasets are used to verify the proposed algorithm. The experiment is conducted using 50% VS 50% resampling method. Our proposed algorithm obtains eight wins among nine datasets for all competing methods.

Unconventional reservoir systems can best be described as petroleum (oil and/or gas) accumulations which are difficult to be characterized and produced by conventional technologies. In this work we present the development of a systematic procedure to evaluate well performance in unconventional (i.e., low to ultra-low permeability) reservoir systems. The specific tasks achieved in this work include the following: ● Integrated Diagnostics and Analysis of Production Data in Unconventional Reservoirs: We identify the challenges and common pitfalls of production analysis and provide guidelines for the analysis of production data. We provide a comprehensive workflow which consists of model-based production analysis (i.e., rate-transient or model matching approaches) complemented by traditional decline curve analysis to estimate reserves in unconventional reservoirs. In particular, we use analytical solutions (e.g., elliptical flow, horizontal well with multiple fractures solution, etc.) which are applicable to wells produced in unconventional reservoirs. ● Deconvolution: We propose to use deconvolution to identify the correlation between pressure and rate data. For our purposes we modify the B-spline deconvolution algorithm to obtain the constantpressure rate solution using cumulative production and bottomhole pressure data in real time domain. It is shown that constant-pressure rate and constant-rate pressure solutions obtained by deconvolution could identify the correlation between measured rate and pressure data when used in conjunction. ● Series of Rate-Time Relations: We develop three new main rate-time relations and five supplementary rate-time relations which utilize power-law, hyperbolic, stretched exponential, and exponential components to properly model the behavior of a given set of rate-time data. These relations are well-suited for the estimation of ultimate recovery as well as for extrapolating production into the future. While our proposed models can be used for any system, we provide application almost exclusively for wells completed in unconventional reservoirs as a means of providing estimates of time-dependent reserves. We attempt to correlate the rate-time relation model parameters versus model-based production analysis results. As example applications, we present a variety of field examples using production data acquired from tight gas, shale gas reservoir systems.

Thesis (Ph. D.)--University of Missouri-Columbia, 2001. / Typescript. Vita. Includes bibliographical references (leaves 112-115). Also available on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 2001. / Typescript. Vita. Includes bibliographical references (leaves 112-115). Also available on the Internet.

Thesis (Ph.D.)--University of Oklahoma, 1991. / Includes bibliographical references (leaves 148-153).

A Rolling Dynamic Deflectometer (RDD) is a nondestructive testing device for determining continuous deflection profiles of pavements. Unlike discrete testing methods, the RDD performs continuous measurements. The ability to perform continuous measurements makes the RDD a powerful screening/evaluation tool for quickly characterizing large sections of pavement, with little danger of missing critical pavement features. RDD testing applications have involved pavement forensic investigations, delineations of areas to be repaired, selection of rehabilitation treatments, measurements of relative improvements due to the rehabilitation, and monitoring of changes with time (trafficking and environmental loading). However, the speed of RDD testing with the current rolling sensors is between 1 and 2 mph (1.6 to 3.2 km/hr). Improvements in testing speed and data analysis procedures would increase its usefulness in project-level studies as well as permit its used in some pavement network-level studies. A three-part study was carried out to further improve the RDD. The first part involved the development of speed-improved rolling sensors (referred as the third-generation rolling sensor). Key benefits of this new rolling sensor are: (1) increased testing speed up to 5 mph (8.0 km/hr), and (2) reduced level of rolling noise during RDD measurements. With this rolling sensor, the RDD can collect more deflection measurements at a speed of 3 to 5 mph (4.8 to 8.0 km/hr). Field trials using the first- and third-generation rolling sensors on both flexible and rigid pavements were performed to evaluate the performance of the third-generation rolling sensor. The second part of this study involved enhancements to the RDD data analysis procedure. An alternative data analysis method was developed for the third-generation rolling sensor. This new analysis method produces results at higher speeds that are comparable to the existing analysis method used for testing at 1 to 2 mph (1.6 to 3.2 km/hr). Key benefits of this analysis method that were not previously available are: (1) distance-based deflection profiles (report RDD deflections based on a selected distance interval), (2) improved-spatial resolution without sacrificing the filtering performance, and (3) analysis of the rolling noise characteristics and signal-to-noise and distortion ratios better characterize the deflection profiles and their accuracy. The third part of this study involved investigating the effects of parameters affecting RDD deflection measurements which include: (1) force level and operating frequency, (2) in-situ sensor calibration, (3) load-displacement curve, and (4) pavement temperature variations. These parameters need to be considered in testing and data analysis procedures of the RDD because small errors from these parameters can adversely influence calculations of the RDD deflections. Criteria are presented for selecting the best operating parameters for testing. / text

ITC/USA 2014 Conference Proceedings / The Fiftieth Annual International Telemetering Conference and Technical Exhibition / October 20-23, 2014 / Town and Country Resort & Convention Center, San Diego, CA / Upon completion of a test where telemetry(TM) data was collected, the resulting TM file will usually contain millions of data points. Traditionally MatLab™, Mathlab™, or some third party software is used to plot the data. These methods may not always be desirable due to the expense of licensing, restrictions on the ability to create custom graphs and the inability to quickly plot large amounts of data. These problems were solved by using Windows Presentation Foundation (WPF) graphics capabilities in conjunction with C# to develop a unique set of algorithms to display custom graphs of unlimited size with quick response.