In response to a need for more primary care physicians and patients' growing attraction to alternative health care, greater numbers of individuals are applying to naturopathic colleges. With increasing numbers of applicants, better methods of predicting potential effectiveness as an N.D. are needed. This study examined factors (both academic and psychosocial) that best predict success in naturopathic school. Demographic, academic, and psychosocial survey data were collected from thirty-three students who had just completed their second year of naturopathic medical school. This information was correlated with scores on the NPLEX Basic Science exams which were taken the following summer. Because of the small sample size, a bootstrap resampling technique was used to produce estimates for a hierarchical regression. Demographic variables (sex, age, whether or not English was the first language) and undergraduate major, explained almost 10% of the variance in Basic Science Exams (BSE) scores; however, none of these variables were significant predictors in the first step of the regression. As predicted, the addition of undergraduate grade point average (GPA) significantly increased the amount of variance accounted for (to 39.9%) in BSE scores. Also as predicted, adding the psychosocial variables to the model increased the amount of variance accounted for to 52%. This addition also made sex a significant predictor, but external locus of control was the only psychosocial variable which was significant in any of the models. The best model contained the psychosocial variables of both internal and external locus of control but not commitment and accounted for 51 % of the variance in BSE scores. Sex, undergraduate GPA, and external locus of control were significant predictors. Results are consistent with previous research using data on students from allopathic medical schools. However, complex relationships exist among the psychosocial variables and between the psychosocial variables and gender. The suppression effect of the psychosocial variables with gender, multicolinearity between the commitment and locus of control variables, and suppression due to common method variance between the internal and external locus of control variables are discussed. Limitations of bootstrap methodology are considered.

This study has been concerned with investigating several aspects of error, including human error, and the underlying causes in construction.It has attempted to calculate the realistic error rate and as a result has identified the optimum inspection rate in construction.Error prediction in construction is a new field of study, particularly with regard to repetitive construction processes.Several methodologies and probalistic approaches have been considered.Case studies have been presented and the findings shown. The results from several publications suggest that the learning rate varies with experience and with the skill of the worker, task complexity, environmental factors and interruptions due to inclement weather, inspection delays, and equipment breakdowns.A learning theory using a Straight-Line Power model was used to predict future performances,and the descrete event simulation model using 'iThink' simulation software,in conjunction with CPM, was developed in this study to calculate project durations.A simulation model was developed using the Event Tree Analysis (ETA) to calculate a more realistic error rate for the repetitive tasks. / Doctor of Philosophy (PhD)

Ground deformation due to volcanic magma intrusion is recognised as an important precursor of eruptive activity at a volcano. The Global Positioning System (GPS) is ideally suited for this application by being able to measure three-dimensional coordinate changes of the monitoring points over time. Due to the highly disturbed ionosphere in equatorial regions, particularly during times of maximum solar activity, a deformation monitoring network consisting entirely of single-frequency GPS receivers cannot deliver baseline solutions at the desired accuracy level. In this thesis, a mixed-mode GPS network approach is proposed in order to optimise the existing continuous single-frequency deformation monitoring system on the Papandayan volcano in West Java, Indonesia. A sparse network of dual-frequency GPS receivers surrounding the deformation zone is used to generate empirical 'correction terms' in order to model the regional ionosphere. These corrections are then applied to the single-frequency data of the inner network to improve the accuracy of the results by modelling the residual atmospheric biases that would otherwise be neglected. This thesis reviews the characteristics of existing continuously operating GPS deformation monitoring networks. The UNSW-designed mixed-mode GPS-based volcano deformation monitoring system and the adopted data processing strategy are described, and details of the system's deployment in an inhospitable volcanic environment are given. A method to optimise the number of observations for deformation monitoring networks where the deforming body itself blocks out part of the sky, and thereby significantly reduces the number of GPS satellites being tracked, is presented. The ionosphere and its effects on GPS signals, with special consideration for the situation in equatorial regions, are characterised. The nature of the empirically-derived 'correction terms' is investigated by using several data sets collected over different baseline lengths, at various geographical locations, and under different ionospheric conditions. Data from a range of GPS networks of various sizes, located at different geomagnetic latitudes, including data collected on Gunung Papandayan, were processed to test the feasibility of the proposed mixed-mode deformation monitoring network approach. It was found that GPS baseline results can be improved by up to 50% in the midlatitude region when the 'correction terms' are applied, although the performance of the system degrades in close proximity to the geomagnetic equator during a solar maximum.

Thesis (M.S.)--Eastern Illinois University, 1973. / Includes bibliographical references (leaf 49).

<p>Proteins play a crucial roll in all biological processes. The wide range of protein functions is made possible through the many different conformations that the protein chain can adopt. The structure of a protein is extremely important for its function, but to determine the structure of protein experimentally is both difficult and time consuming. In fact with the current methods it is not possible to study all the billions of proteins in the world by experiments. Hence, for the vast majority of proteins the only way to get structural information is through the use of a method that predicts the structure of a protein based on the amino acid sequence.</p><p>This thesis focuses on improving the current protein structure prediction methods by combining different prediction approaches together with machine-learning techniques. This work has resulted in some of the best automatic servers in world – Pcons and Pmodeller. As a part of the improvement of our automatic servers, I have also developed one of the best methods for predicting the quality of a protein model – ProQ. In addition, I have also developed methods to predict the local quality of a protein, based on the structure – ProQres and based on evolutionary information – ProQprof. Finally, I have also performed the first large-scale benchmark of publicly available homology modeling programs.</p>

Thesis (Ph. D.)--University of Washington, 1984. / Vita. Bibliography: leaves [124]-137.

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This study proposes two methods, (1) a probabilistic method based on historical oil prices and (2) a method based on Gaussian simulation, to model future prices of oil. With these methods to model future oil prices, we can calculate the ranges of uncertainty in traditional probability indicators based on cash flow analysis, such as net present values, net present value to investment ratio and internal rate of return. We found that conventional methods used to quantify uncertainty which use high, low and base prices produce uncertainty ranges far narrower than those observed historically. These methods fail because they do not capture the "shocks" in oil prices that arise from geopolitical events or supply-demand imbalances. Quantifying uncertainty is becoming increasingly important in the petroleum industry as many current investment opportunities in reservoir development require large investments, many in harsh exploration environments, with intensive technology requirements. Insight into the range of uncertainty, particularly for downside, may influence our investment decision in these difficult areas.

The goals of gene prediction using computational approaches are to determine gene location and the corresponding functionality of the coding region. A subset of gene prediction is the gene structure prediction problem, which is to define the exon-intron boundaries of a gene. Gene prediction follows two general approaches: statistical patterns identification and sequence similarity comparison. Similarity based approaches have gained increasing popularity with the recent vast increase in genomic data in GenBank. The proposed gene prediction algorithm is a similarity based algorithm which capitalizes on the fact that similar sequences bear similar functions. The proposed algorithm, like most other similarity based algorithms, is based on dynamic programming. Given a genomic DNA, X = x1 xn and a closely related cDNA, Y = y1 yn, these sequences are aligned with matching pairs stored in a data set. These indexes of matching sets contain a large jumble of all matching pairs, with a lot of cross over indexes. Dynamic programming alignment is again used to retrieve the longest common non-crossing subsequence from the collection of matching fragments in the data set. This algorithm was implemented in Java on the Unix platform. Statistical comparisons were made against other software programs in the field. Statistical evaluation at both the DNA and exonic level were made against Est2genome, Sim4, Spidey, and Fgenesh-C. The proposed gene structure prediction algorithm, by far, has the best performance in the specificity category. The resulting specificity was greater than 98%. The proposed algorithm also has on par results in terms of sensitivity and correlation coeffcient. The goal of developing an algorithm to predict exonic regions with a very high level of correctness was achieved.

The purpose of this thesis was to develop a means of predicting future injury among nursing personnel working in a hospital system. Nursing has one of the highest incidence rates of musculoskeletal injuries among U.S. occupations. Endemic to the job are tasks such as rolling, sitting, standing, and transferring large, and often times, uncooperative patients. These tasks often place large biomechanical stresses on the musculoskeletal system and, in some cases, contribute to or cause a musculoskeletal injury. Given the current nursing shortage, it is imperative to keep nurses injury-free and productive so they can provide patient care services. Even though a large number of nursing personnel are injured every year and most are exposed to these high levels of biomechanical stress, the majority of nurses are injury-free. The question then arises "Why do some nurses have injuries while others do not?" The purpose of this thesis was to determine whether individual attributes in a population of nurses were associated with risk of future injury. The subject population was comprised of 140 nursing personnel at a local hospital system hired between April 1995 and February 1999. Data on individual attributes, such as patient demographics, previous injuries, posture, joint range of motion, flexibility, and muscular strength, was ascertained during a post-offer screening on these personnel. Twenty six (19%) nurses experienced an injury associated with the axial skeleton. Chi square test for homogeneity for the categorical predictor variables, and the Student's T-test for continuous predictor variables were used to determine if any individual attributes were associated with future injuries. None of the variables were associated with a risk of future axial skeletal injury. Practical application of these results for St. Joseph Regional Health Center, and possibly other acute care facilities, directs us to stop costly pre-employment/post-offer testing for the purpose of identifying injury prone nurse applicants. Secondly, it allows the focus of limited resources to be on making the job safer through administrative and engineering controls.