The goal of this thesis was to explore the effects of dose resolution, respiratory variation and dose calculation method on dose accuracy. To achieve this, two models of lung were created. The first model, called TISSUE, approximated the connective alveolar tissues of the lung. The second model, called BRANCH, approximated the lungs bronchial, arterial and venous branching networks. Both models were varied to represent the full inhalation, full exhalation and midbreath phases of the respiration cycle.<p> To explore the effects of dose resolution and respiratory variation on dose accuracy, each model was converted into a CT dataset and imported into a Monte Carlo simulation. The resulting dose distributions were compared and contrasted against dose distributions from Monte Carlo simulations which included the explicit model geometries. It was concluded that, regardless of respiratory phase, the exclusion of the connective tissue structures in the CT representation did not significantly effect the accuracy of dose calculations. However, the exclusion of the BRANCH structures resulted in dose underestimations as high as 14\% local to the branching structures. As lung density decreased, the overall dose accuracy marginally decreased.<p> To explore the effects of dose calculation method on dose accuracy, CT representations of the lung models were imported into the Pinnacle$^3$ treatment planning system. Dose distributions were calculated using the collapsed cone convolution method and compared to those derived using the Monte Carlo method. For both lung models, it was concluded that the accuracy of the collapsed cone algorithm decreased with decreasing density. At full inhalation lung density, the collapsed cone algorithm underestimated dose by as much as 15\%. Also, the accuracy of the CCC method decreased with decreasing field size.<p> Further work is needed to determine the source of the discrepancy.

The goal of this thesis was to explore the effects of dose resolution, respiratory variation and dose calculation method on dose accuracy. To achieve this, two models of lung were created. The first model, called TISSUE, approximated the connective alveolar tissues of the lung. The second model, called BRANCH, approximated the lungs bronchial, arterial and venous branching networks. Both models were varied to represent the full inhalation, full exhalation and midbreath phases of the respiration cycle.<p> To explore the effects of dose resolution and respiratory variation on dose accuracy, each model was converted into a CT dataset and imported into a Monte Carlo simulation. The resulting dose distributions were compared and contrasted against dose distributions from Monte Carlo simulations which included the explicit model geometries. It was concluded that, regardless of respiratory phase, the exclusion of the connective tissue structures in the CT representation did not significantly effect the accuracy of dose calculations. However, the exclusion of the BRANCH structures resulted in dose underestimations as high as 14\% local to the branching structures. As lung density decreased, the overall dose accuracy marginally decreased.<p> To explore the effects of dose calculation method on dose accuracy, CT representations of the lung models were imported into the Pinnacle$^3$ treatment planning system. Dose distributions were calculated using the collapsed cone convolution method and compared to those derived using the Monte Carlo method. For both lung models, it was concluded that the accuracy of the collapsed cone algorithm decreased with decreasing density. At full inhalation lung density, the collapsed cone algorithm underestimated dose by as much as 15\%. Also, the accuracy of the CCC method decreased with decreasing field size.<p> Further work is needed to determine the source of the discrepancy.

It has been suggested that depressed individuals have difficulties decoding emotional facial expressions in others contributing to a negative cycle of interpersonal difficulties. Some studies have demonstrated global deficits in the processing of emotional facial expressions compared to non-depressed participants, whereas others have noted differences for specific emotions. Methodological issues, including the operationalization of accuracy and bias and the examination of a limited range of emotion and intensity, can partially explain the mixed findings. The aim of the current study was to examine differences in accuracy in the detection of emotional facial expressions in participants with MDD (currently depressed, partially remitted, and those with a lifetime history of MDD) and non-depressed comparisons. Methodological limitations of previous studies were addressed by: (a) using the unbiased hit rate (Wagner, 1993), which is a more precise measure of accuracy for specific emotion, (b) using a more precise measure of judgment bias, taking into account the overuse or underuse of specific emotion categories, (c) including the six basic emotions, and (d) incorporating expressions ranging from 20%-100% intensity. Of secondary interest was to determine whether transient mood state is predictive of accuracy scores regardless of diagnostic status. Thirty-seven depressed and 34 non-depressed participants recruited from the ROHCG Mood Disorders program and the University of Ottawa took part in this study. Clinical status was assessed using the Structured Clinical Interview for the DSM-IV (SCID-IV) and the Beck Depression Inventory-II (BDI-II). Participants also completed the Profile of Mood States-Bipolar (POMS-BI) form to assess mood state at the time of testing. The facial recognition task consisted of happiness, sadness, anger, fear, disgust, and surprise at 20%-100% intensity, presented for 500 ms. Participants pressed a computer key to identify the emotion that was presented. Results indicated that both groups of depressed participants were more accurate than non-depressed participants in detecting anger at 20% intensity. Depressed participants also showed a bias away from surprise. Group differences at high intensity were non-significant, however, participants with current depression and partial remission showed a bias towards anger at 50% intensity. Regression analyses were performed using the POMS-Agreeable Hostile (POMS-AH) and POMS-Elated Depressed (POMS-ED) scales to determine whether mood state was predictive of accuracy in the detection of anger and sadness. Regression models predicting accuracy were non-significant. Results of this study are considered in the context of cognitive and cognitive-interpersonal theories of MDD.

No description available.

There are numerous techniques used to measure strain. Most are only capable of taking surface measurements. The penetrating nature of X-rays has been used to measure deformation, and thus strain, but only with radiographic images. Radioscopic techniques are faster and do not require film processing, but produce less detailed results than digitised radiographic images. The research covered by this thesis tested radioscopic images and showed them to be suitable for strain measurement. The thesis includes details of the design and capabilities of the radioscopic equipment. Pin cushion distortion is a common feature of radioscopic images, and an automatic method of identifying, and correcting for the distortion was implemented.

Abstract Manufacturers are under tremendous pressure to improve product quality in terms of dimension while maintaining high productivity. To maintain product quality, it is necessary to know the accuracy level of machine tools so that defective parts can be prevented in manufacturing. Different machine tools deviate from their ideal situation to an error prone state over time. Even new machine tools may cause errors due to faulty installation, an extra heat source etc. Roll, pitch and yaw errors are common problems in machine tools for the manufacturing industries. The origins of these errors are kinematics parameter deviations resulting from manufacturing errors, assembly errors or quasistatic errors. By considering the geometric description of any machine tool, one should be able to predict the actual tool tip as compared to ideal tool tip for every controlled point in the machine's workspace. By counting the forward kinematics of the machine it is possible to predict the tool tip deviation for every point. A number of measuring methods can be adopted to describe the actual geometry of machine tools. Each method has it's own advantages and disadvantages. Often machine tool experts measure the machine with different types of measuring devices to obtain error traces based on its error sources and magnitude. In this thesis, a theoretical and practical relation has been established between static and dynamic measuring systems. These relations are important when we are measuring machine tools with different measuring devices to validate the measurement results. In this work, traces obtained by one measuring system have been compared and simulated with the traces obtained by other methods. A number of systematic mathematical models have been developed, and compared with the results obtained by other measuring methods. The outcome of this can lead to the development of a software system that can be used to validate measuring results obtained from different measuring systems and those can be compared with each other. The VM182 measurement result simulates closer than the laser measurement result when both are compared using the traces obtained by DBB measurement. Several methods for improving the positioning accuracy of machine tools have been studied. One of the methods is NC code modification. This method has been applied to develop an NC program processor based on the error found by the measurements. An aluminium test piece has been cut with the modified program to test the developed model. The finding of NC code modification is that for repeatable error, we can obtain a better dimensional accuracy for work pieces when we use a modified NC program based on the algorithms developed. The arch replacement technique has given a circularity improvement from 22 to 12 with DBB measurement, and circularity has been improved from 12.59 to 8.10 when it has been applied to cut aluminium work piece.

It has been suggested that depressed individuals have difficulties decoding emotional facial expressions in others contributing to a negative cycle of interpersonal difficulties. Some studies have demonstrated global deficits in the processing of emotional facial expressions compared to non-depressed participants, whereas others have noted differences for specific emotions. Methodological issues, including the operationalization of accuracy and bias and the examination of a limited range of emotion and intensity, can partially explain the mixed findings. The aim of the current study was to examine differences in accuracy in the detection of emotional facial expressions in participants with MDD (currently depressed, partially remitted, and those with a lifetime history of MDD) and non-depressed comparisons. Methodological limitations of previous studies were addressed by: (a) using the unbiased hit rate (Wagner, 1993), which is a more precise measure of accuracy for specific emotion, (b) using a more precise measure of judgment bias, taking into account the overuse or underuse of specific emotion categories, (c) including the six basic emotions, and (d) incorporating expressions ranging from 20%-100% intensity. Of secondary interest was to determine whether transient mood state is predictive of accuracy scores regardless of diagnostic status. Thirty-seven depressed and 34 non-depressed participants recruited from the ROHCG Mood Disorders program and the University of Ottawa took part in this study. Clinical status was assessed using the Structured Clinical Interview for the DSM-IV (SCID-IV) and the Beck Depression Inventory-II (BDI-II). Participants also completed the Profile of Mood States-Bipolar (POMS-BI) form to assess mood state at the time of testing. The facial recognition task consisted of happiness, sadness, anger, fear, disgust, and surprise at 20%-100% intensity, presented for 500 ms. Participants pressed a computer key to identify the emotion that was presented. Results indicated that both groups of depressed participants were more accurate than non-depressed participants in detecting anger at 20% intensity. Depressed participants also showed a bias away from surprise. Group differences at high intensity were non-significant, however, participants with current depression and partial remission showed a bias towards anger at 50% intensity. Regression analyses were performed using the POMS-Agreeable Hostile (POMS-AH) and POMS-Elated Depressed (POMS-ED) scales to determine whether mood state was predictive of accuracy in the detection of anger and sadness. Regression models predicting accuracy were non-significant. Results of this study are considered in the context of cognitive and cognitive-interpersonal theories of MDD.

No description available.

As a process designed to collect information about jobs, job analysis is one of the most fundamental aspects of personnel psychology. It forms the foundation upon which almost every other human resource management component is built, including selection, compensation, performance appraisal, and training program development. Despite the considerable evidence of human fallibility in other judgment processes, many have followed the implicit assumption that job analysis information is accurate without actually examining this proposition. This study considers two potential sources of job analysis rating inaccuracy — the source of the ratings and the type of instrument utilized to collect ratings. By utilizing less job-familiar job analysis raters and shorter, more holistic job analysis instruments, industrial-organizational psychologists have attempted to attenuate the time and costs associated with the job analysis process; however, findings regarding the reliability and accuracy of such practices are questionable. Hypotheses tested in the current study indicated that decomposed measures of job behavior converged to a greater degree with an external job analysis than did holistic measures. Interrater agreements for all types of raters and across all types of instruments are concluded to be inadequate. Potential explanations from the cognitive and social psychological domains for these findings are conjectured and directions for future research are noted. / Ph. D.

This study examines the tone of management disclosures and their impact on capital markets. In particular, I examine the positive and negative tone, as defined by the Harvard IV-4 Dictionary, during conference calls and the impact on analyst accuracy, dispersion of analysts' estimates, cumulative abnormal returns, abnormal trading volumes, and the number of days after the end of the quarter. Results indicate that pessimism is significantly related to decreased analyst accuracy. A one percent increase in the pessimism of a conference call results in a decrease in analyst accuracy by approximately 10%. In addition, an increase in pessimism is associated with an increase in the dispersion of analysts' estimates. Pessimism is related to negative abnormal returns in the 30 days after the end of the conference call and also to increased trading volume in the three days after the conference call. A one percent increase in the pessimism of a conference call is related to a negative abnormal return of approximately .4%. These findings are consistent with the theory that the positive and negative tone of a conference call provides incremental information to the capital markets. I am unable to find significant results for an increase in the number of days between the end of the quarter and the conference call date. These results are robust to using a more financially oriented dictionary created by Loughran and McDonald (2011) / Ph. D.