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The overall objective of this research was to investigate the feasibility of using artificial neural networks to detect the incidence of clinical bovine mastitis and to determine the major factors influencing it. The first part of this research was devoted to a general examination of the learning ability of artificial neural networks by training them with relatively small data sets. These data sets (a total of 460,474 records) contained suspected indicators of mastitis such as milk production, stage of lactation and somatic cell count, and it was hoped that artificial neural networks would be able to detect what statistical modelling had already established elsewhere in the literature. The second part of this research was extended to examine the roles of more information resources such as conformation traits and their genetic values---factors that have not been studied extensively, with either conventional approaches or emerging technologies like artificial neural networks. (Abstract shortened by UMI.)

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The presence of distributed micro-calcifications can be an indicator of early breast cancer. On the mammogram, they appear as bright smooth particles superimposed on the normal breast image background. Radiologists determine the occurrence of this lesion by detecting the individual micro-calcifications and then examining their distribution within the breast tissue. Due to the visual complexity of the mammogram, the detection sensitivity is usually less than 100%. The digital environment has the potential to increase the radiologist's accuracy. We have developed a computer aided detection (CAD) scheme that can identify clinically indicative clusters of micro-calcifications. The CAD algorithm emulates some aspects of the radiologists' approach by using contrast texture energy segmentation and morphological distribution analysis. On a local database of 61 mammograms digitised at 100μm with 8 bits intensity resolution, the CAD returns: a) 85% sensitivity (91% for malignant lesions and 78% for those that are benign), b) 0.33 false positive clusters (FPC) per image and c) 92% specificity. Therefore, the output from the CAD is shown to compare favourably with the performance of an expert radiologist. It also compares favourably with other CAD techniques, exceeding many algorithms which employ a higher level of mathematical complexity. The scheme is tested on an international database provided by the Mammographic Image Analysis Society. In this case it returns a) 96.4% sensitivity (100% for malignant lesions and 92% for those that are benign) b) 2.35 FPC rate per image and c) 33% specificity. The higher FPC rate is attributed to the different acquisition and production of the digital mammograms. It is concluded that this can be reduced by employing a shape analysis procedure to the CAD's final output. It is shown that the image processing principles we have implemented are generally successful on databases which are produced at other centres under different technical conditions.

Formulas for predicting the absolute X-ray intensity spectra from both Bremsstrahlung and K characteristic X-rays have been developed. These formulas cover a wide range of target materials and target geometry conditions (incident and emission angle) and tube voltage range from 20 to 200kV. For the Bremsstrahlung Intensity Spectrum:vskip 3.5cm Where U tube voltage(kV); J tube current(mA); n&61 1.6; <i>P</i> &61 1.08x10^-6(<i>A/Z</i>^2.5;<i>k_{s}</i> &61 0.32; <i>k</i> &61 <i>K_{m}Z</i>²/<i>A; K</i> = <i>k</i>3/511. The theoretical value of <i>K_{m}</i> is 4.73x10¹⁴<i>keV/mAs.sr.keV</i>. For a Fluency with a Total Filtration of <i>d_{Al} g/cm</i>² Al: <i>F(E)dE = I(E)E^{-1}e^{-μAldAl}dEphotons/keV.mAs.sr</i>. This formula can be used at various incident angles (θi) andemission anglesfor different target material (A, Z, μ) for <i>U</i> from 20-200kV. The angular distribution <i>f</i>(θ) (defined = 1 when θ = 90^o and varies with <i>U</i>) requires definition by experimental measurements. According to this formula the Photon Fluency spectra at different tube voltages, different target angles and different emission directions are calculated and illustrated as spectral curves. There is a good agreement between the formula results and spectra measured by other authors. For K-Characteristic X-ray fluency:vskip 1.5cm Where n&61 1.61; <i>P</i> &61 1.08x10^-6(<i>A/Z</i>)^2.5; k is a factor of 1.0-2.5, which represents the effective depth of K photo production and increases with Z; θ<i>_{i}</i> and θ<i>_{r}</i> the electron incident and X-ray emission angles; Ψ(θ<i>_{i}</i>) &61 <i>cos</i>(7.89x10^-4θ2.6_i) an empirical angle function; <i>N(E_{i}</i>) the fluency of <i>E_{i}</i> characteristic X rays per mAs per solid angle; <i>E_{k}</i> the binding energy of the K sell; <i>F</i>_k the efficiency constant depends on Z; <i>f(E_{i}</i> the fractional emission of the <i>E_{i}</i> characteristic X rays; <i>J</i> the tube current; <i>U</i> the tube voltage. The recommended value of <i>F</i>_Kα, based on measured data, is 3.8-4.7x10^11<i>photon/mAs.sr</i> for values of Z from 25 to 50. The relationship between the target attentuation factor and different target materials, angles and voltages are discussed. This formula can be used at much wider situations and has good agreement with the measured data.

Purpose: The primary purpose of this research study was to assess the degree of agreement between simple anthropometric measurements (i.e. body mass index, waist circumference, hip circumference, and waist-to-hip ratio) and the measures of regional adiposity, with a primary focus on the androidlgynoid ratio, assessed using dual energy x-ray absorptiometry (DXA). This secondary purpose of the study was to identify any significant correlations between the measures of regional adiposity, physical activity, and cardiovascular risk factors. Methods: Forty-eight subjects, 19 males (48.7 ± 16.9 years) and 29 females (43.6 ± 16.2 years), volunteered to participate in this study. Subjects underwent laboratory testing compromised of resting blood pressure, blood lipid analysis, waist & hip circumference, total body DXA scan, and a one week physical activity assessment. Results: Significant correlations were observed between body mass index and region body fat % (r = 0.84, 0.79), waist circumference and android fat % (r = 0.79, 0.75), and waist-to-hip ratio and androidlgynoid ratio (r = 0.72, 0.61) for men and women, respectively. Fasting insulin was correlated with region body fat %, android body fat %, trunk body fat %, and the android/gynoid ratio. The android/gynoid ratio was correlated with high density lipoproteins, very low density lipoproteins, triglycerides, and fasting glucose. There was a statistically significant negative relationship observed between average steps per day and body mass index, waist circumference, hip circumference, region body fat %, android body fat %, and trunk body fat %. Conclusions: This study found that there are strong relationships between simple anthropometric measures and regional body fat measures from the DXA. According to the data in the present study, body mass index, waist circumference, and waist-to-hip ratio provide simple yet sensitive methods for the estimation of regional body fat in Caucasian males and females. In addition, this study found significant correlations between measures of the blood lipid profile, physical activity, and both simple anthropometric and DXA measures of regional body fat. Key words: android fat, body mass index, dual-energy x-ray absorptiometry, gynoid fat, obesity, waist circumference. / School of Physical Education, Sport, and Exercise Science

This study examined the utility of the Dean-Woodcock Emotional Status Examination (D-WESE). As part of the Dean-Woodcock Neuropsvchology Battery, the D-WESE is a 50-item measure of emotional functioning. Its ability to predict pathology as compared to the MMPI Clinical Scales was investigated. The D-WESE gains information about a wide range of symptoms within a short period of time.An existing data set was utilized in this study. It included 207 patients referred for psychological and neuropsychological evaluation from a large outpatient Midwestern neurology practice. The age ranged between 13 and 96 years (M = 56.53).The results of an exploratory factor analysis with oblique rotation revealed an 11factor solution consisting of eigenvalues greater than 1.00. The D-WESE factors were significantly correlated with the MMPI Clinical Scales and a poor to good percent (1670%) of the variance could be accounted for through Stepwise Multiple Regressions. The results were discussed in terms of their relationship with established MMPI codetypes. Overall, the D-WESE was found to have a moderate degree of overlap and predictive validity as compared to the MMPI. / Department of Educational Psychology

A speech discrimination test for use with young children, specifically preschool age children, was devised utilizing monosyllabic words as the stimulus items and a picture identification task as the response. The test was administered to subjects in three experimental groups: hearing impaired adults, hearing impaired children, and normal hearing preschool age children. The CID Auditory Test W-22 was also administered to the adult subjects.Results of the testing suggested that the picture test is a reliable and stable instrument for assessing speech discrimination ability in normal hearing and hearing impaired children over the age of five years. It appeared that the use of this test with children below five years of age should be with caution because of the variability in scores obtained for this age group. There were indications that further study is needed to collect normative data for normal hearing preschool age children to use as a basis for comparison of clinically obtained scores.

Diagnosing performance problems in modern datacenters and distributed systems is challenging, as the root cause could be contained in any one of the system’s numerous components or, worse, could be a result of interactions among them. As distributed systems continue to increase in complexity, diagnosis tasks will only become more challenging. There is a need for a new class of diagnosis techniques capable of helping developers address problems in these distributed environments. As a step toward satisfying this need, this dissertation proposes a novel technique, called request-flow comparison, for automatically localizing the sources of performance changes from the myriad potential culprits in a distributed system to just a few potential ones. Request-flow comparison works by contrasting the workflow of how individual requests are serviced within and among every component of the distributed system between two periods: a non-problem period and a problem period. By identifying and ranking performance-affecting changes, request-flow comparison provides developers with promising starting points for their diagnosis efforts. Request workflows are obtained with less than 1% overhead via use of recently developed end-to-end tracing techniques. To demonstrate the utility of request-flow comparison in various distributed systems, this dissertation describes its implementation in a tool called Spectroscope and describes how Spectroscope was used to diagnose real, previously unsolved problems in the Ursa Minor distributed storage service and in select Google services. It also explores request-flow comparison’s applicability to the Hadoop File System. Via a 26-person user study, it identifies effective visualizations for presenting request-flow comparison’s results and further demonstrates that request-flow comparison helps developers quickly identify starting points for diagnosis.This dissertation also distills design choices that will maximize an end-to-end tracing infrastructure’s utility for diagnosis tasks and other use cases.