Stress has been shown to influence learning and memory in humans and rats (Diamond et al, 1996; Diamond et al, 1999; Krugers et al, 1997; Kirschbaum et al, 1996; Lupien et al, 1997). The hippocampus and is an area of the brain involved in memory function in humans and rats (Kirschbaum et al, 1996; Lupien et al, 1997) and is highly susceptible to stress (Diamond et al, 1990). Research has indicated that a number of stressors such as exposure to a predator (Diamond et al, 1999) can lead to stress effects. Recently efforts have been made to counteract the effects of stress on brain function and related behavioral performance. The antidepressant drug tianeptine has been used in this setting. Little is known about tianeptine's role in blocking stress effects on behavior and memory performance with regard to interactions with stress hormones, such as corticosterone. Here a set of experiments delineates the role of corticosterone and its link to stress effects on memory as well as an investigation into the actions of tianeptine and ADX in the blockade of stress effects on memory. First, I examined the effects of tianeptine on multi-day RAWM working memory training and a novel one-day learning and memory training task. Second, the effects of propranolol, an anti-anxiety medication, were tested with regard to the alleviation of stress effects on memory, allowing for a comparison between two anti-anxiety drugs, tianeptine and propranolol. Third, adrenalectomy (ADX) and the resultant depletion of adrenal hormones were examined in connection with learning and memory in the one-day learning task. Fourth, the effects and interactions of tianeptine and ADX were examined to see if tianeptine can exert its effects in the absence of adrenal hormones. Tianeptine blocked stress-induced memory errors in two different tasks and under ADX conditions. All effects were independent of corticosterone levels. In contrast, propranolol was ineffective in blocking stress-induced memory changes. The current data may prove useful in the development of antidepressant drugs and further the study of the mechanisms by which stress affects memory.

Using high resolution optical spectra from Mount John University Observatory, Mount Stromlo Observatory and the Anglo-Australian Observatory, new, high accuracy radial velocity curves have been obtained for the two bright southern Cepheids l carinae (HR 3884) and beta doradus (HR 1922). An indepth investigation into period variations, cycle-to-cycle and long-term variations in the velocity curves and the reliability of the combination of velocity data from different observatories is carried out. Evidence for shock waves in the atmosphere of l car and resonance in beta dor is discussed. A grid of static model atmospheres incorporating plane-parallel geometry is compared with the observational spectra of both Cepheids, using line depth ratios, to determine the variation in effective temperature, surface gravity and microturbulence with phase. This information is used to determine the phase dependence of the surface-brightness for both Cepheids. The surface brightness variation with phase was found to follow an almost linear relationship. The distance to and radius of the Cepheids are determined using both a near-infrared version of the Barnes-Evans method and the Fourier Baade-Wesselink (BW) method. The derived radii and distances agree within the limits of the errors for both methods. The Fourier BW method was found to be very sensitive to phase shifts between the photometric and spectroscopic data and the derived distance highly dependent on the assumed reddening. An investigation into line profile variations in l car and beta dor has revealed the magnitude of these phenomena increase as the pulsational period of the Cepheid increases. It is estimated that line level variations introduce an additional uncertainty into derived radii of approximately 4 per cent for beta dor and 10 per cent in lcar. The uncertainty introduced into derived distances and radii by line profile asymmetries was estimated to be of the order of 6 per cent in beta dor and 10 per cent in l car. A comparative analysis is made of the hydrogen line radial velocity curves of l car and beta dor. A trend in the properties of these radial velocity curves with period has been revealed. In longer period Cepheids, the Halpha line seems to be forming in a region that does not partake in the pulsation as a whole, probably in a chromospheric shell. A quantitative analysis of the asymmetries in these lines reveal large redward asymmetries near maximum infall velocity. The magnitude of these asymmetries and the period for which they are present are larger in l Car than in beta dor. The blueward asymmetries in the Halpha line in l Car are comparable in magnitude to the redward asymmetries while the other lines exhibit only small blueward asymmetries. A qualitative analysis of these line profiles with phase reveal no conclusive evidence for line doubling in these Cepheids. Evidence of emission is found in the Halpha and H Beta lines of beta dor and l car. The strength and duration of the emission is found to be greater in the longer period Cepheid. Although it is likely that this emission is shock-related, theoretical work is needed to determine the exact origin of the emission. A non-LTE radiative hydrodynamic model for l Car has been created. This atmosphere will be used in further work to calculate synthetic spectral line profiles which will aid the interpretation of our observational results.

<p>The demands for ride comfort quality in today's long haulage trucks are constantly growing. A part of the ride comfort problems are represented by internal vibrations caused by rotating mechanical parts. This thesis work focus on the vibrations generated from radial runout on the wheels. These long haulage trucks travel long distances on smooth highways, with a constant speed of 90 km/h resulting in a 7 Hz oscillation. This frequency creates vibrations in the cab, which can be found annoying. To help out with the vibration diagnosis when a truck enters a mechanical workshop, this work studies methods for radial runout detection using the wheel speed sensors.</p><p>The main idea is to represent the varying radius signal with a sinusoid, where the calculations are based on Fourier series. The estimated radial runout value is then the amplitude of the sinusoid. In addition to the detection part, the work also present results regarding how the relative phase difference between two wheels with radial runout effects the lateral motion of the cab.</p><p>This thesis work was performed at Scania CV AB in Södertälje, Sweden and all measurements have been full scale experiments on real trucks.</p>

<p>Neurography is the most objective and reliable measure of the peripheral nerve function, and it is used to diagnose both local and generalized neuropathies. Neurography can measure both motor and sensory nerve functions. The principle for sensory neurgraphy is to stimulate over the nerve and record proximal or distal from the stimulated electrode.</p><p>At the Department of Clinical Neurophysiology, University Hospital Uppsala, a problem has been identified, in that young adult patients tend to show unexpected abnormal neurography values in relation to the expected, indicated by the reference limits, without clinical correlates. This concerns foremost the sensory amplitudes in median and ulnar nerves. The hypothesis is that the requirement of young adults’ amplitudes is too high. A reference material better including more subjects in this age group may solve the problem.</p><p>Sensory nerve conduction studies were performed in 33 subjects, aged 15-30. The nerve functions were tested on median, ulnar and radial nerves. Surface electrodes were used for both recording and stimulation.</p><p>The result shows that the presently used reference material for some nerves indeed has too high requirement for young adults. After increasing the reference material for younger age groups, the new reference limits has been changed and this should cause fewer false positive findings.</p>

Nonlinear multivariate statistical techniques on fast computers offer the potential to capture more of the dynamics of the high dimensional, noisy systems underlying financial markets than traditional models, while making fewer restrictive assumptions. This thesis presents a collection of practical techniques to address important estimation and confidence issues for Radial Basis Function networks arising from such a data driven approach, including efficient methods for parameter estimation and pruning, a pointwise prediction error estimator, and a methodology for controlling the "data mining'' problem. Novel applications in the finance area are described, including customized, adaptive option pricing and stock price prediction.

Wire mesh vibration dampers have been the subject of some very encouraging experiments at the Texas A&M Turbomachinery laboratories for the past several years and have emerged as an excellent replacement for squeeze film dampers. Their capability to provide damping for a wide range of temperatures (even cryogenic), fluid free operation and ability to perform even when soaked with lubricants makes them a suitable option as a bearing damper. Experiments were conducted to investigate the effect of design parameters like axial thickness and axial compression that influence the characteristics of wire mesh as a bearing damper. Two groups of wire mesh were tested to show that the stiffness and damping are directly proportional to the axial thickness, if all the other parameters are kept constant. Tests on four wire mesh donuts of different radial thickness showed that stiffness and damping vary inversely with radial thickness. Rigorous tests were also conducted to quantify the effects of axial compression, radial interference and displacement amplitude on stiffness and damping of the wire mesh. Another novel kind of mesh damper tested was comprised of two small segments instead of a whole donut. The results showed that wire mesh exhibited good damping characteristics even when used in small segments. Empirical expressions were developed using MathCADTM worksheets, and an existing ExcelTM design worksheet was modified to include these factors. The effect of frequency variation was also included to give a comprehensive design tool for wire mesh. A new design worksheet was developed that can predict rotordynamic coefficients for a wire mesh bearing damper having a different size as well as different installation and operational conditions.

The EEC (Electron Energy Corporation) in conjunction with the National Aeronautics and Space Administration is researching the magnetic bearings for an alternative to conventional journal or ball bearings. The purpose of this research was to design and develop a high-temperature (1000ºF) hybrid Magnetic Bearing using High Temperature Permanent Magnets (HTPM), developed by the EEC for high performance jet engines at high speeds that supply loads of 500 lbf. Another objective is to design and build a test rig fixture to measure the load capacity of the designed bearing. The permanent magnet bias of the Homopolar radial magnetic bearing reduces the amount of current required for magnetic bearing operation. This reduces the power loss due to the coil current resistance and improves the system efficiency because the magnetic field of the HTPM can suspend the major portion of the static load on bearing. A high temperature radial magnetic bearing was designed via an iterative search employing 3D finite element based electromagnetic field simulations. The bearing was designed to produce 500 lbf of force at 1000ºF and the design weight is 48 lbs. The bias flux of the Homopolar radial bearing is produced by EEC HTPM to reduce the related ohmic losses of an electromagnetic circuit significantly. An experimental procedure was developed to measure actual load capacity of the designed bearing at the test rig. All the results obtained from the experiment were compiled and analyzed to determine the relation between bearing force, applied current and temperature.

In this work the problem of calibrating Flush Air Data Sensing (FADS) has been addressed. The inverse problem of extracting freestream wind speed and angle of attack from pressure measurements has been solved. The aim of this work was to develop machine learning and statistical tools to optimize design and calibration of FADS systems. Experimental and Computational Fluid Dynamics (EFD and CFD) solve the forward problem of determining the pressure distribution given the wind velocity profile and bluff body geometry. In this work three ways are presented in which machine learning techniques can improve calibration of FADS systems. First, a scattered data approximation scheme, called Sequential Function Approximation (SFA) that successfully solved the current inverse problem was developed. The proposed scheme is a greedy and self-adaptive technique that constructs reliable and robust estimates without any user-interaction. Wind speed and direction prediction algorithms were developed for two FADS problems. One where pressure sensors are installed on a surface vessel and the other where sensors are installed on the Runway Assisted Landing Site (RALS) control tower. Second, a Tikhonov regularization based data-model fusion technique with SFA was developed to fuse low fidelity CFD solutions with noisy and sparse wind tunnel data. The purpose of this data model fusion approach was to obtain high fidelity, smooth and noiseless flow field solutions by using only a few discrete experimental measurements and a low fidelity numerical solution. This physics based regularization technique gave better flow field solutions compared to smoothness based solutions when wind tunnel data is sparse and incomplete. Third, a sequential design strategy was developed with SFA using Active Learning techniques from the machine learning theory and Optimal Design of Experiments from statistics for regression and classification problems. Uncertainty Sampling was used with SFA to demonstrate the effectiveness of active learning versus passive learning on a cavity flow classification problem. A sequential G-optimal design procedure was also developed with SFA for regression problems. The effectiveness of this approach was demonstrated on a simulated problem and the above mentioned FADS problem.

Surface reconstruction of anatomical structures is an integral part of medical modeling. Contour information is extracted from serial cross-sections of tissue data and is stored as "slice" files. Although there are several reasonably efficient triangulation algorithms that reconstruct surfaces from slice data, the models generated from them have a jagged or faceted appearance due to the large inter-slice distance created by the sectioning process. Moreover, inconsistencies in user input aggravate the problem. So, we created a method that reduces inter-slice distance, as well as ignores the inconsistencies in the user input. Our method called the piecewise weighted implicit functions, is based on the approach of weighting smaller implicit functions. It takes only a few slices at a time to construct the implicit function. This method is based on a technique called variational interpolation. <p> Other approaches based on variational interpolation have the disadvantage of becoming unstable when the model is quite large with more than a few thousand constraint points. Furthermore, tracing the intermediate contours becomes expensive for large models. Even though some fast fitting methods handle such instability problems, there is no apparent improvement in contour tracing time, because, the value of each data point on the contour boundary is evaluated using a single large implicit function that essentially uses all constraint points. Our method handles both these problems using a sliding window approach. As our method uses only a local domain to construct each implicit function, it achieves a considerable run-time saving over the other methods. The resulting software produces interpolated models from large data sets in a few minutes on an ordinary desktop computer.

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