<p>The aim of this project was to compare the use of the Swedish Reference Group of Antibiotics and the Clinical Laboratory standard institute disc diffusions method using a variety of antibiotics on Klebsiella species and Pseudomonas aeruginosa. After the disc diffusions methods a Vitek system and Etest where preformed on those isolates with a different or abnormal result.</p><p>Klebsiella spp. and P. aeruginosa was colleted in St James’s hospital over a period of four months. These two isolates were chosen because of their resistance pattern to many commonly used antibiotics and the threat of upcoming resistance to other antibiotics.</p><p>The SRGA method is know to have lower breakpoints to catch low degree of resistance bacteria, while the CLSI often is blamed for not having species specific MIC breakpoints.</p><p>The results in this study showed that the SRGA-m often caught more resistant and</p><p>intermediate isolates then the CLSI-m.</p>

In this paper we study sensitivity analysis of the uncapacitated single level economic lot-sizing problem, which was introduced by Wagner and Whitin about thirty years ago. In particular we are concerned with the computation of the maximal ranges in which the numerical problem parameters may vary individually, such that a solution already obtained remains optimal. Only recently it was discovered that faster algorithms than the Wagner-Whitin algorithm exist to solve the economic lot-sizing problem. Moreover, these algorithms reveal that the problem has more structure than was recognized so far. When performing the sensitivity analysis we exploit these newly obtained insights.

The aim of this project was to compare the use of the Swedish Reference Group of Antibiotics and the Clinical Laboratory standard institute disc diffusions method using a variety of antibiotics on Klebsiella species and Pseudomonas aeruginosa. After the disc diffusions methods a Vitek system and Etest where preformed on those isolates with a different or abnormal result. Klebsiella spp. and P. aeruginosa was colleted in St James’s hospital over a period of four months. These two isolates were chosen because of their resistance pattern to many commonly used antibiotics and the threat of upcoming resistance to other antibiotics. The SRGA method is know to have lower breakpoints to catch low degree of resistance bacteria, while the CLSI often is blamed for not having species specific MIC breakpoints. The results in this study showed that the SRGA-m often caught more resistant and intermediate isolates then the CLSI-m.

A systematic procedure is proposed in this thesis to study the effect of temperature change to the power system load demand by using the typical load patterns of customer classes. The billing data of all service customers are retrieved to derive the daily load profile of the selected Taipower district. To verify the accuracy of the estimated load composition, the simulation results are compared to the actual load profile collected by the SCADA system. The sensitivity analysis of load demand with respect to the temperature change for each customer class is performed by statistic regression according to the actual customer power consumption and temperature data. With temperature rise, the load contribution by each customer class is updated by the corresponding temperature sensitivity and integrated together to form the new load profile of the service district. To investigate the effect of customer load characteristics to system stability, the equivalent circuit of Taipower 345 KV network is created. With the integration of the load composition by load survey study and temperature sensitivity of customer load, the load demand of each load bus is derived. For fault contingency of system buses, the transient stability analysis has performed to determine the critical clearing time under different temperature conditions.

Streamline-based models have shown great potential in reconciling high resolution geologic models to production data. In this work we extend the streamline-based production data integration technique to naturally fractured reservoirs. We use a dualporosity streamline model for fracture flow simulation by treating the fracture and matrix as separate continua that are connected through a transfer function. Next, we analytically compute the sensitivities that define the relationship between the reservoir properties and the production response in fractured reservoirs. Finally, production data integration is carried out via the Generalized Travel Time inversion (GTT). We also apply the streamline-derived sensitivities in conjunction with a dual porosity finite difference simulator to combine the efficiency of the streamline approach with the versatility of the finite difference approach. This significantly broadens the applicability of the streamlinebased approach in terms of incorporating compressibility effects and complex physics. The number of reservoir parameters to be estimated is commonly orders of magnitude larger than the observation data, leading to non-uniqueness and uncertainty in reservoir parameter estimate. Such uncertainty is passed to reservoir response forecast which needs to be quantified in economic and operational risk analysis. In this work we sample parameter uncertainty using a new two-stage Markov Chain Monte Carlo (MCMC) that is very fast and overcomes much of its current limitations. The computational efficiency comes through a substantial increase in the acceptance rate during MCMC by using a fast linearized approximation to the flow simulation and the likelihood function, the critical link between the reservoir model and production data. The Gradual Deformation Method (GDM) provides a useful framework to preserve geologic structure. Current dynamic data integration methods using GDM are inefficient due to the use of numerical sensitivity calculations which limits the method to deforming two or three models at a time. In this work, we derived streamline-based analytical sensitivities for the GDM that can be obtained from a single simulation run for any number of basis models. The new Generalized Travel Time GDM (GTT-GDM) is highly efficient and achieved a performance close to regular GTT inversion while preserving the geologic structure.

Impedance-based methods are used to locate faults on distribution systems because of their simplicity and ease of implementation. These methods require fault voltage and current data along with the positive- and zero-sequence line impedance values (in ohm per unit length) to estimate the reactance or distance to fault location. Inaccuracies in line impedance values, which arise from circuit model errors, have an adverse impact on fault location estimates of the impedance-based methods. Measurement errors in current and voltage transformers can also lead to inaccuracy in estimation. Further, all methods use simplistic models to represent the system load. The load in a practical distribution system does not conform to the oversimplified models leading to errors in estimation of fault location. This thesis presents sensitivity analysis of four impedance-based methods. It focuses on the Takagi, positive-sequence reactance, loop reactance and balanced-load methods. Amongst these four methods, the first three are commonly used for fault location. The fourth method was developed as a part of this work. The objective of sensitivity analysis is to study and quantify the effect of circuit model, measurement and load model errors, on the fault location estimates of the four methods. The results of this analysis are used to establish upper and lower bounds on the estimation errors for each method. The analysis begins with creation of a baseline case using a modified version of the IEEE 34 Node Test Feeder. All the methods estimate the reactance to fault location as a part of this analysis. The baseline case uses accurate line impedances and measurement values in the four methods. The fault location estimates for this case serve as a means of comparison for all subsequent analyzes. Secondly, various circuit model errors are introduced while computing the line impedance values. These errors include inaccurate modeling of four parameters viz. phase conductor distances, conductor sizes, phase to neutral conductor distances and earth resistivity. The erroneous line impedance values, which arise from these circuit model errors, are used in the four methods. The resultant location estimates are compared with those for the baseline case. It is observed that modeling errors in earth resistivity can cause estimation errors of 2% to 5% in the Takagi and positive-sequence reactance methods. These errors can be positive or negative depending upon whether the modeled earth resistivity value is more than or less than the accurate value. The effect of inaccurate modeling of the other three parameters is marginal. Additionally, the Takagi and positive-sequence reactance methods assume line impedances to be uniform while estimating fault location. Although this assumption is a type of circuit model error, it does not lead to significant errors in estimation. The loop reactance and balanced-load methods are insensitive to circuit model errors as they do not use line impedance values while estimating reactance to fault location. The next part is analysis of effect of measurement errors on fault location estimates. Ratio and phase angle errors are deliberately introduced in the current and voltage transformers and the erroneous measurements are used to conduct fault location. This causes 5% to 6% errors in estimation for the Takagi and positive-sequence reactance methods. These estimation errors can be positive or negative depending upon the magnitude of the CT and VT ratio errors and the sign of the phase angle errors. For the loop reactance method, erroneous measurements introduce 8% to 30% errors in fault location. This indicates that the loop reactance method is highly sensitive to measurement errors. The balanced-load method is moderately sensitive and experiences 6% to 7% errors in fault location estimates. Lastly, the effect of load current on fault location estimates is analyzed. When the Takagi and positive-sequence reactance methods are used on a heavily loaded system, they estimate fault location with an error of 5% to 8%. The loop reactance method is severely affected by the level of load current in the system. This method can estimate fault location with nearly 100% accuracy, on a lightly loaded system. However, the estimation errors for this method increase significantly and are in the range of 15% to 30%, as load current in the system increases. In case of the balanced-load method, unbalanced, heavy loads can cause estimation errors of 7% to 25%. The combined effect of all the error sources is taken into account by creating a confidence interval for each method. For the Takagi and positive-sequence reactance methods, the actual fault location can be expected to lie within ±10% of the estimated value. The fault location estimation error for the loop reactance and balanced-load methods is always positive. The actual reactance-to-fault is within -30% of the value estimated by these methods. / text

Objective: The aim of this study was to investigate the differential effects of breastfeeding practice and having an intention to breastfeed (during pregnancy) on a mother’s maternal responsiveness to her infant after birth. Methods: Using longitudinal data from a subsample of 962 mother-infant dyads from a UK cohort study (Avon Longitudinal Study of Parents and Children), we investigated the influence of intention to breastfeed at 3 months pregnancy and breastfeeding practice and on mother-infant interactions at 12 months after birth. Breastfeeding intent and practice were assessed by questionnaires administered to the mothers. Intention to breastfeed in the first 3 months postpartum was measured at 32 weeks into the pregnancy, while breastfeeding practice (over first 12 months postpartum) was measured retrospectively at 15 months post partum. Results: Using logistic regression analyses, we found that intending to breastfeed at 32 weeks gestation significantly predicted maternal responsiveness, namely that an intention to breastfeed increased the odds of positive maternal responsiveness, independently of breastfeeding practice. However, we found the practice of breastfeeding was not an independent predictor of positive maternal responsiveness once intention to breastfeed was accounted for. Using a life course epidemiology approach we further demonstrated that maternal responsiveness is most positive when both the intention to breastfeed and breastfeeding practice are present. Conclusions: To our knowledge this is the first study to report that having the intention to breastfeed an infant is more strongly associated with positive maternal responsiveness than the act of breastfeeding itself. This may suggest that more responsive mothers choose to breastfeed rather than breastfeeding practice directly causing enhanced responsiveness. Further research will be needed to understand the nature of this intention and its relationships with maternal responsiveness. However, the results may also highlight the potential importance of parenting intentions/ preparations during pregnancy for a mothers developing abilities to be responsive to her infant after birth.

The sensitivity of the important inputs and the savings prediction function reliability for the WinAM 4.3 software is studied in this research. WinAM was developed by the Continuous Commissioning (CC) group in the Energy Systems Laboratory at Texas A&M University. For the sensitivity analysis task, fourteen inputs are studied by adjusting one input at a time within ± 30% compared with its baseline. The Single Duct Variable Air Volume (SDVAV) system with and without the economizer has been applied to the square zone model. Mean Bias Error (MBE) and Influence Coefficient (IC) have been selected as the statistical methods to analyze the outputs that are obtained from WinAM 4.3. For the saving prediction reliability analysis task, eleven Continuous Commissioning projects have been selected. After reviewing each project, seven of the eleven have been chosen. The measured energy consumption data for the seven projects is compared with the simulated energy consumption data that has been obtained from WinAM 4.3. Normalization Mean Bias Error (NMBE) and Coefficient of Variation of the Root Mean Squared Error (CV (RMSE)) statistical methods have been used to analyze the results from real measured data and simulated data. Highly sensitive parameters for each energy resource of the system with the economizer and the system without the economizer have been generated in the sensitivity analysis task. The main result of the savings prediction reliability analysis is that calibration improves the model’s quality. It also improves the predicted energy savings results compared with the results generated from the uncalibrated model.

With the focus of the automotive industry on decreasing vehicle weight and improving fuel efficiency, aluminum is being used for structural components in automobiles. Given the high strain rates associated with vehicle impact, it is necessary to understand the rate sensitivity of any potential alloy (eg. AA5754) in order to accurately predict deformation behaviour. Furthermore, the magnitude and strain path associated with the residual strains remaining after forming of the component also play a major role in how the material will behave. It has been found that AA5754 sheet exhibits negative rate sensitivity up to a strain rate of 0.1/s, and positive strain rate sensitivity at strain rates between 0.1/s and 1500/s. Increasing the strain rate also has the effect of increasing the yield stress as well as the ductility. When a strain path change is involved between the prestrain stage and subsequent uniaxial loading, it has the effect of reducing the rate sensitivity of the material as well as reducing the overall flow stress. A rate-sensitive adaptation of the Voce material model was successfully implemented in LS-DYNA and used to predict the response of AA5754 sheet in bending for applied strain rates of 0.001/s and 0.1/s. / Thesis (Ph.D, Mechanical and Materials Engineering) -- Queen's University, 2008-09-23 20:11:30.829

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