Zinc supplementation at conventional doses does not improve the disturbance of taste perception in hemodialysis patients

Objective: To determine the effect of zinc supplementation on taste perception in a group of hemodialysis patients. Design and Setting: Double-blind randomized placebo-controlled study in a teaching hospital dialysis unit. Patients: Fifteen stable hemodialysis patients randomized to placebo (6 male, 2 female; median age, 67; range, 30 to 72 years) or treatment (5 male, 2 female; median age, 60; range, 31 to 76 years). Intervention: Treatment group received zinc sulfate 220 mg per day for 6 weeks, and the placebo group received an apparently identical dummy pill. Main Outcome Measures: Taste scores by visual analogue scales, normalized protein catabolic rate and plasma, whole blood and red cell zinc levels. Results: At baseline, sweet and salt tastes were identified correctly by both groups. Sour was often confused with salt. Sour solutions of different concentrations were not distinguishable. Taste scores were not different after 6 weeks for either group. There was no significant increment in zinc levels or normalized protein catabolic rate for either group. Conclusion: We found a disturbance of taste perception in hemodialysis patients, particularly for the sour modality, which was not corrected by this regimen of zinc supplementation. These results cast doubts on the conclusions of earlier studies that indicated an improvement in taste after zinc supplementation.

TCSC control design for transient stability improvement of a multi-machine power system using trajectory sensitivity

This paper discusses the effects of thyristor controlled series compensator (TCSC), a series FACTS controller, on the transient stability of a power system. Trajectory sensitivity analysis (TSA) has been used to measure the transient stability condition of the system. The TCSC is modeled by a variable capacitor, the value of which changes with the firing angle. It is shown that TSA can be used in the design of the controller. The optimal locations of the TCSC-controller for different fault conditions can also be identified with the help of TSA. The paper depicts the advantage of the use of TCSC with a suitable controller over fixed capacitor operation.

The application of neural networks to induction machine control

Neural networks (NNs) are discussed in connection with their possible use in induction machine drives. The mathematical model of the NN as well as a commonly used learning algorithm is presented. Possible applications of NNs to induction machine control are discussed. A simulation of an NN successfully identifying the nonlinear multivariable model of an induction-machine stator transfer function is presented. Previously published applications are discussed, and some possible future applications are proposed.

Vector control of a high power induction machine

The design and implementation of a high-power (2 MW peak) vector control drive is described. The inverter switching frequency is low, resulting in high-harmonic-content current waveforms. A block diagram of the physical system is given, and each component is described in some detail. The problem of commanded slip noise sensitivity, inherent in high-power vector control drives, is discussed, and a solution is proposed. Results are given which demonstrate the successful functioning of the system

Effect of acupressure on thirst in hemodialysis patients

Background: Thirst and dry mouth are common among hemodialysis (HD) patients. This paper reports a study to evaluate the impact of an acupressure program on HD patients’ thirst and salivary flow rates. Methods: The acupressure program included placebo, followed by true acupressure each applied for 4 weeks. Twenty-eight patients (mean age 57.6, SD = 16.13 years) first received a sticker as placebo acupressure at two acupoints CV23 and TE17 three times a week for 4 weeks, and then received true acupressure in the same area for the next 4 weeks. Salivary flow rate and thirst intensity were measured at baseline, during and after treatment completion for both the placebo and true acupressure program. Results: The true acupressure program was associated with significantly increased salivary flow rate (0.09 ± 0.08 ml/min at baseline to 0.12 ± 0.08 ml/min after treatments completion, p = 0.04). The mean thirst intensity also improved from 4.21 ± 2.66 at baseline to 2.43 ± 2.32 (p = 0.008) after treatment completion in HD patients. There was no statistically significant difference in pre-post program salivary flow rate; however, significant improvement in thirst intensity scores was observed (p = 0.009) in the placebo acupressure program. Conclusion: This study provides preliminary evidence that acupressure may be effective in improving salivary flow rates and thirst intensity.

Accurate urban road model reconstruction from high resolution remotely sensed imagery based on Support Vector Machine and Gabor filters

In this paper, we presented an automatic system for precise urban road model reconstruction based on aerial images with high spatial resolution. The proposed approach consists of two steps: i) road surface detection and ii) road pavement marking extraction. In the first step, support vector machine (SVM) was utilized to classify the images into two categories: road and non-road. In the second step, road lane markings are further extracted on the generated road surface based on 2D Gabor filters. The experiments using several pan-sharpened aerial images of Brisbane, Queensland have validated the proposed method.

Empirical comparison of machine learning algorithms for image texture classification with application to vegetation management in power line corridors

This paper reports on the empirical comparison of seven machine learning algorithms in texture classification with application to vegetation management in power line corridors. Aiming at classifying tree species in power line corridors, object-based method is employed. Individual tree crowns are segmented as the basic classification units and three classic texture features are extracted as the input to the classification algorithms. Several widely used performance metrics are used to evaluate the classification algorithms. The experimental results demonstrate that the classification performance depends on the performance matrix, the characteristics of datasets and the feature used.

Machine learning techniques to improve software quality

A significant proportion of the cost of software development is due to software testing and maintenance. This is in part the result of the inevitable imperfections due to human error, lack of quality during the design and coding of software, and the increasing need to reduce faults to improve customer satisfaction in a competitive marketplace. Given the cost and importance of removing errors improvements in fault detection and removal can be of significant benefit. The earlier in the development process faults can be found, the less it costs to correct them and the less likely other faults are to develop. This research aims to make the testing process more efficient and effective by identifying those software modules most likely to contain faults, allowing testing efforts to be carefully targeted. This is done with the use of machine learning algorithms which use examples of fault prone and not fault prone modules to develop predictive models of quality. In order to learn the numerical mapping between module and classification, a module is represented in terms of software metrics. A difficulty in this sort of problem is sourcing software engineering data of adequate quality. In this work, data is obtained from two sources, the NASA Metrics Data Program, and the open source Eclipse project. Feature selection before learning is applied, and in this area a number of different feature selection methods are applied to find which work best. Two machine learning algorithms are applied to the data - Naive Bayes and the Support Vector Machine - and predictive results are compared to those of previous efforts and found to be superior on selected data sets and comparable on others. In addition, a new classification method is proposed, Rank Sum, in which a ranking abstraction is laid over bin densities for each class, and a classification is determined based on the sum of ranks over features. A novel extension of this method is also described based on an observed polarising of points by class when rank sum is applied to training data to convert it into 2D rank sum space. SVM is applied to this transformed data to produce models the parameters of which can be set according to trade-off curves to obtain a particular performance trade-off.