Association of antihypertensive monotherapy with serum sodium and potassium levels in Chinese patients

<b>Background</b> International guidelines on management of hypertension recommend any major classes of antihypertensive drugs. However, the low prescribing rate of thiazides has been attributed to concerns about electrolyte disturbances and studies between antihypertensive drug classes and hyponatremia/hypokalemia among Chinese patients were scarce. <p></p> <b>Methods</b> From clinical databases we included 2,759 patients who received their first-ever antihypertensive monotherapy from January 2004 to June 2007 in a large territory of Hong Kong. We studied the plasma sodium and potassium levels 8 weeks after prescriptions and factors associated with hyponatremia and hypokalemia by multivariable regression analyses. <p></p> <b>Results</b> Among major antihypertensive drug classes, thiazide users had the lowest sodium level (139.6 mEq/l, 95% confidence interval (CI) 139.3, 140.0, P < 0.001) and patients-prescribed calcium channel blockers (CCBs; 3.92 mEq/l, 95% CI 3.89, 3.95) or thiazide diuretics (3.99 mEq/l, 95% CI 3.93, 4.04) had the lowest potassium levels (P < 0.001). Multivariate analysis reported that advanced age (>/=70 years, odds ratio (OR) 7.49, 95% CI 2.84, 19.8, P < 0.001), male gender (OR 2.38, 95% CI 1.45, 3.91, P < 0.001), and thiazide users (OR 2.42, 95% CI 1.29, 4.56, P = 0.006) were significantly associated with hyponatremia, while renin-angiotensin system (RAS) (OR 0.31, 95% CI 0.13, 0.73, P = 0.008) and beta-blockers (BBs) (OR 0.35, 95% CI 0.23, 0.54, P < 0.001) users were less likely to present with hypokalemia. However, the proportions having normonatremic (95.1%) and normokalemic (89.4%) levels were high. <p></p> <b>Conclusions</b> In view of the low prevalence of hyponatremia and hypokalemia associated with thiazides, physicians should not be deterred from prescribing thiazide diuretics as first-line antihypertensive agents as recommended by most international guidelines

Anomalous Soft Photons in Hadron Production

Anomalous soft photons in excess of what is expected from electromagnetic bremsstrahlung have been observed in association with the production of hadrons, mostly mesons, in high-energy (K+)p, (pi+)p, (pi-)p, pp, and (e+)(e-) collisions. We propose a model for the simultaneous production of anomalous soft photons and mesons in quantum field theory, in which the meson production arises from the oscillation of color charge densities of the quarks of the underlying vacuum in the flux tube. As a quark carries both a color charge and an electric charge, the oscillation of the color charge densities will be accompanied by the oscillation of electric charge densities, which will in turn lead to the simultaneous production of soft photons during the meson production process. How the production of these soft photons may explain the anomalous soft photon data will be discussed. Further experimental measurements to test the model will be proposed.Comment: 19 pages, 2 figures, to be published in Physical Review

Random Feature Maps via a Layered Random Projection (LaRP) Framework for Object Classification

The approximation of nonlinear kernels via linear feature maps has recently gained interest due to their applications in reducing the training and testing time of kernel-based learning algorithms. Current random projection methods avoid the curse of dimensionality by embedding the nonlinear feature space into a low dimensional Euclidean space to create nonlinear kernels. We introduce a Layered Random Projection (LaRP) framework, where we model the linear kernels and nonlinearity separately for increased training efficiency. The proposed LaRP framework was assessed using the MNIST hand-written digits database and the COIL-100 object database, and showed notable improvement in object classification performance relative to other state-of-the-art random projection methods.Comment: 5 page

A unified model of the electrical power network

Traditionally, the different infrastructure layers, technologies and management activities associated with the design, control and protection operation of the Electrical Power Systems have been supported by numerous independent models of the real world network. As a result of increasing competition in this sector, however, the integration of technologies in the network and the coordination of complex management processes have become of vital importance for all electrical power companies. The aim of the research outlined in this paper is to develop a single network model which will unify the generation, transmission and distribution infrastructure layers and the various alternative implementation technologies. This 'unified model' approach can support ,for example, network fault, reliability and performance analysis. This paper introduces the basic network structures, describes an object-oriented modelling approach and outlines possible applications of the unified model

Update of an early warning fault detection method using artificial intelligence techniques

This presentation describes a research investigation to access the feasibility of using an Artificial Intelligence (AI) method to predict and detect faults at an early stage in power systems. An AI based detector has been developed to monitor and predict faults at an early stage on particular sections of power systems. The detector for this early warning fault detection device only requires external measurements taken from the input and output nodes of the power system. The AI detection system is capable of rapidly predicting a malfunction within the system. Artificial Neural Networks (ANNs) are being used as the core of the fault detector. In an earlier paper [11], a computer simulated medium length transmission line has been tested by the detector and the results clearly demonstrate the capability of the detector. Today’s presentation considers a case study illustrating the suitability of this AI Technique when applied to a distribution transformer. Furthermore, an evolutionary optimisation strategy to train ANNs is also briefly discussed in this presentation, together with a ‘crystal ball’ view of future developments in the operation and monitoring of transmission systems in the next millennium

Power system fault prediction using artificial neural networks

The medium term goal of the research reported in this paper was the development of a major in-house suite of strategic computer aided network simulation and decision support tools to improve the management of power systems. This paper describes a preliminary research investigation to access the feasibility of using an Artificial Intelligence (AI) method to predict and detect faults at an early stage in power systems. To achieve this goal, an AI based detector has been developed to monitor and predict faults at an early stage on particular sections of power systems. The detector only requires external measurements taken from the input and output nodes of the power system. The AI detection system is capable of rapidly predicting a malfunction within the system . Simulation will normally take place using equivalent circuit representation. Artificial Neural Networks (ANNs) are used to construct a hierarchical feed-forward structure which is the most important component in the fault detector. Simulation of a transmission line (2-port circuit ) has already been carried out and preliminary results using this system are promising. This approach provided satisfactory results with accuracy of 95% or higher

Digital measurement of lightning impulse parameters using curving fitting algorithms

This paper describes the application of curve fitting algorithms to aid the evaluation of lightning impulse parameters. A number of popular curve fitting algorithms have been evaluated and compared. Investigations using the genetic algorithm and other optimisation methods for the purpose of curve fitting have also been carried out and will be described