Calculating tables, giving the products of every two numbers from one to one thousand and their application to the multiplication and division of all numbers above one thousand.

Added t.-p. in French and German. 9. ed 1904. p. III-X in French and German in parallel columns.

Principles and clinical application of assessing alterations in renal elimination pathways

Drugs and metabolites are eliminated from the body by metabolism and excretion. The kidney makes the major contribution to excretion of unchanged drug and also to excretion of metabolites. Net renal excretion is a combination of three processes - glomerular filtration, tubular secretion and tubular reabsorption. Renal function has traditionally been determined by measuring plasma creatinine and estimating creatinine clearance. However, estimated creatinine clearance measures only glomerular filtration with a small contribution from active secretion. There is accumulating evidence of poor correlation between estimated creatinine clearance and renal drug clearance in different clinical settings, challenging the 'intact nephron hypothesis' and suggesting that renal drug handling pathways may not decline in parallel. Furthermore, it is evident that renal drug handling is altered to a clinically significant extent in a number of disease states, necessitating dosage adjustment not just based on filtration. These observations suggest that a re-evaluation of markers of renal function is required. Methods that measure all renal handling pathways would allow informed dosage individualisation using an understanding of renal excretion pathways and patient characteristics. Methodologies have been described to determine individually each of the renal elimination pathways. However, their simultaneous assessment has only recently been investigated. A cocktail of markers to measure simultaneously the individual renal handling pathways have now been developed, and evaluated in healthy volunteers. This review outlines the different renal elimination pathways and the possible markers that can be used for their measurement. Diseases and other physiological conditions causing altered renal drug elimination are presented, and the potential application of a cocktail of markers for the simultaneous measurement of drug handling is evaluated. Further investigation of the effects of disease processes on renal drug handling should include people with HIV infection, transplant recipients (renal and liver) and people with rheumatoid arthritis. Furthermore, changes in renal function in the elderly, the effect of sex on renal function, assessment of living kidney donors prior to transplantation and the investigation of renal drug interactions would also be potential applications. Once renal drug handling pathways are characterised in a patient population, the implications for accurate dosage individualisation can be assessed. The simultaneous measurement of renal function elimination pathways of drugs and metabolites has the potential to assist in understanding how renal function changes with different disease states or physiological conditions. In addition, it will further our understanding of fundamental aspects of the renal elimination of drugs.

Optimisation of strain rate imaging for application to stress echocardiography

We sought to improve the feasibility of strain rate imaging (SRI) during dobutamine stress echocardiography (DSE) in 56 subjects at low risk of coronary disease. The impact of several SRI changes during acquisition were studied, including: (1) changing from fundamental to harmonic imaging; (2) parallel beam-forming; (3) alteration of spatial resolution and (4) narrow sector acquisition. We assessed SR signal quality, a quantitative measure of signal noise and measurements of SRI. Of 1462 segments evaluated, 6% were uninterpretable at rest and 8% at peak stress. Signal quality was optimised by increasing temporal (p = 0.01) and spatial resolution (p<0.0001 vs. baseline imaging) at rest and peak. Increasing spatial resolution also minimised signal noise (p<0.0001). Inter-observer variability of time to peak SR and peak SR were less with high temporal and spatial resolution. SRI quality can be improved with harmonic imaging and higher temporal resolution but optimisation of spatial resolution is critical. (C) 2004 World Federation for Ultrasound in Medicine Biology.

The application of proteomics to the human alcoholic brain

Alcoholism results in changes in the human brain that reinforce the cycle of craving and dependency, and these changes are manifest in the pattern of expression of proteins in key cells and brain areas. Described here is a proteomics-based approach aimed at determining the identity of proteins in the superior frontal cortex (SFC) of the human brain that show different levels of expression in autopsy samples taken from healthy and long-term alcohol abuse subjects. Soluble protein fractions constituting pooled samples combined from SFC biopsies of four well-characterized chronic alcoholics (mean consumption > 80 g ethanol/day throughout adulthood) and four matched controls (< 20 g/day) were generated. Two-dimensional electrophoresis was performed in triplicate on alcoholic and control samples and the resultant protein profiles analyzed for differential expression. Overall, 182 proteins differed by the criterion of twofold or more between case and control samples. Of these, 139 showed significantly lower expression in alcoholics, 35 showed significantly higher expression, and 8 were new or had disappeared. To date, 63 proteins have been identified using MALDI-MS and MS-MS. The finding that the expression level of differentially expressed proteins is preponderantly lower in the alcoholic brain is supported by recent results from parallel studies using microarray mRNA transcript.

Design and implementation of a windows-based parallel computing environment for large scale optimization

A parallel computing environment to support optimization of large-scale engineering systems is designed and implemented on Windows-based personal computer networks, using the master-worker model and the Parallel Virtual Machine (PVM). It is involved in decomposition of a large engineering system into a number of smaller subsystems optimized in parallel on worker nodes and coordination of subsystem optimization results on the master node. The environment consists of six functional modules, i.e. the master control, the optimization model generator, the optimizer, the data manager, the monitor, and the post processor. Object-oriented design of these modules is presented. The environment supports steps from the generation of optimization models to the solution and the visualization on networks of computers. User-friendly graphical interfaces make it easy to define the problem, and monitor and steer the optimization process. It has been verified by an example of a large space truss optimization. (C) 2004 Elsevier Ltd. All rights reserved.

A carbon activation model with application to longan seed char gasification

In this paper a new structural model is presented to describe the evolution of porosity of char during the gasification process. The model assumes the char structure to be composed of bundles of parallel graphite layers, and the reactivities of each layer with the gasification agent are assumed to be different to represent the different degree of heterogeneity of each layer (i.e. each layer will react with the gasification agent at a different rate). It is this difference in the reactivity that allows micropores to be created during the course of gasification. This simple structural model enables the evolution of pore volume, pore geometrical surface area and the pore size distribution to be described with respect to the extent of char burn-off. The model is tested against the experimental data of gasification of longan seed-derived char with carbon dioxide and it is found that the agreement between the model and the data is reasonably satisfactory, especially the evolution of surface area and pore volume with burn-off.

An object-oriented designed finite-difference time-domain simulator for electromagnetic analysis and design in MRI - application to high field analyses

This paper presents a finite-difference time-domain (FDTD) simulator for electromagnetic analysis and design applications in MRI. It is intended to be a complete FDTD model of an MRI system including all RF and low-frequency field generating units and electrical models of the patient. The pro-ram has been constructed in an object-oriented framework. The design procedure is detailed and the numerical solver has been verified against analytical solutions for simple cases and also applied to various field calculation problems. In particular, the simulator is demonstrated for inverse RF coil design, optimized source profile generation, and parallel imaging in high-frequency situations. The examples show new developments enabled by the simulator and demonstrate that the proposed FDTD framework can be used to analyze large-scale computational electromagnetic problems in modern MRI engineering. (C) 2004 Elsevier Inc. All rights reserved.

A study in the application of domestic solar assisted heat pumps for heating and cooling

In the present work, the more important parameters of the heat pump system and of solar assisted heat pump systems were analysed in a quantitative way. Ideal and real Rankine cycles applied to the heat pump, with and without subcooling and superheating were studied using practical recommended values for their thermodynamics parameters. Comparative characteristics of refrigerants here analysed looking for their applicability in heat pumps for domestic heating and their effect in the performance of the system. Curves for the variation of the coefficient of performance as a function of condensing and evaporating temperatures were prepared for R12. Air, water and earth as low-grade heat sources and basic heat pump design factors for integrated heat pumps and thermal stores and for solar assisted heat pump-series, parallel and dual-systems were studied. The analysis of the relative performance of these systems demonstrated that the dual system presents advantages in domestic applications. An account of energy requirements for space and hater heating in the domestic sector in the O.K. is presented. The expected primary energy savings by using heat pumps to provide for the heating demand of the domestic sector was found to be of the order of 7%. The availability of solar energy in the U.K. climatic conditions and the characteristics of the solar radiation here studied. Tables and graphical representations in order to calculate the incident solar radiation over a tilted roof were prepared and are given in this study in section IV. In order to analyse and calculate the heating load for the system, new mathematical and graphical relations were developed in section V. A domestic space and water heating system is described and studied. It comprises three main components: a solar radiation absorber, the normal roof of a house, a split heat pump and a thermal store. A mathematical study of the heat exchange characteristics in the roof structure was done. This permits to evaluate the energy collected by the roof acting as a radiation absorber and its efficiency. An indication of the relative contributions from the three low-grade sources: ambient air, solar boost and heat loss from the house to the roof space during operation is given in section VI, together with the average seasonal performance and the energy saving for a prototype system tested at the University of Aston. The seasonal performance as found to be 2.6 and the energy savings by using the system studied 61%. A new store configuration to reduce wasted heat losses is also discussed in section VI.

Parallel geostatistics for sparse and dense datasets

Very large spatially-referenced datasets, for example, those derived from satellite-based sensors which sample across the globe or large monitoring networks of individual sensors, are becoming increasingly common and more widely available for use in environmental decision making. In large or dense sensor networks, huge quantities of data can be collected over small time periods. In many applications the generation of maps, or predictions at specific locations, from the data in (near) real-time is crucial. Geostatistical operations such as interpolation are vital in this map-generation process and in emergency situations, the resulting predictions need to be available almost instantly, so that decision makers can make informed decisions and define risk and evacuation zones. It is also helpful when analysing data in less time critical applications, for example when interacting directly with the data for exploratory analysis, that the algorithms are responsive within a reasonable time frame. Performing geostatistical analysis on such large spatial datasets can present a number of problems, particularly in the case where maximum likelihood. Although the storage requirements only scale linearly with the number of observations in the dataset, the computational complexity in terms of memory and speed, scale quadratically and cubically respectively. Most modern commodity hardware has at least 2 processor cores if not more. Other mechanisms for allowing parallel computation such as Grid based systems are also becoming increasingly commonly available. However, currently there seems to be little interest in exploiting this extra processing power within the context of geostatistics. In this paper we review the existing parallel approaches for geostatistics. By recognising that diffeerent natural parallelisms exist and can be exploited depending on whether the dataset is sparsely or densely sampled with respect to the range of variation, we introduce two contrasting novel implementations of parallel algorithms based on approximating the data likelihood extending the methods of Vecchia [1988] and Tresp [2000]. Using parallel maximum likelihood variogram estimation and parallel prediction algorithms we show that computational time can be significantly reduced. We demonstrate this with both sparsely sampled data and densely sampled data on a variety of architectures ranging from the common dual core processor, found in many modern desktop computers, to large multi-node super computers. To highlight the strengths and weaknesses of the diffeerent methods we employ synthetic data sets and go on to show how the methods allow maximum likelihood based inference on the exhaustive Walker Lake data set.

Parallel implementation of image segmentation algorithms on a network of transputers

Image segmentation is one of the most computationally intensive operations in image processing and computer vision. This is because a large volume of data is involved and many different features have to be extracted from the image data. This thesis is concerned with the investigation of practical issues related to the implementation of several classes of image segmentation algorithms on parallel architectures. The Transputer is used as the basic building block of hardware architectures and Occam is used as the programming language. The segmentation methods chosen for implementation are convolution, for edge-based segmentation; the Split and Merge algorithm for segmenting non-textured regions; and the Granlund method for segmentation of textured images. Three different convolution methods have been implemented. The direct method of convolution, carried out in the spatial domain, uses the array architecture. The other two methods, based on convolution in the frequency domain, require the use of the two-dimensional Fourier transform. Parallel implementations of two different Fast Fourier Transform algorithms have been developed, incorporating original solutions. For the Row-Column method the array architecture has been adopted, and for the Vector-Radix method, the pyramid architecture. The texture segmentation algorithm, for which a system-level design is given, demonstrates a further application of the Vector-Radix Fourier transform. A novel concurrent version of the quad-tree based Split and Merge algorithm has been implemented on the pyramid architecture. The performance of the developed parallel implementations is analysed. Many of the obtained speed-up and efficiency measures show values close to their respective theoretical maxima. Where appropriate comparisons are drawn between different implementations. The thesis concludes with comments on general issues related to the use of the Transputer system as a development tool for image processing applications; and on the issues related to the engineering of concurrent image processing applications.

Transition in plane parallel shear flows heated internally

The stability of internally heated inclined plane parallel shear flows is examined numerically for the case of finite value of the Prandtl number, Pr. The transition in a vertical channel has already been studied for 0≤Pr≤100 with or without the application of an external pressure gradient, where the secondary flow takes the form of travelling waves (TWs) that are spanwise-independent (see works of Nagata and Generalis). In this work, in contrast to work already reported (J. Heat Trans. T. ASME 124 (2002) 635-642), we examine transition where the secondary flow takes the form of longitudinal rolls (LRs), which are independent of the steamwise direction, for Pr=7 and for a specific value of the angle of inclination of the fluid layer without the application of an external pressure gradient. We find possible bifurcation points of the secondary flow by performing a linear stability analysis that determines the neutral curve, where the basic flow, which can have two inflection points, loses stability. The linear stability of the secondary flow against three-dimensional perturbations is also examined numerically for the same value of the angle of inclination by employing Floquet theory. We identify possible bifurcation points for the tertiary flow and show that the bifurcation can be either monotone or oscillatory. © 2003 Académie des sciences. Published by Elsevier SAS. All rights reserved.

Parallel strategy for optimal learning in perceptrons

We developed a parallel strategy for learning optimally specific realizable rules by perceptrons, in an online learning scenario. Our result is a generalization of the Caticha–Kinouchi (CK) algorithm developed for learning a perceptron with a synaptic vector drawn from a uniform distribution over the N-dimensional sphere, so called the typical case. Our method outperforms the CK algorithm in almost all possible situations, failing only in a denumerable set of cases. The algorithm is optimal in the sense that it saturates Bayesian bounds when it succeeds.

In-Motes EYE:a real time application for automobiles in wireless sensor networks

Wireless sensor networks have been identified as one of the key technologies for the 21st century. In order to overcome their limitations such as fault tolerance and conservation of energy, we propose a middleware solution, In-Motes. In-Motes stands as a fault tolerant platform for deploying and monitoring applications in real time offers a number of possibilities for the end user giving him in parallel the freedom to experiment with various parameters, in an effort the deployed applications to run in an energy efficient manner inside the network. The proposed scheme is evaluated through the In-Motes EYE application, aiming to test its merits under real time conditions. In-Motes EYE application which is an agent based real time In-Motes application developed for sensing acceleration variations in an environment. The application was tested in a prototype area, road alike, for a period of four months.

High performance numerical modeling of ultra-short laser pulse propagation based on multithreaded parallel hardware

The focus of this study is development of parallelised version of severely sequential and iterative numerical algorithms based on multi-threaded parallel platform such as a graphics processing unit. This requires design and development of a platform-specific numerical solution that can benefit from the parallel capabilities of the chosen platform. Graphics processing unit was chosen as a parallel platform for design and development of a numerical solution for a specific physical model in non-linear optics. This problem appears in describing ultra-short pulse propagation in bulk transparent media that has recently been subject to several theoretical and numerical studies. The mathematical model describing this phenomenon is a challenging and complex problem and its numerical modeling limited on current modern workstations. Numerical modeling of this problem requires a parallelisation of an essentially serial algorithms and elimination of numerical bottlenecks. The main challenge to overcome is parallelisation of the globally non-local mathematical model. This thesis presents a numerical solution for elimination of numerical bottleneck associated with the non-local nature of the mathematical model. The accuracy and performance of the parallel code is identified by back-to-back testing with a similar serial version.

Generalized mean field approximation for parallel dynamics of the Ising model

The dynamics of the non-equilibrium Ising model with parallel updates is investigated using a generalized mean field approximation that incorporates multiple two-site correlations at any two time steps, which can be obtained recursively. The proposed method shows significant improvement in predicting local system properties compared to other mean field approximation techniques, particularly in systems with symmetric interactions. Results are also evaluated against those obtained from Monte Carlo simulations. The method is also employed to obtain parameter values for the kinetic inverse Ising modeling problem, where couplings and local field values of a fully connected spin system are inferred from data. © 2014 IOP Publishing Ltd and SISSA Medialab srl.