Maximal Instantaneous Mitral Valve Velocities Measured with a Digital Echocardiographic Tracking System

Analysis of the motion of various cardiac structures is attaining increasing clinical significance. By coupling a digital tracking system to a commercially available echocardiograph, continuous position and velocity data from selected intracardiac structures were obtained. This tracking system, which employs a dynamic range gate, has been designed to isolate and lock on to a selected target within the heart. Position of the target is calculated every 8 ms; velocity is obtained by determining the difference between consecutive positions. This system has been used to study mitral valve motion in 20 normal subjects. Maximal instantaneous velocities of the anterior leaflet of the mitral valve were recorded. These instantaneous velocities are to be distinguished from the average velocities obtained by manually measuring the slopes of the curve segments. This system demonstrates a new technique for obtaining more detailed information about the dynamic characteristics of selected cardiac structures.     

Implementation and Development of an Adaptive Steering-Control System

In this paper, an adaptive steering-control system for a steer-by-wire system, which consists of a vehicle directional-control unit and a driver-interaction unit, is developed. The adaptive online estimation method is used to identify the dynamic parameters of the vehicle directional-control and driver-interaction units. A nonlinear 4-degree-of-freedom (DOF) vehicle model, including the longitudinal, lateral, yaw, and quasi-static roll motions, is derived using Newtonian mechanics to simulate and test the adaptive steering-control system. Experimental results are performed to demonstrate the efficacy of the proposed adaptive steering-control system.      

On the Modeling of Elastic Contact between Rough Surfaces

The contact force and the real contact area between rough surfaces are important in the prediction of friction, wear, adhesion, and electrical and thermal contact resistance. Over the last four decades various mathematical models have been developed. Built on very different assumptions and underlying mathematical frameworks, model agreement or effectiveness has never been thoroughly investigated. This work uses several measured profiles of real surfaces having vastly different roughness characteristics to predict contact areas and forces from various elastic contact models and contrast them to a deterministic fast Fourier transform (FFT)-based contact model. The latter is considered “exact” because surfaces are analyzed as they are measured, accounting for all peaks and valleys without compromise. Though measurement uncertainties and resolution issues prevail, the same surfaces are kept constant (i.e., are identical) for all models considered. Nonetheless, the effect of the data resolution of measured surface profiles will be investigated as well. An exact closed-form solution is offered for the widely used Greenwood and Williamson (GW) model (Greenwood and Williamson, Proceedings of the Royal Society of London A, vol. 295, pp. 300–319), along with an alternative definition of the plasticity index that is based on a multiscale approach. The results reveal that several of the theoretical models show good quantitative and qualitative agreement among themselves, but though most models produce a nominally linear relationship between the real contact area and load, the deterministic model suggests otherwise in some cases. Regardless, all of the said models reduce the complicated surface profiles to only a few key parameters and it is therefore unrealistic to expect them to make precise predictions for all cases.     

Gain conditions and convergence of simple adaptive control

Recent publications have presented successful implementations of simple direct adaptive control techniques in various applications. However, they also expose the fact that the convergence of the adaptive gains has remained uncertain. The gains may not converge to the ideal constant control gains predicted by the underlying linear time‐invariant system considerations. As those prior conditions that were also needed for stability may not hold, this conclusion may raise doubts about the robustness of the adaptive system. This paper intends to show that the adaptive control performs perfect tracking even when the linear time‐invariant solution does not exist. It is shown that the adaptation performs a ‘steepest descent’ minimization of the errors, ultimately ending with the appropriate set of control gains that fit the particular input command and initial conditions. The adaptive gains do asymptotically reach an appropriate set of bounded constant ideal gain values that solve the problem at task. Copyright © 2004 John Wiley & Sons, Ltd.     

An anodic behaviour study of an analogical sintered system of austenitic stainless steel in H2SO4 solution

The anodic behaviour of sintered austenitic stainless steel was investigated on an analogical system using wrought plate stainless steel of Type 316. Potentiodynamic polarization measurements were carried out on wrought plate stainless steel of Type 316 in an analogical concentration cell using a special method. The effect of carbides precipitation on the anodic behaviour was investigated on carburized 316 wrought stainless steel containing an average carbon concentration of 0.06 and 0.15 (wt. %). The carburization treatment results in an increase in the critical current density although a clear active passive transition is observed. The anodic behaviour of sintered stainless steel in H₂SO₄ solution is mainly affected by the existence of interconnected open pores. The local intergranular sensitivity to the corrosive environment is increased due to carbide precipitation.     

A fast method for the implementation of common vector approach

In this paper a novel computation method is proposed to perform the common vector approach (CVA) faster than its conventional implementation in pattern recognition. While conventional CVA calculations perform the classification with respect to the distance between vectors, the new method performs the classification using scalars. A theoretical proof of the equivalence of the proposed method is provided. Next, in order to verify the numerical equivalence of the proposed computation method to the conventional (vector-based) method, numerical experiments are conducted over three different face databases, namely the AR Database, extended Yale Face Database B, and FERET Database. Since the computational gain may depend on (i) the dimension of the feature vectors, (ii) the number of feature vectors used in training, and (iii) the number of classes, the effects of these items are clearly verified via these databases. Our theoretically equivalent (but faster) method provided no difference in the classification rates despite its improved classification speed as compared to the classical implementation of CVA. The new method is found to be about 2.1-3.0 times faster than the conventional CVA implementation for the AR face database, 1.9-3.3 times faster for the extended Yale Face Database B, and 1.9-3.1 times faster for the FERET Database.     

With a little help from my friend: Effect of single or multiple act aid as a function of donor and task characteristics.

96 Israeli males were asked to bring a good same-sex friend with them to the experiment. Half the Ss were told that their teammate, with whom they were to interact at a later stage of the experiment, was their friend. The other half were told that they had been teamed up with someone whom they did not know. Ss were instructed to solve a detective story, the performance of which was presented to half the Ss as related to ego-relevant dimensions (e.g., intelligence, creativity) and to the other half as related to non-ego-relevant dimensions (e.g., luck and momentary mood). The assignment was insolvable, and Ss were helped by their supposed teammate to obtain the "correct" solution. Half received help on one detective story, and the other half received help on 2 stories. Results show that Ss who had been helped twice on an ego-relevant task by a good friend had the least-favorable affect and self-evaluations. Ss who had been helped twice by a good friend on a non-ego-relevant dimension tended to have the most-favorable affect and self-evaluations. Similar patterns were observed for measures of external perception (i.e., liking for helper, evaluations of helper, and relations with the helper). Implications for research on recipient reactions to aid, social comparison processes, and close interpersonal relations are discussed. (27 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Replica Management in the European DataGrid Project

Within the European DataGrid project, Work Package 2 has designed and implemented a set of integrated replica management services for use by data intensive scientific applications. These services, based on the web services model, enable movement and replication of data at high speed from one geographical site to another, management of distributed replicated data, optimization of access to data, and the provision of a metadata management tool. In this paper we describe the architecture and implementation of these services and evaluate their performance under demanding Grid conditions.This work was partially funded by the European Commission program IST- 2000-25182 through the European DataGrid Project.     

Speeding up the scaled conjugate gradient algorithm and its application in neuro-fuzzy classifier training

The aim of this study is to speed up the scaled conjugate gradient (SCG) algorithm by shortening the training time per iteration. The SCG algorithm, which is a supervised learning algorithm for network-based methods, is generally used to solve large-scale problems. It is well known that SCG computes the second-order information from the two first-order gradients of the parameters by using all the training datasets. In this case, the computation cost of the SCG algorithm per iteration is more expensive for large-scale problems. In this study, one of the first-order gradients is estimated from the previously calculated gradients without using the training dataset. To estimate this gradient, a least square error estimator is applied. The estimation complexity of the gradient is much smaller than the computation complexity of the gradient for large-scale problems, because the gradient estimation is independent of the size of dataset. The proposed algorithm is applied to the neuro-fuzzy classifier and the neural network training. The theoretical basis for the algorithm is provided, and its performance is illustrated by its application to several examples in which it is compared with several training algorithms and well-known datasets. The empirical results indicate that the proposed algorithm is quicker per iteration time than the SCG. The algorithm decreases the training time by 20–50% compared to SCG; moreover, the convergence rate of the proposed algorithm is similar to SCG.