Fractal and fractal-rate stochastic point processes (FSPPs and FRSPPs) provide useful models for describing a broad range of diverse phenomena, including electron transport in amorphous semiconductors, computer-network traffic, and sequences of neuronal action potentials.A particularly useful statistic of these processes is the fractal exponent α, which may be estimated for any FSPP or FRSPP by using a variety of statistical methods. Simulated FSPPs and FRSPPs consistently exhibit bias in this fractal exponent, however, rendering the study and analysis of these processes non-trivial. In this paper, we examine the synthesis and estimation of FRSPPs by carrying out a systematic series of simulations for several different types of FRSPP over a range of design values for α. The discrepancy between the desired and achieved values of α is shown to arise from finite data size and from the character of the 1 Corresponding author.

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We applied multiresolution wavelet analysis to the sequence of times between human heartbeats (R-R intervals) and have found a scale window, between 16 and 32 heartbeat intervals, over which the widths of the R-R wavelet coefficients fall into disjoint sets for normal and heart-failure patients. This has enabled us to correctly classify every patient in a standard data set as belonging either to the heartfailure or normal group with 100% accuracy, thereby providing a clinically significant measure of the presence of heart failure from the R-R intervals alone. Comparison is made with previous approaches, which have provided only statistically significant measures. [S0031-9007(97)05278-2] Multiresolution wavelet analysis [1][2][3][4][5] has proved to be a useful technique for analyzing signals at multiple scales, even in the presence of nonstationarities which often obscure such signals [6,7]. The sequence of times between human heartbeats (R-R intervals) is a prototype of a nonstationary time series that carries information about the state of cardiovascular health of the patient [8,9].By projecting this sequence into a wavelet space, a new set of variables is obtained, whose statistics allow us, for the first time, to correctly classify every patient in a standard data set as either heart-failure or normal, with 100% accuracy. It is clear from our results that the R-R intervals alone suffice as a measure for the presence of heart failure; the full electrocardiogram is not required. This remarkable result arises from the ability of multiresolution analysis to simultaneously and compactly monitor multiple time scales and thereby to expose a hitherto unknown scale window (between 16 and 32 heartbeat intervals) over which the widths of the R-R wavelet coefficients fall into disjoint sets for normal and heart-failure patients. The emergence of this particular scale window should help shed light on the underlying dynamics of cardiovascular function [9]. Previous approaches [10-12], even those that have made use of wavelets [13], have been successful only in providing a statistically significant measure, rather than the clinically significant one we have developed. The analysis method we have used is applicable to a wide variety of nonstationary physical and biological signals, regardless of whether the underlying fluctuations have stochastic origins or arise from nonlinear dynamical processes.The series of intervals between adjacent heartbeats t i [known as R-R or interbeat intervals in cardiology; see Figs. 1(a) and 1(b)] is thought to result from a complex superposition of multiple physiological processes at their respective characteristic time scales [9]. The object of this Letter is to demonstrate that is is possible, without any a priori knowledge of the physiological time scales or underlying heart dynamics, to determine a range of scales over which a statistic of the wavelet coefficients permits each heart-failure and normal patient to be correctly categorized.Scale-dependent statistics are constructed by tran...

We study topological properties of the QCD vacuum on an 8 3 ϫ4 lattice at finite temperature in both phases of QCD. We analyze the distributions of instanton and monopole densities around static color sources and find a suppression of the densities close to external sources. In the confinement phase the suppression occurs in the whole flux tube between a static quark-antiquark pair. We investigate the relation between instantons and Abelian projected monopoles by calculating local correlation functions between topological charge densities and monopole densities. It turns out that topological quantities are correlated over approximately two lattice spacings. The monopole-instanton correlations are rather insensitive under cooling of gauge fields.

Fifty-eight consecutive patients with clinical symptoms of pulmonary embolism/infarction were examined by ultrasound as the first imaging modality. The diagnosis was confirmed in 35 patients by ventilation-perfusion scintigraphy; 13 underwent pulmonary angiography for verification of clinical diagnosis. Seven patients died, necropsy was performed and the diagnosis of pulmonary embolism was confirmed in six cases; three patients were submitted to transthoracic lung biopsy. Intercostal space and an additional small pleural effusion in 48% of the examined patients served as a sonic window for the 5 MHz sector scanner. In 42 of the 54 cases with a final diagnosis of pulmonary embolism/infarction a total of 69, hypoechoic, lesions with a pleural basis were detected. These were conspicuous, predominantly triangular, of a mean size 4.6 x 3.7 cm (range 9 x 8 to 2 x 1.5). A hyperechoic structure with reverberation artefacts suggestive of air was frequently visible in the centre: a sign of segmental involvement. The ultrasound examination yielded a true positive result in 41 cases. The overall sensitivity was 98% and the specificity 66%. The prevalence of pulmonary embolism was 83% and the diagnostic accuracy 90%. This suggests that chest sonography can be an efficient technique in the detection of pulmonary infarction.

This paper proposes a new model for studying the new product development process in an artificial environment. We show how connectionist models can be used to simulate the adaptive nature of agents' learning exhibiting similar behavior as practically experienced learning curves. We study the impact of incentive schemes (local, hybrid and global) on the new product development process for different types of organizations. Sequential organizational structures are compared to two different types of team-based organizations, incorporating methods of Quality Function Deployment such as the House of Quality. A key finding of this analysis is that the firms' organizational structure and agents' incentive system significantly interact. We show that the House of Quality is less affected by the incentive scheme than firms using a Trial & Error approach. This becomes an important factor for new product success when the agents' performance measures are conflicting.

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Renormalization group transformations as discussed recently in deriving fixed point actions are used to analyse the vacuum structure near to the deconfinement temperature. Monte Carlo configurations are generated using the fixed point action. We compare equilibrium configurations with configurations obtained by inverse blocking from a coarser lattice. The absence of short range vacuum fluctuations in the latter does not influence the string tension. For the inversely blocked configurations we find the following: (i) the topological susceptibility χtop is consistent with the phenomenological value in the confinement phase, (ii) χtop drops across the deconfinement transition, (iii) density and size of instantons are estimated, (iv) the topological density is found to be correlated to Abelian monopole currents and (v) the density of spacelike monopole currents becomes a confinement order parameter.