We postulated that aging is associated with disruption in the fractallike long-range correlations that characterize healthy sinus rhythm cardiac interval dynamics. Ten young (21-34 yr) and 10 elderly (68-81 yr) rigorously screened healthy subjects underwent 120 min of continuous supine resting electrocardiographic recording. We analyzed the interbeat interval time series using standard time and frequency domain statistics and using a fractal measure, detrended fluctuation analysis, to quantify long-range correlation properties. In healthy young subjects, interbeat intervals demonstrated fractal scaling, with scaling exponents (alpha) from the fluctuation analysis close to a value of 1.0. In the group of healthy elderly subjects, the interbeat interval time series had two scaling regions. Over the short range, interbeat interval fluctuations resembled a random walk process (Brownian noise, alpha = 1.5), whereas over the longer range they resembled white noise (alpha = 0.5). Short (alpha s)- and long-range (alpha 1) scaling exponents were significantly different in the elderly subjects compared with young (alpha s = 1.12 +/- 0.19 vs. 0.90 +/- 0.14, respectively, P = 0.009; alpha 1 = 0.75 +/- 0.17 vs. 0.99 +/- 0.10, respectively, P = 0.002). The crossover behavior from one scaling region to another could be modeled as a first-order autoregressive process, which closely fit the data from four elderly subjects. This implies that a single characteristic time scale may be dominating heartbeat control in these subjects. The age-related loss of fractal organization in heartbeat dynamics may reflect the degradation of integrated physiological regulatory systems and may impair an individual's ability to adapt to stress.

Current methods for detecting nonlinear determinism in a time series require long and stationary data records, as most of them assume that the observed dynamics arise only from the internal, deterministic workings of the system, and the stochastic portion of the signal (the noise component) is assumed to be negligible. To explicitly account for the stochastic portion of the data we recently developed a method based on a stochastic nonlinear autoregressive (SNAR) algorithm. The method iteratively estimates nonlinear autoregressive models for both the deterministic and stochastic portions of the signal. Subsequently, the Lyapunov exponents (LE) are calculated for the estimated models in order to examine if nonlinear determinism is present in the deterministic portion of the fitted model. To determine if nonlinear dynamic analysis of heart-rate fluctuations can be used to assess arrhythmia susceptibility by predicting the outcome of invasive cardiac electrophysiologic study (EPS), we applied the SNAR algorithm to noninvasively measured resting sinus-rhythm heart-rate signals obtained from 16 patients. Our analysis revealed that a positive LE was highly correlated to a patient with a positive outcome of EPS. We found that the statistical accuracy of the SNAR algorithm in predicting the outcome of EPS was 88% (sensitivity=100%, specificity=75%, positive predictive value=80%, negative predictive value=100%, p=0.0019). Our results suggest that the SNAR algorithm may serve as a noninvasive probe for screening high-risk populations for malignant cardiac arrhythmias.

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Invasive coronary angiography is considered to be the gold standard for diagnosis and follow-up of coronary artery aneurysms, thrombosis and stenosis in patients with Kawasaki Disease. However, the availability of multi-detector CT coronary angiography provides a viable alternative as a non-invasive imaging modality for sequential follow-up of patients with Kawasaki disease. High quality multidetector CT angiography images of coronary arterial anatomy can be obtained after adequate heart rate control using beta blockers.

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levels, equivalent continuous sound level, and formulation of annoyance criteria. Chapter 29 treats the various aspects of occupational hearing loss and hearing conservation. This leads directly into the effect of noise on people. The concluding chapter reports upon human response to vibration. In summary, this is an excellent book. Due to the broad contents of the various subjects, the authors can only provide highlights but the references are numerous. In reality, this is a handbook. The reviewer believes that discussion on acoustic intensity and finite elements should be expanded. The table of contents should be expanded to include subsections. Due to the cost, the reviewer recommends that the book should be published in a paperback edition. Furthermore, due to its size, the book should be divided into two parts, i.e. fundamental theory and applications. The 23 authors should be commended for their prodigious efforts. Except for the minor criticisms, this book will assume the exalted role of a much sought-after reference book.

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This study presents the development of a methodology for the construction of data-driven, parametric, multifidelity reduced-order models to emulate aerodynamic flowfields with nonlinear and discontinuous features. Realistic computational budgets often constrain the size of the high-fidelity data set required to build a model with the desired predictive accuracy. In such cases, multifidelity models can be advantageous, as they leverage an abundance of inexpensive low-fidelity data in conjunction with high-fidelity training data to improve the model’s predictive accuracy. This study formulates a multifidelity reduced-order modeling method that uses nonlinear dimension reduction and Procrustes manifold alignment to project and transform data from disparate sources such that they lie in a common latent space. An initial feasibility assessment of the method is performed for emulating the flow over a two-dimensional transonic airfoil and a high-speed blunt body. It is observed that, for problems with high input space dimension and complex features, the predictive accuracy of the multifidelity nonlinear reduced-order models improves substantially over their linear counterparts. However, multifidelity linear models were superior to equivalent nonlinear models for smaller input space dimensions, which may provide a useful intuition for practitioners when constructing reduced-order models for their respective problems.