Irving O. Morales

Heart Rate and Systolic Blood Pressure Variability in the Time Domain in Patients with Recent and Long-Standing Diabetes Mellitus

Diabetes Mellitus (DM) affects the cardiovascular response of patients. To study this effect, interbeat intervals (IBI) and beat-to-beat systolic blood pressure (SBP) variability of patients during supine, standing and controlled breathing tests were analyzed in the time domain. Simultaneous noninvasive measurements of IBI and SBP for 30 recently diagnosed and 15 long-standing DM patients were compared with the results for 30 rigorously screened healthy subjects (control). A statistically significant distinction between control and diabetic subjects was provided by the standard deviation and the higher moments of the distributions (skewness, and kurtosis) with respect to the median. To compare IBI and SBP for different populations, we define a parameter, α, that combines the variability of the heart rate and the blood pressure, as the ratio of the radius of the moments for IBI and the same radius for SBP. As diabetes evolves, α decreases, standard deviation of the IBI detrended signal diminishes (heart rate signal becomes more “rigid”), skewness with respect to the median approaches zero (signal fluctuations gain symmetry), and kurtosis increases (fluctuations concentrate around the median). Diabetes produces not only a rigid heart rate, but also increases symmetry and has leptokurtic distributions. SBP time series exhibit the most variable behavior for recently diagnosed DM with platykurtic distributions. Under controlled breathing, SBP has symmetric distributions for DM patients, while control subjects have non-zero skewness. This may be due to a progressive decrease of parasympathetic and sympathetic activity to the heart and blood vessels as diabetes evolves.

Behavior of Early Warnings near the Critical Temperature in the Two-Dimensional Ising Model

Among the properties that are common to complex systems, the presence of critical thresholds in the dynamics of the system is one of the most important. Recently, there has been interest in the universalities that occur in the behavior of systems near critical points. These universal properties make it possible to estimate how far a system is from a critical threshold. Several early-warning signals have been reported in time series representing systems near catastrophic shifts. The proper understanding of these early-warnings may allow the prediction and perhaps control of these dramatic shifts in a wide variety of systems. In this paper we analyze this universal behavior for a system that is a paradigm of phase transitions, the Ising model. We study the behavior of the early-warning signals and the way the temporal correlations of the system increase when the system is near the critical point.

High-spin structures of 136 54Xe, 137 55Cs, 138 56Ba, 139 57La and 140 58Ce: a shell model description

In the present work recently available experimental data (Astier et al 2012 Phys. Rev. C 85 064316) for the high-spin states of five N = 82 isotones, 136 54Xe, 137 55Cs, 138 56Ba, 139 57La and 140 58Ce have been interpreted with shell model calculations. The calculations have been performed in the 50–82 valence shell composed of 1g7/2, 2d5/2, 1h11/2, 3s1/2 and 2d3/2 orbitals. We have compared our results with the available experimental data for excitation energies including high-spin states, occupancy numbers and transition probabilities. As expected the structure of these isotones are due to proton excitations across the Z = 50 shell. The structure of the positive-parity states are mainly from (πg7/2πd5/2)n and (πg7/2πd5/2)n − 2(πh11/2)2 configurations, while the negative-parity states have a (πg7/2πd5/2)n(πh11/2)1 configuration. Additionally, for the 136 54Xe, 137 55Cs and 138 56Ba isotones the excitation of the neutrons across the N = 82 gap is important.

How good are the Garvey–Kelson predictions of nuclear masses?

Abstract The Garvey–Kelson relations are used in an iterative process to predict nuclear masses in the neighborhood of nuclei with measured masses. Average errors in the predicted masses for the first three iteration shells are smaller than those obtained with the best nuclear mass models. Their quality is comparable with the Audi–Wapstra extrapolations, offering a simple and reproducible procedure for short range mass predictions. A systematic study of the way the error grows as a function of the iteration and the distance to the known masses region, shows that a correlation exists between the error and the residual neutron–proton interaction, produced mainly by the implicit assumption that V n p varies smoothly along the nuclear landscape.

THE ART OF PREDICTING NUCLEAR MASSES

A review of recent advances in the theoretical analysis of nuclear mass models and their predictive power is presented. After introducing two tests which probe the ability of nuclear mass models to extrapolate, three models are analyzed in detail: the liquid drop model (LDM), the liquid drop model plus empirical shell corrections (LDMM) and the Duflo–Zuker mass formula (DZ). The DZ model is exhibited as the most predictive model. The Garvey–Kelson mass relations are also discussed. It is shown that their fulfillment probes the consistency of the most commonly used mass formulae, and that they can be used in an iterative process to predict nuclear masses in the neighborhood of nuclei with measured masses, offering a simple and reproducible procedure for short range mass predictions.

Enhancement of early warning properties in the Kuramoto model and in an atrial fibrillation model due to an external perturbation of the system

When a complex dynamical system is externally disturbed, the statistical moments of signals associated to it can be affected in ways that depend on the nature and amplitude of the perturbation. In systems that exhibit phase transitions, the statistical moments can be used as Early Warnings (EW) of the transition. A natural question is thus to wonder what effect external disturbances have on the EWs of system. In this work we study the impact of external noise added to the system on the EWs, with particular focus on understanding the importance of the amplitude and complexity of the noise. We do this by analyzing the EWs of two computational models related to biology: the Kuramoto model, which is a paradigm of synchronization for biological systems, and a cellular automaton model of cardiac dynamics which has been used as a model for atrial fibrillation. For each model we first characterize the EWs. Then, we introduce external noise of varying intensity and nature to observe what effect this has on the EWs. In both cases we find that the introduction of noise amplified the EWs, with more complex noise having a greater effect. This both offers a way to improve the chance of detection of EWs in real systems and suggests that natural variability in the real world does not have a detrimental effect on EWs, but the opposite.

PREDICTING NUCLEAR MASSES BY IMAGE RECONSTRUCTION

The differences between measured masses and Liquid Drop Model (LDM) predictions have well known regularities, which can be analyzed as a two-dimensional texture on the N-Z plane. The remaining microscopic effects, obtained after removing the smooth LDM mass contributions, have proved difficult to model. They contain all the information related to shell closures, nuclear deformation and the residual nuclear interactions, and display a well defined pattern. In the present work the more than 2000 known nuclear masses are studied as an array in the N-Z plane viewed through a mask, behind which the approximately 7000 unknown unstable nuclei that can exist between the proton and neutron drip lines are hidden. Employing a Fourier transform deconvolution method these masses can be predicted. Measured masses are reconstructed with and r.m.s. error of less than 100 keV. Potential applications of the present approach are outlined.

Self similitude in the power spectra of nuclear energy levels

Protons and neutrons interact inside the nuclei with a non integrable interaction. It is shown that is possible to reproduce the energy statistics of nuclear energy levels analyzing the fluctuations of an ensemble of nuclear states produced by random interactions. In this contribution we analyze the characteristics of the fluctuations and distributions of the energy levels in order to understand the independent scale nature of the 1/f-noise nuclear energy levels.

Scale Invariance, Self Similarity and Critical Behavior in Classical and Quantum Systems

Symmetry and self-affinity or scale invariance are related concepts. We explore the fractal properties of fluctuations in dynamical systems, using some of the available tools in the context of time series analysis. We carry out a power spectrum study in the Fourier domain, the method of detrended fluctuation analysis and the investigation of autocorrelation function behavior. Our study focuses on two particular examples, the logistic module-1 map, which displays properties of classical dynamical systems, and the excitation spectrum of a schematic shell-model Hamiltonian, which is a simple system exhibiting quantum chaos.

Shape coexistence and phase transitions in the platinum isotopes

The matrix coherent-state approach of the interacting boson model with configuration mixing is used to study the geometry of the platinum isotopes. With a parameter set determined in previous studies, it is found that the absolute minimum of the potential for the Pt isotopes evolves from spherical to oblate and finally to prolate shapes when the neutron number decreases from N = 126 (semi-magic) to N = 104 (mid-shell). Shape coexistence is found in the isotopes 182,184,186,188 Pt. A phase diagram is constructed that shows the coexistence region as a function of the number of bosons and the strength of the mixing parameter.