Deuteron production and elliptic flow in relativistic heavy ion collisions

The hadronic transport model \textsc{art} is extended to include the production and annihilation of deuterons via the reactions $BB \leftrightarrow dM$, where $B$ and $M$ stand for baryons and mesons, respectively, as well as their elastic scattering with mesons and baryons in the hadronic matter. This new hadronic transport model is then used to study the transverse momentum spectrum and elliptic flow of deuterons in relativistic heavy ion collisions, with the initial hadron distributions after hadronization of produced quark-gluon plasma taken from a blast wave model. The results are compared with those measured by the PHENIX and STAR Collaborations for Au+Au collisions at $\sqrt{s_{NN}^{}} = 200$ GeV, and also with those obtained from the coalescence model based on freeze-out nucleons in the transport model.Comment: 9 pages, 10 figures, REVTeX, version to be published in Phys. Rev.

Effects of hadronic potentials on elliptic flows in relativistic heavy ion collisions

Within the framework of a multiphase transport (AMPT) model that includes both initial partonic and final hadronic interactions, we show that including mean-field potentials in the hadronic phase leads to a splitting of the elliptic flows of particles and their antiparticles, providing thus a plausible explanation of the different elliptic flows between $p$ and ${\bar p}$, $K^+$ and $K^-$, and $\pi^+$ and $\pi^-$ observed in recent Beam Energy Scan (BES) program at the Relativistic Heavy-Ion Collider (RHIC).Comment: 5 pages, 7 figure

Implementation of a Multimaps Chaos-Based Encryption Software for EEG Signals

In the chapter, we adopted a chaos logic map and a quadratic map to develop the chaos-based multi-maps EEG encryption software. The encryption performances of the chaos-based software were studied. The percent root-mean-square difference (PRD) is used to estimate the accuracy of a correctly decrypted EEG signal with respect to the original EEG signal. Pearson correlation coefficient (PCC) is used to estimate the correlation between the original EEG signal and an incorrectly decrypted EEG signal. The seven encryption aspects were testing, the average PRD value of the original and correctly decrypted EEG signals for the chaos-based multi-maps software is 2.59 x 10-11, and the average encryption time is 113.2857 ms. The five error decryption aspects were testing, the average PCC value of the original and error decrypted EEG signals for the chaos-based multi-maps software is 0.0026, and the average error decryption time is 113.4000 ms. These results indicate that the chaos-based multimaps EEG encryption software can be applied to clinical EEG diagnosis

Economic variables and their relationship to the returns of listed and unlisted commercial properties in South Africa

The purpose of this research is to investigate the relationship between unlisted and listed commercial property returns and the macroeconomic factors identified, which are the stock market, economic activity, inflation and interest rates, in South Africa for the period from 1995 to 2013 (for unlisted properties) and from 2002 to 2013 (for listed properties). It is commonly understood that relevant macroeconomic variables impact asset prices; it is therefore easy to see why it is important to examine the dynamic interactions between the macroeconomic variables and property returns. Previous studies identified stock market performance, economic growth, interest rate and inflation as significant macroeconomic variables. The empirical research in this work is conducted using regression and vector auto regression (VAR) methodologies consistent with prior studies. Regression analysis considers the statistical dependence of the dependent variable on one or more explanatory variables. VAR analysis permits inferences to be drawn about how a particular variable helps to explain property returns and to see how a shock from the same variable affects that return. The work concluded that unlisted property has insufficient historical data to perform the relevant statistical testing. It also established that unlisted property has shown a high correlation (69%) to listed property. Finally, for listed property it was determined that interest rates were found to be a significant negative variable. This result was consistent with the impulse response analysis conducted. Variance decomposition also showed that the interest rate variable explained almost 49% of the volatility of listed property. No other economic variables identified in this work were found to be statistically significant. This research is the first of its kind relating to commercial property in South Africa. The findings of this research reaffirm the theoretical argument that the relationship between interest rates and returns of commercial property is negative. The findings of this research are of significance to investors, analysts and policymakers wishing to acquire a better understanding of this market

A Shallow Ritz Method for Elliptic Problems with Singular Sources

In this paper, a shallow Ritz-type neural network for solving elliptic equations with delta function singular sources on an interface is developed. There are three novel features in the present work; namely, (i) the delta function singularity is naturally removed, (ii) level set function is introduced as a feature input, (iii) it is completely shallow, comprising only one hidden layer. We first introduce the energy functional of the problem and then transform the contribution of singular sources to a regular surface integral along the interface. In such a way, the delta function singularity can be naturally removed without introducing a discrete one that is commonly used in traditional regularization methods, such as the well-known immersed boundary method. The original problem is then reformulated as a minimization problem. We propose a shallow Ritz-type neural network with one hidden layer to approximate the global minimizer of the energy functional. As a result, the network is trained by minimizing the loss function that is a discrete version of the energy. In addition, we include the level set function of the interface as a feature input of the network and find that it significantly improves the training efficiency and accuracy. We perform a series of numerical tests to show the accuracy of the present method and its capability for problems in irregular domains and higher dimensions