Moderate Deviations for the SSEP with a Slow Bond

We consider the one dimensional symmetric simple exclusion process with a slow bond. In this model, particles cross each bond at rate $N^2$, except one particular bond, the slow bond, where the rate is $N$. Above, $N$ is the scaling parameter. This model has been considered in the context of hydrodynamic limits, fluctuations and large deviations. We investigate moderate deviations from hydrodynamics and obtain a moderate deviation principle.Comment: 24 page

Baryon Number Fluctuations in Quasi-particle Model

Baryon number fluctuations are sensitive to the QCD phase transition and QCD critical point. According to the Feynman rules of finite-temperature field theory, we calculated various order moments and cumulants of the baryon number distributions in the quasi-particle model of quark gluon plasma. Furthermore, we compared our results with the experimental data measured by the STAR experiment at RHIC. It is found that the experimental data can be well described by the model for the colliding energies above 30 GeV and show large discrepancies at low energies. It can put new constraint on qQGP model and also provide a baseline for the QCD critical point search in heavy-ion collisions at low energies.Comment: 13 pages, 5 figure

Herding evidence in Chinese stock market : a study of the relationship between stock price index and trading volume based on behavioral finance theory

Over the last couple decades, more evidence has been found supporting the notion that investors are not always rational. Herding behaviors have been observed in both the stock market crash1 and financial bubbles2, which were beyond the understanding of modern finance theory. In this paper, the herding phenomenon was explored in the Chinese stock market by the study of the relationship between stock prices and trading volume over the past 7 years. It was found that the change of price is statistically significant to have caused the change of trading volume, but the reverse is not true. Theoretically, this identifies persistent herding phenomenon in the Chinese stock market. The findings provide useful investment guidance for investors and new considerations in financial reform for the government

Trade-Based Manipulation Or Speculative Bubble: A Case Study

This paper examines the unusual and puzzling stock price performance of USEC Inc. during July 2013.  The stock price surged as much as ten times during merely sixteen trading days without apparent value-changing information being released.  Four possible reasons are analyzed - mean reversion, information-based manipulation, speculative bubble, and trade-based manipulation.  Trade-based manipulation and speculative bubble resulting from investors’ overconfidence seem to be more plausible and better explain the dramatic stock price changes

Approximation Methods for the Standard Deviation of Flow Times in the G/G/s Queue

We provide approximation methods for the standard deviation of flow time in system for a general multi-server queue with infinite waiting capacity (G / G / s ). The approximations require only the mean and standard deviation or the coefficient of variation of the inter-arrival and service time distributions, and the number of servers. These approximations are simple enough to be implemented in manual or spreadsheet calculations, but in comparisons to Monte Carlo simulations have proven to give good approximations (within ±10%) for cases in which the coefficients of variation for the interarrival and service times are between 0 and 1. The approximations also have the desirable properties of being exact for the specific case of Markov queue model M / M / s, as well as some imbedded Markov queuing models ( Ek / M / 1 and M / Eα / 1). The practical significance of this research is that (1) many real world queuing problems involve the G / G / s queuing systems, and (2) predicting the range of variation of the time in the system (rather than just the average) is needed for decision making. For example, one job shop facility with which the authors have worked, guarantees its customers a nine day turnaround time and must determine the minimum number of machines of each type required to achieve nine days as a “worst case” time in the system. In many systems, the “worst case” value of flow time is very relevant because it represents the lead time that can safely be promised to customers. To estimate this we need both the average and standard deviation of the time in system. The usefulness of our results stems from the fact that they are computationally simple and thus provide quick approximations without resorting to complex numerical techniques or Monte Carlo simulations. While many accurate approximations for the G / G / s queue have been proposed previously, they often result in algebraically intractable expressions. This hinders attempts to derive closed-form solutions to the decision variables incorporated in optimization models, and inevitably leads to the use of complex numeric methods. Furthermore, actual application of many of these approximations often requires specification of the actual distributions of the inter-arrival time and the service time. Also, these results have tended to focus on delay probabilities and average waiting time, and do not provide a means of estimating the standard deviation of the time in the system. We also extend the approximations to computing the standard deviation of flow times of each priority class in the G / G / s priority queues and compare the results to those obtained via Monte Carlo simulations. These simulation experiments reveal good approximations for all priority classes with the exception of the lowest priority class in queuing systems with high utilization. In addition, we use the approximations to estimate the average and the standard deviation of the total flow time through queuing networks and have validated these results via Monte Carlo Simulations. The primary theoretical contributions of this work are the derivations of an original expression for the coefficient of variation of waiting time in the G / G / s queue, which holds exactly for G / M / s and M / G /1 queues. We also do some error sensitivity analysis of the formula and develop interpolation models to calculate the probability of waiting, since we need to estimate the probability of waiting for the G / G / s queue to calculate the coefficient of variation of waiting time. Technically we develop a general queuing system performance predictor, which can be used to estimate all kinds of performances for any steady state, infinite queues. We intend to make available a user friendly predictor for implementing our approximation methods. The advantages of these models are that they make no assumptions about distribution of inter-arrival time and service time. Our techniques generalize the previously developed approximations and can also be used in queuing networks and priority queues. Hopefully our approximation methods will be beneficial to those practitioners who like simple and quick practical answers to their multi-server queuing systems. Key words and Phrases: Queuing System, Standard Deviation, Waiting Time, Stochastic Process, Heuristics, G / G/ s, Approximation Methods, Priority Queue, and Queuing Networks

Approximate Nonlinear Modeling of Aircraft Engine Surge Margin Based on Equilibrium Manifold Expansion

AbstractStable operation of aircraft engine compressions is constrained by rotating surge. In this paper, an approximate nonlinear surge margin model of aircraft engine compression system by using equilibrium manifold is presented. Firstly, this paper gives an overview of the current state of modeling aerodynamic flow instabilities in engine compressors. Secondly, the expansion form of equilibrium manifold is introduced, and the choosing scheduling variable method is discussed. Then, this paper also gives the identification procedure of modeling the approximate nonlinear model. Finally, the modeling and simulations with high pressure (HP) compressor surge margin of the aircraft engine show that this real-time model has the same accuracy with the thermodynamic model, but has simpler structure and shorter computation time

Asymptotic distributions of the average clustering coefficient and its variant

In network data analysis, summary statistics of a network can provide us with meaningful insight into the structure of the network. The average clustering coefficient is one of the most popular and widely used network statistics. In this paper, we investigate the asymptotic distributions of the average clustering coefficient and its variant of a heterogeneous Erd\"{o}s-R\'{e}nyi random graph. We show that the standardized average clustering coefficient converges in distribution to the standard normal distribution. Interestingly, the variance of the average clustering coefficient exhibits a phase transition phenomenon. The sum of weighted triangles is a variant of the average clustering coefficient. It is recently introduced to detect geometry in a network. We also derive the asymptotic distribution of the sum weighted triangles, which does not exhibit a phase transition phenomenon as the average clustering coefficient. This result signifies the difference between the two summary statistics

Non-equilibrium Fluctuations of the Weakly Asymmetric Normalized Binary Contact Path Process

This paper is a further investigation of the problem studied in \cite{xue2020hydrodynamics}, where the authors proved a law of large numbers for the empirical measure of the weakly asymmetric normalized binary contact path process on $\mathbb{Z}^d,\, d \geq 3$, and then conjectured that a central limit theorem should hold under a non-equilibrium initial condition. We prove that the aforesaid conjecture is true when the dimension $d$ of the underlying lattice and the infection rate $\lambda$ of the process are sufficiently large