Present status on experimental search for pentaquarks

It has been ten years since the first report for a positive strangeness pentaquark-like baryon state. However the existence of the pentaquark state is still controversial. Some contradictions between the experiments are unsolved. In this paper we review the experimental search for the pentaquark candidates $\Theta^+$, $\Theta^{++}$, $\Xi^{--}$, $\Theta_c^0$ and $N^*$ in details. We review the experiments with positive results and compare the experiments with similar conditions but opposite results.Comment: 20 latex pages, 2 figures, to appear in IJMPA as a revie

Research on the Influence of Brand Personality on Brand Forgiveness

As market competition becomes more and more fierce, brand building and customer retention are important ways for modern enterprises to find competitive advantages. Some brand problems arise inevitably in the development of modern enterprises. How to survive in a crisis, retain the original customers, which have been the primary businesses for enterprises to find ways to solve. The aim of the present study is to explore the mechanism between brand personalities and brand forgiveness. The explanations for this study is based on the framework of the Theory of Similarity. Combined with the theory, the study explains the influence of brand personality on brand forgiveness from the path of brand trust. And in the context of emotional brand attachment, this mechanism is more obvious. Keywords: Brand personality, Brand trust, Brand forgiveness, Emotional brand attachment DOI: 10.7176/EJBM/11-20-02 Publication date:July 31st 2019

Bayesian analysis of spatial and survival models with applications of computation techniques

Title from PDF of title page (University of Missouri--Columbia, viewed on October 29, 2012).The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file.Dissertation advisor: Dr. Dongchu SunIncludes bibliographical references.Vita.Ph. D. University of Missouri--Columbia 2012."July 2012"This dissertation discusses the methodologies of applying Bayesian hierarchical models to different data with geographical characteristics or with right-censored failure time. A conditional autoregressive (CAR) prior is used for the model to capture spatial effects. Markov chain Monte Carlo (MCMC) methods are used in the sampling. The Ancillary-Sufficient Interweaving Strategy (ASIS) is applied to improve the performance for some parameters. The convergence of some of the parameters improved greatly, but the others do not have very significant improvement. However, the overall performance has improved greatly since it needs much fewer iterations than using regular Gibbs sampling to achieve convergence. For the survival analysis, we propose a generalized linear mixed model with different effects for the hazard rates, and adopte a cure rate model in Chen et al. (1999) for the hazards. A ratio-of-uniforms method is used to get the posterior density of some parameters that can not be simply sampled by common methods. Both the Weibull model and cure rate models are compared. Moreover, for the same data set, competing risks model is considered by incorporating spatial effect to a latent competing risk model from Gelfand et al. (2000). The sampling method mentioned in Berger & Sun (1993) is adapted for efficiency. Finally, spatial confounding occurs when incorporating spatial effects in a regression model. Several estimators of the coefficients are compared for their Mean Squared Errors. The corresponding prediction errors are also discussed.Includes bibliographical reference

Unified Gas-kinetic Wave-Particle Methods III: Multiscale Photon Transport

In this paper, we extend the unified gas-kinetic wave-particle (UGKWP) method to the multiscale photon transport. In this method, the photon free streaming and scattering processes are treated in an un-splitting way. The duality descriptions, namely the simulation particle and distribution function, are utilized to describe the photon. By accurately recovering the governing equations of the unified gas-kinetic scheme (UGKS), the UGKWP preserves the multiscale dynamics of photon transport from optically thin to optically thick regime. In the optically thin regime, the UGKWP becomes a Monte Carlo type particle tracking method, while in the optically thick regime, the UGKWP becomes a diffusion equation solver. The local photon dynamics of the UGKWP, as well as the proportion of wave-described and particle-described photons are automatically adapted according to the numerical resolution and transport regime. Compared to the $S_n$ -type UGKS, the UGKWP requires less memory cost and does not suffer ray effect. Compared to the implicit Monte Carlo (IMC) method, the statistical noise of UGKWP is greatly reduced and computational efficiency is significantly improved in the optically thick regime. Several numerical examples covering all transport regimes from the optically thin to optically thick are computed to validate the accuracy and efficiency of the UGKWP method. In comparison to the $S_n$ -type UGKS and IMC method, the UGKWP method may have several-order-of-magnitude reduction in computational cost and memory requirement in solving some multsicale transport problems.Comment: 27 pages, 15 figures. arXiv admin note: text overlap with arXiv:1810.0598

Sequential Change-point Detection for Compositional Time Series with Exogenous Variables

Sequential change-point detection for time series enables us to sequentially check the hypothesis that the model still holds as more and more data are observed. It is widely used in data monitoring in practice. In this work, we consider sequential change-point detection for compositional time series, time series in which the observations are proportions. For fitting compositional time series, we propose a generalized Beta AR(1) model, which can incorporate exogenous variables upon which the time series observations are dependent. We show the compositional time series are strictly stationary and geometrically ergodic and consider maximum likelihood estimation for model parameters. We show the partial MLEs are consistent and asymptotically normal and propose a parametric sequential change-point detection method for the compositional time series model. The change-point detection method is illustrated using a time series of Covid-19 positivity rates

Sequential Change-point Detection for Binomial Time Series with Exogenous Variables

Sequential change-point detection for time series enables us to sequentially check the hypothesis that the model still holds as more and more data are observed. It's widely used in data monitoring in practice. Meanwhile, binomial time series, which depicts independent binary individual behaviors within a group when the individual behaviors are dependent on past observations of the whole group, is an important type of model in practice but hasn't been developed well. We first propose a Binomial AR($1$) model, and then consider a method for sequential change-point detection for the Binomial AR(1)