Observational constraints on the unified dark matter and dark energy model based on the quark bag model

In this work we investigate if a small fraction of quarks and gluons, which escaped hadronization and survived as a uniformly spread perfect fluid, can play the role of both dark matter and dark energy. This fluid, as developed in [1], is characterized by two main parameters: beta, related to the amount of quarks and gluons which act as dark matter; and gamma, acting as the cosmological constant. We explore the feasibility of this model at cosmological scales using data from type Ia Supernovae (SNeIa), Long Gamma-Ray Bursts (LGRB) and direct observational Hubble data. We find that: (i) in general, beta cannot be constrained by SNeIa data nor by LGRB or H(z) data; (ii) gamma can be constrained quite well by all three data sets, contributing with approximate to 78% to the energy matter content; (iii) when a strong prior on (only) baryonic matter is assumed, the two parameters of the model are constrained successfully. (C) 2014 The Authors. Published by Elsevier B.V.

Temperature fields of 355 nm HR coatings based on the interface absorption model

Temperature fields of 355 nm high-reflectance (HR) coatings were investigated based on the interface absorption model. It was found that the highest temperature in the HR coatings increased with an increase in the extinction coefficient of the interface A, B, C, Al2O3 and MgF2. The highest temperature of HR coatings that can be reached increased quickly with the increase in the extinction coefficient of interface A in particular. The temperature rises of 355 nm HR coatings at different layers and different deposition temperatures were investigated based on experiments also. The damage mechanism of 355 nm HR coatings was confirmed with temperature fields and the interface absorption model.

Defining plausible migration rates based on historical tagging data: a Bayesian mark-recapture model applied to English sole (Parophrys vetulus)

A generalized Bayesian population dynamics model was developed for analysis of historical mark-recapture studies. The Bayesian approach builds upon existing maximum likelihood methods and is useful when substantial uncertainties exist in the data or little information is available about auxiliary parameters such as tag loss and reporting rates. Movement rates are obtained through Markov-chain Monte-Carlo (MCMC) simulation, which are suitable for use as input in subsequent stock assessment analysis. The mark-recapture model was applied to English sole (Parophrys vetulus) off the west coast of the United States and Canada and migration rates were estimated to be 2% per month to the north and 4% per month to the south. These posterior parameter distributions and the Bayesian framework for comparing hypotheses can guide fishery scientists in structuring the spatial and temporal complexity of future analyses of this kind. This approach could be easily generalized for application to other species and more data-rich fishery analyses.

A simple spreadsheet model to incorporate seasonal growth into length-based stock assessment methods

The paper describes a method by which seasonal growth can be incorporated into length-converted catch curves and cohort analyses using a spreadsheet. The method is based on calculating the length of fish using seasonal growth parameters on a daily basis. A LOOKUP function is then used to find the age corresponding to the length.