Summer Male Call Index Relative to Nesting Chronology and Autumn Density of the Northern Bobwhite

We studied breeding season male call counts and breeding behavior of the Northern Bobwhite (Colinus virginianus) to determine the relationship between male calling activity and nesting chronology. Additionally, we examined the relationship between breeding season call counts and fall population size. Standardized call count routes were conducted on 6 different sites located in southwest Georgia and north Florida during the breeding season months (1 Apr - 31 Sep) in 2001 and 2002. An information theoretic approach was used to evaluate a set of 7 candidate, linear-mixed models describing breeding season calling of bobwhite males. Of the candidate models, the model in which call counts depended on year and a quadratic effect of the number of incubating hens was the best approximating model, suggesting that the percentage of incubating hens had the greatest influence on activity of calling males. We also used multiple linear regression models to predict autumn northern bobwhite abundance from mean numbers of calling male bobwhites detected during the breeding season. Peaks in male calling activity occurring during June and July demonstrated a strong relationship (R2 = 0.987) with autumn population size, suggesting breeding season call counts were useful indices of autumn bobwhite abundance

Exploration of High Entropy Ceramics (HECs) with Computational Thermodynamics - A Case Study with LaMnO3±δ

The concept of the new category materials high entropy ceramics (HECs) has been proposed several years ago, which is directly borrowed from high entropy alloys (HEAs). It quickly attracts a lot of interests and displays promising properties. However, there is no clear definition of HECs differentiating it from HEAs, as it is still in its early research stage. In the current work, we are trying to use the classic perovskite LaMnO3±δ (LMO) to demonstrate the fundamental differences between HECs and HEAs. We have adopted the integrated defect chemistry and CALPHAD approach to investigate the mixing behavior and how it is affected by the control parameters, i.e. PO2, T, and composition. We have developed a new way to visualize the mixing behavior of the species including the cations, anions, and defects (vacancies), which linked the mixing behavior to the thermo-chemical properties including enthalpy, entropy, and Gibbs energy. It was found that entropy plays the most important role on the mixing behavior in LMO. The present work paves the way for the HECs investigation and the design of new HECs for the various applications

IMECE2003-42258 SPECIFYING EB-PVD PROCESS PARAMETERS FOR COATING OF A SECOND STAGE TURBINE BLADE USING AN EXPERIMENTALLY VERIFIED CAD MODEL OF THE PROCESS

ABSTRACT: A model was developed to predict the thickness of the thermal barrier coating (TBC) applied to specific points on a rotating PW4000 second stage turbine blade using electron beam physical vapor deposition (EB-PVD). The theoretical model of coating deposition rates as a function of position in the PVD vapor cloud (Knudsen cosine law) was experimentally verified. The experimental work consisted of a series of four turbine blades coated under various coating conditions. Based on the verified model, a UniGraphics (UG) CAD model of the process was built. A UG User Function (UFunc) was programmed to predict coating thickness for a wide variety of EB-PVD process parameters to populate a database of contoured coating profiles. A software tool was then developed to specify the manufacturing process parameters to fabricate a contoured EB-PVD TBC of partially stabilized zirconia. A coating profile matching routine was included in the software to identify the process parameters closest to the desired coating profile. The focus of this paper is on the experimental methods, the CAD model and the software tool

Bayesian Parameter Estimation for Latent Markov Random Fields and Social Networks

Undirected graphical models are widely used in statistics, physics and machine vision. However Bayesian parameter estimation for undirected models is extremely challenging, since evaluation of the posterior typically involves the calculation of an intractable normalising constant. This problem has received much attention, but very little of this has focussed on the important practical case where the data consists of noisy or incomplete observations of the underlying hidden structure. This paper specifically addresses this problem, comparing two alternative methodologies. In the first of these approaches particle Markov chain Monte Carlo (Andrieu et al., 2010) is used to efficiently explore the parameter space, combined with the exchange algorithm (Murray et al., 2006) for avoiding the calculation of the intractable normalising constant (a proof showing that this combination targets the correct distribution in found in a supplementary appendix online). This approach is compared with approximate Bayesian computation (Pritchard et al., 1999). Applications to estimating the parameters of Ising models and exponential random graphs from noisy data are presented. Each algorithm used in the paper targets an approximation to the true posterior due to the use of MCMC to simulate from the latent graphical model, in lieu of being able to do this exactly in general. The supplementary appendix also describes the nature of the resulting approximation.Comment: 26 pages, 2 figures, accepted in Journal of Computational and Graphical Statistics (http://www.amstat.org/publications/jcgs.cfm