North Carolina State University

About: North Carolina State University is a education organization based out in Raleigh, North Carolina, United States. It is known for research contribution in the topics: Population & Thin film. The organization has 44161 authors who have published 101744 publications receiving 3456774 citations. The organization is also known as: NCSU & North Carolina State University at Raleigh.

Scattering Theory Derivation of a 3D Acoustic Cloaking Shell

Abstract: Through acoustic scattering theory we derive the mass density and bulk modulus of a spherical shell that can eliminate scattering from an arbitrary object in the interior of the shell—in other words, a 3D acoustic cloaking shell. Calculations confirm that the pressure and velocity fields are smoothly bent and excluded from the central region as for previously reported electromagnetic cloaking shells. The shell requires an anisotropic mass density with principal axes in the spherical coordinate directions and a radially dependent bulk modulus. The existence of this 3D cloaking shell indicates that such reflectionless solutions may also exist for other wave systems that are not isomorphic with electromagnetics.

Fate of polynuclear aromatic compounds (PNAs) in soil-plant systems

Abstract: Polycyclic aromatic hydrocarbons (PAH) consist of three or more fused benzene rings in linear, angular, or cluster arrangements. Substitution of carbon in the benzene ring with nitrogen, sulfur, oxygen, or other elements creates heterocyclic aromatic compounds (Blumer 1976). Excluding diphenyl types there are some 70 possible isomers of 4-6 fused rings. Additionally these isomers may be substituted by a variety of substituents (Erskine and Whitehead 1975). The aromatic hydrocarbons and heterocycles, unsubstituted and substituted, are referred to alternatively as polynuclear aromatics (PNAs), polycyclic aromatic compounds (PCAs), and polycyclic organic matter (POM) (NAS 1972, U.S. EPA 1975 b).

Fundamentals of Nonparametric Bayesian Inference

Abstract: Explosive growth in computing power has made Bayesian methods for infinite-dimensional models - Bayesian nonparametrics - a nearly universal framework for inference, finding practical use in numerous subject areas. Written by leading researchers, this authoritative text draws on theoretical advances of the past twenty years to synthesize all aspects of Bayesian nonparametrics, from prior construction to computation and large sample behavior of posteriors. Because understanding the behavior of posteriors is critical to selecting priors that work, the large sample theory is developed systematically, illustrated by various examples of model and prior combinations. Precise sufficient conditions are given, with complete proofs, that ensure desirable posterior properties and behavior. Each chapter ends with historical notes and numerous exercises to deepen and consolidate the reader's understanding, making the book valuable for both graduate students and researchers in statistics and machine learning, as well as in application areas such as econometrics and biostatistics.