Oklahoma State University–Stillwater

About: Oklahoma State University–Stillwater is a education organization based out in Stillwater, Oklahoma, United States. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 18267 authors who have published 36743 publications receiving 1107500 citations. The organization is also known as: Oklahoma State University & OKState.

Family Resilience: Moving into the Third Wave

Abstract: Family resilience has progressed through two waves and is poised for Wave 3. During Waves 1 and 2, family resilience perspectives were conceptualized, researched, and applied as a strengths-based approach focused on positive family adaptation despite significant risk using an integration of concepts from individual resilience, general systems perspectives on families, and family stress theory. For Wave 3, the authors advocate for increased consistency in terminology and present the family resilience model (FRM) within which existing models interface with family adaptive systems (meaning systems, emotion systems, control systems, maintenance systems, and family stress-response systems). The authors also argue for increased focus on trajectories and cascades, and enhanced prevention, intervention, and policy. The authors provide a hypothetical case study applying the FRM.

Measurement of Bs0 Mixing Parameters from the Flavor-Tagged Decay Bs0→J/ψ

Abstract: From an analysis of the flavor-tagged decay B-s(0)-> J/psi phi we obtain the width difference between the B-s(0) light and heavy mass eigenstates, Delta Gamma(s)=0.19 +/- 0.07(stat)(-0.01)(+0.02)(syst) ps(-1), and the CP-violating phase, phi(s)=-0.57(-0.30)(+0.24)(stat)(-0.02)(+0.08)(syst). The allowed 90% CL intervals of Delta Gamma(s) and phi(s) are 0.06

An efficient blood vessel detection algorithm for retinal images using local entropy thresholding

Abstract: This paper presents an efficient method for automatic detection and extraction of blood vessels in retinal images. Specifically, we also delineate vascular intersections/crossovers. The proposed algorithm is composed of four steps: matched filtering, local entropy thresholding, length filtering, and vascular intersection detection. The purpose of matched filtering is to enhance the blood vessels. Entropy-based thresholding can well keep the spatial structure of vascular tree segments. Length filtering is used to remove misclassified pixels. The algorithm has been tested on twenty ocular fundus images, and experimental results are compared with those obtained from a state-of-the-art method and hand-labeled ground truth segmentations.

Does diversity beget diversity? A case study of crops and weeds

Abstract: The ecological literature is ambiguous as to whether the initial diversity of a plant community facilitates or deters the diversity of colonizing species. We experimentally planted annual crop species in monoculture and polyculture, and ex- amined the resulting weed communities. The species compo- sition of weeds was similar among treatments, but the species richness of weeds was significantly higher in the polycultures than in the monocultures. This supports the 'diversity begets diversity' hypothesis. Environmental microheterogeneity, di- versity promoters, and ecological equivalency do not seem able to explain the observed patterns.

A neural network approach for the blind deconvolution of turbulent flows

Abstract: We present a single-layer feed-forward artificial neural network architecture trained through a supervised learning approach for the deconvolution of flow variables from their coarse-grained computations such as those encountered in large eddy simulations. We stress that the deconvolution procedure proposed in this investigation is blind, i.e. the deconvolved field is computed without any pre-existing information about the filtering procedure or kernel. This may be conceptually contrasted to the celebrated approximate deconvolution approaches where a filter shape is predefined for an iterative deconvolution process. We demonstrate that the proposed blind deconvolution network performs exceptionally well in the a priori testing of two-dimensional Kraichnan, three-dimensional Kolmogorov and compressible stratified turbulence test cases, and shows promise in forming the backbone of a physics-augmented data-driven closure for the Navier–Stokes equations.