Brown University

About: Brown University is a education organization based out in Providence, Rhode Island, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 35778 authors who have published 90896 publications receiving 4471489 citations. The organization is also known as: brown.edu & Brown.

MDS Cognitive Performance Scale

Abstract: Background Chronic cognitive impairment is a major problem in U.S. nursing homes, yet traditional assessment systems in most facilities included only limited information on cognitive status. Following the Congressional mandate in the Omnibus Reconciliation Act of 1987 (OBRA '87), U.S. nursing homes now complete the Minimum Data Set (MDS), a standardized, comprehensive assessment of each resident's functional, medical, psychosocial, and cognitive status. We designed a Cognitive Performance Scale (CPS) that uses MDS data to assign residents into easily understood cognitive performance categories. Methods Information was drawn from three data sets, including two multistate data sets constructed for the Health Care Financing Administration. The prevalence and reliability of the MDS cognitive performance variables were established when assessed by trained nursing personnel. Five selected MDS items were combined to create the single, functionally meaningful seven-category hierarchical Cognitive Performance Scale. Results The CPS scale corresponded closely with scores generated by the Mini-Mental State Examination and the Test for Severe Impairment, nursing judgments of disorientation, and neurological diagnoses of Alzheimer's disease and other dementias. Conclusions The new CPS provides a functional view of cognitive performance, using readily available MDS data. It should prove useful to clinicians and investigators using the MDS to determine a resident's cognitive assets.

Stirring by chaotic advection

Abstract: In the Lagrangian representation, the problem of advection of a passive marker particle by a prescribed flow defines a dynamical system. For two-dimensional incompressible flow this system is Hamiltonian and has just one degree of freedom. For unsteady flow the system is non-autonomous and one must in general expect to observe chaotic particle motion. These ideas are developed and subsequently corroborated through the study of a very simple model which provides an idealization of a stirred tank. In the model the fluid is assumed incompressible and inviscid and its motion wholly two-dimensional. The agitator is modelled as a point vortex, which, together with its image(s) in the bounding contour, provides a source of unsteady potential flow. The motion of a particle in this model device is computed numerically. It is shown that the deciding factor for integrable or chaotic particle motion is the nature of the motion of the agitator. With the agitator held at a fixed position, integrable marker motion ensues, and the model device does not stir very efficiently. If, on the other hand, the agitator is moved in such a way that the potential flow is unsteady, chaotic marker motion can be produced. This leads to efficient stirring. A certain case of the general model, for which the differential equations can be integrated for a finite time to produce an explicitly given, invertible, area-preserving mapping, is used for the calculations. The paper contains discussion of several issues that put this regime of chaotic advection in perspective relative to both the subject of turbulent advection and to recent work on critical points in the advection patterns of steady laminar flows. Extensions of the model, and the notion of chaotic advection, to more realistic flow situations are commented upon.

Random sampling with a reservoir

Abstract: We introduce fast algorithms for selecting a random sample of n records without replacement from a pool of N records, where the value of N is unknown beforehand. The main result of the paper is the design and analysis of Algorithm Z; it does the sampling in one pass using constant space and in O(n(1 + log(N/n))) expected time, which is optimum, up to a constant factor. Several optimizations are studied that collectively improve the speed of the naive version of the algorithm by an order of magnitude. We give an efficient Pascal-like implementation that incorporates these modifications and that is suitable for general use. Theoretical and empirical results indicate that Algorithm Z outperforms current methods by a significant margin.