approximate


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ap·prox·i·mate

(ă-prok'si-māt), To bring close together. In dentistry:
1. Proximate, denoting the contact surfaces, either mesial or distal, of two adjacent teeth.
2. Close together; denoting the teeth in the human jaw, as distinguished from the separated teeth in certain lower animals.
[L. ad, to, + proximus, nearest]
Farlex Partner Medical Dictionary © Farlex 2012

approximate

1. Not exact, thereabouts, more or less, sort of, approximal Medtalk → Vox populi verb To make closer
McGraw-Hill Concise Dictionary of Modern Medicine. © 2002 by The McGraw-Hill Companies, Inc.

ap·prox·i·mate

(ă-proksi-māt)
1. dentistry To bring close together.
2. (ă-proksi-măt) Proximate, denoting the contact surfaces, either mesial or distal, of two adjacent teeth.
3. Close together; denoting the teeth in the human jaw, as distinguished from the separated teeth in certain of the lower animals.
[L. ad, to, + proximus, nearest]
Medical Dictionary for the Health Professions and Nursing © Farlex 2012

ap·prox·i·mate

(ă-proksi-măt)
1. Proximate, denoting the contact surfaces, either mesial or distal, of two adjacent teeth.
2. Close together; denoting the teeth in the human jaw, as distinguished from the separated teeth in certain lower animals.
[L. ad, to, + proximus, nearest]
Medical Dictionary for the Dental Professions © Farlex 2012
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References in periodicals archive ?
We can then approximate C([x.sub.i], [y.sub.j], [t.sub.n]) by [C.sup.n.sub.i,j] the value of the difference approximation of C(x, y, t) at point the grid point ([x.sub.i], [y.sub.j], [t.sub.n]) is defined by [x.sub.i] = i[DELTA]x for all i = 0, 1, 2, ..., L, = j[DELTA]y for all j = 0, 1, 2, ..., M, and [t.sub.n] = n[DELTA]t for all n = 0, 1, 2, ..., T, in which L, M, and T are positive integers.
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In the subsequent subsections we illustrate implementation of the above procedure, and we provide simulation results and approximate similarity solutions for different cases of values of parameter [p.sub.1].
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* A Prediction-based Energy-conserving Approximate StoragE (P-EASE) is brought up, which improves the trade-off between the query precision and the energy conserving by using prediction method to return the approximate querying result dynamically to cut down the location error.
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