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Light hadronic decays and decay rates of quarkonia

AIP Conference Proceedings, 2011
The Light hadronic decay and decay rates of mesons (QQ) are studied in the NRQCD formalism in terms of their short distance and long distance coefficients. The long distance coefficients are obtained through phenomenological potential model description of the mesons.
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Decay Rates for the Hollow Circular Cylinder

Journal of Applied Mechanics, 1992
The self-equilibrated end load problem for a hollow circular cylinder is considered using the Papkovitch-Neuber solution to the elastostatic displacement equations of equilibrium; both axi- and nonaxisymmetric solutions are derived. The requirement of zero traction on the surface generators of the cylinder leads to an eigenequation whose roots ...
Stephen, N. G., Wang, M. Z.
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Determination of the decay rate of nitrifying bacteria

Biotechnology and Bioengineering, 2006
AbstractThe growth and decay of nitrifying organisms determines the amount of nitrifying bacteria in activated sludge systems. The growth rate of the nitrifying organisms is reasonable, well defined, and studied, while the decay rate is still rather uncertain. Experiments in previous studies were over periods up to 14 days and obtained results were not
S, Salem   +2 more
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On the decay rate of (p, A)-lacunary series

Journal of Mathematical Sciences, 2006
Summary: A power series \(\sum_{k=0}^\infty a_kx^{n_k}\) with radius of convergence equal to 1 is said to be \((p,A)\)-lacunary if \(n_k\geq Ak^p\), \(A>0 ...
Nazarov, F. L., Shirokov, N. A.
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Energy decay rates in linear thermoelasticity

Funkcialaj Ekvacioj, 1992
A one-dimensional linear thermoelastic system is studied, and a new elementary proof that the energy decays exponentially to zero as \(t \to \infty\) is given. To this end the energy method, the regularity results and some technical ideas are used.
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Rates of decay

International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts, 1996
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Learning to optimize based on rate decay

The Computer Journal
Abstract Learning to optimize (L2O) is a technique that uses neural networks to learn optimization algorithms automatically. While it holds promise for diverse optimization problems, achieving consistently ideal results remains a challenge. Typically, L2O through a parameterized optimization method (i.e.
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Quality and quantity control of gene expression by nonsense-mediated mRNA decay

Nature Reviews Molecular Cell Biology, 2019
Lynne Maquat, Tatsuaki Kurosaki
exaly  

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