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We review recent methodological developments in integrative structural modeling by satisfying spatial restraints on networks of FRET pairs (hybrid-FRET).
MODELLER can implement comparative protein structure modeling by satisfying spatial restraints in terms of probability density functions.
MODELLER is a general program that implements comparative protein structure modeling by satisfying spatial restraints in terms of probability density functions.
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IMP translates the data into spatial restraints, computes an ensemble of models by satisfying these restraints as well as possible, and finally assesses the ensemble against the data used or not used in its construction (Schneidman-Duhovny et al., 2014).
More precisely, these randomly occurring phenomena are modeled by stochastic variables satisfying the Bernoulli distribution.
This paper is concerned with the non-fragile synchronization issue for neural networks with discontinuous activation functions, time-varying delays and random feedback gain uncertainties, where the randomly occurring phenomena are modeled by stochastic variables satisfying the Bernoulli distribution.
We extend our earlier FinFET based MGG model to a cylindrical NWFET by satisfying three additional requirements.
The Energy Service Company (ESCo) business model is designed to reward businesses by satisfying consumers' energy needs at less cost and with fewer carbon emissions via energy demand management and/or sustainable supply measures.
Using the p-version with higher order polynomials, the structure is modeled by one element while satisfying the exactitude requirement.
The mathematical model of the 2-D system is established upon the well-known Roesser model, and the measurement missing phenomenon, which appears typically in a network environment, is modeled by a stochastic variable satisfying the Bernoulli random binary distribution.
In contrast, interchangeability could be demonstrated by satisfying the requirements of a model for individual bioequivalence (IBE).
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