Sentence examples for dynamic analytic from inspiring English sources

An adaptive multi-objective particle swarm optimization (AMOPSO) algorithm is proposed and developed based on dynamic analytic hierarchy process (AHP).

We have sketched a possible future of adaptive learning systems where the next leap of innovation will come from real-time dynamic analytic systems that provide just-in-time feedback in the learning moment to learners and instructors.

In order to get reasonable and precise dynamic analytic result, the fractal theory is employed here to express the backlash instead of the existed way of setting backlash as a fixed value or a steady random number that meets the normal distribution.

In order to pursue the proposed sociotechnical systems approach to workplace safety, we need more innovative and dynamic analytic methods that consider individual and momentary variation (Hancock, Hancock, and Warm 2009).

This dynamic decision analytic technique allows the progression from LTBI to active TB and the treatment outcome to be modelled over time.

As compared with a traditional tracking scheme based on Kalman filtering (KF) algorithms derived from Bayesian dynamic model, the analytic result and the numerical simulations show that the proposed forward and one-step backward tracking approach not only can achieve an accurate location very close to the traditional KF tracking scheme, but also has a lower computational complexity.

We demonstrate the convergence characteristics of the proposed algorithms with a nonlinear dynamic problem having analytic solutions, and test these algorithms with several three-dimensional nonlinear elastodynamic problems involving large deformations and rotations, employing St. Venant-Kirchhoff and compressible Neo-Hookean hyperelastic material models.

Transmission dynamic and decision analytic models were combined to determine the expected impact of hand hygiene interventions on MRSA-BSI incidence and evaluate their cost-effectiveness.

An innovative aspect of this analysis is our use of dynamic MSM, an analytic approach that permits, under explicit assumptions, proper adjustment for time-dependent confounders on the causal pathway between early exposures and the outcome and enables proper adjustment for selection bias due to informative censoring.

With regard to dynamics, Leutner et al. (2005) distinguish dynamic problem solving from analytic problem solving.

Polyhedral and polygonal intersection algorithms work together with a quad-tree mesh subdivision and cutting adaptive process to become the kernel of the domain setup and intra-simulation dynamic modification, on which analytic formulations of pressure calculations are evaluated.