Sentence examples for solution for time from inspiring English sources

Gong et al. [33] presented a parallel solution for time fractional reaction-diffusion equation with explicit difference method.

One of the most notable advantages of using HLA for this purpose is that it already has a trustworthy and widely used solution for time synchronization.

We describe a Monte Carlo solution for time dependent photon transport, in the difference formulation with the material in local thermodynamic equilibrium, that is piecewise linear in its treatment of the material state variable.

Subject to the conditional use of a lumped material energy in the vicinity of strong gradients, possible with a linear treatment of the material energy, our approach provides a robust solution for time dependent transport of thermally emitted radiation that can address a wide range of problems.

We then emphasize the correlation matrix as V d ( k ) ≐ ∑ l ∈ I k λ k - l v d ( l ) v d H l l ) and the cross-correlation vector as r v ( k ) ≐ ∑ l ∈ I k λ k - l r ~ d ∗ ( l ) v d ( l ) ; the LS solution for time instant k is then stated as follows: q T ( k ) = - r v H ( k ) V d ( k ) - 1. (38).

PayrollHero is a comprehensive solution for time, attendance, scheduling and payroll in Southeast Asia.

This is an effective solution for time-varying communications channels.

This paper presents an analytical solution for time-dependent behaviors of a hollow sphere made of functionally graded piezoelectric material (FGPM) under the coupling of multi-fields.

Furthermore, Boufoussi et al. [22, 23] investigated the existence and uniqueness of a mild solution for time-dependent neutral stochastic functional differential equations driven by the fBm.

The solution, which employs Laplace transforms, has the advantage that a solution for time-varying conditions may be constructed from the solution for constant conditions by means of the Heaviside procedure.

Compared to the traditional non-parametric method, e.g., relying on the Kolmogorov-Smirnov test, this method provides a better solution for time-activity data that are not normally distributed, because we were able to simultaneously assess both the likelihood and the duration of the time spent in microenvironments/activities.