Sentence examples for models of problems from inspiring English sources

It illustrated the method in the framework to index models of problems by using constraints as knowledge descriptions and demonstrated generation of explanation with the framework.

They arise in many realistic models of problems in science, engineering, and medicine, where there is a time lag or after-effect.

Due to its extraordinary utility and broad applicability in many areas of applied mathematics (most notably, fully-discretized models of problems in image reconstruction from projections, in image processing, and in intensity-modulated radiation therapy), algorithms for solving convex feasibility problems continue to receive great attention (see, for instance, [2 5] and also [6 10]).

Due to their extraordinary utility and broad applicability in many areas of appliedmathematics (most notably, fully discretized models of problems in imagereconstruction from projections, in image processing, and in intensity-modulatedradiation therapy), algorithms for solving convex feasibility problems continue toreceive great attention; see for instance [1 11].

Due to its extraordinary utility and broad applicability in many areas of applied mathematics (most notably, fully discretized models of problems in image reconstruction from projections, in image processing, and in intensity-modulated radiation therapy), algorithms for solving convex feasibility problems have been received great attention (see, for instance [10 13] and also [14 18]).

By 1965, the field had experimented with several models of problem solving: some were based on formal logic; some used heuristic reasoning; some, called "neural networks," were inspired by the brain.

Here, we examine whether models of problem solving based on this work are useful within the domain of evolutionary biology.

Accordingly, teachers and educators have focused on models of problem solving as guidelines for structuring inquiry-based processes in their subject lessons (Oser and Baeriswyl [2001]).

It employs conceptual models of problem domains to drive configuration of an application generator suitable for a related set of applications, such as reservation and resource allocation.

Such grids have many advantages in the numerical modeling of problems such as contaminant transport in rivers and also in areas of 3D visualization such as terrain modeling.

Numerical approximations and computational modeling of problems from Biology and Materials Science often deal with partial differential equations with varying coefficients and domains with irregular geometry.