Sentence examples for solving whereas from inspiring English sources

In other words, experts tend to allocate more of their time to the early or preparatory stages of problem solving, whereas novices tend to spend relatively more of their time in the later stages.

In general, cooperation is more effective for higher-order tasks (e.g., problem solving), whereas competition is more effective for lower-order tasks (e.g., rote learning) (e.g., Johnson et al. 1981; Johnson et al. 1980).

Use of schematic spatial representations is associated with success in mathematical problem solving, whereas use of pictorial representations is negatively correlated with success [ 29].

The use of schematic spatial representations is positively correlated with success in mathematical problem solving, whereas the use of pictorial representations is negatively correlated with success [ 29].

She was equally rigorous and stiff-spined in the television series "Prime Suspect," but there we had an array of tasty mysteries to be savored and solved, whereas "The Clearing," written by Justin Haythe and directed by Pieter Jan Brugge, is a thriller stripped of thrills — or, even worse, a thriller that thinks of itself as somehow rising above the vulgar pleasures of excitement.

In academia, important problems can take years or even decades to solve, whereas in industry, one always has to work by a deadline, says one early-career mathematician.

Overall altered problems posed increased in the solving group, whereas partially altered problems posed increased in the production-s, production-f, and evaluation groups.

Furthermore, the results of residual analysis indicated that the number of not altered problems was significantly high in the control group but significantly low in the solving group, whereas the number of fully altered problems was significantly high in the solving group but significantly low in the control group.

Specifically, Section 4.1 formulates the strategy to select the optimal TM and PA vector solving OP (9), whereas Section 4.2 solves the PA problem in closed-form.

Whereas solving a large number of BG imputation problems is avoided, the task is shifted to the restricted set of CGs.

This first group consisting of first two papers mainly deals with the algorithms of nonconvex multiobjective optimization and Nash genetic algorithms for solving inverse problems whereas the second group consisting of remaining four papers studies the dynamical modeling, control design for elastic systems and contact optimization problems.