Sentence examples for considerations for generating from inspiring English sources

This chapter outlines some pedagogical considerations for generating meaningful content for both the novice and the advanced learner, and presents examples of instructional cognitive theories and how elements of the theories can be integrated into tutorials and learning objects.

We then summarize design considerations for generating polymeric materials via spinodal decomposition, bound by thermodynamics of available polymer systems, coarsening kinetics governed by rheology, as well as by engineering constraints.

In this regard, all possible contacts and transition within depo-facies were taken into consideration for generating final stochastic facies model.

This is an important consideration for generating data and platform selection.

With these considerations in mind we explored an alternative strategy for generating nongermline chimeric GEMMs that fulfill all requirements for efficient use in translational research.

On the other hand, models can be built to determine the underlying requirements for generating the organization of mitochondrial networks without considerations of their functional impact.

Consideration must be given when utilising hospital discharge data alone for generating comorbidity indices, as disease burden at the time of admission is under-reported.

(Appropriate for generating buzz, no?).

These include handling data dependencies in accessing indirectly referenced data and design considerations in generating code for execution on a cluster of multi-threaded CPUs and GPUs.

Differences in telomere length and maintenance between mice and humans are an important consideration when generating mouse models for Werner syndrome and Ataxia telangectasia; in both cases Wrn-/ and Atm-/ deficient mice exhibited progeriod features only after they were crossed into a telomerase-deficient background [ 38, 60].

Additionally, this finding highlights the importance of taking model architecture into consideration when generating models and datasets for the evaluation of algorithms.