Sentence examples for model to utilise from inspiring English sources

We conclude that minimal signs of tolerance and changes observed in hippocampal neurogenesis suggest that schedule of LPS injections used might be a suitable model to utilise in future studies looking into immunological aspects of mood disorders.

Use of profile data to construct a graph structure was carried out [39] and proposed a simple model to utilise both observable connectivity relation and profile graph.

36 Notably increased complexity of finger movements increases activation of the basal ganglia circuit, and thus provides an ideal model to utilise fMRI to locate function specific regions of the cortex associated with finger movements.

Inclusion of the group 1 calves in this analysis allowed the model to utilise the partial information on disease status (ie. not clinical BNP but not known whether normal) available from this large group of calves.

Studies using a number of different accelerometers have been reported [ 12- 17], with researchers required to weigh the differing costs, unit dimensions, technical specifications, outputs and evidence for reliability and validity when deciding which model to utilise.

This is also the first member of the CHAOS series of field models to utilise spatial field differences as data from both CHAMP and SWARM satellites (Finlay et al. 2016).

66– 68 The use of the DES model, over the more commonly used Markov model, was designed to utilise its flexibility when comparing various treatment scenarios, with the authors arguing that a Markov model "forces a disease into a few mutually exclusive states within a fixed time", eg, fixed EDSS stages.

In order to state the defining relation for the time-discrete solution and its reformulation as time-discrete variational inequality, we find it useful to focus on the model problem and to utilise an approach via abstract evolutionary differential inclusions, as this resembles the approach for evolution equations exploited in [ 15].

Benefits to using an SEM framework include exploring multiple mediation and multiple outcome variables in a single model, the capacity to utilise alternative estimation methods such as maximum likelihood (MLE), or asymptotically distribution-free (ADF) estimation and the ease of producing robust standard errors or bias corrected confidence from bootstrapping techniques.

In an earlier work we have shown that a second order Markov model, compared to non-Markovian and first-order models, is able to utilise nucleotide correlations and suffices in accurately partitioning portions of DNA in a statistically significant manner [12].

There is an exciting potential for such a model to be utilised to help analyse/process existing EISCAT and future EISCAT_3D data through a Bayesian approach.