Sentence examples for model combinations that from inspiring English sources

There are few model combinations that are consistent with the experimental data at Km(ADP) value of 0.15 mM, with the largest number of combinations at lower values of Km(ADP) (≤0.1 mM).

We see this very clearly with cyber-physical systems (CPS), which combine computing and networking with physical dynamics, and hence require model combinations that integrate dynamics described using differential equations with models of software.

EGFAg-Nil was the most frequently occurring marker in the top three-analyte GDA model combinations that most accurately differentiated cases from contacts irrespective of QFT results, while EGFNil and EGFAg-Nil were the most frequent in the top SVM model combinations (figure 5).

Methods would have to exist by which experiments would reveal the technology-business model combination that could succeed.

Therefore, not only do different models predict different long term dynamics; within one model, different parameter combinations that describe the data equally well can give rise to different predictions regarding the long-term dynamics.

Compared with most existing protein structure prediction systems, our approach contains a unique and novel model combination step that can refine protein models by averaging complementary good models or fragments.

In epidemiological models, all intervention parameter combinations that achieve eradication can be combined with econometrics in computing the most cost-effective strategy for eradicating a disease [ 16].

We think that it is exactly this mechanism that makes the mating types victorious in the spatial model at many parameter combinations that allowed for coexistence in the mean-field approximation.

Here, we first describe in detail our global local model combination algorithm that MULTICOM-REFINE, MUProt and MULTICOM all use.

Different models include variations of hypotheses combinations that explain the variation in language co-editing ties.

Hierarchical partitioning is a multiple-regression technique designed to identify - by using all possible model combinations - the variables that have the greatest independent influence on the dependent variable, providing a measure of the effect of each variable that is largely independent from that of other variables [50] [51].