Sentence examples for make these approaches from inspiring English sources

The natural semantics of these systems and their ability to reason about default rules make these approaches quite appealing.

Hence, these issues make these approaches unsuitable for infrastructure-less environments, such as 802.11-based ad hoc networks, due to the resource constraint devices and distributed nature of the network [22, 25, 26].

Current state-of-the-art quantitative assessments of abnormal neuro-mechanics (e.g., spasticity, rigidity, dystonia) require sophisticated measurement systems that, together with the lengthiness of the data acquisition, make these approaches impractical for the clinical setting.

Consequently, making decisions on such evolving high rates of mutations in human solid tumors make these approaches fraudulent ('molecular false flags') and irresponsible as evident from the high failure rate outcomes of 'molecular target' therapies [18, 22, 36 38, 44, 65].

The recent description of PCR-derived dsDNA templates (Paix et al. 2014) will make these approaches even more powerful; the abundance of potential genetic markers in other model organisms should make these widely applicable approaches.

In practical applications, however, a range of potential pitfalls need considering: model probabilities can show strong dependence on model and parameter priors; and the computational effort needed to evaluate these posterior distributions can make these approaches cumbersome.

However, rapid advances since 2013 have made these approaches particularly relevant to assessing genetic architecture.

Attempts to correct for these contaminations can be done by along-track analysis (Maus et al. 2006a; Thébault et al. 2012) or statistically (Lesur et al. 2013), but the transient nature of these disturbances makes these approaches imperfect.

Most of the conventional approaches are not introduced to support the smaller number of mobile cloud users because many resources are required, making these approaches unsuccessful in real situations.

It is well known that phosphoproteomics and MS-based recent advancements have made these approaches the ideal way by which to study signal transduction although it implies high speciality and tedious research studies.

Thus, handling a larger number of parameters requires increasing the memory on each computational node which makes these approaches harder or even infeasible to scale, where the number of parameters are very large.