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Vertical handover approaches generally require new network elements, a new layer in TCP/IP stack, or fixing a protocol at a particular layer.
However, these approaches generally require complicated synthetic procedures and result in a low yield of the products [13].
Likelihood based approaches generally require problem-specific programmes and therefore are not generally readily available.
On the downside, these approaches generally require a great deal of data, very good knowledge of the species distribution, and make the sampling design critical as it must be representative of the postdispersal distribution of individuals (evaluation of long-distance dispersal might be especially difficult due to constraints in the size of the study area).
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These approaches generally required complex multistep synthetic strategies, resulting in low overall radiochemical yields.
Articular cartilage repair using tissue engineering approach generally requires the use of an appropriate scaffold architecture that can support the formation of cartilage tissue.
For one thing, Awe.sm's API-based approach generally required its users to have some sort of technical or engineering knowledge to take advantage of its platform.
The second approach generally requires a higher computational complexity than the first approach as the underlying algebra in calculating a contextual anomaly is computationally expensive.
The technical feasibility of either approach generally requires a sophisticated user to reverse-engineer the app in question -- simply launching the app and writing down relative locations isn't going to be sufficient.
Although informative, this approach generally requires extensive experimental validation.
This approach generally requires data from diverse sources to be integrated and analyzed together.
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