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The basis sets and calculation methods have strong effects on the thermodynamics and kinetics calculations.
In addition, the spectral learning methods have strong theoretical grounding, which is absent for deep-learning based approaches.
In the models, the training and testing results have shown that ANN and FL methods have strong potential for predicting the fc and Ed of crushed tile concretes exposed to elevated temperatures.
The training, testing and validation set results of the explicit formulations obtained by the genetic programming models show that artificial intelligent methods have strong potential and can be applied for the prediction of the compressive strength of concrete containing fly ash with different specimen size and shape.
In spite of recent progresses optical methods have strong limitations due to the light transmission to and from deep-seated organs.
Computational methods have strong potential for efficient and large-scale epitope prediction for many antigen candidates at much lower cost.
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While for large volumes of data, distributed and parallel learning methods have stronger advantages.
If only a part of the exons of a transcript is matched, IPred tests if the overlapping transcripts predicted by other methods have stronger support (i.e. they differ from the currently leading transcript, but agree with each other).
For example, both Pcellmax (maximum light-saturated photosynthesis – a measure of photosynthetic capacity) and αcellmax (maximal instantaneous light utilization – a measure of photosynthetic efficiency, see Materials and Methods) had strong periodicities (Figure 3B), with the former reaching a maximum at mid-day, and the latter reaching one closer to dusk.
The failure to understand these important research design methods has strong implications on obtaining a valid informed consent.
The implicit assumption in phylogenetic footprinting is that sequences detected by the method have strong conservation because they are under natural selection.
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