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Recent reviews on modeling methods reported that the number of soft computing based engineering models is increasing (Dote and Ovaska 2001).
Therefore the need for predictive performance models is increasing but model development has generally lagged behind the introduction of new coatings.
The complexity of neural models is increasing with the investigation of larger biological neural network, more various ionic channels and more detailed morphologies, and the implementation of biological neural network is a task with huge computational complexity and power consumption.
The quantity of data now available for building models is increasing rapidly, which has the advantage that more accurate models can be created, for a wider range of properties.
The interest in the development, validation, and clinical application of clinical prediction models is increasing [ 1].
While risk assessment, utilizing risk prediction models, is increasing [ 5], quantitative health impact assessment remains relatively rare in healthcare [ 4].
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Physical effects are progressively introduced, and the realism of lumped-parameter models is increased step-by-step.
Furthermore, it has been pointed out that the efficiency of carcinogenesis in tumour models is increased when combining mutagenic agents with agents that induce cell proliferation [73].
After the number of input sequences, or the sequence length, or the number of available models is increased, the computing task of ML burdens the system.
We found that although studies incorporating predictive models are increasing, there are still far fewer modelling studies than observational or experimental studies and relatively few studies include both empirical and modelling components.
I think on the usership side — the popularity of ride hailing, the ride sharing model, is increasing — yet, the aspect of personal ownership is still really, really high.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com