Lack of sufficient data and low adequacy of experimental protocols hinder comprehensive risk assessment of nanoparticles (NPs).
Lack of sufficient data and their inherent epistemic and aleatory uncertainty due to human and equipment errors are, however, major limitations for these correlations.
Lack of sufficient data on transmission dynamics, virulence spectrum and antimicrobial resistance of certain pathogens such as the uropathogenic E. coli (UPEC) from countries with high infection burden, such as India, hinders the infection control and management efforts.
Lack of sufficient data on the fluorodeoxyglucose (FDG) uptake of sclerosing hemagioma confounds the problem even further.
However, the utility of ERCs has not been fully realized mainly due to the lack of sufficient data resources.
Due to the lack of sufficient data and appropriate ecological information parameterizing predictive population dynamical models usually is a difficult task.
However, at a specified site of engineering interest, these empirical models are associated with large number of uncertainties due to lack of sufficient data.
For most software developers, there have historically been many barriers to entry in machine learning, most notably software libraries designed more for academic researchers than for software engineers as well as a lack of sufficient data.
This study demonstrates how models can be developed and different forms of evaluation can be performed despite a lack of sufficient data, by presenting a habitat suitability model for the rare Astacopsis gouldi, the giant freshwater crayfish.
However, model parameters may not be precisely represented due to some factors in engineering practices, such as lack of sufficient data, data with fuzziness and unknown or non-constant reproduction conditions.
However, the standard acknowledges the lack of sufficient data for the case of high strength concrete with limestone aggregate and there are no provisions for fibre reinforced concrete at high temperatures.
