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In practical, this method can be readily used for solving more complex cases within a reasonable accuracy.
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In practical applications, this method can be integrated to customized recommendation systems that can recommend interesting hot topics associated with learning resources to cater for learners' individual learning demands.
However, on practical application, this method presents a potential pitfall (Hirschmann and Dasgupta 2007) given by the uncertainty in estimating the starting "equilibrium" composition; a deviation from equilibrium composition may lead to shifts in bulk compositions affecting final phase compositions, and ultimately phase assemblage.
Logic also strongly suggests it means prioritising possession over a quick, forward progression, as the action occurs across a smaller area (although this is not explicitly proved by the study).Dr Gyarmati foresees powerful practical applications for this method.
The practical application of this method was discussed.
Some practical drawbacks make this method difficult to be easily utilized in low-resource laboratories.
Lastly, a numerical example is employed to prove the practical utility of this method.
The result of the observability analysis provides theoretical support for the practical application of this method.
Prospective practical users of this method are people from energy industry, energy planners, engineers, city planners, and climate concern citizens.
For practical purposes, however, this method is not very helpful, as the space of prices and goods bundles is very large.
To illustrate the practical utility of this method, this research applied the methodology to the Winnipeg, Canada road network.
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