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The frequency-domain methodology proposed has wider application in the design of sensor suites for high-performance navigation systems.
The methodology proposed has allowed the production of particles with different sizes and morphologies in a controlled manner.
The methodology proposed has potential applications for the reduction of the response of railway bridges subjected to the transit of high-speed trains.
The methodology proposed has been validated through a simple example which illustrates the exploitability of the method in relation to the modeling of damaged composite structures.
The design methodology proposed has been given step-wise and the same has been verified with the help of an example in this paper.
The methodology proposed has shown competitiveness in all the metrics selected, when compared to other machine learning techniques and efficiently complements the MELD score based on the principles of efficiency and equity.
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The presented methodology proposed here has been applied to the water system of the desert megacity of Lima/Peru.
The methodology proposed herein has more Pareto optimal solutions, however, it is desirable to find the ones scattered uniformly over the Pareto frontier to provide a variety of compromise solutions to the decision maker.
In order to evaluate the generalization capabilities of the methodology proposed it has been applied to two types of biosignals with different kinds of non-stationary behaviors: electroencephalographic and phonocardiographic biosignals.
The methodology proposed here has several strengths.
The methodology proposed here has a number of limitations.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com