Sentence examples for processing of experimental data from inspiring English sources

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Present work involves detailed experimentation on pump-mixer, and processing of experimental data to model its head and power characteristics using multi-variable least square based empirical correlations and artificial neural network (ANN).

This center is integrated with the Bauer Core for effective processing of experimental data.

Moreover, we also unambiguously confirm the deterministic nature of the dynamics via numerical processing of experimental data.

The processing of experimental data within the standard linear elasticity approach would lead to an overestimation of effective work of adhesion.

Early-time and late-time solutions for the model are also presented for convenient application for post processing of experimental data.

The processing of experimental data indicates that the yield compositions may be explained by the radial temperature profile in the catalyst.

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The present paper deals with the post-processing of experimental data on the flow and heat transfer in a nanofluid-based double tube heat exchanger using an artificial neural network.

The application interfaces with the MDSplus data acquisition system and it has been designed to capture metadata which is generated by scientists from the post-processing of experimental data.

Briefly, systems biology could be considered in a number of ways: 1) philosophy and way of integrative thinking for biological systems, 2) methodology for processing huge arrays of experimental data and dealing with exploding new information and 3) predictive models for experiments and biological objects.

For examples and details in physics, chemistry, biology, biophysics, blood flow phenomena, control theory, wave propagation, signal and image processing, viscoelasticity, identification, fitting of experimental data, economics, etc., we refer the reader to the texts [1 4].

Fractional differential equations arise in many engineering and scientific disciplines as the mathematical modeling of systems and processes in the fields of physics, chemistry, control theory, biology, economics, blood flow phenomena, signal and image processing, biophysics, aerodynamics, fitting of experimental data, etc.

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