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Dr Jonathan Cumming's Outputs (4)

A Bayesian non-linear hierarchical framework for crop models based on big data outputs (2020)
Presentation / Conference Contribution
Hasan, M. M., & Cumming, J. (2020, December). A Bayesian non-linear hierarchical framework for crop models based on big data outputs. Paper presented at 13th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2020), King's College London, England

Assessing the Non-Uniqueness of a Well Test Interpretation Model Using a Bayesian Approach (2020)
Presentation / Conference Contribution
Cumming, J., Botsas, T., Jermyn, I., & Gringarten, A. (2020, December). Assessing the Non-Uniqueness of a Well Test Interpretation Model Using a Bayesian Approach. Presented at SPE Virtual Europec 2020

Objectives/Scope: A stable, single-well deconvolution algorithm has been introduced for well test analysis in the early 2000’s, that allows to obtain information about the reservoir system not always available from individual flow periods, for exampl... Read More about Assessing the Non-Uniqueness of a Well Test Interpretation Model Using a Bayesian Approach.

Statistical Approach to Raman Analysis of Graphene-Related Materials: Implications for Quality Control (2020)
Journal Article
Goldie, S. J., Bush, S., Cumming, J. A., & Coleman, K. S. (2020). Statistical Approach to Raman Analysis of Graphene-Related Materials: Implications for Quality Control. ACS Applied Nano Material, 3(11), 11229-11239. https://doi.org/10.1021/acsanm.0c02361

A statistical method to determine the number of measurements required from nanomaterials to ensure reliable and robust analysis is described. Commercial products utilizing graphene are in their infancy and recent investigations of commercial graphene... Read More about Statistical Approach to Raman Analysis of Graphene-Related Materials: Implications for Quality Control.