The coupled statistical and process-based modeling demonstrates an end-to-end methodology for generating reasonable estimates of wildland fire PM concentrations and health effects at resolutions compatible with syndromic surveillance data.
Based on results from this study and best practices for high-dimensional model validation, we conclude with an end-to-end methodology for effective application of penalized regression to diverse genomic data for prediction and variable selection.
At any given moment, a field may be dominated by squabbles, but, in the end, the methodology prevails.
To this end, the methodology used in[13, 42] is adhered to; that is, the encoding and decoding complexity are evaluated using execution time measurements under regulated conditionsb.
To this end, a methodology for simulation of fuel cell operation under uncertainty is presented by considering a one-dimensional nonisothermal description of the governing physical phenomena.
Towards that end, a methodology for manufacturing networks design via a smart search algorithm is proposed, targeting the adoption of frugal innovation in a new manufacturing network.
To this end, the methodology introduced in Part I is consistently adopted in this paper, but the volumetric center is additionally monitored in the whole transient process of cloud propagation.
Toward this end, the methodology for development of surrogate models is provided, and Pt catalyst dissolution in proton exchange membrane fuel cells (PEMFCs) is used as an example to demonstrate the approach.
To this end, the methodology makes use of the attainable region and driving force concepts as well as employs several process models with differing level of complexity (simple, steady-state, dynamic).
To this end, a methodology based on model simulations is used that employs three statistical methods: (i) Monte-Carlo procedure: to find proper initial conditions, (ii) Identifiability analysis: to find an identifiable parameter subset of the fuzzy controller and (iii) minimization algorithm: to fine-tune the identifiable parameter subset of the controller.
