We address the need for improvements of sensor calibration/characterization, atmospheric correction, and bio-optical algorithms to make operational and quantitative use of these medium-resolution bands.
We suggest that this method can be used as the non-operational semi-quantitative definition of cell differentiation status and developmental potency.
Now, to be clear, data scientists come in many shapes and sizes — quantitative, operational, marketing, product, predictive modelers, statisticians, engineers, miners, visualizers, warehousers, machine learners and so on — and they perform many different roles within an organization.
The proposed research incorporates carbon emission parameters with various decision variables and modifies traditional integrated forward/reverse logistics model into decision-making quantitative operational model, minimizing both the total cost and the carbon footprint.
In particular, the Escherichia coli cell is chosen because it has been the subject of systematic study for many years, much is known about its operational mechanisms, and quantitative data are available on its structure and parameters.
The country-specific stratification, the forest management practices and their associated quantitative operational criteria (steps 1 and 2 above) were defined according to information provided by the countries, found in the literature or through an expert assessment (see [30, 53] for a summary of country data sources).
The use of quantitative objectives, operational targets and optimisation approaches is also infrequent, in spite of repeated commitments of European forest ministers (MCPFE) to establish coherent, comprehensive and representative forest conservation networks.
This paper details these dimensions of the anomaly/failure attribution process and provides data from a variety of operational examples to illustrate quantitative and specific actions to enhance the anomaly/failure attribution process short-term and long-term.
The paper proposes a unified simplified cost model for all design objectives (throughput, space utilization, process capital and operational cost) and a quantitative selection criterion to allow for an optimal automation design.
Nearly all of these a priori criteria are operational definitions that are quantitative.
