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This paper focuses on the second level optimization utilizing data envelopment analysis (DEA).
In past work the authors proposed a set of metrics by which to measure a technology and a means to measure its progress over time utilizing data envelopment analysis (DEA).
The approach is derived through statistical nonlinear regression analysis, utilizing data generated from finite element analysis.
Here, we established a simple and effective quantitative analysis of protein markers, utilizing data obtained through routine immunofluorescence analysis on CTCs non-selected according to EpCAM expression, thus expanding and complementing a previously established CTC identification methodology.
The new methodology which adapts the same concept as the Stand-Alone Hybrid System Power Pinch Analysis utilizes data extracted from an ideal (considering no efficiency losses) graphical Power Pinch Analysis and Power Cascade Table and multiplying the extracted data with a probability factor to obtain an estimated target (considering efficiency losses) for the power system.
First, the analysis utilized data contained in one health plan located in the Southeastern United States.
This analysis utilized data from a large population-based cohort trial in northwest rural Bangladesh.
The current analysis utilizes data from SEER-MHOS cohorts who completed surveys between 2001 and 2007.
This analysis utilizes data from the first two rounds of the MEPI annual survey and site visits.
First, our analysis utilized data from an administrative database, and detailed clinical information was missing, including hemoglobin levels or other transfusion triggers.
This analysis utilized data from two sources in order to capture overall YPLL for the United States, as well as to assess YPLL and YLLD by specific histologies (Fig. 1).
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