The data synthesis processes involve categorizing and classifying extracted data results, tabulation of each characteristic of the study and the major findings related to TPN, and then drawing a conclusion or making a coherent integrated report from the individual studies.
This paper presents a formal verification methodology of high-level data-flow synthesis process.
How to verify the correctness of the synthesis results is a key issue within the high-level data-flow synthesis process.
Multisensor navigation data synthesis (MNDS) is the process of fusing outputs from inertial sensors with information from other sensors and information processing blocks into one representational form.
The process of data synthesis is thus: (1) study selection following the literature search; (2) data extraction, including calculation of study weighting factors; (3) regression analysis.
To ensure accuracy and reliability in reporting the findings, we engaged in an iterative process of data synthesis, which involved going back and forth between the data extraction tables and the original articles.
The recent trend toward direct-to-consumer advertising of whole-genome analysis by several companies underscores the importance of a credible process for data synthesis and evaluation of the validity and utility of claims related to genetic prediction of disease risks (4– 7).
The results of the retrieved primary studies were reviewed and summarized in a coherent manner by using the NHS Center for Reviews and Dissemination [ 80] non-quantitative data synthesis guidelines, and classifying and structuring processes [ 100].
Thus, as testify mentioned above data, doping by copper of ZnS particles, which are formed during synthesis process is realized in different way depending on their sizes.
