Sentence examples for each database and from inspiring English sources

Initial results are explained, indicating the relevance of each database and quantifying the overlap among them.

For each database and given minimum support, we chose the set ({mathcal A}^{F}) of all frequent items.

We have set four different time window bounds such as 5, 10, 16 and 20 min for each database and we find out recognition rates (Fig. 9).

First, we investigate the impact of data encoding into cover images (i.e., synthetic fingerprints from DB4 of each database) and accuracy of data decoding from stego images.

Table 7 shows the total number of paths in each database and the number of pattern paths for a support threshold of 2%%.

The values that gave the best accuracy rates for each database and for each methodology (1D and 2D) are summarized in Table 6 (methods 4 and 8).

The Hit box will show the number of matches found in each databases and provides links to the results.

The following search keywords and logic were adapted and complemented when necessary for each database: Diabetes AND (Gaps OR Barriers OR Needs Assessment OR Health Care Needs OR Challenges OR Versorgungsforschung) AND (Hausarzt OR Arzt für Allgemeinmedizin OR Praktischer Arzt OR Allgemeinmedizin).

The following search keywords and logic were adapted and complemented when necessary for each database: Diabetes AND (Gaps OR Barriers OR Needs Assessment OR Health Care Needs OR Challenges) AND (Family Practice OR Family Medicine OR General Practice).

Importantly, we perform a separate regression and correction for each database pair and for interactions common to all three databases.

We here report the results for each database query and discuss search capabilities and problems (Table 1).