For the detection of segmental gains and losses of one or more copies in 35 NB samples, performance was very high even when measured in a numerical background (Table 1).
Finite sample performances are investigated and compared in a simulation study.
The effect of cohesion on sampler performance is explored.
For the investment to employee and government, non-connected samples' performance is better than connected samples'.
As shown in Fig. 8, compared with the non-AFH results, the WiFi RSSI sampling performance is improved in the AFH-enabled test case such as AP 1 2, 3, and 6, where the RSSI values are higher than −80 dBm.
Besides, difference of the three samplers on the scope of the target analytes and exposure time, as well as the effects of environmental factors, e.g. hydrodynamic conditions, temperature, pH, ionic strength, DOM, on sampling performance were also introduced.
For these reasons, the estimates of out-of-sample performance are likely to be generalizable to other current settings.
We calculated standardized error as a metric for evaluating the performance of the prediction models as follows: (1) (2) (Estd, standardized error; Eave, averaged error; yp, prediction value; yt, teacher signal value; N, sample number; V, variance of all samples) Performance is considered to be improved with a decrease in the standardized error.
(13) S e n s i t i v i t y = T P T P + F N (14) S p e c i f i c i t y = T N T N + F P Due to the limited number of samples, performance is evaluated using 10 fold cross-validation.
Given the significant effect of age and education on story recall performance, the present normal control sample's performance was divided into three age-groups: 50 64 years, 65 74 years and 75 85 years and these are presented in Table 2.
