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The experimental data are consistent with the theoretical predictions.
NQR spectra of sodium nitrite According to the frequency spectra of three groups above, we obtain the NQR signal amplitudes of the test samples, and the data are listed in Table 2.
Through fitting curves to the data of three groups, we obtain the correlation coefficients 0.9991, 0.9994 and 0.9992 of the urea, urotropine and sodium nitrite respectively.
According to the analysis of the data above, we can see that the nitrogen content of urea is highest and the relaxation time is longest, however, the NQR signal amplitude is smallest.
Conclusions The following conclusions can be derived from the analysis of the experimental data and theoretical results in this paper: 1.

Buffer layer addition between the hard alloys cladding shows a reduction in crack formation and the dilution rates.
The temperature data is recorded at a 20 Hz sampling rate using LabVIEW software through National Instruments (NI) data acquisition system (Model 9213).
The online temperature data for samples S1 and S4 are shown in Fig. 3, which observes to be of similar trends in both cases.
Hence, the data acquired from P5 is considered to be the maximum temperature generated in the substrate, shown in Fig. 4.

Initial Data and Preliminary Calculations Grain size distribution.
The data of Table 3 and the graphs in fig. 3 show that the flow parameters of the slurry are highly dependent on the solids concentration.
The results obtained must be compared with the experimental data on the pipes [11].
Fig. 8 shows the graphical dependences of head loss on the flow velocity, based on the experimental data.
Let us insert the data τ0=68 Pa (fig. 5a), hef=0.3258 Pa∙s (fig. 5b); γ= 8vhDpipe= 8∙1,00.05=160 s-1.

The ECG signals were filtered to remove the noise and all the dataset were re-sampled to 250 Hz, and the data were segmented into 1.2 seconds.
The SVM is a supervised learning statistical algorithm used for regression analysis and classification data [25,26].
The framework of the SVM is to find the maximum margin between the decision boundary and the training data.
The support vectors are the closest data subsets to the decision boundary [27].
Ghodsi, Dimensionality reduction a short tutorial.

The total surface area, total pore volume and BJH pore size distribution of all ImIL-SBA-15 nanocomposites decreasing with the increasing amount of ImIL from 393.27 to 354.39 m2/g which indicated that the pore channel and/or surface of SBA-15 were occupied by ImIL without significant reduction of the quality.
The intense diffraction peaks (1 0 0) for the 1.0 ImIL-SBA-15, 2.0 ImIL-SBA-15 and 4.0 ImIL-SBA-15 nanocomposites were shifted slightly toward higher 2θ values (1.00°), implying the reduction of pore diameter of SBA-15 owing to a certain amount of ImIL was trapped inside the mesoporous channels.
The inset shows the corresponding 2D images of scattering patterns for all nanocomposites The data summarized in Table 2 showed the unit-cell parameter decreased from 10.69 nm for SBA-15 to 10.21 nm for ImIL-SBA-15 (ImIL = 1.0, 2.0 and 4.0 mmol).
Table 2 SAXS data of SBA-15 and ImIL-SBA-15 nanocomposites Samples 2θ (º) Intensity of d100 d100 (nm)a q (nm-1) ao (nm)b SBA-15 0.96 14699.95 9.25 0.68 10.69 1.0 ImIL-SBA-15 1.00 18581.54 8.84 0.71 10.21 2.0 ImIL-SBA-15 1.00 17043.37 8.84 0.71 10.21 4.0 ImIL-SBA-15 1.00 16378.77 8.84 0.71 10.21 6.0 ImIL-SBA-15 0.90 34102.71 9.82 0.64 11.34 8.0 ImIL-SBA-15 0.90 12027.37 9.82 0.64 11.34 10.0 ImIL-SBA-15 0.90 8863.01 9.82 0.64 11.34 ad-spacing of the (1 0 0) plane calculated by using the formula of d100 = 2π/q; bhexagonal unit-cell parameter calculated by using the formula of ao = 2 d100 / √ 3 In order to study the microscopic morphology, SBA-15 was further characterized by FESEM.
Siemieniewska, Reporting Physisorption Data for Gas/Solid Systems with Special Reference to the Determination of Surface Area and Porosity.

The results show that the proportion-derivative precedence type (I-PD) control is suitable for reduction of measurement times in vector magnetic property measurements.
Since it was very difficult to measure accurately the magnetic properties under the high magnetic flux density conditions, we have not yet performed analysis so far due to lack of the data of vector magnetic properties over 1.6T [5-7].

The predicted compressive strengths using 7-day maturity data were comparable to actual strength results.
The UPV results were taken from 3 samples of each mixture, and each measurement was taken three times to get more accurate and precise data.
The strength-maturity relationship was established by expressing the strength as a linear function of the logarithm of TTF to determine a best-fit straight equation through the plotted data.
For example, using slope values and Eq. 2, 28-d strength of mixtures were predicted based on 7-d data.
· The predicted compressive strengths using 7-day maturity data were comparable to actual strength results.

Pugh method analysis shown that the second design give more advantages in handling, easy to operate, least cost in manufacture, and reduction of time in doing the total knee replacement than the first design.
The comparison was carried out via Pugh scoring method by referring to a design datum, which is benchmarked industrial holder.
The common commercial jig holder in the market is selected as reference datum in this study.
The first and second concept is compared with reference datum according to design criteria that has been discussed previously.

Temperature conductor Fig. 3 Diagram of piping layout determination 2.3 Determination Data Processing 2.3.1 Analysis Results of Gas Samples To find out the variation laws of temperature of residual coal and gas injection parameters in the fully mechanized caving working face, 3237 working face of Zhaogezhuang mine has been made temperature measurement, sampling and analysis.
Table 1 Analysis results of gas samples Depth from the working face（m） CO（ppm） O2（％） 12.4 14 14.1895 15.4 13 11.8565 34.3 11 11.0326 39.1 9 7.4096 43.9 5 7.2781 54.1 0 4.0733 56.7 0 4.627 59.5 0 4.1933 64 0 4.2211 67.1 0 3.9652 Analysis of the data in Table 1, φ (O2) reduced with the increase of the depth in gob.
According to data in the Table, concentration curve of CO is drawn by L3 in Figure 4.With calculation and regression analysis, the regression curve is drawn by L4 in Figure 4.
Therefore, according to data in Table 2, the regression equation is obtained, that is Table 2 Changing data of temperature measurement points in the goaf Date Temperature（k） Promoting degree（m） Date Temperature（k） Promoting degree（m） 20/7 294 2 15/8 303 46.5 22/7 294 5 16/8 304 48.5 24/7 294 8 17/8 304 50.2 28/7 296 9.6 18/8 303 52.1 29/7 300 12.4 19/8 303 31/7 298 15.4 20/8 301 2/8 300 21/8 300 54.1 4/8 305 22/8 301 54.6 6/8 301 23.4 23/8 302 56.7 7/8 302 27.5 24/8 301 57.4 8/8 313 30.5 25/8 301 59.5 9/8 305 34.3 26/8 300 62.5 10/8 306 38.1 27/8 300 64 11/8 305 39.1 28/8 300 66.1 12/8 304 41.4 29/8 298 67.1 13/8 304 43.9 30/8 299 68.7 14/8 305 45.4 31/8 300 69.3 .
The temperature peak determined in gob is 313K, the maximum increasing variation is 19K, and the maximum reduction variation is 13K.

This is due to several reasons: the complexity of formulating the query, lacking of familiarity with the data collection procedures, or inadequacy of the available features.
Our data will consist of (), withwhere n is the total number of images, is the feature vector, and is the user evaluation (1 = positive and 0 = negative).
Logistic regression [6] takes a binary (0/1, yes/no) dependent variable, it is the most important model for categorical response data.
Silverman, Functional Data Analysis, Springer Series in Statistics, Springer, New York, 1997 [5] T.
Rubin, Bayesian Data Analysis,second ed., Chapman & Hall and CRC, London, Boca Raton, FL, 2003 [9] http://www.corel.com