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Ali et al. [6] presented a new system which can be appropriate for rating tunnel sites to evaluate the potential of groundwater inflow according to the preliminary site investigation data based on the fuzzy Delphi AHP method.
On the basis of the relevant data, the permeability coefficients of the lining is 1.03×10-4 m/d, and that of the surrounding rock is 0.54 m/d.
According to the exploration data, the water head (h) of the model is 60m.
According to the Fig. 7, with the reduction of h, Q decreased sharply at the beginning, and then, the reduction trend gets inconspicuous.
(3) Based on the Fig. 7, with the reduction of ground water level, the seepage discharge decreased sharply at the beginning, and then, the reduction trend gets inconspicuous.

Although rhodium is very similar to platinum in chemical sense, by comparing these data with the ones published elsewhere [11], one may see that there is a considerable difference between rhodium and platinum deposits obtained in the same potentiodynamic way: while platinum builds microspheres imbedded randomly in the TiO2 layer [11], rhodium tends to spread over the whole surface of titanium support, in the form of a layer.
Along the potential axis, the region of hydrogen adsorption/desorption is clearly separated from the region of oxide formation/reduction, the transition potential being 0.0 V vs.
With a scan rate of 150 mV s -1, the current of oxide formation and reduction is between 12 and -12 mA cm-2, for a smooth Rh foil [17].
As a consequence of high surface roughness, the currents of hydrogen underpotential deposition are comparable to diffusion current of oxygen reduction in an oxygen-saturated solution.
Voltammograms in the region of O2 reduction, on Rh/TiO2 electrode in oxygen saturated 0.1 M NaOH solution, at rotation rate 5 cps (closed curve labeled by double arrows) and 10 cps.

Data mining in medical domain has gained great potential in discovering the hidden patterns from large data sets.
Classifying the raw medical data is a tedious task, because the data may have some missing or irrelevant data.
Discretization is an important step in data processing to convert the data into discrete intervals
“Effective dimension reduction methods for tumor classification using gene expression data”, Bioinformatics, vol.19, no.5, 563–70, 2003
Prather et al., “Medical data mining: knowledge discovery in a clinical data warehouse,” in Proc.

The vacuum of 10 Pa and temperature level of 1200°C were maintained in 4.5 hours for the MgO reduction.
Table 1 Weight data of input (briquettes) and output (products) materials for the early pilot tests and F contents analyzed in the products, % (weight percent) Test Briquette materials [kg] Products from test [kg] F content in products [wt%] No Dolime FeSi Fluorite Mg slag Mg ingot K and Na condensate Mg slag Mg ingot K and Na condensate 1 5.0 0.84 0.25 4.1 0.9 0.031 2.6 0.064 0.169 2 4.5 0.84 0.25 4.2 0.8 0.023 2.95 0.042 0.166 3 8.13 1.67 0.25 7.1 1.8 1.57 0.022 — 4 8.13 1.67 0.25 8.3 1.85 0.061 1.24 0.047 0.07 Table 2 Results of F balance calculated using the data in Table 1 for input and output materials of the early pilot tests Early pilot test 1 2 3 4 Input of F [g] In briquettes 115.7 115.7 115.7 115.7 Output of F [g] In Mg slag 106.6 123.9 111.47 102.92 In Mg ingot 0.58 0.34 0.4 0.87 In K and Na condensate 0.05 0.04 0.04 Total F-output 107.23 124.28 111.87 103.83 (F-output)/(F-input) [%] 92.68 107.42 96.69 89.74 (F in Mg slag)/(F-input) [%] 92.13 107.09 96.34 88.95
Table 2 lists results of F balance computed by using the data in Table 1.
These data are presented in Table 3 and used in Equation 1 to calculate recovery of F in Mg slag from F added in the charged briquettes.
The high rates of F recovery indicate little or no F compounds leaving the retorts during MgO reduction. 3.

The reduction of iron oxides generally takes place in two steps.
The degree of reduction (DRT) is defined as the ratio of hydrogen consumed for the complete reduction of metal oxides from ambient temperature to 800°C to the amount of hydrogen calculated for this complete reduction [8].
Hydrogen consumed, given by the TPR profiles, was used to calculate the degree of reduction.
Chemisorption data for the CNTs-supported nanocatalysts Catalyst DRT(%)a Amount CO adsorbed [µmol/g] Co 89.3 9.0 70Co30Fe 93.3 13.2 50Co50 Fe 72.0 7.7 30Co70 Fe 78.0 5.8 Fe 60.0 6.3 Figure 3 shows the XRD patterns of the monometallic and bimetallic nanocatalysts on CNTs.
The catalytic activity and product selectivity data were calculated after 5 hours of time on stream.

Relationship between Correlation Distance and Sample Spacing In order to analyze the effect of sampling space of data on the calculation correlation distance, the data is collected of 10 static cone penetration holes from some a construction field of high-rise building in Taiyuan in China.
The relationship is described in figure 2 between correlation distance and different sampling space of data.
At present the sampling space of exploration data is generally between 1m and 2m and sometime is even larger, which makes us not to find the real correlation distance because of littler data and larger sampling space.
So we can obtain reliable calculated result of correlation distance when sampling space of data is equal to correlation distance on the whole.
According to exploration datum, the vertical and horizontal correlation distances of typical stratum are analyzed in statistics based on a large amount of investigation data and the representative values of correlation distance of local area are obtained, which can be seen in Table 2 and Table 3.

Reduction of glass defects.
Cost reduction of refractory.
The matching of each refractory's service life is also achieved with this inspection because the inner structure of each block is recognized and installed at the proper position based upon these data.
Thus, the inner structure inspection of the sidewall blocks can be performed and the data of the blocks are available for the installation positioning of the blocks.
Such data are also available for the determination of the electrode hole position after the campaign started.

Of course, this growth and reduction are controlled by policy input. 
Comparing with the real statistical data, the model can be verified.
Fig.2. shows the comparison of simulated data and statistical data of China from the year 2001 to 2009.
The statistical data is from the “China Statistical Yearbook” and the data of carbon emissions is based on literature data[12].
Verification of the model by comparing the simulation data and statistical data of China Future scenario simulation According to China’s carbon emission reduction target, the emission intensity of 2020 will be reduced by 40% to 45% compared to the emissions level of 2005.

From the experimental data (N=18), C, a, and b for the rolling results v1, v2, ω 1, ω 2, and φ , can be obtained as given in Table 3.
From these figures, it is known that the errors between the predicted values and the experimental data are all within 10%.
From the comparisons of the predicted values and the experimental data, it is known that the proposed empirical equations can predict effectively the axial and angular speeds at the entrance and exit of the roll-gap and the twisting angle under these rolling conditions.
NSC-89-2212-E110-042. 3 6 9 12 15 3 6 9 12 15 Experimental Data Predicted 1 (rpm) 3 6 9 12 15 3 6 9 12 15 Experimental Data Predicted 2 (rpm) Fig. 8 Comparisons of the predicted values Fig. 9 Comparisons of the predicted values and the experimental data.
and the experimental data References [1] T.

And it has been used of solving the redundancy of attribute and data.
Decision tree has been widely used in data mining techniques, because it is efficient, fast and easy to be understood in terms of data classification.
It is a discrete-valued function approximation method of noise data with good robustness and can learn disjunctive expression.
Decision tree is easy to understand and implement, it is also possible to directly reflect the characteristics of its data.
Definition4 Information gain [9] is a measure of the standard with respect to the ability to attribute classification training data.