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First the defuzzification module uses the rough reduction method to mine the input information, then quantifies the natural properties of the reduction information, and sends the quantified data to the signal component extraction layer.
By adjusting the input layer weights , using wavelet decomposition operator and decomposing the coefficient of the input data, we obtain the varying frequency component signals in a small period of time, and input these different frequency component signals to the decision support layer.
At the moment , the input in the decision support layer is , where is the output data in the component extraction layer.
At the moment , the input value in the matching layer is , where is the output data in the decision support layer.

The trajectory of the plotted data showed a distorted arc, attributed to the superposition of the impedance of the crystalline phase, glass phase, grain boundary and electrode.
As shown Fig. 5, sdc data appeared to be linear. i. e. σdc increased exponentially with increasing temperature, suggesting that the mechanism for ionic conduction is thermal activation.
To estimate the activation energies, the plotted data were fitted by the following thermal-activated formula: , Fig. 5 Temperature dependence of sdc for KAlSi3O8.
This result possibly suggests that the existence of a glass phase causes the reduction in activation energy.
That is, if the data were plotted on log(s(n)/sdc) versus log(n /sdcT), all spectra with the same ionic transport property would fall onto one master curve.

Modelling of recrystallization The developed model was used to simulate the recrystallization process in copper rolled up to 70% reduction.
Stored energy values versus grain orientations were estimated from EBSD (Electron Backscattered Diffraction) data.
Deformation texture (Fig.7b) and stored energy distribution (Fig.7c) obtained from EBSD measurements were used as an input data for modeling.
Deformation texture and stored energy distribution obtained from EBSD measurements for copper (70% reduction) were used as an input data for the model, 3.
Piękoś, Modeling of recrystallization using Monte Carlo method based on EBSD data, ICOTOM 13, Seoul, Korea; printed in: Materials Science Forum, Vol. 408 - 412 (2002), p. 395 [11] F.

If we consider the results given in reports [8, 3], and we take into account that the strength of a medium piece of exploded mine rock mass is a function of the distance from the explosion source, then we can calculate the coefficient of strength reduction for granites in question in relation to unbroken material in each zone from the change of specific energy consumption.
Relation between linear compressive strength of the rock mass and relative distance Validation of Eq. (3) for the experimental conditions of the open cast mine was performed on the basis of blasting data and by comparison with the obtained Eq. (2).
The example of output data for blasting No. 1 is illustrated in Fig. 4.
Substituting the calculated data into equations of strength weakening for zones illustrated in Fig. 3 we obtain the uniaxial compressive strength for averaged rock in shotpile.
On the basis of calculations and experiments has been established that variation of uniaxial compressive strength of average rock as a function of ES specific energy consumption in the range from 2.5 MJ/kg3 to 3.5 MJ/kg3 for the considered rocks is determined according to the following equations: While comparing the experimental dependence with calculated Eq. (2) it can be seen that it satisfactorily approximates experimental data and is suitable for calculation of average rock strength in general and in individual fracture zones on the basis of ES specific energy consumption.

According to their data, addition of the silica nanoparticles increases the surface and interfacial tension.
Surface and interfacial tension data of silica nanoparticles and some type of ionic and nonionic surfactant systems also has been reported by Ma et al. [5] The reported data shows that silica particles have no effect on the surface or interfacial tension of nonionic surfactant solutions, while increasing the surface activity of the anionic sodium dodecyl sulfate molecules, and consequently decreasing the interfacial and surface tension.
Increasing the concentration of nanoparticles promotes the reduction.
The IFT and surface tension reduction of nanofluids contains C12TAB is not as much as SDS, according to the chemical interactions taking place between the surfactants and ZrO2 nanoparticles (Fig. 5.).
Data, 2001, 46 (5), 1086–1088.

The given data demonstrated that anisotropy of mechanical properties of sheets was low.
According to the data of paper [5], extruded AK4-1ch alloy semiproducts have the following properties in longitudinal direction: UTS = 420 MPa, 0.2%YS = 380 MPa, El = 9 %.
Table 4 Characteristics of high-temperature strength of В-1213 sheets and extruded strips Product UTS150ºC [MPa] σ100 150ºC [MPa] σ0,2/100 150ºC [MPa] Sheet 390 345 335 Extruded strip 405 355 - We compared the obtained data with high-temperature properties of commercial heat-resistant AK4-1ch-type alloys.
The following data on high-temperature strength were given in paper [7] for 2-3 mm thick sheets from AK4-1ch alloy in artificially aged state (T1): UTS150ºC = 350 MPa, σ100 150ºC = 245 MPa, σ0,2/100 150ºC = 220 MPa.
The presence of Mg in solid solution causes diffusion of copper and contributes to an increase in hightemperature properties of aluminium alloys due to a reduction of coagulation rate of hardening precipitates [1].

The container with water and covered with the membrane was measured under specified conditions of moisture passed in time of the data sample, see Fig 2.
The paper deals with the development of experimental facilities for research moisture transfer and present some experimental data.
Figure. 4 Experimental arrangement, 1 - source of compressed dry air, 2 - separator filter, 3 - pressure regulator, 4 - T–junction to “dry” and “humidified” way, 5 - closing valve, 6 - reduction valve, 7 - two intensity modes humidifier, 8, 9 - rotameters, 10 - experimental enthalpy exchanger (side view), 11 - experimental enthalpy exchanger (ground plan), 12 – ALMEMO 2590-4S data logger, 13 - PC (AMR-CONTROL software).
Volume flow rates were controlled and equalized by reduction valves (6).
Relative humidity φ, temperature t, dew point temperature td and total pressure pb were scanned by ALMENO 2590–4S data logger and sent to personal computer.

This method puts the verification on the PC-side on which it hardly guarantees condition or data safety, not in the cards, verification function is limited.
It’s expectation to get the bytecode verification algorithms that don’t require to get the prior information of the structure, but instead by check to get the information, or get it at the same time during the data flow analysis.
This method does reduce the space complexity, while the degree of reduction depends on the size of M, that the number of OB block.
Compared to a traditional verification method on the left in the Fig.1, in accordance with the data flow to verify subroutine, then at subroutine entry instruction i there will be facing a confluence circumstance of three branches, the first thing is doing LUB calculation, which reduces the type accuracy of the local variables and stack.
But the reduction of individual snippets memory consumption is not obvious, the reason for this lies primarily with the increases of multivariate verification state set, and the other is on the memory allocation which is not only appeared in the verification stage.

In this paper, Eh—pH diagram of Au-I--I2-H2O system was established through calculating the equilibrium potentials of the main chemical reactions based on the thermodynamic data of the actual iodide leaching of gold system.
Eh-pH diagram can be drawn based on calculating the equilibrium potentials of the chemical reactions according to the thermodynamics data of the actual leaching system.
AuI+e=Au+I− (25) Au+ 4I−=AuI4−+3e (26) Au+2I−＝AuI2−+e (27) Au+ I−+ I3−=AuI4−+e (28) As shown in Fig.1, the reduction potential of the complex of Au-iodide in iodine-iodide leaching gold system is 0.578V, which is far lower than the reduction potential of free Au ions (1.824 V).
I3-+H2O ⇄IO-+2I-+2H+ (36) 3I3-+3H2O ⇄IO3-+8I-+6H+ (37) Conclusions In the present work, Eh-pH diagram of Au-I--I2-H2O system was established based on the actual thermodynamic data of the leaching system.

Tikalsky et al. completed a study on different binary and ternary based HPC mixtures electrical resistivity testing and found that resistivity data is well correlated with RCPT data for different binary and ternary based HPC mixtures.
That resistivity data can be utilized to compute the diffusion coefficient of HPC mixtures [11].
The readings are obtained twice by swapping two ends of the concrete cylinder within the clamp attached with the electrode plates and the data logger attached to the computer records the bulk conductivity data.
Altogether 8 data points are obtained for each concrete mixture (2 data points from each cylinder).
First group consists of the measured diffusion coefficient data in Table 1.