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Fig. 2 shows Arrhenius plot of RIC for CaZr0.9In0.1O3-δ which were calculated using Ohm's law from the experimental data between from 0 to +5 V in Figs. 1(a) and (b), as compared with base conductivity for CaZr0.9In0.1O3-δ unirradiated.
The data measured at the initial fluence and the several temperatures were plotted.
The RIC increased with increasing the reactor power and was in good agreement with the theoretical value, where σ 0=2.7x10-9 S/m, K=0.2x10-6 and d=0.9 were took into account by hitting to the experimental data as a function of the reactor power (R), except for one at 50 MW.
The reduction of the RIC at the second stage in Fig. 3(b) is attributed to annihilation of sub-band between valence and conduction bands by the neutron irradiation and change of energy band by their defect formations.
The distortion between the experimental and theoretical values for the RIC at 50 MW in Fig. 3(a) and the quick reduction of the RIC the first stage may show protonic or ionic excitations by the irradiation.

During rolling, depending on pass design and reduction, the midsection of the billet experiences a compressive stress state whilst at the edges and subsurface the stress state is more complex, with presence of tensile triaxiality.
In order to measure the flow stress response of the material, the reduction in notch diameter was measured using a silicon C-gauge transducer, placed on one of the notches.
The length of the gauge was determined by recorded strain data resulting from trial linear velocity tensile tests.
Loveday, Validation of a Code of Practice for notched bar creep rupture testing: procedures and interpretation of data for design, Materials at High Temperatures. 16 (1999) 143–158
Purper, et al., A Code of Practice for conducting notched bar creep tests and for interpreting the data, Fatigue Fracture of Engineering Materials and Structures. 27 (2004) 319–342

The purpose of this work is to develop a model of the roll gap in reversing mill rolling process based on alloy grade, slab width, thickness, reduction and temperature.
Data entry (see Fig.2) is made by mill operator.
Data entry The second box is Materials Handbook containing coefficients for calculating metal deformation resistance (to be discussed in the next chapter) and physical constants for calculating temperature, specific heat and thermal conductivity of the material.
Some advanced alloys data are given in Table 1 [1,10-12].
Rolling force is determined by known formula [13] P=pm∙lc∙В, (3) where lc = arc of action determined by expression: lc=DhR , (4) where Dh = absolute reductions, R = work roll radius, В = plate width, pm = normal contact stress.

The experimental design data are shown in Table 3.
Table 3 Experimental design data Experimental variables Value Spindle speed n (r/min) 2000, 3000, 4000, 5000 Feed rate vf (mm/min) 30, 60, 90, 120 Vibration frequency f (kHz) 20 Amplitude A (μm ) 0, 5, 10 3.3 Measurement methods The vibration frequency and amplitude of end face of the tool were measured by a Laser Doppler Vibrometer (Sunny instruments Singapore Pte Ltd).
The data from the dynamometer were acquired and processed using DynoWare. 4 Results and discussion 4.1 Influence of process parameters on rotary ultrasonic drilling force The drilling force obtained through RUM experiments is recorded in Table 4.
Compared with conventional drilling, the drilling force for RUM decreased by 15.1%–22.5% respectively under the same process parameters, and the percentage of reduction increased with increasing of feed rate.
The percentage of reduction increases with increasing of feed rate.

Improving the inventory processes of spare parts can offer significant benefits in terms of cost reduction and increased the productivity of the maintenance department.
This method helps in reduction of the bullwhip effect on the forecasting accuracy and also give better performance compared to others forecasting methods which are double exponential smoothing (DES), trend analysis (TA), winters’ method (WM), fuzzy neural network (FNN) and enhanced fuzzy neural network with connection weights initialized randomly (EFNNR) method.
The CIMS Framework The CIMS Performance: Results and Discussions Overall the CIMS has been successfully performed the intended functions such as storing new data, updating data etc.
Besides that, the system presents all the data or reports of the spare parts in a systematic way.

MTT was used as an indicator of cell viability as determined by its mitochondrial-dependent reduction to formazone.
Data were determined per milligram proteinSection Headings.
The result illustrates that after one month storage, the DPPH scavenging percentage of PAMAM-OH/Trolox is similar to that of Trolox free form (the concentration of Trolox is same in the above experiment), indicating that the stability of Trolox can be enhanced by using PAMAM-OH as drug nanocarrier (data not shown).
Data are expressed as means ± S.
Preparation by a size-reduction technique.

StressDiff software is dedicated to the acquisition and processing of the X-ray data for the evaluation of the constraints.
When some data were outside the tolerance interval after iteration, they were deleted for the new calculation.
The appendix of this paper presents the certificate of one sample which includes the main information about it (reference, fabrication, mean values and variances for the material, mean values and variances for the given sample, X-ray parameters, or how to use the data in relation to the EN15305 standard).
Guillen, Round Robin test for X-ray stress analysis standards: optimisation for discrepancy reduction, J.
Guillen, Round Robin test for X-ray stress analysis standards: optimisation for discrepancy reduction, J.

Tested data shows that the direction changing rate can reach 0.5 degree per second [10], which is quicker than the normal yawing rate of large wind turbines.
WMO(World Meterolagical Organization) analized the tested tropical data, the report recommends gust factor for four different surface, e.g In-Land, Off-Land,Off-Sea and At-Sea.
The tested data [13] shows that the wind speed distribution conforms to exponential function within the height of 40 meters, but abover that the wind shear could be very large.
From the analysis of this time serials response TMD is very helpful for the extreme load reduction when the wind changes quickly, which can represent transient reaction of wind turbine structure system.
Therefore, TMD system is helpful for the reduction of extreme and fatigue.

Experimental Procedures Previously, studies were conducted using a statistical method of data analysis to determine the regularity of pearlite dispersity distribution in the wire rod cross section and the reproducibility of this distribution on the tested samples [11, 14 - 21].
Estimation data of wire rod pearlite grain grade with 6.5 mm diameter from steel with 0.58-0.65 % carbon content № Sample Diagonal 1st grade grain lamellar pearlite content in the view field, % 1 2 3 4 5 6 7 8 9 10 11 12 13 1 1 71.5 71.5 71.5 66 66 60.5 60.5 55 50 50 50 44 49.5 2 70 70 70 66 66 60 60 55 50 50 49.5 50 50 2 1 70 70 70 70 65 55 60 55 50 45 38.5 40 38.5 2 75 75 75 60 65 60.5 60 55 50 50 45 45 40 3 1 70 70 70 70 65 65 60 60 44 50 44 45 38 2 66 66 66 65 65 65 55 60 55 50 50 45 45 № Sample Diagonal 1st grade grain lamellar pearlite content in the view field, % 14 15 16 17 18 19 20 21 22 23 24 25 26 27 1 1 40 44 44 44 50 49.5 50 55 50 60 60.5 66 66 66 2 40 45 45 45 50 55 50 55 49.5 60 60 70 70 75 2 1 40 45 45 50 50 50 55 60 60 65 65 75 75 75 2 40 45 45 45 50 55 55 60 55 65 65 70 70 75 3 1 38 50 50 50 55 50 55 60 65 66 65 70 70 70 2 38.5 40 45 45 50 45 55 60 55 65 60.5 66 70 71.5 Table 2.
Estimation data of wire rod pearlite grain grade with 6,5 mm diameter from steel with 0.68-0.77 % carbon content № Sample Diagonal 1st grade grain lamellar pearlite content in the view field, % 1 2 3 4 5 6 7 8 9 10 11 12 13 1 1 75 75 72 70 70 65 65 60 55 55 50 50 49.5 2 75 75 70 71.5 66 70 65 60 55 56 51.5 50 50 2 1 80 75 71.5 75 70 70 65 60.5 57 55 50 51 50 2 75 75 75 70 71.5 65 65 60 60 55 51.5 51.5 48 3 1 80 75 72 70 70 65 66 60 55 55 50 49.5 45 2 75 75 75 71.5 70 65 65 60 55 51.5 50 47.5 45 № Sample Diagonal 1st grade grain lamellar pearlite content in the view field, % 14 15 16 17 18 19 20 21 22 23 24 25 26 27 1 1 45 50 50 50 55 60 60 65 65 70 70 75 75 75 2 45 49.5 50 50 55 60 55 65 66 70 71.5 72 75 75 2 1 42 45 50 55 58 60 60 65 60 70 75 75 75 75 2 45 45 50 51.5 55 60 61 65 65 70 68.5 70 70 75 3 1 48 50 50 50 55 57.5 60 60 65 70 70 75 75 75 2 45 45 50 51.5 55 55 60 65 66 70 75 75 75 75 Results and Discussion The results of pearlite dispersion determination confirmed the general
In the study of the possibility of the pearlite dispersion estimated value deviation reduction from the base value we can note the following: the microstructure formation in the profile cross section occurs during the rolled product cooling and is determined by thermal rules.
Conclusions The study results confirm the possibility of the error reduction in the estimated value determination of the high-carbon steel wire rod microstructure pearlite dispersion.

We selected the electricity demand in Hebei Province in 2004 - 2011 to build the model, The data were rounded handled.
Use MATLAB2012 software to calculate the model data.
We obtained the original data sequence of variables x(0) by the raw data.
au=(BTB)-1BTYn, Can be obtained: a=-0.10972, u=1315.3 Accumulate sequence prediction equation:x1k+1=13279e0.10972k-11988 (k=0,1,2,…) The original series’ gray prediction model:x0k+1=132791-e-0.10972e0.10972k Get 2004-2011electric demand prediction data and actual data, shows in Table 3: Table 3: 2004-2011 energy electric demand predictive data and actual data(Unit: 100 million kwh) Year 2004 2005 2006 2007 2008 2009 2010 2011 actual data 1291.40 1501.92 1734.83 2013.67 2095.02 2343.85 2691.52 2984.90 predictive data 1379.9 1539.9 1718.5 1917.8 2140.3 2388.5 2665.4 2974.6 Accuracy posteriori testing of the model.
Tabel 4: Residual(Unit: 100 million kwh) Year 2004 2005 2006 2007 2008 2009 2010 2011 actual data 1291.40 1501.92 1734.83 2013.67 2095.02 2343.85 2691.52 2984.90 predictive data 1379.9 1539.9 1718.5 1917.8 2140.3 2388.5 2665.4 2974.6 Residual 88.5 37.98 -16.33 -95.87 45.28 44.65 -26.12 -10.3 Use MATLAB2012 software to calculate the model data: Average residuals: ε=8.481,Variance of the history data: s12=2872.3 ,Average historical data: x=2082.1,Residual value of the variance: s22=292780,Posterior margin ratio: C=s2s1=0.099047 Small error probability: Pεk-ε<0.6745×s12=1>0.95 Therefore we can determine that the accuracy of the model is level 1.