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This model applies the temperature dependent Buckley-Model [15, 16] and is used in this study to predict the time-dependent displacement and thickness reduction.
This is regarded as a reasonable assumption because at the point in time when the temperature locally drops below Tg, the mechanical deformation and therewith the thickness reduction are assumed to be progressed to a great extent.
For the temperature region of interest, this study uses the following linear approximation of the experimental data from [21]: c = (9.9/K∙T - 1.7e3)∙J/(kg∙K).
To prevent an overestimation, the heat flux is corrected by a factor of 0.7 to match the experimental data in [14].

This paper employs the environment statistic data[3.4] of the whole city to compare the IHW generation from 2006 to 2010 (As shown in Table 1, Fig.1 & Fig.2).
IHW generation equivalent and its attenuation coefficient are calculated based on the statistic data discussed in "Chongqing Statistical Annual (2007-2011)".
It is urgent demand for calling clearer production, green technological transformation and energy-saving & emission-reduction process[7].
As a mature process of clean production, the non-calcium roasting technology has been widely used in developed countries, realizing the reduction, reuse and recycle of chromium residue.

After the obtained liquid-solid coexistence of black gel material standing for 4 h in a breaker, heated at 140℃ for 1.5 h so that hydrolysis and oxidation-reduction reaction happened.
Using the curve of -ln(ct/co)~ t to express the data in Fig. 4 we get Fig.5.
It shows that the photodegradation of RhB with the catalyst comply with the rule of pseudo-first-order reaction (data in Table 2).
When the energy of irradiating photon is greater than the bandgap of TiO2, it will produce conduction band electron with strong reduction and balance band holes with strong oxidizing.

The reduction content of spinel [(Ni,Cr)2(Cr,Al)2O4], chromium oxide and nickel oxide (NiO) in TGO can be achieved after thermal exposure.
The weight gain data of five specimens varies gradually during the initial 0-5 h stage of isothermal oxidation, subsequently all of them change dramatically and in consistent with the sharp increase of the TGO growth rate during the 5-15 h stage of isothermal oxidation.
However, the weight gain curve of VHT 1000 specimen presents a flat trend among all the specimens during the 150-275 h stage of isothermal oxidation, and a relatively larger weight gain data at the 75-200 h stage of isothermal oxidation can be attributed to the formation and growth of (Al, Cr)2O3 oxide at the BC/TGO interface.
Meanwhile, the R2 peak shift of both VHT 1000 and N-VHT specimens show a reduction trend during the 50-100 stage of isothermal oxidation, this is probably due to certain mechanism stress relaxation.

The XRD data measured were refined with the version 4.0 of Rietveld analysis program TOPAS (Bruker AXS) to calculate the microstructure features of powder samples.
On the other hand, assuming that the saturation magnetization (Ms) manifests the variation of the phase in composition, the reduction of copper oxide by the precipitation of (CoNi) oxide and the partially change FCC-Co to HCP-Co (Fig.1), can be explained by the constant decreasing trend of Ms as is observed in Fig. 4, and comply with X-ray diffraction patterns as a function of milling time.
The mean strain values of as-milled alloys, measured by XRD data refined with Rietveld explains grain fragmentation increase of residual microstress by the continuous grain fragmentation and the lattice deformation due to the formation of metastable CuCoNi solid solution.
The saturation magnetization (Ms) decreased monotonically with increasing milling time, as a consequence to the variation of the phase in the composition, the reduction of copper oxide by the formation of (CoNi) particle and oxide, and the partially change of fcc-Co to hcp-Co.

This can increase the reduction rate and stability of the nanoparticles.
The sambiloto extract is mixed with a solution of AuCl3, which will cause the reduction of Au3+ ions into Au0 atoms and form stable nanoparticles in the presence of redox enzymes and metabolites.
UV-Vis data of gold nanoparticles at the optimization stage.
TEM characterization data is shown in Fig. 6.

This happens because the driving force for grain growth is the decrease in energy caused by the reduction of the number of grain boundaries per unit volume.
The total surface area of boundaries is decreased as grain size is increased, causing a reduction in the surface energy, i.e., when grains grow, the number of their boundaries is decreased and their total surface area energy decreases.
Their data for the AISI 316 were used in the present investigation to determine the activation energy for grain growth by using Eq. 5.
Results Statistical data taken from Stanley and Perrota [2] were used to calculate the activation energy for grain growth in AISI 316 steels.

Because the limits to reduction of these impurities are set of cost, many commercially available 7xxx alloys contain significant amounts of Fe and Si, which react with Al and alloying elements to form a large number of IM phases [1,2].
All specimens were taken from the forged 50 mm thick pancake-type plates that have been deformed at 430 o C for 85 % height reduction after homogenization at 465 o C for 9h and 475o C for 16h.
Table 2 Results of image analysis along with fatigue tests data.
The fatigue crack growth rate data are presented in Fig. 4.

Then, although transient strength reduction due to buckling wave reversing occurred, the strength increased under the influence of forming tension field.
The displacements at their maximum strengths and the inclinations of strength reduction correspond between the specimens of the same section.
A jig problem caused the difference in lines of data plots of CY3 but this can be ignored.
In this figure, solid line and broken line respectively indicate data lines obtained from the points when the fifth cycle of each δF and δy was conducted.

GM(1,1) model has the characteristic with less data, poor information, simple calculation and high precision.
The linear regression model for long-term prediction has strong anti-interference, but highly dependences on raw data, and requires a lot of data.
More original data, more laws, and higher accuracy.
The first is to do accumulate the original data to generate, that is a new data exponentially law, after get the original series accumulate.
Table 4 shows that the combined average relative error of prediction model is minimum, compared to three models to predict the data.