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As shown in Fig.2, the power low (1) fits the experimental data quite well at least as long as the number of pressings is small and the dominant mechanism of strengthening remains to be the dislocation accumulation and the subs-structure formation.
The hardness of ECAP treated K18 alloy is higher than that of conventionally rolled to 75% area reduction specimen even after the first ECA-pass, as shown in Fig.2.
Since the strength of f.c.c. metals typically reaches its maximum after approximately 3-4 passes through the die (see [5], for example) and since the reduction of ductility is not a problem in the present case (in fact, some ductility is even redundant in ordinary Au and Au alloys, which is inconvenient for many artwork), no further ECA-pressing is actually necessary and the desired compromise between the strength and ductility can be readily achieved after first ECA-pressings.
ECAP is particularly beneficial for strengthening of concentrated solid solutions, probably due to considerable reduction of the stacking fault energy.

Another way to reduce the damage of ecological environment from mining is “resource reduction” which considers environment issues caused by mining and takes eco-costs into pit design to achieve eco-costs incorporated in ultimate pit optimization.
So the correlation parameters for calculating ecological costs are basically consistent with Wang et al[7]and the d is 0 for all of data based on the current market economy.
However, the profit of the pit with eco-costs which we advocate “source reduction” is 188.49 MUS$ and 97.3% more than the pit without eco-costs which adopts the way of “end treatment”.
Conclusion It is an attempt to take eco-costs into account in pit optimization for practicing the idea of design for environment and achieving “source reduction”.

Loss reduction and good voltage profile are therefore the important factors to be considered in the operation of these DG units.
The related detailed data is shown in [5].
The simulation results also revels the apparently achievements in the reduction of power losses and improvement in the voltage profile.
Network reconfiguration in distribution systems for loss reduction and load balancing.

Replication, or repeated tests at the same stress amplitude, is used to provide statistical confidence in life data during the development of S-N curves.
However, with the increasing interest in gigacycle fatigue and the development of high frequency fatigue testing machines capable of tests carried out up to 1012 cycles, methods of further data reduction are desirable in order to be able to economically gather adequate data for design purposes.
The parameters A and B estimated from various combinations of the S-N data measured on the Mg specimens.
The recorded S-N data are plotted in the semi-log system of stress amplitude (sam) versus fatigue life (Nf), and are analysed in terms of replication.
In the present study, the two parameters were estimated by fitting the data using Eq. (2).

Activities data in this study are collected in Rayong province.
This data will serve and assist in developing the emission database under PRTR program.
These data are use as activity data to compute with emission factor in order to obtain emission amount.
However, further implementations in other areas depend on availability of such data of the inventory area.
These data can be used as an input data for air pollution dispersion modeling and exposure analysis of air pollutant charactristic particularly for Rayong province in other researches.

The density data was statistically analysed by on-way (ANOVA) and the values were significantly effect when addition of rubber particles in PMMA denture base composite (p < 0.05).Therefore, the density issue can be easily adjusted in retention and stability of denture base in the patient mouth.
In the present work, the study is focused on the reduction of PMMA denture base density through incorporation of nitrile butadiene rubber (NBR) particles and further adjusted by ceramic fillers.
The data was subjected to one-way analysis of variance (ANOVA) followed by Tukey’s post-hoc.
0.30 0.26 - SiO2 0.044 0.09 99.14 0.38 TiO2 - 0.11 - 2.031 Fe2O3 0.04 0.7 0.068 - ZnO - - 0.013 0.63 Y2O3 - 6.38 - - ZrO2 0.006 90.39 0.027 - HfO2 - 2.45 - - Ga2O3 Trace - - - NiO 0.012 - 0.017 Trace CaO 0.011 - 0.016 2.10 CuO - - 0.012 0.008 K2O - - 0.01 0.068 Na2O - - 0.43 0.14 N - - - 2.38 SO3 - - - 1.85 CL - - - 0.016 SrO - - - Trace BaO - - - 0.05 C - - - 90.34 X-ray diffractometer (XRD): The XRD analysis for the three different ceramic fillers (Al2O3, YSZ and SiO2) with reference to the International Centre for Diffraction Data
The reduction in density values in this study is in agreement with the finding that made by Jansen et al. [13].

From the UV-Vis data, we observed that the more concentration of rGO is being ablated, the more secondary absorption peak at 255.1 nm appeared.
Further confirmation obtained from UV-Vis spectrum data (Fig.2) that fit with assumed GQDs absorption spectrum [16].
However, the absorption peak in our data is not so sharp, and therefor the bandgap of GQDs could be determined using Tauc’s plot [24].
Ruoff, Stable aqueous dispersions of graphitic nanoplatelets via the reduction of exfoliated graphite oxide in the presence of poly (sodium 4-styrenesulfonate), Journal of Materials Chemistry. 16.2 (2006) 155-158
Santoso, Effect of Chemical Reduction Temperature on Optical Properties of Reduced Graphene Oxide (rGO) and its Potentials Supercapacitor Device, Material Science Forum, 901 (2017) 55-61

Even though the FCG tests have generated a large number of data, the data deviation may still be found.
Furthermore, the probabilistic assessment of FCG data was evaluated by generating a large number of FCG data with the MCM.
The LSFM tends to give the lower C, and higher m values than the experimental data since the FCG data distribution influences the LSFM.
About 5% of data lies below the lower bound, and 90% data lies above the upper bound.
All predicted of FCGR data lay on an 85% confidence interval.

azjxiang111@163.com , b1209916571@qq.com Keywords: the load rate, grey correlation analysis, the analysis of influence factors Abstract: With the rapid development of power industry, the load rate which describes the power characteristics is playing a more and more important role, this paper uses the grey correlation analysis to find the relationship between the load rate and the influence factors based on the historical data of the load rate influence factors and analyzes the weight of each influence factor quantitatively through the grey correlation coefficient so as to reflect the influence degree of different factors on load rate, meanwhile, through the analysis of various influencing factors of load rate, this paper hopes to clarify its effects on the load rate which includes strength, size and the order and help the power grid enterprise choose the key factors influencing the load rate which provide a basis for the future to improve the load rate.
Economic factors mainly include the proportion of different industry, the proportion of light and heavy industries, temperature climate factors mainly include the effects of climate change, the summer average temperature and winter average temperature, the DSM measures factors mainly include the influence of electricity price policy, the influence of the energy conservation and emission reduction policy and smart grid construction factors mainly include the user's selection, real-time electricity price, the extent of the load monitoring as well as the access of distributed energy ratio, specifically is shown in Figure 1-1 [2-3]: Figure 1-1 The influence factors of load rate 2.2 Grey correlation analysis model of load rate influence factors Gray correlation analysis is a statistical analysis method which can well reflect the influence of different factors on load rate, quantitative analysis with the grey correlation analysis theory is introduced to the weight of each influence factor
The following table 3-1 is S city’s detailed data in recent years: Table 3-1 All factors’ data of S city from 2005 to 2012 Year Annual load rate the summer average temperature（℃） the summer average temperature（℃） Primary industry. proportion Second industry proportion tertiary industry proportion New energy proportion 2005 0.67 26.50 -3.10 0.03 0.55 0.42 0.074 2006 0.69 25.12 -1.20 0.02 0.55 0.43 0.075 2007 0.69 26.43 0.25 0.02 0.55 0.43 0.078 2008 0.69 25.63 -1.87 0.02 0.55 0.43 0.086 2009 0.69 26.17 -1.87 0.02 0.53 0.45 0.087 2010 0.68 26.03 -2.93 0.02 0.52 0.46 0.094 2011 0.71 26.37 -2.67 0.01 0.52 0.47 0.088 2012 0.72 25.70 -3.30 0.01 0.52 0.47 0.103 Calculating in accordance with the above model and finally getting the correlation coefficient matrix: λi=1.001.001.001.001.001.000.870.460.610.950.980.970.950.330.610.950.980.970.900.560.610.950.980.810.930.560.610.890.930.790.950.900.620.900.860.680.890.740.430.830.900.810.850.980.430.820.920.64 Grey

Trained by the temperature distribution data of ceramic sintering analyzed with ANSYS under linear sintering curves including different slopes, the neural network can be used to simulate that under irregular sintering curve at certain precision, so the temperature evolution of the ceramic hot geometry centroidal point (HGCP) can be fast obtained by the result simulated with the trained neural network.
The analyzed data has been used as the training sample of the neural network.
For the sake of reduction or elimination of the Platform phenomenon, neural network has been analyzed and adjusted according to following several aspects [5,6].
The BP neural network is trained by the analyzed data and tested by the sample which is analyzed with ANSYS under non-linear sintering curve.