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Introduction Amorphous oxide semiconductors have potential for application to the next-generation electronics because of their wide energy gap (transparent to visible light), no grain boundaries, and relatively low growth temperature [1].
A number of studies have been proposed for achieving the higher performance of indium-based oxide thin film transistors such as the treatment methods, including the material dopant, surface treatment for the wettability, annealing in various temperatures and various ambient atmospheres [4-8].
The conductive behavior suggests the existence of a considerable number of mobile carriers without application of VGS.
It may cause oxygen defects, and the observed device behavior, high off current (low on/off ratio), is consistent with ISO films with considerable number of oxygen defects.

Experimental design In order to analysis all impacts on asphalt mixture to skeleton, study on variation of the coarse aggregate clearance rate with the particle size and grading.The aggregate is divided four factors: 16.0mm～13.2mm,13.2mm～9.5mm，9.5mm～4.75mm，4.75mm～2.36mm, their respective coarse aggregate content is means with X1, X2, X3, X4, The number of levels:n = 16.
Table 1 Coarse aggregate mixture uniform design Particle size and proportion serial number 16.0~13.2mm X1（％） 13.2~9.5mm X2（％） 9.5~4.75mm X3（％） 4.75~2.36mm X4（％） 1 68.51 14.79 8.87 7.83 2 54.58 10.42 1.09 33.91 3 46.14 2.59 30.44 20.83 4 39.75 36.44 2.23 21.58 5 34.48 20.66 29.44 15.42 6 29.95 8.13 9.67 52.25 7 25.94 60.97 8.59 4.50 8 22.32 32.14 9.96 35.58 9 19.01 15.38 47.16 18.45 10 15.95 1.32 23.27 59.46 11 13.10 46.26 31.75 8.89 12 10.42 24.29 22.44 42.85 13 7.90 7.50 71.38 13.22 14 5.51 65.56 11.75 17.18 15 3.23 35.09 55.90 5.78 16 1.05 15.06 39.32 44.57 Analysis on uniform test result The basalt stone is produced Tangshan City in the test, two kinds of forming method are inserted pound and vibration, VCA and skeleton strength value can be measured,date is shown in Fig.1 32.00 33.00 34.00 35.00 36.00 37.00 38.00 39.00 40.00 41.00 42.00 1 2 3 4 5 6 7 8 9
10 11 12 13 14 15 16 Sequence number Tamping vibration VCA(%) Fig. 1 VCA of uniform design test results As can be seen from Fig1, through two cases of ramming and vibrating, VCA trend combination is largely the same, that in the external power role in the case, the change trend of VCA and various aggregate formulation has good correlation.
The CBR test has some experience about the actual value of pavement structure, but it reveals the graded strength of asphalt mixture. 2 VCADRC of coarse aggregate clearance rate with uniform design test Grain size 16.0~13.2 mm X1（％） 13.2~9.5 Mm X2（％） 9.5~4.75 mm X3 （％） 4.75~2.36 mm X4（％） inserted pound VCADRC (%) vibration VCADRC (%) CBR 1 68.51 14.79 8.87 7.83 40.72 38.51 52.72 2 54.58 10.42 1.09 33.91 37.20 34.65 98.53 3 46.14 2.59 30.44 20.83 38.37 35.63 41.37 4 39.75 36.44 2.23 21.58 38.60 35.92 39.24 5 34.48 20.66 29.44 15.42 39.75 35.65 50.78 6 29.95 8.13 9.67 52.25 37.75 34.64 76.05 7 25.94 60.97 8.59 4.50 41.39 39.36 30.95 8 22.32 32.14 9.96 35.58 38.94 34.56 59.13 9 19.01 15.38 47.16 18.45 39.74 36.51 37.93 10 15.95 1.32 23.27 59.46 39.49 36.03 34.87 11 13.10 46.26 31.75 8.89 41.12 38.49 40.94 12 10.42 24.29 22.44 42.85 38.08 34.86 92.30 13 7.90 7.50 71.38 13.22 40.09

Instead, a number of evolutionary microstructure morphologies can be observed during the eutectic solidification with increasing departures from equilibrium.
In particular, the progressive transition from a regular lamellar to an anomalous eutectic structure has been reported in a number of systems, e.g.
Microstructure analysis reveals a number of complex morphologies in the droplets with decreasing diameter (increasing cooling rate), including the eutectic, cellular and dendrite morphologies.
Cui, Microstructural evolution from dendrites to core-shell equiaxed grain morphology for CoCrFeNiVx high-entropy alloys in metallic casting mould.

As the core is desaturated, the water in the pore is emptied, leaving only small amount of water in the interstitial near the grain contacts.
Lithology Number of cores Holes Fractures Obviously Small amount Barely none Obviously Small amount Barely none Rhyolite 50 7 4 39 3 10 37 Tuff 36 4 1 31 4 4 28 Volcanic Breccia 39 5 2 32 2 5 32 Based on these results, it is obvious that 25mm or 38mm diameter core samples are not accurate in representing the complex characteristics of volcanic reservoir correctly due to its size limit.
Lithology Number of cores Measured T2 cutoff (ms) Distribution range Average Rhyolite 21 8.03-179.46 87.91 Tuff 16 11.57-103.72 52.02 Volcanic breccia 15 3.22-86.4 36.28 Table 3 Static of measured T2 cutoff sub-classified by lithology.
Lithology Number of cores Measured T2 cutoff (ms) Distribution range Average Gray rhyolite 3 41.60～71.97 54.50 White rhyolite 3 103.72～179.46 135.90 Green rhyolite 3 49.94～86.40 69.43 Gray spherules rhyolite 3 124.52～124.52 124.52 White spherules rhyolite 2 34.65～86.40 60.53 Rhyolitic crystal tuff 4 71.97～103.72 83.51 Rhyolitic volcanic breccia 4 11.57～41.60 22.78 Crystal volcanic breccia 3 71.97～86.40 81.59 Andesitic tuff breccia 2 3.22～3.87 3.54 Purple andesite 2 24.04～28.86 26.45 4.

Introduction The flow stress values of metal is fundamental quantity to characterize the pressure processing properties .Besides, The flow stress values is also the main parameters to calculate processing power and essential parameters to institute sensible process planning [1, 2].The good grains structure which make the steel with excellent overall performance can be obtained through the rational control of rolling process In the Pressure processing [3].
Where P is the input vector; T is the target output vector; goal is set to the error sum of squares, Its default value is 0, spread is the width coefficient, the default value of 1, MN is the largest number of the hidden layer neurons, Its default value is the number of the input samples group, DF is the displaying frequency of neurons on the increase.
Table 1 Training parameters and training results of neural network Training Parameters Training results The largest Spread Training times Training error Acturality/Target number of 200 0.5 78 0.000995708/0.001 hidden layer 1 63 0.000956971/0.001 neurons 1.5 65 0.000956083/0.001 Training target 0.001 2 66 0.000987726/0.001 2.5 63 0.000935329/0.001 Table 2 Simulation results of RBF neural network Experiment Simulation Relative Simulation Relative Simulation Relative Value[MPa] Value s=1 error(%) Value

Conventional damage mechanics studies, since then, are getting of prosperity by probabilistic theory: damage evolution functions of solid structure under random loading condition were, based on micro-mechanics-model, set up by Silberschmidt and Chaboche[2]; damage-rupture development mechanism on discontinuous stochastic composite material reinforced by fibers was analyzed with statistics by Wu and Li [3]; mechanical characters of solid brittle material coupled with plane grain cracks submitting to correlated random distribution were investigated by Ju and Tseng who, on the basis of micro-mechanics and mean-volume theory, invited Legedre-Tchebycheff orthogonal polynomial algorithm into analyses[4]; by the help of flat noise generator simulation for random factors influential over medium damage mechanism from both external and internal aspects, continuous damage mechanics was furthered by Silberschmidt based on his early studies [5].
Acknowledgements This work was supported by the National Natural Science Funds (Grant No: 51109118), the China Postdoctoral Science Foundation (Grant No: 20100470344), Fundamental project fund of Zhejiang Ocean University (Serial number: 21045032610), Initiating project fund for doctors of Zhejiang Ocean University (Serial number: 21045011909) and Project of Education Department in Zhejiang (Serial number: Z201119560) References [1] W.

In these alloys studies of their defect structure (vacancies, dislocations, grain boundaries) are essential for better understanding of time and temperature instabilities of physical properties and stabilisation of the martensite.
Alloy Heat treatment Number of cycle Temperature [ ]KSA Alloy I Quenching from 973 K/30 min.
This influences the martensite stabilisation effect as well as the number of elementary processes responsible for the decay of quenched-in vacancies.
The number of elementary processes responsible for the decay of quenched-in vacancies during ageing process may be explained by different values in the formation energy of vacancies.

At the Saint Mary's University in Canada and at the Hokkaido University in Japan the issue of heating water in production ponds, grain drying and pasteurization of milk has been addressed [2].
(R2=0.988) [°C] (2) where: d – number of days from the beginning of the heating season, i.e. from 19 September 2012 The minimum temperature difference between the temperature tr which was measured 1.0 m from the heat exchanger at a depth of 1.5 m and the temperature at a depth of 1.5 m in the vicinity of the heat exchanger t was -0.1 K.
(R2=0.993) [°C] (3) where: d – number of days from the beginning of the heating season, i.e. from 19 September 2012 Fig. 2 shows a gradual increase of the temperature of the ground massif t and the reference temperature tr in the period from 14 April to 31 May 2013.
(R2=0.993) [°C] (5) where: d – number of days from the beginning of the heating season, i.e. from 14 April 2013 In this time period the temperature of the ground massif t gradually increased from 2.82 °C to 11.40 °C.

According to the company’s financial statements, along with a decrease in demand for pipes, the number of applications for multi-series products increased.
Steel consumption [8] Methods At the final stage of producing steel products, heat treatment is a mandatory procedure, since on coming in contact with the metal surface in the adjacent layer there is a possibility of a sharp jump in temperature, resulting in formation of a fine-grained crust, thereby impairing the quality of metal [9-14].
Finished products include a number of products in the form of pipes, discs, as well as sheets.
MAGSTRONG high-alloyed steels have a number of advantages compared to similar samples and are able to compete with products by other manufacturers.

In this article, we made visual grading, four-point bending edge-wise and axial tensile test to grain some data according to ASTM D245 and ASTM D4716-05 on the basis of pervious studies.
Table.4 Analysis of correlation coefficient of tensile strength and combined parameters Size(mm):3900×120×32 Tension strength(σw)(r) E4s E4s+ρ E4s+Kus E4s+ρ+Kus 0.654 0.662 0.749 0.750 We can get the related coefficient between combined parameters E4s+ρ/E4s+ Kus/E4s+ρ+Kus and tension strength increased as the number of predicting parameters.
The related coefficient increased clearly when visual grading Kus combined with E4s+ρ.The more the number of combined parameter -s, the better relevance which provide stronger evidence to the prediction of larch timber strength.
So, to increase the number of predict factors is a better way to evaluate the parallel tensile strength, but further study is need.