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After the deformation process the alloys were annealed at the temperature range 350 – 500oC for 1 hour.
Table 1.
Figure 1.
References [1] M.
Sci. & Eng.A 481-482 (2008), 479 [3] M.

Reference [1] J.K.
Journal of natural resources, 2010, 25(1): 112-120
Journal of world economy, 2000, 1(7): 65-74
The Review of Economics and Statistics, 2009, 91(1): 1-19
Nature, 2011, 470(7335): 479-485.

., 480-1, Takamiya, Hikone, Shiga, 522-0292, Japan a*tokuyama@screen.co.jp, bk.shinohara@screen.co.jp Keywords: Convolutional neural network, Machine learning, Wet etch.
Having a model that can predict the desired etch profile will help reduce time and resources to develop new etch conditions, Figure 1.
Figure 1: A system which finds process conditions to obtain the desired etch profile.
Therefore, multiple training datasets (14, 27, 53, 104, 185, 305, 479, and 709 training data) were prepared by extracting the conditions from each grouped cluster.
References [1] LeCun, Y., et al., Contour and Grouping in Computer Vision, Lecture Notes in Computer Science, 1681 (1999) 319-345

Introduction Liquid crystal (LCs), was a kind of phase state [1-3].
The preparative routes for the liquid crystalline compounds of trifluoromethyl substitutent were shown in Figure 1.
Figure 1.
References [1] R.
Forum Vol. 479-481 (2012), P. 2165

The discharge capacities obtained was 143 mAhg-1 in the first cycle for both materials.
The electrolyte was 1.0 M LiPF6 in 1:1 volume ratio of ethylene carbonate (EC) and dimethylcarbonate (DMC).
Results and Discussions The XRD pattern for samples L700 and L800 are shown in Fig. 1.
References [1].
Power Sources 174 (2007) 473–479

Results Fig. 1 shows the TGA plot of MG30 and TMG30 polymer electrolytes.
The peak of ion pairs (LiCF3SO3, Li(CF3SO3)2-, Li(CF3SO3)32-) is located at 1045-1040 cm-1 and the peak of ion aggregates (Li2CF3SO3+, Li3CF3SO32+) is located at 1053-1049 cm-1 [20].
References [1] H.
Stab. 87 (2005) 479–486
Gupta, Natural polymer-based electrolytes for electrochemical devices: a review, Ionics (Kiel). 17 (2011) 479–483

The measurement error in the wetting angle is 1%.
Results The obtained density polytherms are shown in Figure 1.
Density, kg/m3 Figure 1.
Table 1.
Vol. 223 (3), (2014), 469–479

Especially, the multisplitting algorithm in Ref. [1] is the most popular at present.
Analysis of convergence Theorem 1.
Numerical examples Example 1.
Table 1 The results for model 1(the algorithm in the paper) P 1 2 4 8 T 963.9996 492.8244 251.7298 132.3971 S E 1.9746 0.9873 3.8658 0.9664 7.3501 0.9188 L 4114 4124 4126 4126 9.8879e-11 9.9845e-11 9.8893e-11 9.8908e-11 Table 2 The results for model 1(the multisplitting method) P 1 2 4 8 T 134.2484 69.4497 39.6882 25.3379 S 1.9330 3.3826 5.2983 E 0.9665 0.8456 0.6623 L 1053 1067 1067 1067 1.0002e-10 1.4842e-10 1.4842e-10 1.4842e-10 5.
Hill, Triangle mesh methods for the Neutron transport equation, Los Alamos Scientific Laboratory, Report LA-UR-73-479, 1973

Table 1.
CFL number of 1 was used and all solutions were run up to t=2.
This can also be observed from L2 norm in Table.1.
Hill:Triangular Mesh Methods for the Neutron Transport Equation,Tech.report LA-UR-73-479, Los Alamos ScientificLaboratory, ( 1973)
Comp. 46, 1(1986)

Under the change in population, urbanization and energy efficiency, this paper gives analysis model of energy demand change. 1.
Energy demand change model depends on a variety of changing factors （1） Energy demand change model depends on mutative GDP and energy intensity The relational model among energy demand, GDP and Energy demand: (3) stands for the ith energy intensity, stands for the ith energy demand, stands for GDP growth rate in the base case, stands for the GDP growth rate in the Jth condition, means the change rate for energy intensity in the base case, means the change rate for energy intensity in the Jth condition, meanwhile there are relationships among them in the following： = (1+) (4) = (1+) (5) = (1+) (6) means the change rate for GDP growth rate in Jth condition, means change rate for i’s energy intensity in the Jth condition, means change rate for i’s energy demand in the Jth condition.
References: [1] YU Chao, TAN Zhong-fu, WANG Lu-hua.
Electric Power, 2010, 43(10):1-5 [2] BOYD G A, HANSON D A, STERNER T.
Application of Statistics and Management, 2010，29 (3) : 473-479