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Table 1.
Probability distribution for x and y y x 1 2 1 0.15 0.25 2 0.4 0.2 The mathematical expectation for y is computed as follows: Ey=1×(0.15+0.25)+2×(0.4+0.2)=1.6 And here are the conditional mathematical expectations for y: E(y|x=1)=1×0.15/0.4+2×0.25/0.4=1.625 E(y|x=2)=1×0.4/0.6+2×0.2/0.6=1.33 Conditional mathematical expectation also considers the relationship between random variables.
Figure 1 In Figure 1, the horizontal line represents time, and the vertical line represents the value.
And the accurate value for day 8 is 461, and the accurate value for day 9 is 479.
References [1] E.W.T.

Table 1.
The results are presented in the Fig. 1.
a b Fig. 1.
References [1] L.
Russian Journal of Physical Chemistry A. 87(2013) 479–484.

The samples of Al powders were coded as A-1, A-2 and A-3, respectively.
Results and Discussion Take sample with 5 wt% A-1 for instance, XRD analysis result of the sample sintered at 1600 oC are shown in Fig.1.
From Fig.3 we can see, the apparent porosity and bendiing strength of samples with A-1 descended and ascended with content of A-1 increase, respectively.
References [1] C.
Alloys and Compounds, 479 (2009), 599-602

References [1] H.
Magn., 2007, 43(1):93–98
Magn.,2009,45(1):474-479 [3] Ying W, Marshall R A, Shukang C.
IEEE Transactions on Plasma Science. 2011. 39(1): 466-70
IEEE Transactions on Magnetics, 2009, 45(1): 495-500

The Comparison Study of Lake Carbon Flux in Summer and Autumn at northern Grass-type Lake-Baiyangdian Wei Dong 1,2,3,a, Jianmin Shu 1,2,b, Ping He 1,2,c, Guangwen Ma 1,4,d 1 Chinese Research Academy of Environmental Sciences, Beijing100012,China 2State Environment Protection key Laboratory of Regional Eco-process and Function Assessment, Beijing 100012, China 3 State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China 4 College of Water Sciences, Beijing Normal University, Beijing 100875, China adunnydong@163.com bdunny@sohu.com cguangwenm@163.com dmagw@craes.org.cn Keywords: Comparison Study, northern Grass-type Lake, Carbon Flux, Baiyangdian.
Results and Analysis This paper will illustrate the daily change curves of typical lakes in August (Fig.1), in October (Fig.2) and in November (Fig.3).
Conclusions (1) The daily change of carbon flux of the lake is more obvious, while daily change laws in summer and in autumn are similar with two peaks and the maximum value at 4 am to 7 am, and time of reaching the maximum value is tending to lag behind from summer to autumn. 
References [1] Rydin F, Burnberg A.
Global Change Biology, 2003,9:479-492

In order to contribute to a better understanding,the two constraints of assumptions were raised: 1) The grey values of image objects in subsequent frames do not change over time,2) The optical flow field in the spatial change is smooth.
Using Horn-Schunck and improved algorithm to estimate the optical flow to form the image,which can be seen to use the folowing 4 steps: (1) Using Gaussian low-pass filtering to process the two frames; (2) Using Horn-Schunck optical flow estimation algorithm, wherein = 30, respectively = 200,n=1000,we can get the Fig (c) and Fig (e) (3) Using Horn-Schunck algorithm optical flow estimation, wherein the initial = 30, respectively = 200 ,n=1000,we can get the Fig (d) and Fig (f) (4)Calculating peak signal to noise ratio(PSNR) as below table 1.
References [1] A.
Shen, et al, ‘A MAP approach for joint motion estimation,segmentation, and super resolution’, IEEE Transactions on Image Processing, Vol. 16, No. 2, 2007, pp. 479–490
[7] N.Woods,et al,‘Stochastic methods for joint registration,restoration, and interpolation of multiple undersampled images’, IEEE Transactions on Image Processing, Vol. 15, No. 1, 2006, pp. 201–213.

Numerical Simulation of 2D Flow in a Curved Channel Mingqin Liu 1, a Y.
Introduction Flow characteristics in channel bends are much more complicated than those in straight reaches.A number of numerical methods have been developed to solve these equations, such as the finite difference method [1-3], the finite element method [4], and the finite volume method [5-8].
Fig.1 Transformations from Cartesian x, y to the local coordinates This transformation is obtained by the following shape functions [9] (1a,b, c,d) where and are the coordinates of point k in the plane.
REFERENCES [1] Garcia, R., and Kahawita, R.
Fluids, 1998,27,4, 479–508

Pressure distribution Study of women's wool underwear Lei Xia 1, a, Ling Yin 2 1College of Fashion Technology, Shanghai University of Engineering Science, Shanghai 200065, P.
Pressure comfort was one of the important research directions of clothing comfort, but the study on clothing pressure was not mature[1].
The instrument is shown in Fig 1.
Fig 1 Pressure instrument 1.2 Test points Considering the larger area the back pressure changes concentrated in the scapular region, and the chest, waist were the trunk main[2], so we chosen seven points to test, the points were shown in Fig 2 and Fig 3, the description of the points were shown in Table 1 and Table 2.
In: Proceedings of International Textile Science and Technology Forum. pp. 479–486. (2010) [2] Zhang, L.

See the left picture in Fig.1 Fig. 1 Evolving from the energy property analysis to EFU No matter what kind of product, it’s using activates are just categorized to these three kinds of flows: energy flow; material flow and information flow.
In the end, the topology energy footprint unit (EFU) is built as seen in the right picture in Fig.1.
References [1] Anne-Marie Tillman, Significance of decision-making for LCA methodology.
Computers in Industry. 65 (2014) 470-479
Journal of Cleaner Production. 15 (2007) 1-11

The Impacts of Energy Price Fluctuations on China's Agriculture and Rural Economic Development Yan Wang1,2, Na Wang3, 4 ,Meimei Wu1 1.
(4) The results The impact of the energy price fluctuations on general level of market prices From 2002 to 2006, general level of market prices rose by 17.94% and CPI rose by 8.65% in China.The rise of energy price led to general level of market prices rose by 9.7% and CPI rose by 8.65% . the drving effect of cost of energy price is 50% of actual increase extent of the general level of market prices, and 70%of the CPI(Table 1).
Table 2 The impact of the energy price fluctuations on the income distributions of rural and urban(Unit：100million yuan) The main industries Consumption of Rural Residents Consumption of Urban Residents Rural workers' compensation Urban workers' compensation Planting 237 254 322 0 Accommodation and foodservices and drinking places 26 119 21 3 Food processing industry 118 223 20 6 Industry 537 1,659 370 479 Total 918 2,254 733 488 The impact of the energy price fluctuations on agriculture in different regions With the rise of energy price, agricultural net incomes increase in coastal provinces and some central provinces where have higher intensification,The impact on planting decrease in some central regions and western regions where have lower intensification(Table3).
References [1] Mehdi,S.
[5] Xiaoqun He,Taoyuan Wei.Impacts on Chinese Economics of The World Oil Price Rise,Economics Theory and Business Management,4:11-15,(2004)(In Chinese) [6] Jikun Huang,Jun Yang,Management world,1:67-82,(2006)(In Chinese) [7] Jianling Jiao,Ying Fan,Jiutian Zhang,Yiming Wei,Management Review,7:48-53,(2004)(In Chinese) [8] Feng LU,Kaixiang Peng,Relationship between Grain Prices an Inflation in China(1987-1999),China Economics Quarterly,1(4):821-836,(2002)(In Chinese)