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Table 1 shows the main properties of W, B4C and Cu.
In this work, P was chosen as 1, as the former work showed that when P was equal to 1, the B4C/Cu graded coating showed the best thermal stress relaxing effect [14].
References [1] P.
[1] C.
Vol. 475-479 (2005), p. 1371

Li 1, 2,a , C.
Cheung 1,b , L.B.
Kong 1,c , W.
Lee 1,d and S.
Vemuri: Computer Aided Design, Vol.32 (2000) No.8-9, pp.479

There are several forecast models and methods used in domestic and foreign countries: multiple linear regression model, GARCH models [1], BP neural network[1], Type2 fuzzy system[3], although these models can achieve a certain effect, but can’t react how different factors affect gold price fluctuations in different periods, we can easily find a lot of multiple linear regression models fit or predict gold prices, they all suppose various factors keep the same influence of the gold price in the entire sample space.
This paper chooses varying-coefficient model that can solve factors have different forces at different times to gold price, which has improved the prediction accuracy. 1 Varying-coefficient regression model instruction In time series regression analysis, the constant coefficient linear regression model is the most common type of model, which mainly used to identify and analyze the relationship between economic variables, their theoretical system and statistical inference methods are relatively satisfactory, the general form is: (1) Where, is constant, is as explanatory variables (independent variables) observations value at the i moment, as the dependent variable at the i moment observation, as independent with mean 0 and as variance of probability distribution.
(1) Gold price and the U.S. dollar index The gold price is generally priced by the dollar, national domestic gold prices are based on their national currency with the dollar's exchange rate, and the U.S. dollar index integrated response the exchange rate situation between dollars against major world currencies, so we chose the U.S. dollar index as the variable and made the gold and the U.S. dollar relationship chart, referred Fig. 1.
Fig.1 Gold price and U.S dollar index relationship Fig. 2 Gold price and oil price relationship (4) Gold price and silver price Selecting silver as the variable, reflects the gold metal properties, the value of their property has certain convergence that is the same time up or down, gold price and silver price relationships are shown in Fig. 3.
Table 1 Comparison between varying-coefficient and multiple linearity models Se2 Var(σ2) Average residual value Average error rate Varying-coefficient 461785.9 3848.216 38.48 2393.19 Multiple linearity 1061080 8842.33 60.49 5256.863 Table 2 Monthly gold price in 2010 based on varying-coefficient regression model Month 1 2 3 4 5 6 Gold price 1162.69 1166.71 1162.94 1132.92 1201.69 1102.23 Month 7 8 9 10 11 12 Gold price 1094.54 1083.55 1119.03 1101.30 1154.42 1072.80 4 Conclusion This paper analyzes the gold price factors, mainly from the dollar index, oil prices, silver price, stock index, the leading index and the CRB index.

The error between ROM and full order system is defined as: (1) The Eq.1 which represents min-problem can be transferred into a max-problem.
The new POD method can therefore be summarized as follows: (1) Collect snapshots of primary system to construct matrix X
Case Validation Consider a typical 2D aero-servo-elastic system, which is illustrated in Fig.1.
References [1] Rosenfeld A, Kark A C.
International Journal for Numerical Methods in Engineering，2001，51:479-504

(1)data feature selection First, divide all data into regression training set, regression experiment set and regression checking set.
Use n-1 subsets as the training samples to forecast the subset not in.
Prediction results are shown in table 1 Table 1 Prediction results of Support Vector Machine The sample NO.
References [1] Yu Kaining, Wan Li, Dou Xinjun .Positive and negative effects of urbanization on groundwater quality[J].
Journal of Hydrodynamics (Ser.A), 2006, 21 (4):479-485

Fig. 1 shows a typical plate-fin structure.
References [1] Kawashima F, Igari T, Miyoshi Y et al.
Engineering Failure Analysis, 1996, 3(1): 29-43
Journal of Central South University of Technology, 2007, 14(4): 479-484
Materials & Design, 2009, 30(1): 23-27

Table 1 Status of experimental plots year Water content(%) Bulk density (g.cm-3) Organic C(g.kg-1) Total N(g.kg-1) C/N 6 31.28 1.53 4.62 0.15 30.8 15 35.26 1.61 4.03 0.16 25.19 50 25.24 1.95 2.63 0.13 20.23 Soil sampling Soil samples (0–20 cm and 20-40cm depth) were collected in July 2008 from three random sampling quadrat (5m×5 m) at each site described above.
After centrifugation (3500×g, 3min), 0.25ml of the supernatant fraction was mixed with 3.75ml of distilled water and 2ml of a reagent composed of a sodium salicylate/sodium nitroprusiate mixture(17%, w/v and 0.12%, w/v, respectively), 0.3M NaOH, and distilled water (1:1:1, v/v/v).
The filtrate was titrated with 0.1mol l-1 KMnO4 in the presence of sulphuric acid and the results were expressed as umol KMnO4 g-1h-1.
References [1] Z.
Burns: Soil Biol Biochem 1976 (8) 479-484

Orthogonal Experiment Packet Optimization Table 1 is a list of product specifications size of large-diameter welded pipe production enterprises.
Table 3 The levels and factors Level Factors Expanding rate（%） Relative punch radius Edge of the tube angle (T is the tube wall thickness) 1 0.5% 0.95 0.5T 2 0.75% 1.0 0.75T 3 1.0% 1.05 1.0T 4 1.25% 1.1 1.25T 5 1.5% 1.15 1.5T Experimental results The orthogonal experimental results on the pipe size for Ф610mm ~ Ф813mm three models to simulate the orthogonal experiment, respectively, the optimal combination of the three groups for the indicators of the size of the error and indicators of form error.
(2) For one of the factors of expanding rate, this indicator of the size of the error on the optimal combination achieved the same level, i.e. level 1
References [1] Tsuru, E., Asahi, H.
Technol, 2003,142: 479-486

Results and discussions XRD analysis The indexed XRD patterns of the prepared samples of Co0.5Ni0.4Mg0.1Fe2O4 are presented in Fig. 1.
Fig. 1: The XRD patterns of Co0.5Ni0.4Mg0.1Fe2O4 ferrites sintered at 700 and 1000°C.
Table 1: The characteristic parameters for each sample 1 and 2 for sintering temperature at 700 and 1000°C, respectively at room temperature.
References [1] A.B.
Solid State Commun. 147 (2008) 479-483

Awschalom4,i, Adam Gali1,9,j 1 Wigner Research Centre for Physics, Hungarian Academy of Sciences, P.O.
Table 1.
SNIC 2013/1-331, the Lendület program of the Hungarian Academy of Sciences.
References [1] T.D.
Nature 479, 84 (2011)