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could be expressed as samples’ combination: (2) Among them, is the Lagrange multiplier, is the sample’ vector, is the number of the total samples.
Therefore, the original expression of could be rewritten as: (3) mean the sample label of the number of i , it is equal to 1 or -1.
Among them, refers to the target of the training data set, the label of P2P traffic is 1 and non-P2P traffic is -1; is a integer marking the number of the value from 1; is a real number represent the special characteristic value. 4)In the environment of DOS, scaling the data, getting the optimal parameter and training data.
In paper [9] mainly uses traffic parameters, traffic duration, byte number and mean length of data packet as the identify characters.
But this paper not only involves the byte number, traffic throughput capacity, traffic parameters in window, but also refers to the port number at server, SYN packet and RTT, combing them aim at differ P2P traffic from other traffic better.

According to the adopted stress ratio, the tests are divided into two groups, and the specimen is numbered from sample 1 to sample 12.
When the cycle number N reaches 5851, a short fatigue crack whose propagation grows slowly with increasing cycle number emerges at the rigth side of notch root, as shown in Fig. 1(a).
In the case of cycle number at 124443, the third crack appears at the left side of notch root, as shown in Fig. 1(c), and the two short cracks at the right side tend to stop propagation.
As the cycle number achieves 142129, the propagation path of main fatigue crack appearing on the specimen’s surface is shown in Fig. 1(d).
Melin, Characteristics of short fatigue crack growth in the vicinity of a low angle grain boundary, International Journal of Fatigue, 36 (2012), 59-67

As the grain size increases to a certain degree, the inhomogeneous surface lattice distortion is mainly localized to the outer shell with a thickness of 2-3 lattice parameters. 1.
It is well established that the lattice parameters of nanoparticles decrease with the reduction of size as a result of large numbers of surface dangling bonds and a high surface-to-volume ratio [4-9].
The mean atomic bond length is defined as following: ∑== n j ij i bnb 1 )/1( where n is the number of atoms which are bonded to the considered atom i, i.e.
coordinate number, and bij is the bond length between atoms i and j.
On the outmost surface, since atoms have low coordinate number and lots of dangling bonds, they contract toward inner core as a breakage of equilibrium of force from bulk material, and their atomic mean bond length decrease sharply.

The results show that proper amount Ti combines with C to form a certain number particle TiC in the wear-resistant and heat-resistant steel.
TiC can hinder grain growth under high temperature effectively, so it improves the steel’s hot strength and creep rupture strength.
Carbide improves the wear resistance of the steel under high temperature. 1# sample and 2# sample contain a certain amount of Ti, so their structure has a certain number of granular TiC.
Doubtlessly, the increasing of the carbide number contributes to the hardness and wear resistance of material. 2# sample was added more Ti and formed more TiC particles compared with 1# sample, so 2# sample has greater hindering effect to abrasive.
When the carbide number increases under some wear loads, on one hand it decreases wear load of each carbide particle and reduces the abrasion.

In addition, the effective contact width between polishing tool and mold surface based on the grain size and the principal radii of curvature is also determined.
The contact width in the maxR direction, 1w , is calculated by the "effective contact zone model (see Fig. 2(a))" as: 2 max 2 max 1 ) (2 hpRRw −−−= , 22 max max irRRh −−= (9) Where p is abrasive grain size (� m).
The weaving frequency, fw, is defined as the number of revolutions that the weaving path rotates around the main path per minute.

In this paper, For the sake of researching the microstructure, the morphology, the size and the matrix distribution characteristics of the reinforced grain of prepared composite material , those test methods are used, such as optical microscopy, scanning electron microscope, X-ray diffraction analysis and so on.
You have to stir it ceaselessly so as to make K2ZrF6+KBF4 powder and overheat and liquid aluminum can produce the following reaction and gain Al3Zr+ZrB2 grain.
Here are critical load of Samples 1-5: Sample Numbers 1 2 3 4 5 The Critical Load (N) 38.8 58.8 68.8 74.8 78.8 From the above a series of data, we know that the existence of enhanced particles will increase the critical load and delay the change of wear materials from mild to severe wear.

Some inoculants containing rhizobial strains were efficient in IAA secreting as plant hormone and appeared symbiotic effectiveness to increase grain protein contents, grain yield and leaves chlorophyll concentration [8].
The quality of Rhizobia inoculant products due to many factors, include sufficient numbers of live rhizobia and minimal contamination [10].
There are no significant differences in nodule numbers between inoculation treatments.
The key to inoculant products with good quality is presented as sufficient viable cell number [47], and the viable cell number may be affected by storage temperature, accordingly, the viable cell would contaminate microbes presented in inoculants [16].
These are consistent with Fages [16] results that the viable cell numbers may be affected by storage temperature and contaminate microbes presented in inoculants.

The alloy (0.7Sn-0.15Ag-0.1Cu-0.05Zn) containing minimal amount of zinc with high amount of Ag resulted to high Vickers hardness number.
The alloy A2 (0.7Sn-0.15Ag-0.1Cu-0.05Zn) displayed the highest Vickers number of 7.13.
Vickers Hardness Number result.
A1 (0.7Sn-0.1Ag-0.1Cu-0.1Zn), A2 (0.7Sn-0.15Ag-0.1Cu-0.05Zn), A3 (0.7Sn-0.15Ag-0.05Cu-0.1Zn), and A4 (0.7Sn-0.1Ag-0.05Cu-0.15Zn) Vickers Hardness number (HV) Sample Trial 1 Trial 2 Trial 3 Mean A1 5.77 6.78 6.64 6.40 ± 0.547 A2 7.03 6.9 7.47 7.13 ± 0.299 A3 6.39 6.51 6.41 6.44 ± 0.064 A4 6.53 7.01 6.79 6.78 ± 0.240 Figure 3 shows the x-ray diffractogram of the SAC-Zn alloys.
A1 (0.7Sn-0.1Ag-0.1Cu-0.1Zn), A2 (0.7Sn-0.15Ag-0.1Cu-0.05Zn), A3 (0.7Sn-0.15Ag-0.05Cu-0.1Zn), and A4 (0.7Sn-0.1Ag-0.05Cu-0.15Zn) Conclusion The low addition of zinc in SAC alloys resulted to the formation of Cu6Sn5 intermetallic and made Ag3Sn less dendritic due to grain refinement.

However, the accuracy and reliability of the least square method relies on the condition number of the coefficient matrix of its normal equation.
The solution of the linear equation is unreliable while the condition number of the coefficient matrix of the linear equation is very big.
It can be seen that the condition number of the coefficient matrix of the normal equation of general polynomial linear least square is big correspondingly with the consecutive natural numbers. 1.2.
The condition number of the coefficient matrix of this normal equation is very tiny, so the fitting accuracy and practicability is high.
Application of Combination forecasting method in China's grain output prediction [J].

In fact, the back side was in direct contact with the heated glass substrate during deposition and drying and shows a smoother surface with larger grains.
We attribute the bright needle structures to conducting zones of PANI-PEG-PVDF blends at the surface and between grains.
At smaller scan size ((Fig. 5b), 2.5 µm x 2.5 µm) merged submicrometric grains are observed.
The grains are found dense and partially merged forming connected area between the fibres of the network (Fig. 4a).
Acknowledgements This work has been financially supported by the HES-SO projects PRONANO (grant number 81265/IA-EXT17-57) and PROINDAV (900668/UA-EXT18-23).