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With decrease of Ni2+/Co 2+ ions ratio among 2D fern-like dendrites, 3D dendrites and different agglomerates of a size of about 100 µm, being either compact (typical for pure Co powder) or composed of a large number of small 3D dendrites on their surface were obtained.
At a relatively low current density of 3.3 A dm-2, individual coarse-grained particles were formed, while at higher current density of 10.0 A dm-2 gradual coalescence and refinement of particles to form nodular clusters was achieved.
With the decrease of Ni 2+/Co 2+ ions ratio among 2D fern-like dendrites, agglomerates of the size of about 100 µm, composed of a large number of small 3D dendrites on their surface were obtained.
At the lowest Ni 2+/Co 2+ concentration ratio, three types of particles were detected in the Ni-Co powder: 2D fern-like dendrites, compact agglomerates typical of pure Co powder and smaller agglomerates composed of a large number of 3D dendrites.

Motor has a total length of 530mm, diameter of 170mm, and the grain length of 300mm with 114mm inner diameter.
The mesh grid is Hexahedron with a total number of 70000.
So the number incerease, while the mass decrease slightly.
For convenient, we defined R as initial droplets number divided by finial droplets number, which reflects the intensity of droplets breakup action.
Statistics of particle number along the axis

A phenomenological model of domain wall motion is used in computation, and the effects of ac electric field amplitude and frequency, number of layers, and property gradation on the deflection of the cantilever actuators are examined.
Setting β =0 and ∆l = ∆l0 exp(iωt), from Eqs. (8)-(10), we have x1 l l ∆l E0 exp(iωt) Ps γsO x3 Domain wall Domain (a) (b) Fig. 1 Illustrations of (a) grains in piezoelectric ceramics and (b) basic unit of a crystallite with a displaceable domain wall.

The microstructure of the sintered samples was observed under SEM and the particles were displayed an evidently growth of the grain as function to the soaking time.
This indicates the improvement of particle growth in S5 is solely due to enough time for the particles lowering the surface energy and develop interaction within the particles, forming the grain growth of particles necking then diffusion happened.
According to Feng et al. [1], structural bonding appeared at the region of 800 – 1250 cm-1 was the stretching vibrations of SiO4 with different number of oxygen atoms.

A total number of 19 chemical compositions were studied, including three trials of varying nodularity treatment level; four trials of varying copper content; four trials of varying silicon content; four trials of varying tin content and four trials of varying carbide promoter content.
During this stage the chill grows rapidly perpendicular to the mould wall and due to the brittleness of the cementite fragments keeps breaking of the main direction of the growth causing the ledeburite grain to spread out in a fanlike manner according to Hillert and Subba Rao [2].
During a second stage, the ledeburite grows as a rod eutectic along the c-direction of the cementite consuming the remaining melt located between ledeburite grains.

Alternative classification techniques consider only structure of the XML document. [5,6] treat XML documents as labeled trees, and the measurement of similarity between two XML documents is produced by computing the edit distances between two trees. [7] introduce a structure graph to measure similarity, whose similarity measure is loose-grained.
Next, if the frequency of occurrence for keyword k in element node j is , then the weighted frequency for keyword k is computed as Eq. 2; Finally, according to Eq. 1 and Eq. 2, the total weight of textual keyword k in document d can be computed as Eq. 3: (2)　　 　　 (3) where, n is the number of leaf element nodes containing keyword k in document d，N is the number of documents in document set, nk is the number of document containing the keyword k.
(2) The number of categories is only two.
For a given category, recall is the ratio of the number of correctly classified documents in the category to the total number of documents from this category.
Precision is the ratio of the number of correctly classified retrieved in the category to the total number of documents assigned to this category.

This means that the structure and the parameters are not predefined (e.g. number of processing elements, number of layers etc.) but they are data driven in such a way that the capacity of the model will match the complexity of the data.
This, however, may be problematic due to the curse of dimensionality: as the number of variables under consideration increases so does the number of possible solutions but exponentially.
Each feature is given a feature cost (a number between 0 and 1)
The type of steel used is conventional and high permeability grain oriented electrical steel.
Schölkopf “Support Vector Machines and Kernel Methods The new Generation of Learning Machines”, AI Magazine, Vol. 23, Number 3, pp. 31-41, 2002

Results and analysis Influence of frequency on dynamic pore pressure and strain Figure 4~7 show the dynamic pore pressure ratio and strain’s variation with number of cycles.
For express the variation of pore pressure and strain under small vibration time, number of cycles use logarithmic coordinate.
For a given number of cycles, low frequency results high pore pressure and strain, the frequency is lower and the pore pressure and strain is larger.
Fig.4 Effect of frequency on Number of dynamic pore pressure ratio under s3c = 25kPa Fig.5 Effect of frequency on Number of dynamic pore pressure ratio under s3c = 50kPa Fig.6 Effect of frequency on dynamic strain under s3c = 25kPa Fig.7 Effect of frequency on dynamic strain under s3c = 50kPa Influence of frequency on dynamic strength Dynamic strength refers to the dynamic stress under a specific failure strain with a certain number of cycles[10].
In the test, it use axial strain up to 10% as the failure criterion of soil, and use the curve of cyclic stress ratio and number of cycles of failure to represent change of the dynamic strength.

The influence of the volume fraction and grain size of TiC nanocrystallites on the coating properties has been examined.
The influence of the volume fraction and grain size of TiC nanocrystallites on the coating properties has been examined.
The coatings are coded in such a way that the numbers before the character V indicate the substrate bias in Volt, followed by the flow rate of acetylene gas in standard cubic centimeters per minute (sccm).

Powders have to exhibit narrow grain size distributions in low grain fractions (e. g. 20 µm < d < 45 µm) and convex grain shapes to ensure good flow properties during the feeding process and homogeneous spray particle parameters within the particle jet.
A defined intermediate material - Ottawa Sand with a grain size of 212 µm < d5 - d90 < 300 µm - is continuously brought into the contact zone as constantly renewed loose abrasive.
Due to that, several standards exist concerning test cycle numbers, counter body types and designs.
The specific number depends on the material type, the linear wear loss and the coating thickness.
It is also used to define the relative abrasive effect of any slurry with regard to standardized so-called Miller numbers.