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A great number of studies on the conditions for the appearance of ferromagnetism in the fine particle have so far received much attention as the fundamental investigation applicable to the design of new materials having high-density magnetic memories.
On the other hand, Zeng et al.[5] reported that the FePt nanoparticle assemblies indicated exchange coupling resultant from the reduction of interparticle distances at annealing temperatures more than 600℃ and there is no evidence of carbon residue presented at grain * Present address: Department of Electronic Engineering, Graduate School of Engineering, Tohoku University, Sendai 980-8579, Japan boundaries of the coalesced particles by annealing.
(b)Histogram shows a distribution of particle diameters. d:average diameter, σ:standard deviation. 0 5 10 15 20 0 20 40 60 (b)(b)(b)(b) d=5.6nmd=5.6nmd=5.6nmd=5.6nm σ=±1.3nmσ=±1.3nmσ=±1.3nmσ=±1.3nm Number of particles Number of particles Number of particles Number of particles Diameter (nm) Diameter (nm) Diameter (nm) Diameter (nm) (a) (a) Fig.1 (a)TEM micrograph of FePt nanoparticles as prepared.
(b)Histogram shows a distribution of particle diameters. d:average diameter, σ:standard deviation. 0 5 10 15 20 0 20 40 60 (b)(b)(b)(b) d=3.6nmd=3.6nmd=3.6nmd=3.6nm σ=0.62nmσ=0.62nmσ=0.62nmσ=0.62nm Number of particles Number of particles Number of particles Number of particles Diameter(nm) Diameter(nm) Diameter(nm) Diameter(nm) (a) As shown in Fig.3 (a),(b), observed peaks in the X-ray diffraction patterns of the as- synthesized samples and the one annealed at 350 ℃ exhibited the disordered fcc structure.

A great number of various chemical elements in amorphous and nanocrystalline alloys contribute to the complex process of the structure formation in the course of heat treatment of the amorphous precursor.
Typically, as a result of crystallization, grains of 0.1–1 μm size are formed, which results in dramatic deterioration of magnetic properties, with the coercive force increasing by several orders.
Studies of a number of melts the elements’ content in which ranged B = 10–13, Si = 3.5–4.5, C = 1.5–2 at. % showed that the numerical value of the critical temperature was 1510±10 °С.

The SFAP is developed for preventing lapped surface from damage caused by larger particles (from grain size dispersion or from outside of processing area, larger particles could bring uneven load distribution on processing region).
Because of the limitation of the lapped area and the number of abrasives, the increase of MRR slows down with load and rotating speed increasing.

Although the ejected materials mainly were fine-grained soil, the actual liquefaction was prevalent gravel soil.
The dots represent observed liquefied sites and the numbers mark different seismic intensities which are bounded by elliptical isoseismal lines.

It was found that electroless Ni-P coatings are amorphous with nodules distributed, Ni coating deposited by ion beam sputtering is crystalline and spherical Ni grains are densely packed.
It is noticed that Ni-P coating consists of some nodules and each nodule includes numbers of smaller granules and its average size is about 1 um.

The principal abrasion mechanism of FGM is plastic flowing and the secondary are brittle failure and grain abrasion.
Consequently, the preparation method of FGMs overcomes the limit of traditional FGM technology and a number of new methods and moulding techniques, like metal film casting, negative-pressure casting, die-casting moulding, aqueous extraction, directional solidification, fluidic moulding, etc [3,4] are brought to success.

Hot treatment process of spring steel also involves with the research, aiming to increase the strength of material by fining grain size by Nb element.
Random sampling applied to the rounds based on the lot number.

This second part of the paper treats the problem asymptotically, i.e. from the point of view of a very fine grained silicate composite material with negligible characteristic length which describes the level of the material brittleness (i.e. brittle).
Besides a number of advantages of this test [1-4, 9]) which make it very suitable for utilization in material research [5-7] and computational fracture mechanics (particularly for calibration of material models, often in connection with a form of inverse analysis technique [8-11]); there are also drawbacks of the test which are usually not mentioned in the literature.

Let D be a dataset partitioned into a training set S and a test set T, let M be the number of test instances.
We restrict our attention to the 10 categories (acq, corn, crude, earn, grain, interest, money-fx, ship, trade, wheat).
We can observe that, as the number of test instance varies, ML-FKNN method achieves substantially better performance than other methods.
Fig.2 shows the performance results with the number of test instance added.
We can find that as the number of test instance increases, the improvement becomes more and more significant.

VM is one of the casting processes, which is distinctly different from other sand casting processes as this process requires no binders for holding the sand grains together in the mould [8].
NO OBSERVATION MEAN ABOVE OR BELOW MEAN UP OR DOWN 1 14.9501 14.9554 B 2 14.9416 14.9554 B D 3 14.9551 14.9554 B U 4 14.9601 14.9554 A U 5 14.9603 14.9554 A U 6 14.9652 14.9554 A U MEAN 14.9554 14.9554 RUN=1 U& D=1 A=above the mean, B=below the mean, U=Up from previous reading, D=Down from previous reading Calculation for Z (standard normal deviate) above and below: E (run) AB =(N2+ 1) Where N is the number of observations and E (run)AB is the expected number of run above and below E (run) AB = (62+1) = 4 σAB = √(N-14) Where σAB is the standard deviation of above and below σAB = √(6-14) =1.118 ZAB= {RUNAB - E(run)AB}/ σAB Where RUNAB is the actual number of run obtained above and below ZAB = (1-4)1.118= -2.6834 PAB = NORMSDIST (Z) when the value of Z is negative (using Microsoft excel software) P = 0.003645 For up and down calculations: E (run) UD = 2N-13 Where N is the number of observations and E (run)UD is the expected number of run up and down.
For histogram one require minimum of 50 observations, however more the better and for assessing whether the underlying distribution is normal or not becomes more difficult when the number of observations is fewer.
For cumulative probability plot (Pi) = (S.N-0.5)/N Where S.N is serial number of data observation arranged in ascending order, N is total number of observations in the data set.
So, there are very strong chances that the process is under statistical control however X-bar chart and R-bar chart cannot be drawn due to less number of observational data.