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All the fiber samples were cleaned by Soxhlet extraction with acetone and grain alcohol for 4h, air-dried and then rinsed thoroughly in distilled water to remove reagents, inorganic and proteinaceous materials.
Table 3 Average number of hairs (hairs/m) in different length groups Treatment 1mm 2mm 3mm 4mm 5mm 6mm 7mm 8mm 9mm Untreated 73.71 22.84 10.64 4.42 2.49 1.14 0.78 0.29 0.29 Basolan 81.25 24.55 12.03 5.21 2.75 1.50 1.04 0.48 0.37 Kroy-resin 81.76 27.15 14.01 6.58 3.86 2.19 1.55 0.85 0.76 S3 (hairs/m, number of hairs registered with a length equal to or longer than 3 mm) and Tp(hairs/m, the total number of hairs registered (adding the frequencies of the hairs of different length classes) of spun yarns [4] of control, treated with Basolan®88 and Kroy-resin are shown as Fig. 2.
The numbers of pills of sample treated with Kroy-resin were least in Fig. 3 (c), which probably due to the DFE of wool fiber sample treated with Kroy-resin making it difficult for fiber to move and withdraw in or from yarn and fabric.
The DFE of wool fiber treated with Kroy-resin and the number of hairs (S3) of resulting spun yarn was greatest.
The improved pilling performance observed with the Basolan® 88 and Kroy-resin treatment suggested that the number of hairs was not the only important hairiness aspect that impacted on fabric pilling.

For instance, high performance computing centers may measure work in terms of the number of proteins folded, genomes calculated, or weather models iterated.
Web-search data centers might measure the number of queries served or the number of pages indexed.
However, indexing data at scale presents a number of storage, computational, and energy challenges [6].
Several classes of queries on exascale scientific datasets would thus be more effective if the query engines were tailored around coarse-grained and storage light-weight indexes, e.g., operating by building indexes on the metadata generated by the compression methodology, instead of the actual data.
Fortunately, the execution of many data mining algorithms is dominated by a small number of kernels [3].

Pcurrent={P1, P2 … Pnum}, PcurrentP and num≤n, represents the number of nodes used in the current task scheduling.
Number of chosen applications: 4.
Utilization ratio of resources: r1—75%, r2—67%, r3—67%, r4—100% 2) when all neural cells have the same priority k0, the result is {2,4,5,7,8} Number of chosen applications: 5.
Lewis, “Grain Size Determination for Parallel Processing,” IEEE Software,pp. 23-32, Jan. 2005
Gerasoulis, “DSC: Scheduling Parallel Tasks on an Unbounded Number of Processors,” IEEE Trans.

The depth of crystallization becomes important for applications such as photovoltaics, which depends on a number of factors; with laser beam shape one of the most significant.
The research has been focused on to realising large grained polycrystalline silicon, with minimum surface roughness occurring upon crystallization. [4] A number of beam shapes has been utilized for crystallization, starting with a "top-hat" profile, as shown in Fig. 1(a).

Cold rolling is operated for the work hardening, refinement of crystal grain size, highly dimensional accuracy and improvement of surface quality.
This is a small rolling mill (model number: DBR-70, maximum rolling load: 5 tons).
Thus, the roll gap is opened during casting depending on the mill constant number.
Direct molten metal rolling load was calculated by using mill constant number.
Mill constant number that was estimated by preliminary experiment was 4311 kgf/mm.

The grinding wheel condition monitoring neural network input layer node number is 6，and the inputs of the neural network are the three high-frequency wavelet detail coefficients extracted features in which there are two group data (AE signal characteristic wavelet coefficients number N and wavelet decomposition coefficient greater than wavelet decomposition coefficient threshold Fmax(N)).
The network output node number is 3, which is the fresh, worn and partly worn grinding wheel condition.
Because it has been proven that the three layer network with sufficient number of nodes in the hidden layer is able to model any mathematical function, the number of hidden layers can be limited to 1 and it need not be an objective of this optimization.
The number of nodes in the hidden layer can be decided by the following equation:
(6) Input inputmf rule outputmf out · · Lg(Vw/Vs) Lg(ap) Lg(Vf) Lg(WAE) Lg(Ra) AE In Eq. (6), m is the number of input layer nodes, and n is the number of output layer nodes.

There have been a number of major advances made in this field during the past 30 - 40 years.
Biomaterials and Bioceramics A number of definitions have been developed for the term "biomaterials".
A definite correlation between hardness and a grain size in sintered HA bioceramics was found: the hardness started to decrease at a certain critical grain size limit despite exhibiting high bulk density [104].
Furthermore, dense HA ceramics exhibit superplasticity at 1000 - 1100 °C with a deformation mechanism based on grain boundary sliding.
More to the point, bone-forming functions of cells can be dependent on grain morphology of the scaffolds.

The toxic effects of heavy metals on the biosphere have been demonstrated by a number of studies.
The average grain sizes (D) were calculated from the diffraction peak using the Scherrer formula: θβ λ cos 89.0 =D where λ is the X-ray wavelength employed, θ is the diffraction angle of the peak, and β is defined as the half-width after correction of the instrumental broadening.

The nine components can be regarded as coarse-grained units.
Factoring integers with the number field sieve.
The Factorization of ninth Fermat Number, Math.
Strategies filtering in the number field sieve.
A Montgomery-Like Square Root for the Number Field Sieve.

The results show that the prepared Ni1-xZnxFe2O4(x=0.2～0.7) have good spinel structures, higher saturation magnetization (35.18～77.69 emu/g) and smaller hysteresis hoops, while Ni0.2Zn0.8Fe2O4 grains exhibit some paramagnetic behaviors, such as almost zero hysteresis and non-saturated magnetization.
The structural and static magnetic characteristics of the ferrite grains are characterized respectively by XRD and VSM.
This can be attributed to the increasing migration of Fe3+ ions from the A- to B-sites with increasing Zn content without magnetic moment in order to accommodate to the increased number of Zn ions on A-sites.
This is because the growing of the number of magnetic particles in composites leads to ferromagnetic resonance occurring in lower frequency.