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The texture coefficient TC(hkl) is calculated for the characteristic diffraction planes from the X-ray diffraction data using the well known formula [18] TC(hkl) = (I(hkl)/Io(hkl))/((1/N)∑(I(hkl)/Io(hkl))) where I(hkl) indicates the X-ray diffraction intensity obtained from the films and N is the number of reflections observed in the XRD pattern.
The value of the texture coefficient indicates the maximum preferred orientation of the films along the diffraction plane, meaning that the increase in preferred orientation is as sociated with increase in the number of grains along that plane.

The schist is characteristically foliated, meaning the individual mineral grains split off easily into flakes or slabs.
Calculation results of strength and deformation parameters Specimen Numbers Uniaxial compressive strength (MPa) Elastic modulus (GPa) Deformation modulus (GPa) Poisson's ratio test condition 4-3-1 133.6 52.06 37.86 0.112 natural 4-5-1 135 63.77 37.02 0.224 4-5-3 128.8 59.11 33.65 0.158 4-9-2 113.2 49.6 32.25 0.18 4-10-1 121.5 41.05 18.89 0.222 4-10-3 126 35.62 22.43 0.223 4-3 101.3 44.08 35.85 0.232 saturated 4-3-2 89.4 35.98 21.13 0.151 4-6 90.9 39.76 23.73 0.186 4-7-3 88.9 40.81 28.05 0.253 4-8 79.8 40.35 23.45 0.142 4-10 106.1 33.55 20.91 0.278 Results of triaxial compression test Complete stress-strain curve The complete stress-strain curve obtained by triaxial compression test is presented in figure 5.
Calculation results of strength parameters Specimen Numbers Minimum principal stress (MPa) Principal stress difference (MPa) Maximum principal stress (MPa) C (MPa) Φ (°) test condition 4-10-1 0 121.5 121.5 16.39 64.8 natural 4-7 2 206.3 208.3 4-10-2 4 220.0 224.0 4-9-3 6 292.8 298.8 4-9-1 8 279.6 287.6 4-8 0 79.8 79.8 7.51 62.7 saturated 4-8-3 2 81.6 83.6 4-8-1 4 132.6 136.6 4-8-2 6 132.4 138.4 4-5-2 8 183.1 191.1 Analysis test results Basic mechanical properties The results of uniaxial compression test show that the average uniaxial compressive strength of schist under natural condition is 126.35MPa while the value reduces to 92.73MPa under saturated condition (as is shown in table 1).

(RBS) tsub, [°C] Number of pulses Rep. rate, [Hz] Pulse energy, [mJ] RBS (± 10) SE (± 10) TEM (± 5) 1 Si(100) 30 50 000 30 100 240 245 - 4,9 Si1,0C0,95 2 Si(100) 300 50 000 30 100 240 244 - 4,8 Si1,0C0,95 3 Si(100) 500 50 000 30 100 260 262 - 5,2 Si1,0C0,97 4 Si(100) 700 50 000 30 100 230 232 180 4,6 Si1,0C0,97 5 Si(100) 950 10 000 45 100 - 37 45 4,1 - 6 Si(111) 30 30 000 45 150 - 246 - 8,2 - 7 Si(111) 500 30 000 45 150 - 253 - 8,4 - 8 Si(111) 600 30 000 45 150 - 208 - 6,9 - 9 Si(111) 700 30 000 45 150 - 191 - 6,3 - 10 Si(111) 950 20 000 45 100 74 73 70 3,6 Si1,0C1,0 Taking into account low growth temperature of the samples it can be assumed that the main cause of the deviation from stoichiometry is self-sputtering: some fraction of the ablated material impacting the surface induces a desorption process.
It is known that physical sputtering yield factor depends on the atomic number and cohesive energy of the target elements [8], which may lead to preferential removal (preferential sputtering) of some component from compound semiconductor surfaces [9].
Fig. 2c illustrates Si/SiC heteroboudary area inside a single grain.

Topology model describes the relationship between the various parts of the composition and distribution of plant, the type of organ, the number, location and the structural relationship between each other at every stages of growth; The geometric structure model describes the structure of plant and 3D geometric information of organs, such as organ size, shape and angle.
The research on simulation model of plant uses the traditional mathematical methods such as mathematical statistics, partial differential equations and nonlinear function, artificial intelligence methods such as expert systems, neural networks, the fuzzy system, genetic algorithm, the theoretical generalization and abstract data of plant development are used to take a large number of experimental data plant development and yield to identify the dynamic model of plant growth and relationship with the surrounding environment, and then on the computer plant growth is made simulation in the entire life cycle within a given environment, which is used to direct plant production management and decision-making, then forecast production or breeding[3].
Implementation Growth models include two parts of the program design and dynamic control indicators, the design knowledge model includes the production target, variety selection, sowing time, population density and sowing the amount of calculation, strategy and water management; Dynamic control of knowledge model of regulation indicators includes appropriate growth period, the dynamic leaf age, leaf area index dynamics and dry matter accumulation of dynamic growth index dynamic and effective leaf area ratio, efficient leaf area ratio and the grain-leaf ratio bell leaves the ratio of the source library indicators.

Water cut in floater sample was measured by distillation method, oil cut was gained by organic solvent extraction of distillation remnant after which solvent was evaporated,soil cut was gained by drying of residue of extraction.Results showed that water cut was 65.3%,oil cut was 17.5% and soil cut was 17.2%.After process residual solid presented grey white,in which particles with grain size over 200 mesh accounts for more or less 90%.
Table 1 Energy Spectrum Analysis Plan Number Average element content(%) C 0 Al Si Cl Ca Na Fe S 1 50.14 40.23 4.68 1.89 0.86 0.69 0.62 0.50 0.40 2 49.15 41.89 4.55 1.83 0.78 0.58 0.56 0.40 0.25 As shown in Table 1, element composition and content were almost same in Sample 1 and Sample 2, the main substance of which are organics and non-crystal inorganic component.
A large number of tiny air bubbles were formed during air floation, which could adhere to oil or floater in water .The adhesive action of bubbles depends on wettability of water to particles.

Results and discussion XRD and EDS analysis Fig.1 shows the X-ray diffraction patterns of the bare Zn nanocrystallines, all of the diffraction peaks can be identified as Zn (JCPDS card number 00-004-0831).
All of the diffraction peaks are in good agreement with the data of hexagonal-phase ZnS(JCPDS card number 00-012-0688).
The obtained value is larger than relevant literature [24], this may be caused by quantum effect [25], and also by crystallization, grain size, and the stress and impurities on the films.

However, when the temperature is above 1200°C, the strength of quartz fiber is lost because of the grain growth, which limits its wider application [8-9].
The flow resistance in the impregnation process increases with the increase of the number of impregnation and the decrease of the pore size.
It can be seen that a large number of fibers are pulled out of the matrix, which can play a significant toughening effect and improve the mechanical properties of the materials.

In recent years, the increase in the average age of the population has lead to the growth in the number of surgical procedures, including hip and knee replacements, whereas as the human body ages, the joints become more prone to illness.
The Ti-25Nb-10Zr-8Ta alloys presents a higher content of β phase compared to Ti-21Nb-6Zr-15Ta, where the intensity and number of α’’ peaks are significant.
The Ti-25Nb-10Zr-8Ta and Ti-25Nb-10Zr-8Ta are classified as near β-alloys, presenting similar microstructures, characterized by the presence of β-grains, which contain lamellar structures specific to α” phase.

Increasing of casting speed can refine grain size, decrease the thickness of segregation layer in periphery of billet and improve surface quality of billet [4-6].
Acknowledgments The authors gratefully acknowledge the supports of the doctoral foundation of China Ministry of Education (Grant number 20130042130001) and the Science and Technology Program of Guangzhou, China (Grant number 2015B090926013).

Based on the Inorganic Crystal Structure Database (ICSD) number 98-009-9860, LiFePO4 has a lattice parameter value of a = 10,294 (Å); b = 5.986 (Å); c = 4,675 (Å).
The hope that should occur is the change in electron density in the battery doped with Gd3+ ions at a greater concentration, which can be explained because the atomic number of Fe (56) is smaller than that of the Gd atom (157).
The higher the Gd3+ ion concentration the greater the grain size, which is also accompanied by an increase in homogeneity and density.