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The traverse length and rate of the measured profile were 6 mm and 1 mm/s, respectively, and the number of measurements per each specimen was 10.
[3] Sharkeev Yu.P., Psakhie S.G., Legostaeva E.V., Smolin A.Yu. et. al., Biocomposites based on calcium-phosphate coatings, nanostructured and ultrafine-grained bioinert metals, their biocompatibility and biodegradation, ed.

Simulations of the cutting process can theoretically reduce the number of iterations in the design of experiments and result in substantial cost savings and consumption of energy resources are also reduced.
Wegen era (2013), ‘Simulation of Hexa-Octahedral Diamond Grain Cutting Tests Using the SPH Method’, 14th CIRP Conference on Modeling of Machining Operations (CIRP CMMO) Vol. 8, pp.322-327

Its grain size was conformed to ASTM C136 [13].The specific gravity is 1,320 kg/m3[5] that is nearly the same value specified by [14].
This result may be from improper control curing of samples that caused incompletion hydration process of concrete as well as the limitation number of test specimens, that is a constraint to get better relation test result.

In this study, the permeability of materials seems to be directly influenced by the number, type and size of pores localized in the concrete.
Indeed, Float Goble and Cohen [15] performed mechanical tests on fine concretes with limestone aggregates and a water to cement ratio similar to our materials, and found that concretes with an important grain surface are more mechanically resistant but present a more important strain in compressive behaviour.

Rev.  3(2011) Article number: 63 (2011) [2] X.
O'Donnell, B.K.Tiwari, Effect of non-thermal plasma technology on microbial inactivation and total phenolic content of a model liquid food system and black pepper grains, LWT, 118(2020)108716 [6] A.A.

(7) Where, Nqualified is relative error which is on more than 20%, N is the total number.
Forest Range Cultivated Unused Water Construction Comment 1 15.44 65.09 18.38 0 0.7 0.39 Ⅰ-PGLC; Ⅱ, Ⅲ-RFF; 2 36.39 44.14 18.38 0 0.7 0.39 Ⅰ-LCA; Ⅱ, Ⅲ-RFF; 3 32.2 48.33 18.38 0 0.7 0.39 Ⅰ-LCA; Ⅱ-RFF, Ⅲ-GG 4 29.53 51 18.38 0 0.7 0.39 Ⅰ-LCA; Ⅱ-GG, Ⅲ-RFF 5 11.26 69.27 18.38 0 0.7 0.39 Ⅰ-PGLC; Ⅱ-RFF, Ⅲ-GG 6 8.48 72.05 18.38 0 0.7 0.39 Ⅰ-PGLC; Ⅱ-GG, Ⅲ-RFF 7 8.08 66.57 18.38 5.88 0.7 0.39 Ⅰ-1PGLC; Ⅱ-GG, Ⅲ-RFF 8 13.63 51.59 18.38 15.31 0.7 0.39 Ⅰ-2-LCA; Ⅱ-GG, Ⅲ-RFF 9 15.36 51.59 11.11 20.85 0.7 0.39 Ⅰ-3-RFF; Ⅱ-GG, Ⅲ-RFF 10 23.06 51.59 18.38 5.88 0.7 0.39 Ⅰ-1-LCA; Ⅱ-GG, Ⅲ-RFF 11 15.61 51.59 10.86 20.85 0.7 0.39 Ⅰ-2RFF; Ⅱ-GG, Ⅲ-RFF 12 8.06 58.89 11.11 20.85 0.7 0.39 Ⅰ-3-GG; Ⅱ-GG, Ⅲ-RFF 13 15.36 74.11 3.56 5.88 0.7 0.39 Ⅰ-1-PGLC; Ⅰ-2-GG; Ⅰ-3-RFF; Ⅱ-GG, Ⅲ-RFF base year 4.5 44.45 29.23 20.73 0.7 0.39 2000 Planting grass land clearing(PGLC), Returning farmland to forest(RFF), Land clearing afforestation(LCA), Grain for green(GG) 2000 year was chosen as base year, If taking measures of land clearing

Development of quantitative analysis of fatty acid for monitoring changes of fatty acid profile of camellia oil ZHU Yong1, 2, 3, a, ZHONG Haiyan*, 1, 2, 3, b, SUN Hanzhou4, c, ZHOU Bo1, 2, 3, LONG Qizhi1, 2, 3, ZHU Pei1, 2, 3 1Faculty of Food Science and Engineering, Central South University of Forestry and Technology, Changsha, 410004, Hunan, China; 2National Engineering Laboratory for Further Processing of Grains, Changsha, 410004, Hunan, China; 3Hunan key Laboratory of Deeply Processing and Quality Control of Cereals and oils, Changsha, 410004, Hunan, China; 4Research Institute of Applied Chemistry, Central South University of Forestry and Technology, Changsha 410004, Hunan, China a zhuyonghappycool@163.com, bzhonghaiyan631210@126.com, ceast0000000@126.com
Table 1 Linear regression equations of external standard and r2 of FAMEs standards (1.0-10.0 mg / L) NO. a linear regression equations r2 linear ranges ( mg / L ) 1 Y=0.0001X-0.1214 0.9968 1.193-9.544 2 Y=0.0001X+0.0265 0.9967 1.269-10.15 3 Y=0.0001X-0.3304 0.9966 1.116-8.928 4 Y=1.0×10-4 X -0.13 0.9941 1.160-9.280 5 Y=9.0×10-5 X -0.3136 0.9974 1.340-10.72 6 Y=1.0×10-4 X -0.1523 0.9965 1.099-8.792 7 Y=1.0×10-4 X -0.1297 0.9939 0.9030-7.224 8 Y=0.0001X-0.5352 0.9940 1.102-8.816 9 Y=0.0001X-0.2467 0.9945 0.8890-7.112 10 Y=9.0×10-5X-1.0417 0.9956 1.132-9.056 11 Y=1.0×10-4X+0.1306 0.9968 1.098-8.784 12 Y=1.0×10-4 X -0.3086 0.9962 1.246-9.968 13 Y=0.0001X-0.4814 0.9969 1.062-8.496 14 Y=0.0001X-0.2322 0.9953 1.022-8.176 15 Y=0.0001X+0.2094 0.9934 1.451-11.60 a The numbers reprensented the fatty acids which were corresponding to Fig.1 Table 2 Linear regression equations of external standard and r2 of FAMEs standards (10.0-100.0 mg/L) NO. a linear regression equations
r2 linear ranges ( mg/L) 1 Y=0.0001X+0.8510 0.9982 11.93-95.44 2 Y=9.0×10-5X+2.4925 0.9983 12.69-101.5 3 Y=9.0×10-5X+1.147 0.9963 11.16-89.28 4 Y=8.0×10-5X+3.0603 0.9975 11.60-92.80 5 Y=8.0×10-5X+3.5448 0.9944 13.40-107.2 6 Y=9.0×10-5X+0.2913 0.9990 10.99-87.92 7 Y=9.0×10-5X+1.1934 0.9983 9.030-72.24 8 Y=9.0×10-5X+1.1428 0.9978 11.02-88.16 9 Y=9.0×10-5X+2.1479 0.9982 8.890-71.12 10 Y=9.0×10-5X+0.9725 0.9935 11.32-90.56 11 Y=9.0×10-5X+1.1106 0.9990 10.98-87.84 12 Y=9.0×10-5X+1.0694 0.9989 12.46-99.68 13 Y=1.0×10-4X+1.2391 0.9981 10.62-84.96 14 Y=9.0×10-5X+2.0822 0.9980 10.22-81.76 15 Y=0.0001X+4.6399 0.9981 14.51-116.0 a The numbers reprensented the fatty acids which were corresponding to Fig.1 Table 3 Linear regression equations of internal standard and RRF NO. a linear regression equations r2 RRF b 2 Y=1.0443X+0.0052 0.9998 1.0443 3 Y=1.1009X+0.0069 0.9997 1.1009 5 Y=1.1173X-0.1203 0.9995 1.1173 6 Y=1.0719X+0.0117 0.9995 1.0719 7 Y=1.0783X-0.0035 0.9992 1.0783 8 Y=1.0638X
-0.0049 0.9966 1.0638 9 Y=1.0278X+0.0125 0.9979 1.0278 10 Y=1.0913X-0.0813 0.9924 1.0913 11 Y=1.0403X-0.0015 0.9982 1.0403 12 Y=0.9885X+0.1085 0.9961 0.9885 13 Y=0.9319X+0.0424 0.9973 0.9319 14 Y=1.0627X-0.0154 0.9981 1.0627 15 Y=1.0011X+0.0013 0.9974 1.0011 a The numbers reprensented the fatty acids which were corresponding to Fig.1 b The RRF was consistent with the K of the linear regression equations Table 4 Recovery of FAMEs standards when added to raw camellia oil ( internal standard ) The amount of added (mg) NO.a 1.000 2.000 The amount presented in GC The amount of added Recovery (%) The amount presented in GC The amount of added Recovery (%) 2 1.277 1.269 100.6 2.620 2.538 103.2 3 1.114 1.116 99.82 2.225 2.232 99.69 5 1.385 1.340 103.4 2.701 2.680 100.8 6 1.028 1.099 93.54 2.121 2.198 96.50 7 0.9348 0.9030 103.5 1.811 1.806 100.3 8 1.014 1.102 92.01 2.181 2.204 98.96 9 0.9481 0.8890 106.7 1.867 1.778 105.0 10 1.100 1.132 97.17 2.140 2.264 94.52 11 1.012 1.098 92.17 2.194
2.196 99.91 12 1.266 1.246 101.6 2.577 2.492 103.4 13 1.113 1.062 104.8 2.178 2.124 102.5 14 1.000 1.022 97.85 1.885 2.044 92.22 15 1.492 1.451 102.8 2.923 2.902 100.7 a The numbers reprensented the fatty acids which were corresponding to Fig.1 Table5 Detection and quantitation limit of 15 FAMEs standards analyzed by GC Fatty acid methyl esters DL (ppm)a QL(ppm)b C11:0 0.09278 0.3093 C12:0 0.09198 0.3066 C14:0 0.08582 0.2861 C15:0 0.1035 0.3451 C16:0 0.1080 0.3601 C16:1-9t 0.1117 0.3722 C16:1-9c 0.1200 0.4000 C18:0 0.1365 0.4551 C18:1-9t 0.1338 0.4461 C18:1-9c 0.1338 0.4461 C18:2-9t12t 0.1305 0.4351 C18:2-9c12c 0.1167 0.3890 C20:0 0.1129 0.3763 C18:3-9c12c15c 0.1130 0.3766 C22:0 0.1348 0.4492 a DL: limit of detection based on S/N3.

Based on the principle of stereology, when the number of sections reaches, voidage average of sections will be, namely the true voidage of the asphalt mixture specimen.
The research takes the stereological method to speculate space distribution characteristics of aggregates with grain sizes at all levels and make fractal analysis for different gradations of asphalt mixtures to achieve a quantitative description of asphalt mixture gradation type.
The industrial CT scanning images for which the Otsu method cannot accurately divide different components can be divided into a number of overlapping annular sub-samples from internal to external and the Otsu method is used repetitively for the annular sub-samples to calculate grayscale thresholds of target and background.
After repeated comparisons of a number of materials, Yue-hua Duan et al [15] finally determined the technical solution of the epoxy resin and zirconium glass beads.
Industrial CT Technology-based Asphalt Mixture Virtual Mechanical Test Method Some scholars have carried out over a number of researches on the finite element virtual mechanical test of homogeneous asphalt mixtures, to find these virtual mechanical test methods subject to significant defects.

The results reveal that defect-free joints may be obtained by employing taper and screw-threaded cylindrical pins and that greater rotational speed and lower traverse speed result in larger grain sizes and, as a result, inferior mechanical characteristics [5].
At each simulation time step, complete re-meshing involves, splitting the workpiece into a finite number of meshes.
ALE allows independent mesh movement from material and high-quality meshing in the analysis, so nodes and the number of elements do not vary.
Table 1 Meshed Result Description Value Mesh vertices 8439 Prisms 828 Hexahedra 5010 Triangles 828 Quads 5862 Edge elements 892 Vertex elements 40 Number of elements 5838 Minimum element quality 0.5799 Average element quality 0.9184 Element volume ratio 0.0031966 Mesh volume 6.25x106 mm3 IV.
Acknowledgment This research was supported by INDMET project; grant number 62862 funded by the NORHED II program.

The compacts containing Tin had higher hardness and better densification in comparison to those without [4].Wang et al. [5]indicatedTi coatingprotected diamond from oxidation and a maximum increment was reached to 20% [6, 7].The compositeswere produced at 8 GPa and temperatures of 1800 - 2000 oCfrom diamond powders with micron size grains and showed higher hardness[8-9].
A factorial design of experiment of type of 2k was used in the present study where ‘k ‘corresponds to the number of factors and ‘2’ stands for the number of levels.
Thus, minimum number of trial experiments to be conducted for each material is 8.The plan of experiments and their levels in the present study are shown in Table 1.
Depth of penetration of abrasive particles depends on number of factors such as type, size and hardness of abrasive particles, loading conditions and hardness of matrix materials [16].The depth of deformation grooves increases with increasing the sliding distance since its hardness is about 71 HB, but is found to be less effective in comparison to the load (Fig.1b).The lowest effect on the weight loss of the composite is indicated by the sliding speed (Fig.1c).