Search results

The presence of a phase transition was determined by the behavior of the magnetic susceptibility χ, which was calculated using three order parameters — magnetic Mma, cluster Mcl, and percolation Mpr, which were determined using the following relationships: Mma=1NiNmasi, (8) Mcl=1NiNclsi, (9) Mpr=1NiNprsi, (10) magnetic susceptibility was calculated in a known manner: χ=NtM2-M2, (11) here N=L2; Nma, Ncl and Npr – the number of nodes in the lattice, the number of
In the region far from the phase transition, the average number of ncl clusters is almost constant; therefore, Nma and Ncl are proportional to the concentration of magnetic atoms, unlike Mpr, which is determined by an infinite cluster whose size Npr does not change in this region.
Since the number of magnetic particles in a cluster is less than the number of magnetic particles (NclBinder, Critical properties from Monte Carlo coarse graining and renormalization, Phys.

Fig 1 Experimental process flow diagram 3 Experimental procedure 3.1 The raw coal crushing.Use jaw crusher to crush, in order to prevent crushing excessively, break once will use sets of sieve to sieve, remove the grain size we need, which is larger than the size continues to break, repeat the above procedure. 3.2 Raw coal screening.Vibrating screen grading, dry coal sample, shrink specimen, stop the machine every 5min, check one time by hand screening, when check, remove the sieve on the enamel plate from top to bottom by hand sieve, screening by hand for a minute, the weight of screen underflow is not more than 1% of the weight of oversize, so sieve netly.
Table 1 The experimental results of kerosene as collector Number Kerosene /μL Octanol/μL Clean coal yield/ % Clean coal ash /% Tailings yield /% 1 90 10 89.61 7.91 10.37 2 100 10 89.73 7.19 10.26 3 110 10 88.52 9.49 11.46 Table 2 The experimental results of diesel oil as collector Number Diesel oil /μL Octanol/μL Clean coal yield/ % Clean coal ash /% Tailings yield /% 1 90 10 84.47 8.60 15.52 2 100 10 85.47 9.47 14.51 3 110 10 85.47 9.77 14.52 We use clean coal yield, clean coal ash and tailings yield to draw column chart, do analysis and comparison by it, when select collector is 100μL, the results shown in Figure 2.
Table 3 The experimental results of drainage oil as collector Number drainage oil /μL octanol/μL Clean coal yield/ % Clean coal ash /% Tailings yield /% 1 90 10 89.33 10.57 10.67 2 100 10 88.87 10.60 11.11 3 110 10 89.73 9.16 10.25 Figure 2 The column chart of flotation 4.2 Study flotation use emulsified drainage oil as collector agent.We use OP emulsifier in experiment, which is a condensation compound of alkylphenol and ethylene oxide, is a non-ionic surfactant, the appearance is a viscous liquid which is colorless to pale yellow, solve in water easily, with excellent homogeneously staining, emulsification, wetting, diffusion and other properties.
Table 4 Emulsion drainage oil flotation experimental results Number Vo：Vw Convert dranage oil/μL Clean coal yield / % Clean coal ash /% Tailings yield /% 1 3:7 45 90.00 10.06 9.98 2 4:6 60 88.13 8.76 11.86 3 5:5 75 87.73 8.94 12.25 4 6:4 90 88.67 8.63 11.32 5 7:3 105 88.53 9.86 11.45 Using the above data plot in Figure 3.
Table 5 Emulsion drainage oil collector flotation experimental results Number Convert dranage oil /μL octanol/μL Clean coal yield/ % Clean coal ash /% Tailings yield /% 1 90 10 90.13 9.34 9.85 2 100 10 88.27 9.95 11.71 3 110 10 89.87 9.71 10.12 Draw column chart analysis with clean coal yield, clean coal ash and tailings yield when take collector 110μL and octanol 10μL, the results are shown in Figure 4: Figure 4 Column chart of kerosene, diesel, drainage oil and emulsified drainage oil flotation Seen from Figure 4, adding a certain amount collector, clean coal yield is 89.87%, clean coal ash is 9.71% and tailings yield is 10.12% when collector is emulsified drainage oil.

Year 1950 1960 1970 1980 1990 2000 2010 Number of farms [ten thousand] 538 372 295 244 214 200 220 Average farm size [acres] 212 313 374 426 460 430 418 Table 1 Statistic of the number and average scale of the American Farm Data sources: American "Statistical Abstract" 1975, 1982, 2011
Each year more than 40% of the grain and oil crops and more than 10% of the animal products are for export. with only about 1.8% of the country's population feeding the 300 million people in the United States.
In view of this, the environmental protection agency (EPA) in American has launched a number of new laws and regulations to strengthen environmental protection agriculture in recent years.
In general, use biomass waste in agricultural process as new energy materials, applying biomass biochemistry technology into family farm. 3.2 Implant concept of "green chemistry", implement the agricultural cleaner production The modern agriculture with the feature of highly intensification and a large number of chemical inputs is causing an increasingly serious environment pollution problem.
At the same time, as a business individuals, if family farm flowed out a large number of land, it may lead to a lot of liquidity is occupied by a expensive rents, and the contradiction of no capacity to manage land.

Identification and state section includes identification information and state information, such as data module code, version number, security classification, etc.; Content section includes equipment technical data, such as the descriptive information, maintenance procedures information etc...
Namely Identification information(such as data module code, title, version number, publication date, the language used and etc.) and state information (such as security scale, the responsibility units and the preparation units, technical standards, application information, quality verification status , skill level, for the change reason used and etc.).
The code consists of English characters and numbers but avoids using “I” and “O”.
ICN YYYYYY YY Y Y YYYYY NNNNN XXX AY X prefix type distinguish systems difference systems divide responsibility cooperate sides information order edition number systems difference secret scale data unit code Project coding is a encoding scheme based on the type identification.
Summary Native XML database possess unparalleled advantage and potential camper with other databases in dealing with XML documents, the disadvantages of native XML database are the lack of fine-grained data processing capabilities, not suitable for processing XML documents and so on.

The advantages are manifested due to a significant reduction in labor intensity and cost while processing a large number of parts.
Processing Media Grain-Size Characteristics Choice Principles Taking into account the direction of the research, we will analyze the methods of intensification of vibration processing based on the combination of processing and activating media in relation to finishing and cleaning processing of parts of electronic equipment, which primarily involves removing burrs and preparing surfaces for coating.
Characteristic Designation Experience Number 1 2 3 4 5 6 7 8 The coefficient that defines the ratio of the volume KV Х1 0,1 0,7 0,1 0,7 0,1 0,7 0,1 0,7 Mass of granules of the activating medium, ma Х2 0,1 0,1 3 3 0,1 0,1 3 3 Mass of granules of the processing medium, mm Х3 0,02 0,02 0,02 0,02 0,2 0,2 0,2 0,2 The intensity of the treatment process Р 1 Y1 0,028 0,036 0,035 0,032 0,022 0,03 0,02 0,036 2 Y2 0,026 0.034 0,028 0,038 0,025 0,032 0,024 0,034 3 Y3 0,028 0,034 0,03 0,034 0,022 0,028 0,028 0,03 4 Y4 0,024 0,038 0,034 0,033 0,021 0,033 0,024 0,031 5 Y5 0,021 0,034 0,028 0,036 0,019 0,03 0,025 0,32 Table 2.
The values of the coefficients bi AlMg4.5Mn CuZn38Pb1.5 bi bi b0 0,03159351 0,025324365 b1 0,010814815 0,01375447 b2 -0,029757344 -0,030648148 b3 0,00861552 0,001759259 b11 0,017592593 0,011965812 b22 -0,000574713 -0,00037414 b33 0 0 b12 0,000526511 0,000451632 b13 0 0 Verification of the obtained criteria of dependence on a given significance level a = 0.05 and the number of degrees of freedom f according to the standard confirms the hypothesis that the nature of the distribution of experimental data is close to the normal distribution law of independent random variables.
//MATEC Web of Conferences. - 2018. - Vol. 224: International Conference on Modern Trends in Manufacturing Technologies and Equipment (ICMTMTE 2018). - Article Number: 03011

The cyclic thermal load occurs by nature in a small number of cycles, but the stresses generated by the restrained thermal expansion may be far beyond the elastic limit.
Low cycle fatigue (LCF) is isothermal fatigue where the strain amplitude during fatigue cycling exceeds the yield strength and causes inelastic deformations so that the material suffers from damage in a short number of cycles [7].
LCF is isothermal fatigue where the strain range during fatigue cycling exceeds elastic strain range and causes inelastic deformations so that the material exhibits a short number of cycles to failure [7].
Number LiTH-IKP-S-459.
Thermo-mechanical and isothermal fatigue of a coated columnar-grained durectionally solidified nickel-base superalloy.

Then, the influence on nucleation rate can be detected from the change in the particle numbers.
Fig. 4 shows that the number of ferrite particles does increase in the presence of magnetic field [2].
Hence, probably due to this effect the observed particle number decreased at the time of measurement, see Fig. 5, and the particle numbers soon became similar with and without magnetic field [7].
Fig. 4 Ferrite particle number per unit area of grain boundary vs isothermal holding time plots in three Fe-C base alloys [2].
Fig. 5 Ferrite particle number per unit area of grain boundary plotted against holding time in a Fe-0.1C-3Mn alloy [7].

Fig. 2 shows the dependence of concentration difference variation CrCu CCC −=∆ upon the number of laser impulses N.
At E = 99 mJ, the increasing of pulse number up to 2160 does not result in Cu and Cr appearance at the Ni surface; at E = 132 mJ, the concentration difference ∆C makes on average 4% within the range up to 100 pulses, and then it increases to 10% at N = 1000 pulses.
Mean surface concentration of the diffusing component for the surface accumulation method can be defined by the equation , 2 exp1 2       ' ' −=− l tD L lK C bs δ δ (8) where −bD component diffusion coefficient; t - sample annealing time; l - thickness of material layer in which the diffusion is investigated; L - grain size; δ´ - thickness of the analyzable accumulation layer; δ - grain boundary width; K = Cb/Cs (where Cb is diffusant concentration in the analyzable layer, Cs is the diffusant concentration at drain surface).
Time t in these expressions is the product of t = τN (N -the number of pulses).
Redistribution of the film structure components is determined, first of all, by diffusion coefficient D and total time of laser annealing under the chosen radiation conditions (energy E, pulse duration τ, number of pulses N, Gaussian spot radius rr).

Detail forensic study related to groundwater leakage detection requires lots number of drilling in order to obtain high accuracy layout of the profile investigated.
As reported by [12,13,14,15,16], soil resistivity value can be varied due to the variation of basic geotechnical properties such as moisture content, densities, void ratio, porosity and grain size fraction.
Table 1 2-D Electrical resistivity data acquisition setting No Spread line reference number Array Total survey length, m 1 105 – 109 Schlumberger 116 2 110 – 113 Schlumberger 120 3 117 – 114 Schlumberger 88 4 132 – 136 Schlumberger 160 5 200 – 121 Schlumberger 120 6 123 – 128 Schlumberger 120 Results and Discussions Localize layout of the individual 2-D resistivity lines was given in Fig. 2-7 while globalize layout of the study area was given in 3-D image at Fig. 9-13.
Electrical resistivity value can be influenced by several factors such as the concentration and type of ions in pore fluid and grain matrix of geomaterials via the process of electrolysis where the current was carried by ions at a comparatively slow rate [24].
Ahmad (2013): The influence of soil moisture content and grain size characteristics on its field electrical resistivity, Electronic Journal of Geotechnical Engineering, 18/D, 699-705

An overview of the x-ray diffraction characterization results of Ba0.6Sr0.4Fe11.5Al0.5O19/NiFe2O4 composites using GSAS software. wRp: 3.09, and χ2 : 1.317 Phase System SG Lattice parameters (Å) Fraction V r D a b c (%) (Å3) (g.cm-3) (10-9m) BaFe12O19 Hexagonal P 63/mmc 5.8765(6) 5.8765(6) 23.094(3) 12.01 690.6(2) 5.828 135 NiFe2O4 Cubic F d -3 m 8.3253(3) 8.3253(3) 8.3253(3) 87.99 577.04(7) 5.590 79 Figure 2 displays the surface morphology and grain shape of the Ba0.6Sr0.4Fe11.5Al0.5O19/NiFe2O4 composite sample as a result of SEM characterization.
The Maxwell-Wagner model assumes that the material structure comprises a grain phase that conducts more strongly than the grain boundaries at the interface. [47].
Acknowledgement Thank the Directorate General of Higher Education, Research, and Technology, Ministry of Education, Culture, Research, and Technology, for funding this research under contract number 819/LL3/AL.04/2024, which was awarded by the Ministry of Research, Technology, and Higher Education under contract number 030/LPPM-UPH/VI/2024.