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For the disadvantaged sedimentary facies and shallow depth, the maximum gas content of the Lower Silurian black shale from well site desorption test is 0.59m3/t only, but the result of isothermal adsorption simulate test show that the Lower Silurian black shale have a good adsorption capacity, and can generate a large number of shale gas in Northwestern Hunan where better deposition conditions and suitable depth exist in.
In this period, fine-grained clastic sedimentary rocks were mainly deposited.
Black shale was developed at the bottom of the Lower Silurian with a large number of graptolite fossils and without low biological habitat, indicating that the depositional environment was deep water continental shelf in early Silurian period.
But the result of isothermal adsorption simulate test show that the Lower Silurian black shale had a good adsorption capacity, which showed the Lower Silurian black shale can generate a large number of shale gas in Northwestern Hunan where had better deposition conditions and suitable depth.

The GLCM is a matrix where the number of rows and columns is equal to the number of gray levels G of an image.
Severe fibrosis may be relied on the lack of the number of patients cases with hepatic severe fibrosis.
Number of texture features.
The Angular Second Moment(ASM) is define as [4]: (7) Angular Second Moment has the ability to evaluate grain weight degree in the hepatic magnetic resonance images.
The result showed that optimal number of features was confirmed from 3 to 7.

The first was availability of big number of results of similar materials from previous experiments.
The detailed compositions are following: · Composition 1 (LSHD): Basic adhesive diffusion mortar without any finishing · Composition 2 (LSHD + PS + SZO3): Basic adhesive diffusion mortar + silicate penetration + silicate plaster - maximal grain 3 mm · Composition 3 (LSHD + PAS + AZO3): Basic adhesive diffusion mortar + acrylic-silicon penetration + acrylic plaster - maximal grain 3 mm Three disc samples with diameter about 117 mm from each composition were tested according to the standardization [1] within the experiment.
Table 1 Dimensions of samples of Composition 1 for test of water vapour permeability Sample mark Sample number Sample weight [g] Ø [mm] Average [mm] Sample thickness [mm] Average [mm] LSHD A 30.7 117,3 118.0 117.7 1.8 2.0 1.9 B 30.9 117.7 117.7 117.7 1,9 1,9 1.9 C 34.2 117.2 117.6 117.4 2.4 2.3 2.4 Table 2 Dimensions of samples of Composition 2 for test of water vapour permeability Sample mark Sample number Sample weight [g] Ø [mm] Average [mm] Sample thickness [mm] Average [mm] LSHD+PS +SZO3 A 78.7 117.0 116.9 117.0 5.5 5.5 5.5 B 76.9 116.9 117.0 117.0 5.6 5.5 5.6 C 80.6 117.0 117.0 117.0 5.5 5.6 5.6 Table 3 Dimensions of samples of Composition 3 for test of water vapour permeability Sample mark Sample number Sample weight [g] Ø [mm] Average [mm] Sample thickness [mm] Average [mm] LSHD+PAS +AZO3 A 75.1 117.0 116.9 117.0 5.0 5.0 5.0 B 67.8 116.4 116.5 116.5 4.1 4.2 4.2 C 75.3 117.2 117.1 117.2 4.5 4.6 4.6 Results of diffusion parameters Three samples from each composition
Table 4 Water vapour permeability and water vapour resistance factor of Composition 1 Sample mark Sample number Water vapour rate [*10-8 kg/s] Water vapour permeability [kg/(m2.s.Pa)] Water vapour resistance factor [-] LSHD A 3.44 1.24E-08 8.57 B 3.52 1.33E-08 7.96 C 3.49 1.29E-08 6.59 Average 3.48 1.29E-08 7.7 Table 5 Water vapour permeability and water vapour resistance factor of Composition 2 Sample mark Sample number Water vapour rate [*10-9 kg/s] Water vapour permeability [kg/(m2.s.Pa)] Water vapour resistance factor [-] LSHD+PS +SZO3 A 8.92 1.29E-09 28.3 B 9.27 1.35E-09 26.7 C 9.03 1.39E-09 27.3 Average 9.07 1.34E-09 27.4 Table 6 Water vapour permeability and water vapour resistance factor of Composition 3 Sample mark Sample number Water vapour rate [*10-9 kg/s] Water vapour permeability [kg/(m2.s.Pa)] Water vapour resistance factor [-] LSHD+PAS +AZO A 3.83 4.51E-10 91.1 B 3.71 4.36E-10 110.3 C 3.13 3.64E-10 122.8 Average 3.56 4.17E-10 108.1 Detailed results that

NO2.Cloud computing provide a free download, free installation, on-demand, location and time limitless services which can reduce the large number of IT costs.
For the fact that ERP system on-line is not a once and for all, company facing a growing number of bottlenecks: NO1.
A large number of technical data and information gained in these processes which is unique intelligence for the oil industry.
Basic services layer is the "heart" of the platform, mainly package the common functional components of the coarse-grained and directly communications with infrastructure layer in data connection.
Therefore, public cloud can not only save a large number of information technologies spending, but also enjoy the flexibility in the use of public cloud.

Tauchi’s approach was adopted to reduce the numbers of experiment.
The grain diameter of EPS beads of very smooth and rounded shape was mostly 3mm and the bulk density was 30kg/m3 which could be classified to ultra- lightweight aggregate.
Table 2 L9 orthogonal array and experimental results Mix number Cement A W / C B Volume of EPS C Sand ratio D Density (kg/m3) Compressive strength (MPa) 1 A 1 B 1 C 1 D 1 2060 20.8 2 A 1 B 2 C 2 D 2 1890 14.8 3 A 1 B 3 C 3 D 3 1720 11.2 4 A 2 B 1 C 2 D 3 1880 17.9 5 A 2 B 2 C 3 D 1 1800 7.9 6 A 2 B 3 C 1 D 2 1930 15.7 7 A 3 B 1 C 3 D 2 1730 13.5 8 A 3 B 2 C 1 D 3 1950 18.6 9 A 3 B 3 C 2 D 1 1790 13.0 Taking the levels of experimental factors as X-coordinate, corresponding mean values of density results as Y-coordinate, the main roles plots of parameters with respect to the density of EPS lightweight concrete are drawn in Fig. 2.

The γ' phase was more regular and also increased in size, while a large number of secondary γ' phase appeared.
After HIP treatment, the number of γ/γ' eutectic became less and the γ/γ' eutectic nearly melted into the substrate.
On the other hand, after HIP treatment, the number of the eutectic became less and the size became smaller.
Also the secondary γ′ phase was more regular and large numbers of secondary γ′ phase precipitated, and the typical duplex γ′ phase formed.
Influence of hot isostatic pressing on the fracture transitions in the fine grain MAR-M247 superalloys, Materials Chemistry & Physics. 84 (2004) 284-290.

For each unseen instance, MLFI-KNN takes its k-nearest neighbors in the training set and counts the number of occurrences of each label in this neighborhood, and then utilizes the FP-growth algorithm to obtain the frequent item sets between the labels that these neighboring instances include, in order to determine the predicted label set.
MLFI-KNN Algorithm Rough speaking, the proposed algorithm involves two basic steps: Step 1: Given an unseen instance, we pick up its K nearest neighbors Si from the training set in terms of similarity measure, and count the number of occurrences of each label in this neighborhood to find out the topmost label Lm , Here we regard the label that have the most number of neighbors as the topmost label.
Let D be a dataset partitioned into a training set S and a test set T, let L denote the label set and M denote the number of unseen instances.
We restrict our attention to the 10 categories (acq, corn, crude, earn, grain, interest, money-fx, ship, trade, wheat).
Initially we carry out an experiment to justify the number of nearest neighbor, which has little effect on performance.

The maximum number of latent variables in the model was 20.
The number of variables equal to 11 is thus kept.
The blue circle represents the values of RMSEC and RMSEP, with a chosen number of 11 latent variables.
Williams, Near-infrared Technology-Getting the Best Out of Light, PDK Grain, Canada (2003)
Vivien, Grain 7 : Régression Linéaire, in Chemoocs, Session 1 (2016)

The processing parameters examined were the laser beam power, the scanning speed, the number of laser scan passes, the percentage overlap of the laser tracks between the consecutive passes and the laser beam focal position.
From the preliminary test, it was found that the laser power (W) in continuous wave mode, the rotational speed (rpm), the number of repeated laser passes, the beam focal position below, above, or on the sample surface, and the percentage overlap between the consecutive passes have a significant effect on the final surface roughness.
In the second model, the laser beam power and the number of passes were kept constant on the optimum values obtained from the first DoE, these are 110 W and one respectively.
The microstructure of 316L SST is austenite at room temperature, and the large grains can grow even through the different melt-pool boundaries as shown in areas (1) in Fi. 8 (a).
The presence of some lamellar grains of the hard-martensitic phase were observed near the outside due to the high cooling rates in this region which is caused by the flow of the assist argon gas over the small melt pools.

The emery paper with grain sizes ranging from #300 until #3000 was used and then followed by buff-polished to minimize machining.
Curves of Fatigue crack length versus number of cycles Figure 3.
Another benefit is increasing yield strength of A356 aluminum alloy through the formation of fine β precipitates in large numbers which strengthen the aluminum matrix.
The T6 heat treatment followed by the aging process changes the formation of microstructures of precipitates around grain boundaries and the aluminum matrix.
Grain Refiner Effect on the Microstructure and Mechanical Properties of the A356 Automotive Wheels.