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A significant number of different Rheocasting technologies have been developed over the past decades.
It is also needed to check and adjust the grain modification and nucleation of the AlSi7Mg0.3 with Sr, and grain refinement with TiB2.
Conclusions A quickly growing number of applications in various industries are now using the Rheocasting process to produce heat sinks, leak tight components, etc., and now also structural castings.

The TEM observation results are consistent with the results shown in Fig. 1 (b), indicating a large number of α″ martensite in the interior of Ti-33.5 Nb-4Sn alloy.
Here it is concluded that a large number of lath-shaped α″ martensite forms in the interior of Ti-33.5 Nb-4Sn alloy, and a few ω phase distributes around α″ martensite.
By the measurement of grain surface spacing and the calibration of spots, the orientation relationship between the α″ martensite and β phase is determined to be (110)β∥(002)α″, (020)β∥(022)α″ and [001]β∥[100]α″.
Suppression of isothermal ω phase by dislocation tangles and grain boundaries in metastable β-type titanium alloys, Journal of Alloys and Compounds. 2013, 50, 35-38

The large number of possible crack paths has made systematic research in this area difficult.
However, various factors can cause deviation from this path, including residual stresses, stress gradients, grain boundaries, and other material inhomogeneities.
(2) Where (n) is the total number of items, (k) is the number of items chosen and (!)
factorial (product of all positive integers up to that number).
After excluding these duplicates, the number of unique curved crack paths is reduced to 288, as shown in Fig. 3.

VM is one of the casting processes, which is distinctly different from other sand casting processes as this process requires no binders for holding the sand grains together in the mould [8].
NO OBSERVATION MEAN ABOVE OR BELOW MEAN UP OR DOWN 1 14.9501 14.9554 B 2 14.9416 14.9554 B D 3 14.9551 14.9554 B U 4 14.9601 14.9554 A U 5 14.9603 14.9554 A U 6 14.9652 14.9554 A U MEAN 14.9554 14.9554 RUN=1 U& D=1 A=above the mean, B=below the mean, U=Up from previous reading, D=Down from previous reading Calculation for Z (standard normal deviate) above and below: E (run) AB =(N2+ 1) Where N is the number of observations and E (run)AB is the expected number of run above and below E (run) AB = (62+1) = 4 σAB = √(N-14) Where σAB is the standard deviation of above and below σAB = √(6-14) =1.118 ZAB= {RUNAB - E(run)AB}/ σAB Where RUNAB is the actual number of run obtained above and below ZAB = (1-4)1.118= -2.6834 PAB = NORMSDIST (Z) when the value of Z is negative (using Microsoft excel software) P = 0.003645 For up and down calculations: E (run) UD = 2N-13 Where N is the number of observations and E (run)UD is the expected number of run up and down.
For histogram one require minimum of 50 observations, however more the better and for assessing whether the underlying distribution is normal or not becomes more difficult when the number of observations is fewer.
For cumulative probability plot (Pi) = (S.N-0.5)/N Where S.N is serial number of data observation arranged in ascending order, N is total number of observations in the data set.
So, there are very strong chances that the process is under statistical control however X-bar chart and R-bar chart cannot be drawn due to less number of observational data.

Inner-cluster division It is obvious that the number of reporters increased with the increase of the number of regions, so we adopt as few regions as possible.
Upon receiving such messages, the cluster head can learn the number of nodes located in each region and which type these sensors are.
Where the count means the number of certain type of nodes located in the region, for example, CA,S1 means the number of nodes of type A located in S1.
We denote Nt-CTDA(sim) as the number of transmissions in simulation and Nt-CTDA(tho) as theore- tical value.
Srivastava, “Dynamic fine-grained localization in ad-hoc networks of sensors”, in Proc.

Let D be a dataset partitioned into a training set S and a test set T, let M be the number of test instances.
We restrict our attention to the 10 categories (acq, corn, crude, earn, grain, interest, money-fx, ship, trade, wheat).
We can observe that, as the number of test instance varies, ML-FKNN method achieves substantially better performance than other methods.
Fig.2 shows the performance results with the number of test instance added.
We can find that as the number of test instance increases, the improvement becomes more and more significant.

China has a large area and a huge number of villages and towns, where rural roads are desired to connect villages and towns with rural areas.
For example, if the number of small-sized vehicle is large and travelling speed is slow, the width of intermediate zone and strip should be the smaller value.
In order to ensure the concrete strip roads having long-term moisture stability and freezing stability, the grain material which has a good water stability should be the first choice for base course.
According to the changes of slab thickness which are influenced by the cumulative number of standard axle loads (graded by producing 0.02m slab thickness change), strip roads traffic is divided in these levels: light traffic-1 (the cumulative number of standard axle load is between 30000 and 10000), light-grade 2 (the cumulative number of standard axle load is between 10000 and 3000).
If the cumulative number of standard axle load is less than 3000, it can use the curve of 3000 to design structure.

Feasibility of Railway Washing Wastewater Disposal by Improved CRI Shichao Cao 1,a,Yuanfu Li 1,Zhaoyu Li 1,Xiaoyi Qian 1,Lie Xia 2,Wenlai Xu 3,b＊ 1College of Civil Engineering, Southwest Jiaotong University, Chengdu, China 2China Railway ErYuan Engineering Group Co.Ltd, Chengdu, China 3College of Environment and Civil Engineering, Chengdu University of Technology, Chengdu, China aE-mail: dancen3168@sina.com, bE-mail: 296794884@qq.com Keywords: railway washing wastewater; sponge iron; CRIs; TP; removal efficiency Abstract: Railway washing wastewater, containing a large number of carbohydrates, fat, protein, cellulose, and other organic pollutants, will cause serious pollution, result in production of foul-smelling matter, impact on the survival of aquatic life and hinder the sustainable development of railway transport, if the wastewater is directly discharged into the nature water body.
Introduction The need for treatment of railway washing wastewater which contains a large number of carbohydrates, fat, protein, cellulose, and other organic pollutants is now imperative, because the water can cause serious pollution and impact on the survival of aquatic life and hinder the sustainable development of railway transport, if the wastewater is directly discharged into the nature water body(Wang, 2003).
CRIs is a new type of ecological wastewater disposal technology with high efficiency, low consumption, convenient operation and management, low investment and bright application perspectives, which has been applied to a number of engineering practices successfully in several areas and achieved good social, economic and ecological benefits.
In order to ensure higher the hydraulic loading, infiltration mediums in traditional CRIs are often coarse sand which has little sticky grain and iron, aluminum and calcium and cation exchange capacity of medium is less, therefore, adsorption effect of phosphorus is poor [7].

The number of diamond particles that involved in cutting workpiece gradually increase, so the force between wire saw and the workpiece is increasing.
That is because after the first phase, the effective area of cutting is stable and the number of involved diamond particles is stable.
That is because comparing the first stage, the coolant cannot reach the machined region in time and machined chip cannot be ruled out in time, so there is a mount of chip particles that involved cutting process, which lead to the total number of cutting particles increased and the force between wire saw and workpiece increased compared with the first stage.
Second, such as the above analysis, in the cutting process, the number of abrasive particles per unit area , the proportion of abrasive particles Participated cutting ,Wire radius ,etc ,have some changes in their own.
Furthermore, the silicon atoms of polysilicon arranged into many crystal nucleus in the diamond lattice, and these nucleus grow into grains of different crystal orientation.

Thirdly the calculation time increases sharply by the particle numbers.
According to literature [5], the sample dimension should be greater than 10 times the biggest grain diameter.
The node number of input layer is determined by number of observed deformation data.
The node number of hidden layer is determined by test, which approaches to double of node number of input layer.
The node number of output layer agrees with number of identified parameters.