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Introduction In comparison to other grains, rice is one of the most significant food crops in Malaysian culture.
Germination [%]=Number of seed germinatedNumber of total seeds tested×100% (1) Vigor index=Germination ×Seedling length (shoot length+root length) (2) Fourier Transform Infrared (FTIR) Spectroscopy.
The starch shape changes after neutron irradiation with 14 Gy in PTS and 35Gy in BP, becoming more spherical, shrinking and showing partial aggregation. [19] discovered that the number of small-size granules increased in both inner and outer layer endosperm of rice grain treated with 2 KGy of gamma irradiation.
Padilla-Zakour, Changes in the Glutinous Rice Grain and Physicochemical Properties of Its Starch upon Moderate Treatment with Pulsed Electric Field, Foods 10(2021) 395
Chen, Electron beam irradiation as a tool for rice grain storage and its effects on the physicochemical properties of rice starch, Int.

The higher cooling rates during the quenching process will form smaller grain sizes, improving the hardness and strength of steel, but toughness and ductility are decreased [1].
At a high-temperature tempering of 600oC, the microstructure changes from martensitic to tempered martensitic and ferrite grain.
The greater the number of dissolved carbides in the oxide dispersion strengthened steel matrix as a solid solution will generate higher hardness [9].
This research suggests that the more brittle surface will decrease the elasticity – a reduced number of cycles characterizes this phenomenon.
Wolowiec, Effect of the content of retained austenite and grain size on the fatigue bending strength of steels carburized in a low-pressure atmosphere, Met.

The fracture surfaces of specimens that failed after fewer cycles showed mainly precipitates and micro voids, whereas specimens that fractured after a higher number of cycles indicated that cracks initiated at the surface.
An important feature of this micrograph is elongated grains as a result of rolling.
[MPa] A [%] HB SST at 495°C/1h 306.7 468.3 20.8 129.5 SST at 505°C/1h 310 478 21.3 125 SST at 515°C/1h 306.7 475 21.7 116.8 SST at 495°C/1h and A at 190°C/6h 390 485 13.3 142 SST at 495°C/1h and A at 208°C/4h 373.3 448.3 8.4 134.8 SST at 505°C/1h and A at 190°C/6h 361.7 483.4 18.1 138.3 SST at 505°C/1h and A at 208°C/2h 400 489 13.3 147.7 SST at 515°C/1h and A at 190°C/6h 361.7 478.7 17.6 136.7 SST at 515°C/1h and A at 208°C/2h 386.7 490.3 12.9 137.7 Figure 3 shows the stress versus number of cycles to failure curve (S-N) obtained from fatigue tests on Aluminum Alloy 2024 samples solid solution treated at 505ºC/1h and aged at 208°C/2h.
Conclusions Optical micrographs revealed elongated grains, caused by rolling.

The amount and number density of ε martensite are increased with an increase in Co content, resulting in the improvement of damping capacity.
The grain size of γ austenite in each alloy was adjusted to 120µm by changing the solution treatment time, to exclude its effects on martensitic transformation behavior and resulting microstructure.
Fig. 4 shows the variation in relative numbers of ε martensite plates in unit area with ε martensite content, where the number of ε martensite plates for the Fe-23%Mn-2%Si alloy air-cooled at 298K, was regarded as a standard value.
Change in relative number of ε martensite plates with ε martensite content for experimental alloys.
The addition of Si is detrimental to the amount and number density of ε martensite, resulting in the decay of damping capacity.

If it is present near the interphase, long grains are found to cover almost the whole phase layer.
This is found in most of the cases since the nucleation barrier for new grains to form must be high.
A focused area around this marker plane is shown in which different grain morphologies around K2 is clear.
The number of interdiffusion coefficients to be estimated depends on the number of components in a system.
Moreover, the number of precipitates of bigger sizes decreases.

OPMFS pulp recorded value of 6.42 s, 353 ml, 76.5 %, 36.7 % and 9.35 for drainage time, freeness, moisture content, screened yield percentage and Kappa number respectively.
Then, the fibres were sieved by using chip classifier machine to remove the smallest pollen grain of the spikes.
KAPPA number of the pulp was determined according to TAPPI T236 “Kappa Number of Pulp.
Characteristics of OPMFS fibre pulp Characteristics OPMFS Soda-AQ pulp Drainage time(s) 6.42 Freeness(ml) 353.00 Moisture content (%) 76.5 Screen yield (%) 36.7 Kappa number 9.35 Physical characteristics.
Tensile index, tearing index, bursting index and folding number shows value of 39.10 N.m/g, 8.32 mN.m2/g, 3.15 kPa.m2/g and 38.50 respectively.

The execution efficiency got improved much more based on increasing the number of processor core and thread number executing parallel via using multithread parallel execution technology.
Their work made the sort performance improved to a greater extent via breaking down the sort task into independent computing tasks and digging the fine-grained parallelism of sort algorithms.
Suppose the length of the unsorted sequence is n and the number of parallel thread that will be executed is TNUM it can be deduced that the number of trip needed for two-way merge sort is .
For the ith trip the number of merge should be /2 as the length of the merge segment is 2i and the number of comparison and movement should be 2i removing the tail part merge.
We also can see it is that the efficiency will be improved the best when the number of parallel thread is 4 as it is just equal to the number of core.

Number of neurons is the dimension of study sample vector, namely 6
Number of neurons equals the number of input layer learning samples.
Number of neurons is the dimension of output vector in the learning sample.
According to the fitness function to calculate the fitness value, the speed of the grain is, individual extremum is, population global extremum.
Fig.2 Predicted results compared with the measured results Acknowledgement The research was supported by the Key Subject of the Twelfth-five Scientific Research in the Education Department of Jilin Province with the project number 201356 and the project name Research on Data Management Technology of Large-scale Sensor Network in Agriculture.The work was also Supported by the Opening Project of Key Laboratory of Mine Informatization, Henan Polytechnic University.The project name is Research on Real-time Monitoring Displacement System of Mine Dangerous Area Based on LabVIEW with the project number KY2012-01.

The paper adopted Z-axis as rotation axis of the pump, utilized Pro/E software to process three dimensional model, adopting unstructured grid numbered 310868，as figure 1 shows that.
The grain density is 2600 kG/m3.
TO cut down on the length of paper, the follow only provided the simulate result for particle size is 1mm and 12mm under volume fraction numbered 0.2.
As the four figure shows from figure 2 to figure 5, the paper draws some conclusions as follow: 1）For large particles, they mainly distribute in the field near impeller hub and middle impeller passage, their number are more than that near the cover field.
For large particles，they mainly distribute in the field near impeller hub and middle impeller passage，their number are more than that near the cover field.

Later the Apache foundation and the parallel programming framework Hadoop developed open source distributed.It will be a task is decomposed into many a fine-grained sub task, these sub tasks are assigned to the processing node idle, eventually merged to generate the final results of the method and the specific rule.The MapReduce model will be distributed into two main steps of Map and Reduce, the data through the efficient processing of these two steps, we can get the final result.
The Map process: Data input: centralized document D Output: , where key denotes the < document number, attribute > on the words of a document, value (1) the D analysis for the document number N, the contents of the document, the document properties of content Attri three (2) the content segmentation and to stop words, form words set T (3) for term in T： (4) key= (5) Value=term (6) output The Reduce process: Input: Map output the same key combined with results of Output: , where key is the < document number, property > on, values document "word, word frequency > on the set (1) the content segmentation and to stop words, form words set T (2) for each of the different term are statistical number (3) save results to set () // to save pair set (4) values=set () (5) the output The Map node of the Combine results are merged together.
The Map process: Input: the processing of the text to be classified D Output: key to label the classification, value is the number of documents (1) read from D, the feature vector wD = (w1,w2, … ,wm), Document No. is N，value=N (2) similarity for D and cT , simDT=sim(wD , cT) (3) similarity for D and cF, simDF=sim(wD , cF) (4) If simDT>simDF : (5) key=True (6) cT = a*cT+b*wD (7) else： (8) key=False (9) cF = a*cF+b*wD (10) output The algorithm divides the original single feedback variable feedback for distributed multi.
The Reduce process: Input: the same key combined with results of Output: , key as the class label, values belongs to the key class represents the document number set (1) for DOC in list (value) (2) added doc set (DOC) //set (DOC) set is the collection of Document No