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The requirement regarding the number of irrigation and their timing vary widely for different crops.
(umber of Leaves: The number of leaves produced in each of the treatments were counted.
Plant Measurement: Plant height, numbers of leaves, leaf area index, depth root zone and biomass yield were determined.
Plant Height and number of leaves The result of plant growth is presented in table.
Grain Div.

There is a clear indication of grain growth at 450°C [23].
Sinclair at University of British Columbia are studying grain refinement in AZ80 by cold rolling and annealing, with multiple deformation/recrystallization treatments and cross-rolling.
The objective is to reduce the grain size of as-cast material to less than five microns to improve ductility.
Jonas and colleagues at the University of McGill are studying twinning behavior, the temperature dependence of slip systems, fracture, textures and the mechanical behavior of fine-grained extrusions [24].
However, use of wrought Mg is limited by cost due in part to the large number of processing steps required to produce magnesium sheet.

In contrast to experiments with Nuclear Magnetic Resonance Spectroscopy and Impedance Spectroscopy, only a limited number of tracer based experiments can be found in the literature.
In contrast, with macroscopic tracer methods it is possible to detect long range diffusion processes with a large number of jumps.
This technique is especially sensitive to light elements with atom numbers below 20, like Li.
There, the reflectivity R (the number of incoming neutrons divided by the number of reflected neutron) is plotted as a function of the scattering vector qz = 4 p/l sin(q).
Using SIMS on these samples, the lithium chemical diffusion coefficients in bulk and in grain boundaries were determined at room temperature as D = 1.23 × 10−15 m2/s and Dd = 6.55 × 10−20 m3/ s, respectively.

Their chemical compositions and grain-size distributions were listed in table 1, table 2 and figure 1, respectively.
Pellets naturally fell on the 10mm thick sheet steel from 1m height repeatedly until the pellet cracks down, the number of N times was recorded, and the N-1 fall times was named as pellets drop number.
Finally, after removing the maximum and minimum values, the average of 10 pellets falling number was viewed as drop strength.
Table 3 IR bands of phenolic resin and its functional groups Wave number (cm-1) Assignment Notes 3500 Phenolic —OH stretch a differs with b, c here 3025 Aromatic C—H stretch a differ with b, c here 2925 C—H stretch a different b, c here 1608, 1595, 1505 Aromatic C=C stretch a differs with b here 1104, 1150 Aromatic C—H in-plane deformation a differs with c here 887, 827, 756 Aromatic C—H Out-of-plane deformation a differs with b, c here 3025 Aromatic C—H stretch a differs with b, c here 1650 Aromatic C=O stretch 1451, 1364 CH2 deformation 1238 C—O stretch, O—H deformation IR bands of phenolic resin are shown in Table 3.

A counter recorded the number of revolutions.
This indicates the existence of refined grain[6], arising from rapid solidification of the melt due to high cooling rates experienced in laser process, which can increase the hardness of the glazed regions.
Wear test result Wear rate (weight loss per meter) of wheel disc is plotted in Fig. 5 against number of rolling cycles.
It can be seen that wear rate of treated wheel disc reaches the maximum at around 10 000 cycles, thereafter decreasing with number of rolling cycles until around 20 000 cycles, where the stable wear rate is reached for the remainder of the test.
Fig. 5 Relationship between the wear rate of wheel discs and number of rolling cycles.

Therefore, despite the large number of continuous steel casting studies [2-25], the study of casting technology and bloom quality in specific production conditions allows us to obtain new patterns and improve the production technology.
Quality of the macrostructure of a round bloom Macrostructure defects Range of variation, score Average value, score Allowable value of defect development, score % of templates with exceeding the allowed score* Central porosity 0.5-4.0 1.19 2.0 15.5 Axial liquation 1.0-2.5 0.99 2.0 0.5 Liquation strips and cross-sectional cracks 0.5-2.0 0.34 1.0 6.9 Liquation strips and axial cracks 0.5-3.0 0.42 1.0 18.5 * of the total number of templates studied - 11968 pcs.
Crack spreading within the round bloom occurs along the boundaries of the former austenitic grains.
Analysis of Casting Conditions Bloom quality is influenced by a large number of factors.
Casting parameters Parameter Range of variation Average value Number of melts, [pcs.] 2849 Chemical composition, [%] carbon 0.6-0.62 0.61 manganese 0.64-0.72 0.67 silicon 0.25-0.33 0.28 sulphur 0.002-0.010 0.003 phosphorus 0.005-0.020 0.010 nitrogen 0.004-0.008 0.072 hydrogen, ppm 0.7-2.0 1.4 Withdrawal speed, [m/min] 0.26-0.44 0.36 Spraying plan, [l/kg] 0.30 Steel temperature in the tundish, [ºC] 1490-1520 1500.5 Steel overheat in the tundish, [ºC] 15.4-45.6 26.7 The chemical composition of the steel meets the modern requirements of continuous casting, so the main problem in producing a quality billet is to maintain rational casting parameters.

The interactions of C with lattice imperfections (dislocations, grain boundaries, stacking faults, surfaces and micro-cracks etc.) dominate the influence on the mechanical properties of iron.
It can be defined as [4] �EE E clean b dop b seg − = (1) where N is the total number of impurity atoms in the impurity-doped system and dopbE and cleanbE are the binding energy of the impurity-doped kink system and the clean kink system, respectively.
It is derived as lmmnlnn n lm Haa� E αββα αβ * ∑∑= (3) where �n is the occupation number for molecular orbital nψ , ( ) ( )>=< rra nlln ψφαα | , and lmH αβ is the Hamiltonian matrix element connecting the atomic orbital β of atom m and the atomic orbital α of atom l.
The numbers marked on the plots correspond to the atoms in Fig.2 From Fig.4, we can also see that the C 2s band occurs as a single, which is a well-defined peak at about 12.5eV below EF.
Here, ∆Q = �-Zval, where Zval is the standard number of valence electrons per atom Atom Valence orbital �clean �impC 2s 1.471 C 2p 3.157 ΔQ 0.628 3d 6.352 6.356 Fe2 4s 0.693 0.641 4p 0.950 0.773 ΔQ -0.005 -0.230 3d 6.333 6.338 Fe4 4s 0.716 0.658 4p 0.982 0.860 ΔQ 0.031 -0.144 3d 6.336 6.340 Fe5 4s 0.709 0.595 4p 0.948 0.835 ΔQ -0.007 -0.230 3d 6.338 6.351 Fe7 4s 0.757 0.598 4p 0.942 0.816 ΔQ 0.037 -0.235 In summary, through calculations of the electronic structure of a kink with C we see that the C 2p state strongly hybridizes with its neighboring Fe-atom 3d4s4p states.

It was reported [5] that the degree of reversibility of the anode materials is closely linked with the particle or grain size of the starting materials.
Instead, it is thought that they can be very effectively used as an additive to carbon anode materials to enhance both electrical conductivity and lithium storage capacity. 0 5 10 15 20 25 0 50 100 150 200 250 300 350 400 450 500 550 600 Cycle number Discharge capacity (mAh/g) Fig. 4: The discharge versus cycle number capacity for a Ag nanopowder electrode, produced by reverse micelle method. 20 nm For tin oxide electrode, Sn(OH)4 powders were first prepared by the same method of reverse micelle, followed by thermal decomposition to obtain SnO2 nanopowders.
Scanning rate: 0.1 mV/s. 0 5 10 15 20 25 0 100 200 300 400 500 600 700 800 900 1,000 Cycle number Discharge capacity (mAh/g) Fig. 8: Discharge capacity of a SnO2 nanopowder electrode versus cycle number.

Ding Wuxiu and Feng Xiating [6] tested the rocks corrosive in different chemical solutions, and the results showed that the tie between mineral grains was disturbed and granules were corroded under the effect of chemical solutions, so that the strength of rock was significantly reduced and the structure of rock was damaged.
There were 36 samples in total, which were numbered with C1~C9, D1~D9, E1~E9, and F1~F9 (i.e. acid, alkali, salt, and water environment groups), as shown in table 1.
In figure 4, the contrastive photos of sample D7 before and after freezing and thawing were shown, finding the sample’s surface corrosion degree was serious after freezing and thawing and a large number of particles fall off.
Fig.3.The quality variation of sandstone after freezing-thawing cycle in 4 chemical solutions Fig.4.The photos of sample D7 before and after freezing-thawing cycle After freezing and thawing cycles, the quality variation and the appearance of the samples in the testing process were observed, and then the freezing-thawing damage degradation modes of red sandstone in NaOH, NaCl, and H2SO4: particles-pore damage mode and its damage degradation process was the existence of rock’s micro-pore →the emergence and desquamation of the free particles on the surface →the softening of the surface →the emergence of new micro-pore →the constant expansion of micro-pore →further softening: one-step softening and loosing →a large number of particles fall off →moisture migrated to the inside →pore extension and connection →constantly deepened freezing-thawing damage.
Table 2 Average NMR porosity of sandstone after freezing-thawing cycle Solution Porosity/% Increase percentage/% 0 times of freezing- thawing 10 times of freezing- thawing cycle 20 times of freezing- thawing cycle 30 times of freezing- thawing cycle 10 times of freezing- thawing cycle 20 times of freezing- thawing cycle 30 times of freezing- thawing cycle H2SO4 3.815 4.322 4.851 5.318 13.298 27.147 39.388 NaOH 3.492 4.834 6.472 6.476 38.440 85.328 85.443 NaCl 3.304 4.535 6.362 8.294 37.282 92.584 151.054 Water 3.343 3.786 3.836 3.944 13.250 14.736 17.976 Along with the progress of freezing-thawing cycle, rock porosity increased, suggesting the porosity change of rock was greatly affected by the freezing-thawing cycles; along with the increasing number of freezing-thawing cycles, the production and expansion of new pores in rock were accelerated, and also the porosity change was obvious.

PSE = FSE + KP (1) Here FSE is the average square error within the network that serves to fitting the training data, and KP is the complex penalty within the network, as defined by the following equation: KP = CPM (2) where CPM denotes the complex penalty multiplier, K the number of coefficients within the network, N the number of training data, and a pre-estimated error variance for the model.
The best network structure, number of layers, and functional node types can be determined using the ASPN criterion (Eq. 1, Eq. 2).
Acknowledgements Partially financial support from the National Science Council of Taiwan under grant number NSC96-2221-E-150-015 is acknowledged with gratitude.
Butler: Simulation of precision grinding process, part 1: interaction of the abrasive grain with the workpiece, Int.