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Due to reduction in apical dominance and increase in lateral branch development, some plants change their crown shape with age[16].
Mean of Monthly Temperature and Relative Humidity of Greenhouse Months Temperature(ºC) Mean Relative Humidity (%) Mean minimum Mean maximum Mean Jan. 13 33 19 60 Feb. 11 34 18 60 Mar. 12 37 20 58 Apr. 13 35 21 44 May. 16 37 22 38 Jun. 17.6 38.9 26.6 51 Jul. 20.7 37.7 27.8 65 Aug. 17.0 36.9 26.5 71 Sep. 12.8 31.2 22.0 67 Oct. 4.2 27.6 16.5 50 Nov. 8 32 15.6 70 Dec. 13 33 18.2 70 Experimental Teatments and Data Analysis.
Significant effects of treatments were identified by analyzing data using Microsoft Excel.
The data ( Figure 2) showed that the percent of flowering and fruit set of feijoa plants varied from 0 to 55 in different age tree.

Low cycle fatigue test data of 316L at room temperature and heat to 250℃.
Result and Discussion The data obtained from the fatigue test performed by the MTS testing machine, the fatigue parameters such as the elastic and plastic strain values are brought into the Coffin-Manson relationship, and the elasticity and plasticity are carried out by the life cycle number obtained from the strain value of 0.8% -0.4%.
Low cycle fatigue test data of 316L at room temperature and heat to 250℃.
Fatigue parameters b c 316L (Room temperature) (R.T.) 755.134 -0.06 0.145 -0.455 755.134 316L (250℃) 734.874 -0.04 0.074 -0.390 734.874 According to the fatigue test results in this paper, it is found that after the thermal load effect, we use numerical simulation methods to verify that we have also obtained fatigue life reductions of 35% to 12%, as shown in Table 4.

Data analysis issues The "needle in a haystack" problem.
Next we turn to the question of data analysis.
Each raw data spectrum presents data from a single sample, but of course involves all the wavenumber variables.
Our preference is use PCA for preliminary data reduction and to process the output using discriminant analysis (LDA), which is a "supervised" method in that a priori classes derived from PCA or from prior knowledge are pre-allocated.
After validation of the model, it is used to test the remaining data.

The input data consists of site specific wind data, hub height, rotor diameter and turbine power rating.
Wind site analyses The study of WES is preceded by analysis of the site’s wind data.
From this data the following parameters are calculated [1 - 6].
Using the foregoing equations, a data of different hub heights each with its k, c, Vrate can be established.
The analyses of the 30m hub height data give the following data in table 3.

The proposed criterion and method were validated using data obtained during high speed robotic machining of 7075-T6 blocks.
Introduction The robotic machining is one of the emerging technologies for cost reduction in manufacturing processes.

The derived data were compared to the experimental investigation of cracks and craters as provided by cycling with a correlation with the surface finishing level of samples.
Lubricants contribute to the reduction of damages due to chemical affinity between the aluminum alloy and the steel constituting the die.

The study has been carried out using Computational Fluid Dynamics to obtain aerodynamics data with respect to various sideslip angles (b).
The absence of vertical tail in a BWB aircraft contributes to the reduction of its total drag.

The changes in the physical data were interpreted via microstructural changes observed by scanning- and transmission-electron-microscopy as well as by x-ray investigations.
Chemical composition in weight-% C Cr Ni Nb Ti Mn Mo N Cu Si P S V 0.04 17.60 10.64 0.62 0.02 1.83 0.29 0.007 0.06 0.41 0.02 0.007 0.07 As expected the tensile strength, ultimate strain and reduction in area decrease with increasing temperature (see Tab. 2).

Process data was obtained through electrochemical simulations and subsequently applied to a specially designed micro-galvanic setup.
As the distance increased, the expected homogenization of field lines and reduction of gradual coating effects occurred.

The zero shear viscosity(η0) was obtained by using the Cross model to fit experimental data.
The wide range of CMC applications involves flocculation, detergents, textiles drag reduction, foods, drugs, paper and oil well drilling operation [1].