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(11) We plot h/L as a function of θ in Fig. 3.We find that the equation (11) well fits this data for all values of θ with a single fit parameter.
The fact that our data are well fit by a single parameter K, which falls within this range suggests that our approximation is valid.
Fitted values of the characteristic filling height, h per width, L is plotted against various angles of inclination Summary In this research, we have shown that the data are fitted to our model of h/L as a function of the angle of inclination θ, as described in Eq. (11).
Sloan, Slope stability analysis by means of finite element limit analysis and finite element strength reduction techniques.

- Reduction of vegetative cycles
The display is an output data device built with a two rows screen of 16 columns provided with an ASCII code.
In optimal situation all actuators work, in media heater is deactivated because it consumes many energy and in low charge no body works and wait to charge the battery. 2.- It measures the humidity and fertilizer level and according to data obtained activate the actuators to provide water and fertilizer. 3.- It measures the environment temperature, if it is low and battery is charged, it activates the heater.
If it is high it activates the fans. 4.- It measures the relative humidity and it actives the fans if it is the case. 5.- It storages the data obtained to be used or statics manipulated. 6.- A delay takes place to continue to the next survey.

The Theory of Wavelet Transform Wavelet transform is an emerging discipline of the last decade, which has been widely used in the signal processing, signal noise reduction, signal recognition, and many other areas.
Wavelet neural network output dataare available fitting in wavelet (6) Here, is the fitting data, is the weights, is the expansion factor of wavelet, is the translation factor of wavelet；is the number of wavelet.
The established prediction model based on this method is dynamic, only with the accumulation of the measured data can constantly perfect this evaluation system, so that the prediction results will be more close to reality.
Estimation and Forecasting of Daily Suspended Sediment Data Using Wavelet Neural Networks.

The validity of this method is confirmed by experimental data of other researchers and authors.
The yield stress is 337 MPa, the tensile strength 580 MPa, and the reduction ratio of area 51.5%.
Figs. 2 shown a set typical fatigue data by expressed different stress amplitude.
The predication results agree good with the experimental data.

Gabbay and Mehner et al. used polynomial functions whose coefficients were identified using strain energy data to model a microelectromechanical system[3].
The form of this polynomial can be written as: (4) Where U is the strain energy functional for the system, the coefficientsare found using a least squares fit of strain energy data generated using a finite element package and the are the Lagrange basis functions.
Acknowledgments The research described in this paper was financially supported by National Natural Science Foundation of China (Contract No. 51108345) , opening foundation of State Key Laboratory for Disaster Reduction in Civil Engineering of China (Contract No.
Computers & Fluids, Vol46, (2011), p. 161 [2] Menicovich, D., Gallardo, D., Bevilacqua, R., Vollen, J.O., Generation and integration of an aerodynamic performance data-base within the concept design phase of tall buildings.

Introduction Squeeze casting has many advantages such as porosity reduction, fine grain size microstructure, improvement of mechanical properties and near net shape castings.
The thermo-physical data of the liquid alloy, die and the disposable cores[3] are listed in Table 1.
Table 1 Thermo-physical data of liquid alloy, die, and cores[3] Thermo-Physical Data Materials Liquidus Temperature [°C] Density [kg/m 3] Specific Heat [J/kg/K] Thermal Conductivity [W/m/K] Latent Heat of Solidification [kJ/kg] Al-4.5%Cu 645 2810 960 138 38.9 Die(H13) 1471.2 7600 600 25 209.4 Cores 3750 1200 1.7 Simulation of Mold Filling and Solidification.

The metal temperatures were transmitted, recorded and displayed by a calibrated data logger.
This dramatic reduction in hardness was consistent with the microstructure changes to α-ferritic matrix with coarsening precipitates.
The creep rupture testing is still in progress and the comparison of creep rupture strength of overheated specimens with published creep data will be analyzed after the completion of this experimental research.
[3] Nippon Steel Corporation, Data Package for NF616 Ferritic Steel, January 1993, second edition, March (1994)

The used data is available from different sources and publications, as shown in Table 1.
Time: 0.1 sec Time: 0.2 sec Time: 0.3 sec Time: 0.4 sec Time: 0.5 sec Fig. 2 Finite element simulation of the clinching process (steel-steel thickness 1.2 mm) The reduction in thickness of the flat bottom mentioned in previous studies was evaluated by measuring the clinched resistance (strength) experimentally, where the data was compared with the numerical one [5,6].
The clinching process parameters need to be optimized to get more thickness in the bottom of the joints without affecting the other parameters, and this is achieved not only by optimization but also by obtaining an accurate experimental data for the material behavior and properties in order to reach the maximum strength and the highest quality of the joint.

The design of the turnout offers a smoother run of railway carriages and a reduction of dynamic effects of a carriage on the rails and other structural layers of the railway bed. [5,6] Measurement Methodology The subject of the measurement was the stress measurement in the construction of the railway substructure in the turnout with movable frog No. 5 in the railway station of Vranovce (section of the Czech railway corridor linking Vienna, Prague and Berlin), type J60-1:12-500, provided with a movable frog.
The decomposition was carried out by a special filter working on the principle of the discrete Wavelet Transformation. [9,10] The Discrete Wavelet Transform is an orthogonal function which can be applied to a finite group of data.
The algorithm operates on a finite set of N input data, where N is a power of two; this value will be referred to as the input block size.
These data are passed through two convolution functions, each of which creates an output stream that is half the length of the original input. [12] and for (1) where N is the input block size, c are the coefficients, s is the input signal, A and D are the output functions and c are wavelet coefficients.

Required data for these coefficients are results of mechanical and technological tests [5], which are conducted at uniaxial or biaxial strain.
The received data are in good accordance with one represented at [8], where coefficients r are determined for several steel grades and aluminum alloy AA5005, made at angle 0, 45 and 90° to the rolling direction.
Comparison of μkl values, determined by this way, to data considered above (Fig.2, 3), shows its difference does not exceed 5%.
[7] Masaaki Nakaia, Mitsuo Niinomia, Junko Hiedaa, Ken Choa, Yuya Nagasawab, Takashi Konnoc, Yoshinori Itod, Yoshio Itsumic, Hideto Oyamac, Reduction in anisotropy of mechanical properties of coilable (α+β)-type titanium alloy thin sheet through simple heat treatment for use in next-generation aircraft applications, Materials Science and Engineering: A.