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The experimental data obtained from these measurements were used in the inelastic bending and FEA simulations.
The wires connect the strain gauges mounted at the top sample surfaces to data collection PC.
Slow cutting advance rate (feed) of 20µm min -1 minimised the damage and avoided wire breaking, and ensured the collection of sufficient number of data points.
Each experiment lasted 8 hours; giving about 300 data points for each strain gauge mounted on front (entry) and back faces of the sample.
Eigenstrain analysis The experimental data obtained above were analysed with the help of the eigenstrain finite element methods.

In the fuzzy clustering, the fuzzy c-means (FCM) proposed by Dunn [2] and Bezdek [3] plays an important role in unsupervised data analysis.
Let be a finite unlabeled data set composed by patterns for which every .
The objective function of FCM is given by (1) where () denote the cluster prototypes of the data set .
Feil, Fuzzy clustering and data analysis toolbox, Department of Process Engineering, University of Veszprem, Hungary, 2005
Chen, Clustering incomplete data using kernel-based fuzzy c-means algorithm, Neural Process.

The data was spectrally and statistically analysed.
The sounds resulting from the equal impacts to the plates were transmitted to the data acquisition system through a microphone, and next transferred to computer in order to begin the data processing phase.
The output audio data of the amplifier is transmitted to the computer at a sampling rate of 0.00001 seconds via Advantech 1716L Multifunction PCI card and data analysis is performed using Matlab (Fig. 2).
For a block of data of length N samples the transform at frequency mDf is given by . (6) Where Df is the frequency resolution and Dt is the data-sampling interval.
Taylor, Statistical Techniques for Data Analysis, Lewis Publishers, 1990.

Method and Data Definition of Product Category.
This paper use CML2002 as LCIA methodology Table 1 Data inventory of ready-mixed concrete production Item Substance Unit Quantity Consumption of raw materials Cement t 0.252 Coal ash t 0.045 Mineral powder t 0.054 Sand t 0.841 Gravel t 1.065 admixture t 0.004 Energy consumption Power kWh 7.885 Transportation Road transport t*km 2.261 (3) Collection of inventory data and analysis on results This study collects data about on-site production of several ready-mixed concrete enterprises in China, and obtains the inventory as shown in Table 1.
The background data for the purpose of this study is mainly from the public database at home and abroad, the background process data about raw and auxiliary materials, power and road transportation is mainly from the database of Beijing University of Technology [8] and CLCD database of Sichuan University [9], the background process data about the admixture is mainly from Eco-invent database [10].
Process excluded 1 Cement product production 1 Coal ash production 2 Admixture production 2 Mineral powder production 3 Sand production 3 / 4 Gravel production 4 / 5 Power 5 / 6 Road transport 6 / (2) Determining enterprise’s on-site process data Table 4 Collection of enterprise’s on-site process data Item Substance Unit Remarks Consumption of raw and auxiliary materials Cement t/m3 Mode and distance of transportation Admixture t/m3 Mode and distance of transportation Sand t/m3 Mode and distance of transportation Gravel t/m3 Mode and distance of transportation Energy consumption Power kW·h/ m3 / Environmental emission Particulate matter t/m3 / Based on the above background process included, the enterprise shall collect corresponding on-site process data and raw material consumption data.
Collection range of the enterprise’s on-site process data as finalized is shown in Table 4

PC-1 and PC-2 is the orthogonal vectors, so a new space is they propped up by them, and the data can be expressed using the mapping of original data set in this space.
This space is actually a feature space of the original data set.
In Fig.2, the ellipse represents the data distribution; PC-1 and PC-2 were the two principal components and corresponding to the two feature vectors of data covariance matrix.
So the whole chart formed a data matrix.
The new data matrix is.

Table 3 shows the Potentiodynamic polarization data.
This shows APN majorly influenced the O2 reduction and H2 evolution reactions.
The data depict the resilience of APN adsorption interaction mechanism on Al70 in HCl media.
The ∆G data shows there were no adsorption reaction mechanism between APN inhibitor and Al70 exterior as the ∆G data determined is significantly below the value range for physisorption, physiochemical and chemisorption reaction mechanisms hence lateral interaction effect is negligible [23, 24].
Corrosion thermodynamics data for APN adsorption on AL70 Al70 Samples APN Conc.

Based on test data, Dongiovanni and others use refined second-order accuracy implicit algorithm to obtain a function of density, velocity of pressure wave propagation, and other parameters of ISO 4113 oil (calibration oil) changing with pressure and temperature (according to reference 4).
Compressibility is a volume reduction property of fluids as a response to pressure.
The Barus formula derived based on test data has a certain level of accuracy (according to reference 9) and is listed as follows: (1) In the preceding formula, indicates the dynamic viscosity when pressure is P, indicates the dynamic viscosity at atmospheric pressure, and indicates the Barus pressure-viscosity coefficient.
The specific coefficients can be determined based on test data.
The following formula is obtained combining formulas 16 and 17: (18) The Taylor’s formula is as follows: (19) Accordingly, formula 18 is changed as follows: (20) Specifically, (21) (22) Similarly, the specific coefficients are determined based on test data.

The numerical results are compared with data from the classic Prairie Grass experiment, showing excellent agreement.
Hanna, Hansen and Dharmavaram [1,2], evaluated the performance of the FLACS CFD model in the study of dispersion of pollutants in the atmosphere, in situations with and without obstacles, comparing their data with observations from three field experiments (Kit Fox, MUST and Prairie Grass) and a wind tunnel (EMU).
Their results were compared with experimental data.
The influence of topography, wind speed and source intensity on dispersion of contaminants were evaluated in the work and the results were compared with experimental data.
For the validation of the results we use data from the classic Prairie Grass experiment, originally described by Barad [9, 10], Record and Cramer [11] and Haugen [12].

In order to initialize the collapse in a stable way, the design of the impact attenuator was completed with a trigger which consisted of a very simple smoothing (progressive reduction) of the wall thickness.
The design of sacrificial structure has been completed with a trigger which consists in a smoothing (progressive reduction) of the wall thickness in order to reduce the resisting section locally.
In the current research, is chosen to be 0 due to lack of experimental data.

The experimental thermal data collected by thermocouples positioned along the casting length were used as input information into an Inverse Heat Transfer Code implemented in this work in order to determine the hi variation in time.
The transient hi profile has a typical drastic reduction from a high initial value due to the development of an air gap, followed by a recovery to an essentially constant value.
The success of the applied simulation technique depends on accurate data of heat transfer coefficient at the solder/substrate interface and on the thermophysical properties of the solder alloy and substrate.
The other method is to conduct temperature measurements in the casting and in the chill at several designated locations and use this information as input data in an inverse method to derive the heat transfer coefficient [8-12].