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The experiment was repeated for five times for data accuracy and reliability.
The cost of biomass and addictive (in Thailand) Material Cost Dolomite 0.1 USD / kg Cassava rhizhome 0.03 USD / kg The experimental data were analyzed from the repeating of the experiment and showed as follow Error bar = X ± SE (4) when X is the average value of data, SE = 2SDN-1, SD = 1N-1i=1N(Xi-X)2, N is the number of data and Xi is value of data No. i. 3.
From analyzing process, all data of every dolomite ratio were normalized.
From this result (Figure 8), the LHV data was polynomial decreased, and the fuel cost was straight increased.
The trend line of each data makes an intersection point, and it was an optimization point.

We remove the information redundancy and compress the message data in order to cause the information more convenient to transmit and store, according to the information relevance. [1]That is very essential.
Therefore, the people attribute the visual data by the region which the outline and the texture define.
The human eye visual characteristic will strengthen certain visual data, but neglects certain to the visual effect immaterial information.
The primary data after sampling and quantification must make every effort to transform conforms to the human eye visual characteristic attribute way, once obtained these regions, is belongs identical visual object each region to connect. [2]This attribute way not only may realize to the video data highly effective compression, also to realize object-oriented, region some new functions to pave the way.
FIRtree is the special single instruction multi-data stream form high speed media processing expansion language.

Along the y-axis, data were processed making use of axial symmetry, then a fitted curve was acquired using least squares polynomial.
Data and fitted curve along y-axis, Fig. 19.
Data and fitted curve along z-axis, 2mm in diameter 2mm in diameter Fig. 20.
Data and fitted curve along y-axis, Fig. 21.
Data and fitted curve along z-axis, 1mm in diameter 1mm in diameter Contrasting the data acquired in the 2mm case between that in the 1mm case, it’s shown that bigger particles carry more charge, result in bigger data acquired in the 2mm case, however, the close similarity in form between the fitted curves demonstrates that spatial sensitivity is just related to the positions of the charged particle when the dimensions of the sensor are fixed.

All data were expressed as the mean value ± the standard deviation (SD) or the standard error of means (SEM) of the obtained data as indicated in all figures and tables.
When the microspheres were added after cell adhesion, they did not show a significant inhibitory effect on the functions of NHOst (data not shown).
Pre-coated PS, PE and alumina microspheres showed the potential to suppress functions of NHOst although some of these data did not show statistical differences against NHOst without microspheres.
It was observed that HAp microspheres have no potential to deposit calcium after a 1-week incubation without NHOst (data not shown).
As expected, added various microspheres affected NHOst in a similar manner but less than the pre-coated microspheres (data not shown).

Evaluation method selection For the shallow groundwater quality evaluation, the relevant amount of data is quite huge and the distribution shows discrete points from the sampling data, so many elements are difficult to be controlled and evaluated.
This thesis chose the best representative R-type factor analysis and analyzed the common and specific factors based on the sample observation data in order to further study the correlation among the variables.
Establishment of evaluation models The R-type factor analysis is performed with shallow groundwater environment geochemical survey data in the targeted district as example.
There are 35 samples in total related to the shallow groundwater data with 16 characteristic variables (indexes) extracted from each sample.
The data normalization formula (k=1,2…,12; i=1,2,…,10) is used to normalize the original data, among which , , (i=1,2,…,10) Calculate the coefficient related to such 10 variables according to the standard matrix gained, that is , (i,j=1,2,…,10) (2) So the correlation coefficient matrix is, and for the results with sampling data, see Table 2.

Rietveld analysis of X-ray powder diffraction data is a powerful tool that can quantitatively determine sample phase composition and stoichiometry of the phases.
All XRD data obtained were analysed using the Rietveld method to obtain accurate phase and sample composition.
The lattice parameters of the HA showed an anomalously high volume at the sintering temperature Fig. 1 Multiphase Rietveld analysis of X-ray diffraction data for sample sintered at 800ºC (lines for calculated and difference; crosses for observed data).
Repeated data collection and analysis confirmed the validity of this volume increase which appears to coincide with the decomposition of α-TCP and increasing content of �TCP.
No evidence was found for the reduction in occupancy of the hydroxyl sites in HA, as has been reported to occur in the formation of oxyapatite [14] Conclusions The application of Rietveld analyses on diffraction data has now become a very powerful tool in the study of materials.

To upload stacker’s access accomplishment condition and fault alarm data from optical communication to HMI; 4.
To resolve the instruction data given by HMI and command Stacker PLC finish various movements.
Different frames have different data head and length.
Parity check will be added to the end of each frame data.
When there is transmission error in data transmission, it will realize data retransmission.

The data interface layer supports data persistent, maintenance data and business rules, and support outer interfaces to communicate and share data with other systems inside the same smart utilize district.
In data interface layer, all business objects are modeled with a group of model objects, which is mapped into relation data by Hibernate framework.
Spring framework integrates business logics, underlying services and data access objects.
All the business information collected by network probes will be gathered at information collection server and summed at data interface layer.
All data will be collected by different kinds of sensors and transformed to different events.

The program is run, and the output is compared with experimental data in order to valid the model.
To validate the result, experimental data from selected material were used.
In experimental data the value of table 3 is obtained.
Figure 6 Mass and stress distribution near to the crack tip A curve is then fitted to the data of table 3.
In this study, a crack in a pressure vessel was modeled and validated by experimental data on notched-specimen hydrogen pre-charging.

The model is represented in Equation (5): (5) The modified model displayed a mean deviation of 5.5% from the experimental data of nanorefrigerants.
Experimental data was used to determine the function f(∅,T) and β that relate to particle motion.
This gave an accuracy of 2% with the experimental data, which is a significant improvement when compared to the exponential function.
The prediction from the correlation was 92% in agreement with the experimental data within the deviation of ±15%.
Therefore, further experimental analysis of nanorefrigerants viscosity is needed to obtain data that will be useful in developing models that will accurately predict the viscosity of nanorefrigerants.