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Oxidation Reduction Potential (ORP).
Anaerobic microorganisms can survive only in the lower oxygen redox potential (ORP) environment because some of their dehydrogenases such as coenzymeⅠ, flavin protein and iron reduction remain active only in low ORP.
So during the process of starting and running of USAB reactor, changes in pH should be closely monitored, and then by adding buffer agents, such as sodium carbonate or bicarbonate buffer, to adjust pH according to the measured data

Then, we completed the 3-Dimensional localization after data fusion by weighting the 2-Dimensional localization result.
Table 1 Comparison of actual data with measurement data and measurement error in simulation experiment number test point coordinates error actual measurement 1 67,24,85 67.05,24.00,84.97 0.50 2 327,101,34 327.21,101.07,33.87 0.26 3 136,250,7 135.86,249.78,7.00 0.26 4 312,169,54 312.23,169.07,54.01 0.24 5 88,139,55 87.93,139.00,55.03 0.0 6 370,54,68 370.41,53.79,68.12 0.24 7 409,233,58 409.02,232.96,58.12 0.13 8 141,233,10 141.03,233.02,8.96 1.04 9 432,57,88 432.02,57.11,88.00 0.11 10 127,442,56 127.42,442.25,56.13 0.51 From the Table 1, we can get not only the actual data and measurement data of the 10 test points mentioned above, but also the actual distance and measured distance of them to each base station and the actual angle and measured angle of the receives signals.
In addition, we can calculate the average localization error of this algorithm which is 0.337. 3-Dimensional simulation has been made with the data above using Matlab.
Now, with the same conditions and data as feasibility experiments, localize the test point using the two algorithms, and compare the coordinate errors of these three algorithms, as shown in Figure 6.
In addition, with the same data acquisition environment and data sources, the 3-Dimensional localization algorithm based on weighted fusion with projection coordination has the smallest error.

For this, the study uses the method of mathematical modeling, which will allow forecasting the data necessary for the design of MH systems.
The adequacy of the mathematical model is checked by comparing the existing experimental data with the calculated characteristics of the MH.
Calculated data on the decomposition pressure of the b-phase of LaNi5 are compared in Fig. 2 with the data of experiments (signs) on hydrogen desorption, which were carried out in limited temperature intervals.
The pressures on the plateau of the solubility isotherm calculated in the LaNi5–Н2 system are in good agreement with the available experimental data.
Hao, The hydrogen absorption process prediction of AB2 hydrogen storage device based on data-driven approach.

About 150 °C reduction in densification temperature of Y0.08Zr0.72Ce0.2O1.6-CuO0.02 was obtained when compared with Y0.08Zr0.72Ce0.2O1.6.The YSZDC-Cu0.02 pellets sintered at 1300°C and the YSZDC sintered at 1450°C exhibited relative densities of 95.66% and 94.73%, respectively.
High sintering temperatures result in several disadvantages to CeO2 doped ZrO2-based solid electrolytes such as high manufacture cost, reduction of Ce+4 to Ce+3 also caused a high oxygen partial pressure, which decreased oxygen vacancies and the simultaneous reduction of Ce ions from the tetravalent state Ce4+ to the trivalent state Ce3+ [6].
The diffraction peaks of the XRD data indicated the YSDC powder exhibits cubic fluorite structure (Fm3m space group) without any other traces , certifying that the CeO2 is fully stabilized by YSZ.

This material is usable for actuator (actuators and reduction gears) too.
Isakov, Cutting Data for turning of steel, Industrial Press, New York, 2009, 316 p
Pandová, Natural zeolite-clinoptilolite - raw material serviceable in the reduction of toxical components at combustion engines noxious gases, Gospodarka surowcami mineralnymi 23/4 (2007) 19-25
Dubayová, Natural and modified clinoptilolite testing for reduction of harmful substance in manufacturing exploitation, Advanced Materials Research 518-523 (2012) 1757-1760

Increasing porosity can often lead to the reduction in compressive strength and fracture toughness, although the crack bridging effect of particles (as described in Section 3.1) also plays a role.
The reduction in compressive strength of R2 and RS relative to R4 can also be explained by the higher porosity and increased content of larger capillary pores when smaller rubber particles are used.
The effects of the zigzag surface and interface lead to the reduction in tortuosity of R2 and R4.
With the remaining data, the average hardness values were calculated in Table 3.
However, due to the higher porosity, larger critical pore size and lower bridging stress, compared to R4, the compressive strength and fracture toughness of R2 and RS generally perform the larger reduction.

In this work, three physical models (A I-III) are used to analyze the measured data.
The fit of the neutrality equation to the measured data is performed by a numerical calculation.
Even for higher temperatures, this model matches with the measured data.
The data points show the results of the fit of the neutrality equation using the analysis A I-III (Tab. 1).
The data points show the results of the fit of the neutrality equation using the models A I-III.

The data collected for five consecutive years in a degree level technical institute was recorded.
It provides the visual summary of data and it reveals whether the process is centered on a target value of 75%.
The histogram in Figure 5 clearly shows that the data was not centric and the mean is far beyond the target value of 75% which shows the problem of centering.
Conclusions Based on the data collection and analysis the following conclusions were derived
Chakraborty and Gautam Majumdar, Selection and evaluation of supplier by decision model of hybrid data envelopment analysis, International Journal of Applied Engineering Research. 10(62) (2015) 123-127

The authors propose some considerations related to machining, choice of cutting tools, cutting depths, working mode parameters, reduction of the machining and auxiliary times, etc.
The CAM simulation results provide many useful, tabular data that highlight the options to choose optimal machining variants.
The data obtained from CAM simulation must comply to the working possibilities of the chosen machine tool, availability of the necessary cutting tools, qualified personnel for the machine operation and adjustment, clamping and control devices, supply of stock parts, storage spaces, etc.
Data of the simulation process Srf. no./ fig.
Table 1 contains important data resulted from the simulation process.

We choose TCP data packet Flag to reflect the microscopic view of data packets' internal field changing.
In order to obviously distinguish data packet forgery with real data packet in Flag, we define different weights to different TCP flag.
Thus in microscopic view, the internal data packet field, data packet IP address and port feature will be changed dramatically.
We call triple (IPs, IPd, PORTd) is the I-I-P feature of TCP/IP data packet.
In the microscopic internal data packet’s attribute field, we extract TCP/IP data packet header flag and import describe I-I-P triple of IP address and change port number.