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The first process used vibration amplitude reduction with increment in temperature to identify the desired temperature.
The data acquisition system comprised an accelerometer (Kistler 50g), for vibration data acquisition; a Dewetron module, for signal conditioning; and a National Instruments DAQ card (model: PCI-6023E), for interfacing with the Dell workstation.
Fig. 2: Photograph of the experimental setup Results and Discussions Vibration Amplitude Reduction.
The results obtained were plotted to understand the effect of the work-piece temperature on chatter amplitude reduction.
It was noticed that the highest reduction in amplitude was at 450ºC for both frequency ranges as shown in Fig. 3, below.

Data shown the x-ray wavelength and intensity and plotted.
The theoretical value of mass per-cent between Zn and O are 80.3% and 19.7% which the result is nearly to the theoretical data [18].
From DVR%, smaller size of nanoparticle showed greatest amount of reduction up to 40% reduction of viscosity.
Smaller size of nanoparticle exhibits a larger reduction of viscosity.
Journal of Chemical & Engineering Data, 2010. 55(3): p. 1389-1397

Using measurements data, carried out during the experiment on real bridge structures, in situ, one can estimate the structural parameters of the bridge.
The obtained results of the updated model are useful in the process of further validation of a simulated damage test data.
The aim is to identifying damage by fitting the numerical model to real data, followed by optimization techniques, [7].
These error functions are given by the discrepancy between the predicted response and the measured NDT data of the FE model.
M., Structural parameter estimation incorporating modal data and boundary conditions, Journal of Structural Engineering. 125 (1999) 1048-1055

As discussed in [16], fatigue data is in good agreement with the data reported in [10, 13].
Fitted parameters for power-law approximation of fatigue data for bare AA 2024.
This becomes obvious, if a simple power-law is fitted to the S/N-data, i.e.
The data from Fig. 4 is for both alloys in good agreement with the predicted values in the Kitagawa-Takahashi diagram.
Kitagawa-Takahashi diagram for clad AA2024, data partially taken from [20].

However, about 9% and 37% reduction in compressive strength in HVS cement pastes is observed due to use of 80% and 90% slag, respectively.
However, significant reduction in compressive strength is observed in higher slag-fly ash blends with increasing in fly ash contents.
Mass and differential temperature data were acquired with respect to furnace temperature.
These significant reduction can be attributed to the slow pozzolanic reaction of fly ash and slag in these HVSFA cement pastes.
However, the nano silica used in this study was synthesized by a chemical precipitation method confirmed by the manufacturer whose CO2 emission data is still not know.

Table 3 Crystallization rate of the leaching solution As Te Sb Bi Cu Composition of filtrate (g/L) 16.5 1.83 55.08 29.24 4.32 Composition of precipitate (%) 28.69 0.094 0.76 0.75 0.64 Crystallization rate (%) 43.68 1.23 0.47 0.97 5.18 The data shows that more than 43% of arsenic is crystallized in the form of arsenate, with other valuable metals left in the solution.
Table 4 displays the reduction rate of Te.
Table 6 shows the reduction rate of metals.
Table 6 Reduction rate of the solution after hydrolysis (%) As Bi Cu Composition of filtrate (g/L) 0.06 0.2 0.052 Reduction rate (%) 99.23 98.55 97.45 By the effect of iron powder, As, Bi and Cu are totally reduced and enter into reducing slag with reduction rates more than 97%.
Moreover, the solution after reduction can be recycled.

The simulation results showed that the optimized energy distribution was almost up to 90% and the decoupling degree was greatly improved by comparing the initial data, proving the optimized data played a greater effect on engine vibration isolation and further verifying the feasibility of optimization design method.
Table 6 The energy distribution of energy decoupling simulation Name Total Energy (%) X Y Z RXX RYY RZZ RXY RXZ RYZ Stage 1 100 1.31 89.24 7.37 0.13 0.88 1.05 0.00 Stage 2 0.96 4.99 90.10 0.30 3.57 0.08 0 0.05 0.03 Stage 3 95.54 0.57 3.04 0.19 0.32 0.23 0.05 0 0.03 Stage 4 0.04 0.02 0.91 3.85 92.21 1.97 1.01 0 Stage 5 0.02 0.01 0.94 93.55 3.39 1.73 0 0.16 0.18 Stage 6 0.07 0.97 0.26 2.71 0.88 95.13 0.00 Through the analysis of the simulation data, all the frequency data from the system simulation was between the theoretical values: the maximum was about 16Hz and the minimum was about 6Hz.
Through the analysis of the data, the simulation algorithm in this study was proven to be successful.
Second, the vibration frequencies of the suspension system in the original data were compared after software Adams was used for the simulation, suggesting the optimized result could better play an effect on the vibration reduction of the suspension system.
The simulation data showed the optimized distribution after the optimization using energy decoupling was up to 81% at worst and proved the optimization could make the suspension system achieve a more ideal vibration reduction effect.

It can not meet the policy of energy saving and emission reduction.
The operation data is shown in Table 1.
The data indicate that the operation value of concentration ratio is from 5.27 to 10.50, meeting the zero liquid discharge of Shouyangshan Power Plant.
The operation data is shown in Table 2.
The data indicate that the operation value of concentration ratio is from 5.56 to 6.68, meeting the zero liquid discharge of Baoshan Power Plant.

However, measured data were too limited to evaluate properties of (U, Pu)O2-x.
In this study, the data were expanded to a high temperature region, and the reduction curve was analyzed by fitting to diffusion model with the effect of surface reaction.
(5) The measured data were fitted by Eq. (5) using D and k as parameters.
Good agreement can be found between the experimental data and the fitted profile.
These data correspond to the dominant condition of monovacancy diffusion mechanism.

In the present study, since data from multiple indicators to assess non-precision, non-fully identified data, some indicators there is a direct overlap, it is necessary to adopt appropriate methods of reduction and screening indicators.
This analysis includes the following four steps: (1) Reduction and screening of indicator.
Rough set evaluation method for each dimension reduction targets after the weight calculation, and then calculates the score for each dimension. (3) Integration of the target layer risk evaluation.
each reduction in every dimension.
Table 5 Eigenvectors data table mean 0.648 0.539 0.32 0.122 0.429 0.412 0.230 0.296 0.22 0.320 0.429 0.299 0.122 0.165 0.46 0.558 0.142 0.289 According to data table5, the wrights of, , are 0.412, 0.299, 0.289.