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The test according to Manzo reservoir laboratory test data and geological survey report, using the finite element software of ANSYS to establish the finite element model.
The model put different water level decline speed, different osmotic coefficient and the different infiltration recharge tension cases to analysis separately,the result shows:1)more greater the reduction’s speed, more steeper the saturation line’s luffing, more adverse the slope’s stability;2)more smaller the permeability coefficient, more poor the slope’s drainage capacity,more steeper the infiltration line, more poor the slope’s stability; 3)seepage line’s position higher than no infiltration seepage line’s position.
Since the three Gorges reservoir’ Water storage, reservoir water level have about 30m water level’s fluctuation every year, different level will lead to the bank slope infiltration line’s rise and fall, this test according to Manzo reservoir laboratory test data and geological survey report, using the finite element software of ANSYS to establish the finite element model.
Fig. 2 Plastic displacement of 175m Fig.3 Level dropped 175m-159m horizontal Based on the accumulation layer seepage model’s basic assumptions and calculations, referencing the way of saturation line’s calculation, getting different reservoir water level action’s seepage line,and compared with each other,getting: a.Under the different level decline rate,more greater the reduction’s speed, more steeper the saturation line’s luffing, more adverse the slope’s stability; b.Under the different permeability coefficient influenction,More smaller the Permeability coefficient, more poor the slope’s drainage capacity,more steeper the infiltration line, more poor the Slope’s stability; c.Under different infiltration recharge condition,Seepage line’s Position Higher than no infiltration seepage line’s position.

This method estimate of the threshold between soft threshold and hard threshold, compared with the previous noise reduction method, this method is more close to reality of wavelet coefficients, in order to provide a good removing noise environment, positioning results more accurate and reliable.
In practical application, especially in the signal noise reduction processing real-time demand high, it is difficult to half-and-half soft threshold coefficient of the best value selection and judgment.
For half a soft threshold algorithm [9] of shortages, it is proposed an improvement half soft threshold algorithm, it can be applied to the transmission line fault detection and noise reduction, will get more effective noise reduction effects [10-11].
The data from figure 4, it can be observed that in A phase fault occurred in 0.02 s to 0.03 s, and detect the timebetween initial traveling wave reached testing point and the timeof reflection wave reached testing point in the M.
Power distribution fault cause identification with imbalanced data using the data mining-based fuzzy classification E-algorithm.

The oxidation peaks (DEpa, DIpa) and the reduction peak (DEpc, DIpc) as showed in Figure 2.
Oxidation and reduction peaks can be seen in the potential of 0.92 V and -0.11 V.
O and R are responding to oxidation peak and reduction peak appear.
(a) Whole curve of oxidation and reduction process where O1, O2, O3 and R1 are responding to First oxidation, second oxidation, third oxidation and first reduction (a) First and second oxidation peaks, (c) First reduction, R1 peak appear in this reaction.
Allred, Electronegativity values from thermochemical data, Journal of inorganic and nuclear chemistry, 17 (1961) 215-221

The expression of, and are the mean of each frequency band and n is number of data.
I-kaz analysis provides a 3D data distribution along with the I-kaz coefficient, Z∞.
The Z∞measures the degree of data scattering with respect to the data centroid.
Z∞was adequately corresponds to the degree of data scattering.
The higher value ofZ∞ for sound signal of carbon steel S50C was illustrated by the bigger space of data scattering of I-kaz display compared to the lower Z∞ for cast iron which was illustrated by the smaller space of data scattering.

Most of the evaluation system is evaluated the level of recycling economy based on the idea of AHP method by tertiary structure(objectives, criteria, indicators), and the targets of this integrated structure is clear, indicators layer data is relatively easy to obtain, and it is more widely used in a special plan of circular economy for industry manufacturing in China.
Besides, due to the restrictions of data collection, the input-output analysis method remains theoretical level, and most of them have not the related application under circular economy for industry manufacturing.
For inspecting the waste reduction based on the implementation of circular economy for industry manufacturing in the production sector, this paper defines the coefficient of waste cumulative reduction (also known as the cumulative reduction factor of pollutants), so it is, (1) is the proportion of waste reduction within the production department in waste total output.
is larger, it shows that there have the more significant effects for the pollutant emission reduction in productive sector i based on the circular economy model for industry manufacturing.
Thus the paper defines the direct reduction coefficient matrix A and the complete reduction coefficient matrix B: (4) (5) It is similar to the traditional input-output method, thein complete reduction coefficient matrix B is defined as the waste complete reduction coefficient within the production sector, which indicates that the amount of waste can be reduced directly and indirectly in sectorwhile per unit of waste output produced in the production sector.

The ASR data decreased from 1.68 to 0.645Ωcm2 with the increase of steam contents, while steam content had little effect on ASR data in SOFC mode.
Reduction of NiO-YSZ into Ni-YSZ was accomplished in situ. at 850˚C.
It can be seen that ASR data decreased from 1.68 to 0.645Ωcm2 with the increase of steam content from 20% to 80% correspondingly in SOEC mode, while steam content had little effect on ASR data in SOFC mode.
Fig.3(a) showed the microstructure of hydrogen electrodes after reduction.
The ASR data decrease from 1.68 to 0.645Ωcm2 with the increase of steam contents.

Measure Data: The exhaust fan and cooling tower noise measure data was shown in Table 1, the representative point band level pressure measure data was shown in Table 2.
Gear Reduction Noise: Noise of gear reduction was mainly mechanical noise coming from the gear and bearing.
It could be seen that from the data in Table 2, prominent frequency was 250 Hz, therefore, the fan noise control should be mainly in the low frequency of 250 Hz.
From the data in Table 2 could be seen, prominent frequency is 250 Hz, therefore, the cooling tower noise control should be mainly in the low frequency of 250 Hz.
Contrasting the noise measuring data and the standard limit value, could conclude that the environment noise exceeded the standard.

Wu Han, Hubei 430061, China; a 110190112@qq.com, b 196291812@qq.com, c 360059903@qq.com Keywords: phyllite, support structure, stability analysis, stress changes Abstract: Taking Liang Zhuang phyllite tunnel as the research object, the paper tries to analyze the variation rules of formation stress and initial support stress in the construction process based on numerical simulation of surrounding rocks grade III, IV and V, and also by using the measured data to have a better analysis of the stress state of surrounding rocks and support in the construction process, thus to grasp the rules for better construction.
Taking Liang Zhuang phyllite tunnel as the research object, the paper tries to analyze the variation rules of formation stress and initial support stress in the construction process based on numerical simulation of surrounding rocks graded III, IV and V, and also by using the measured data to have a better analysis of the stress state of surrounding rocks and support in the construction process, thus to grasp the rules for better construction. 2.
Fig.1 Tunnel lining unit model Table 1 Parameters of surrounding rocks and initial support material E /GPa C /MPa j ( o ) μ γ /kg/m3 note Surrounding rocks III 11 0.14 30 0.3 2350 soft rock reduction Surrounding rocks IV 1.3 0.12 28 0.36 2100 soft rock reduction Surrounding rocks V 0.75 0.07 16 0.42 1800 soft rock reduction Initial support 25.7 0. 2 2450 Including reinforced net and steel arch 3.
We will analyze the data of the section of 10 to 20meters in the middle of the model.

This paper deals with the risk assessment within the premises of electric steelworks, in predictable failure situations, based on the sheets of assessment and reduction of the risk associated with explosive atmospheres of methane and oxygen, in the presence of high temperatures existing at the electric steelworks.
The Safety data of the processed substances are found in the literature, and are summarized in the safety data sheets (SDS).
Matrix of the industry risk analyser For the global assessment of the risks identified in the "Electric steelworks premises", we used an unconventional method to analyse and assess the subjective probabilities resulted from the assessments awarded within the estimation and reduction phase, in order to reduce the subjectivity in assessment and obtain plausible final results.
The process of obtaining the final results of assessment consists of a Fuzzy logic inference algorithm, which involves: - entering the data resulted from the system analysis; - fuzzification (transformation of data into Fuzzy variables) / Fuzzy logic inference / defuzzification; - output data (results of assessment); - nuanced assessment of the results.
As can be seen in Figure 2, after applying the measures set out in the risk assessment and reduction sheets, the global value of the risk afferent to the "Electric steelworks premises" has been reduced, finally resulting in a low residual risk, which ensures the prerequisites of safe operation of the electric steelworks.

A combination of Gaussian and Tauc-Lorentz oscillators were used to achieve an optimum fit to the experimental data.
Absorption data obtained using an Avantes AvaSpec 2048 UV-visible spectrometer was also used for the determination of such material parameters as thickness and optical band gap.
The refractive index and extinction coefficient curves for films fabricated under different deposition time conditions were compared (data not shown).
Fig. 2 Optical properties of poly(linalool) thin films deposited at 25 W for 30 min The optical band gap was determined using the Tauc relation applied to the absorption data collected for poly(linalool) samples using UV-visible spectrometer.
The optical band gap determined using the Tauc relation applied to the absorption data for poly(linalool), was calculated to be 2.82 eV, in the semiconducting Eg region.