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., oxidation reaction and thermal reduction reaction).
The SPR is a fingerprint feature of metallic Ag nanoparticles, which has significant potential applications in optical waveguides and optical switches, surface-enhanced Raman scattering, optical data storage technology, solar cells and chemical sensors [1-4].
Thus, only a small part of silver can be oxidized into Ag ions, which then combine with the dangling bonds, and the other part of silver cannot be oxidized by air at high temperature and shows a metallic state due to thermal reduction.
As for the thermal reduction, it can be confirmed by our further experimental result that heat treatment in inert atmosphere (Ar) can reduce the AS sample or AgNO3/mesoporous SiO2 with low or high Ag content.
Here, with the increase of Ag content, the role of oxidation induced by the surface/interface synergetic effect gets weaker, whereas the role of the thermal reduction becomes stronger.

The technologies of nitrate removal include ion-exchange, reverse osmosis, bio-denitration, and chemical reduction, etc [1].
Spherical Sponge Iron (SSI) with 1~5 mm diameter was prepared through the process of palletizing, roasting and direct reduction by hydrogen.
The reduction process has been reported in our previous works [8], and the reduction temperature was selected under T1 and T2.
According to the kinetic experiment datum as shown in Fig. 1, reaction rate equations of nitrate removal by SSI under different reaction temperature could be calculated and illustrated in Table 1.
It was suggested that the limited key of nitrate reduction by SSI was diffusion of the reactant because of lower activity energy.

Research Progress on Low-Carbon Technologies and Assessment Methods in Cement Industry Haitao Zhao1,a, Yu Liu1,b*, Xiaoqing Li1,c and Liwei Hao2,d 1National Engineering Laboratory for Industrial Big-data Application Technology, Beijing University of Technology, Beijing 100124, China 2Beijing Building Materials Academy of Sciences Research/State Key Laboratory of Solid Waste Reuse for Building Materials, Beijing 100041, China azhaoht@emails.bjut.edu.cn, bliuyu@bjut.edu.cn, clixiaoqing@bjut.edu.cn, dhaoliwei@bbma.com.cn Keywords: Cement; Greenhouse gas; Low-carbon technology; Assessment method Abstract.
Li Jinmei et al. [16] compared the calculation boundaries and calculation methods of GB/T32151.8-2015, the "Supplementary Data Sheet" of cement clinker manufacturers and CNCA/CTS0017-2014; combined with the production data of a cement company, making the conclusion that different defined calculation boundaries and emission source types, different prescribed calculation methods and different recommended default values will result in different calculated GHG emission.
Based on the production data of a cement company, Yin Jingyu et al. [18] calculated and analyzed the unit GHG emission of the company's cement products according to the boundaries, emission sources and CO2 emission limits required by CNCA/CTS0017-2014.
In addition to understanding the emission sources, boundaries, and the calculation method of GHG emission, it also needs to grasp the rationality of the key data sources in the certification process and make judgments on the rationality of the data.
(3) A technology that achieves GHG-emission reduction throughout its life cycle can be considered low-carbon technology.

Most of the existing researches are focused on port energy saving and emission reduction where the emission reduction means reducing pollutants rather than carbon, and most of them use qualitative strategy analysis or specific method instead of quantitative research [1-5].
Liu et al. proposed a port evaluation system on energy saving and emission reduction [6].
Since the score intervals of four grades 1, 2, 3 and 4 are (85, 95], (75, 85], (65, 75] and (55, 65], respectively, container terminal's final score of LC performance is G = 90b1 + 80b2 + 70b3 + 60b4. (4) Case Study Based on the collected handling data of a certain area in a container terminal in December 2010, we calculated the quantitative index data listed in Table 6.
Table 6 Quantitative index data Index Data Evaluation Grade C4 12.5 Good C5 4.12 Good C6 9.12 Average C9 21 Average C11 2.07 Good C12 31.2 Good Table 7 Voting result of qualitative indexes Index Excellent Good Average Bad C1 4 9 7 0 C2 12 6 2 0 C3 5 10 5 0 C7 8 7 5 0 C8 7 8 5 0 C10 9 6 5 0 Table 8 Fuzzy evaluation matrix and the weight of each index Index R W C1 0.2 0.45 0.35 0 0.088 C2 0.6 0.3 0.1 0 0.019 C3 0.25 0.5 0.25 0 0.019 C4 0 1 0 0 0.111 C5 0 1 0 0 0.074 C6 0 0 1 0 0.088 C7 0.4 0.35 0.25 0 0.163 C8 0.35 0.4 0.25 0 0.116 C9 0 0 1 0 0.023 C10 0.45 0.3 0.25 0 0.078 C11 0 1 0 0 0.046 C12 0 1 0 0 0.176 Using the fuzzy comprehensive evaluation model, we can get the evaluation result, that is B= W T·R = (0.17, 0.59, 0.24, 0). (5) In line with the principle of maximum membership degree, this terminal’s evaluation result of LC performance is 0.59 and its corresponding grade is Good.
Using the established AHP-fuzzy comprehensive evaluation model, the LC development situation of a container terminal can be evaluated more effectively with sufficient data, and the evaluation result can provide a reference for port enterprises to carry out LC construction work.

So the technology of wavelet transform can raise the level of noise reduction of CMF, increasing accuracy of measuring.
According to property of wavelet transform and it’s noise reduction principle, the procedure of noise reduction of signal in one dimension can be divided into three steps[3] : 1)Wavelet decomposition of signal.
Ten groups of contrast experiment in total are carried out, obtaining data that shown in Table 1.
From this, it’s obvious that data with wavelet transforming is more stable, that is ,wavelet transform can increasing de-noising level of CMF when zero calibration.
Table 1 Zero data with wavelet transforming and without wavelet transforming zero data without wavelet transforming zero data with wavelet transforming Calibration sequence Frequency[] Phase difference[] Frequency[] Phase difference[] 1 72.866049 0.9492534 72.866153 0.9506865 2 72.868977 0.9503442 72.866232 0.9493666 3 72.866469 0.9510302 72.866465 0.9508077 4 72.866392 0.9476064 72.867013 0.9515187 5 72.866965 0.9497614 72.866867 0.9498912 6 72.865848 0.9521442 72.866467 0.9520342 7 72.866367 0.9504672 72.866558 0.9497721 8 72.865018 0.9503404 72.866376 0.9505193 9 72.866392 0.9487854 72.865948 0.9501342 10 72.864866 0.9538039 72.866021 0.9489872 References [1] Xiao Suqin，Han Houyi.

The ANFIS network should be trained to learn about the data and its nature.
In order to determine the error between the simulated and the experimental data, the root mean square error (RMSE) is used.
In Fig. 3, the RMSE of training and checking data sets are shown for each epoch.
The terminated RMSE of training data is 0.0032.
Therefore, Fig. 4 indicates that the results derived from the ANFIS-based model agree with the experimental data well.

Fujian), and handled by 3086 X-Y data recorder (Sichuan Fourth Instruments).
The data were obtained from cyclic voltammograms presented in Figure2 Figure 4.
The symbols represent the experimental data, and the line is the linear fit.
A reduction peak appears at a potential of ca. -0.85 V.
Therefore, the reduction reaction of Ni(II) into metallic nickel on the Pt electrode is an irreversible process.

The IR data is consistent with the IR standard spectrum of tetrahydrofuran.
MS data for the ZrC precursor: m/z=72 (tetrahydrofuran), m/z=58(acetone), m/z=44 (carbon dioxide).
FTIR (Fig. 1) and MS (Fig. 2) data: the wavelengths of 2360cm-1 (carbon dioxide), 3014cm-1 (methane), m/z=16(methane), m/z=44(carbon dioxide).
At relatively low reaction temperature, it could not provide enough activation energy for occurrence of carbothermal reduction.
When the temperature increased from 1000°C to 1200°C, carbothermal reduction occurred, and a little of the ZrO2 was reduced to ZrC.

At this, the project owner decides whether to grant membership and access to specific data or not.
Every project associate can access the required data in an easy way.
An application server controls data processing with integration of application services.
A database server provides back-up processes to ensure availability of data.
In other research groups forums for data transmission are already established.

This paper proposes a new method to predict the spindle deformation based on temperature data.
For building the predictive model, we first extract temperature data from sensors in the spindle, and then they are used as the inputs to train ANFIS.
In order to enhance the robustness of the proposed model, the experimental data sets should spread throughout the normal entire machining process.
The temperature and spindle thermal deformation were measured and recorded at a sampling interval of 5 min, including 110 data sets covering 550 min.
Lee, Integrating fuzzy data mining and fuzzy artificial neural networks for discovering implicit knowledge.