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This paper will discuss what are cloud computing data centers, cloud computing data center construction, cloud computing data center architecture, cloud computing data center management and maintenance, and the relationship between cloud computing data centers and clouds.
Cloud Computing Data Center Construction Construction of cloud computing data centers is a system, a complex and iterative process.
Data Storage and Backup.
The data handled by user are not stored locally, but in the data center of the Internet.
All the data is handled remotely.

So the quantitative analysis on relation between effectiveness and influencing factors had been conducted based on the plot experiment data in this paper.
So the objectives of this study are: 1) to determine degree of association between influencing factors and removal effect of VFS, 2) to develop the empirical models derived from experimental data for the assessment of sediment trapping efficiency, and 3) to describe the physical interpretation of the parameters that define the model equations for evaluating sediment trapping process.
First, it was necessary to standardize original data, and then computed degree of association between sediments removal effect and influence factors by grey correlation analysis.
Relation of VFS width and purification effect It is the core role to determine the reasonable width of VFS in VFS design, so the relation of width and purification effect in this study had been described by counting experiments data, and it was shown in Fig.1.
Thus the quantitative analysis on relation between purification effect of VFS and influencing factors has been conducted based on the plot experiment data.

In terms of economy and efficiency, the carbothermal reduction is the best choice due to introduction of inexpensive starting materials [6].
Therefore, the carbothermal reduction process of oxide by carbon has been most widely used to industrial mass production.
In the Al(OH)3+SiO2+C mixture having the stoichiometric molar ratio of Al4SiC4, there were distinct two-step carbothermal reduction reactions; (i) carbothermal reduction of SiO2 between 1300 and 1600 o C, (ii) carbothermal reduction of Al2O3 above 1500 o C.
However, the XRD datum presents clear difference from that of Barczak, indicating the formation of highly oriented powder along the c-axis.
The XRD datum informs that the synthesized Al4SiC4 powder may have hexagonal plate-like morphology, which was confirmed by SEM observation (fig. 3.).

Research background Data reported by the Ministry of Housing and Urban-Rural Development of the People’s Republic of China(MOHURD) shows that 3513 wastewater treatment plants have been put into operation in China by September 2013[1].
Literature search and on-the-spot investigation show that the study on the way of concentrated activated sludge reflow used in energy saving and consumption reduction at home and abroad is still back.
Feasibility of concentrated activated sludge reflow process used in energy saving and consumption reduction of small and medium-sized wastewater treatment plant is verified through comparing water quality and technical indicators.
Pilot effect of concentrated sludge reflow instead of secondary settling tank reflow used in energy saving and consumption reduction Based on laboratory study, the author selected two wastewater plants as the pilot unit.
The data of operation for 20 days shows that effluent quality initially fluctuates significantly and tends to be stable in later period.

Photocatalytic reduction to useful compounds is a key to the future.
Strong absorbance at 195 nm is observed which well complies with reference and standard data.
Detailed HPLC data of photosynthesized sample is summarized in table 1.
Data is summarized in table 2.
[2] “Annual Data | Atmospheric CO2  | CO2 Now.”

Rough set and neural network have been used in fault diagnosis for many years, rough set is used to make the reduction and neural network is utilized to learn rules and approximate the ideal data.
In this paper, the fault sample reduction is done by the steps are as follows: The binary relation is established and judged according to the sample data, and then the suit rough set model could be founded.
One reduction is selected as the inputs of the network from the three reductions, and the decision is the output.
Table 3 Contrast of the data trained by the network Name Attributes Training time Epochs MSE Before reduction C1-C10 1.43759s 8 0.0275 After reduction C1-C5,C8-C10 1.06484s 8 0.0275 As is shown in Table 3, the change is not obvious in epochs and mean square error (MSE), however, the difference between before and after reduction is the training time, obviously, the training time is decreased greatly after reduction.
The particle swarm scale is configured to 80; the particle dimension is configured to 16; the maximum iterating times is configured to G=200; parameter c1=c2=2; self-adapting inertia =0.5; the maximum speed Vmax=0.3, the network optimized by the PSO is trained with the reduction data, and the test is simulated by the other 40 fault samples, the contrast is listed in Table 4.

Along with reduction of transit performance deploys the company in recent time significantly more energy effective power units for transit of natural gas.
The advantage of remote transition of data lies in data transfer from distant facilities in real time, observation of machine operation and it evaluation. [3] One of the most important features of on-line monitoring is recording of all operation states that means start, turn off or change of rotating regime.
Automated measurement system (AMS) and it technical measurement devices, technical equipment designed for data processing, data evaluation and information means must accomplish operation characteristics according to state-of-art of continual measurement technology, record the processed results and data in digital form, assure the alerting of its failure states and drop-outs and assure recording of at least 72 hours of operating data.
Following relations are used in these calculations: NOx (mgm3)=(NOppm+NO2ppm)∙2,0527 (4) CO (mgm3)=COppm∙1,2497 (5) NOxr (mgm3)=NOx (mg/m3)∙20,95-O2ref/20,95-O2mer (6) COr(mgm3)=CO (mg/m3)∙20,95-O2ref/20,95-O2mer (7) O2 ref - oxygen reference[ 15% ] O2 mer - measured oxygen The most essential condition of automated monitoring system operation is regular check and calibration to assure confidence of measured data.
The particular turbine power reduction in this alternative is realized by automated system, using feedback of mechanical power and NOx concentration in combustion gases, controlled by automated monitoring system.

The established data reflected an outstanding performance on the Hg0 re-emission inhibition from the simulated WFGD liquors by adding DTCR.
Our data suggested that a concentration of 0.0005% (v/v) was enough for Hg0 re-emission inhibition in the simulated WFGD liquors.
The elemental mercury (Hg0) re-emission and the factors that impact DTCR Hg2+ precipitation efficiency and Hg2+ reduction inhibition were investigated by using the simulated scrubber.
The established data reflected an outstanding performance on the Hg0 re-emission inhibition from the simulated WFGD liquors by adding DTCR.
Our data suggested that a concentration of 0.0005% (v/v) was enough for Hg0 re-emission inhibition in the simulated WFGD liquors.

Experimental Study on Unsupported Nano-MoS2 Catalyst for Deep Desulfurization Fuel Oil Yuqin ZHU School of Chemistry & Chemical Engineering, Xian Shiyou University, Xian, China zhuyq@xsyu.edu.cn Keywords: Unsupported MoS2 Catalyst; Hydrotreating Desulfurization; Hydrothermal Reduction.
Five kinds of unsupported MoS2 hydrodesulfurization catalysts and the precursor of MoS2 catalyst, nano-sized MoO3, were synthesized by a novel hydrothermal reduction method.
Experimental materials and preparation methods Experimental materials By analysis and comparison，the hydrothermal reduction is used to prepare MoO3, the precursor of MoS2 catalyst, and the MoS2 catalyst is synthesized by the hydrothermal deoxidization.
Preparation methods Hydrothermal reduction for the preparation of MoO3 The mixture of sodium molybdate and hydrochloric acid in certain proportion was put into the inner of a reactor and it was heated from the ambient temperature to 150℃ at an appropriate speed.
Handbook of thermochemical data for compounds & aqueous species.

Brief introduction of ROSETTA ROSETTA is toolkit software for analyzing tabular data within the framework of rough set theory, which is developed by Norwegian University of Science & Technology (Department of Computer and Information Science) and Warsaw University of Poland (Institute of Mathematics) to cooperate, namely, A Rough Set Toolkit for Analysis of Data.
ROSETTA is designed to support the overall data mining and knowledge discovery process: From initial browsing and preprocessing of the data, via computation of minimal attribute sets and generation of if-then rules or descriptive patterns, to validation and analysis of the induced rules or patterns.
It provides a variety of data preprocessing functions, such as decision table completion, decision table discretization and so on, but also common reduction and rule extraction algorithm within rough set [4], so its support the whole process of rules from the data pretreatment to the prediction and analysis.
The system provides the main function of a data acquisition and output, data preprocessing (decision table completion, discrete), computing (reduction), and other functions.
ROSETTA Discretization Method At present, the data obtained from the experiment mostly are continuous values, but in rough set theory, data reduction is based on discrete data table basis, therefore, for the continuous attributes discretization of real-valued attributes of decision table is a pivotal step in the pretreatment of data.