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However, no computational problem arises if the data set contains zero values [6].
The second one is the existence of zero value in the data series causing an error result by the Divisia index method.
However, for AMDI, the formulae of SAD 1 generate computational problems when zero values appear in the data set.
No computational problem arises if the data set contains zero values.
Fig.2 Changes in China’s industrial COD discharges (2001-2009) (Data resources: ASREC [18]).

In this paper, the background of cloud computing technology to study how to optimize cloud computing data center energy efficiency mechanisms.
Implementation of the energy consumption is defined as: the task to run on a computer, computer hardware instruction and data-driven energy generated during operation.
Cloud data center to the virtual machine as a unit for the user to allocate resources application, the user by running a virtual machine execution services.
Deployment and migration of virtual machines is an important issue cloud data center energy optimization of virtual machine management, you can clearly see the migration process as shown in Figure 1 virtual machine.
Quiroz, et al., Energy-efficient application-aware online provisioning for virtualized clouds and data centers, in Proceedings of the International Conference on Green Computing. 2010. p.31-45.

Conversely, in circumstances where solar irradiation is less than STC, the data plate will have a lesser ISC.
The data nameplate of a CB carries similar labels as that of a fuse.
To find out how important stakeholders felt about the performance of PV systems under different operating situations, quantitative survey data was gathered.
When combined, these techniques provide a thorough understanding of PV performance by fusing real-world operational data with stakeholder viewpoints.
The data was cut across fifty-one locations in Nigeria.

Email: huangfuyun@fzu.edu.cn Keywords: Concrete filled steel tubular, Latticed column, Initial stress degree, Axial loading experiment, Ultimate load-carrying capacity, Reduction ratio Abstract.
Literature [5] based on finite element analysis drew the calculation method of the initial stress reduction coefficient of concrete filled steel tube lattice column, but this method wasn’t verified by tests, it required further research yet.
Literature [10] based on experimental study and finite element parametric analysis draws the calculation method about concrete filled steel tube with initial stress limit bearing capacity, which is through the concrete filled steel tube column (no initial stress) multiplied by the ultimate bearing capacity of the initial stress reduction factor.
The data is adopted by the automatic data collection DH3816.

Foam stability and mobility reduction are the key parameters for foam assisted enhanced oil recovery.
Recipes were evaluated by static foam tests to note the foam height and endurance time data.
The reduction of surfactant concentration tends to lower the cost of surfactant EOR process.

For ensuring fuel consumption and pollution reduction, the researches made in the past decades considerable efforts to replacing steel with aluminum alloys in manufacturing auto bodies, or in naval transportation, because the promising weight saving.
These types of materials include aluminum alloys, titanium alloys or composite materials that ensure reduction of fuel consumption and implicit gas emissions into the atmosphere [1-5].
Sorcoi, R Fako, S Cojocaru, Corrosion Behaviour of some sintered CuNi alloys – Preliminary laboratory data, Advanced Materials Research, Trans Tech Publ, 38, (229-237), (2008), pp.229-237

Evidently, the Raman spectroscopy data are in good agreement with X-ray diffraction data and all of them testify strong fragmentation of carbon layers in the annealed film.
The magnetization, approximation parameters of magnetic susceptibility data and concentration of localized spins in GO and TRGO.
Basing on these data the narrow signal in the EPR spectra of studied samples may be considered as resonance on localized magnetic moments and broad signal as conduction electron spin resonance (CESR).
Makotchenko (Institute of Inorganic Chemistry, SB RAS, Novosibirsk, Russia) for the XRD, Raman spectroscopy, magnetic susceptibility data and for help in synthesis, respectively.
Kalinin, Data mining graphene: correlative analysis of structure and electronic degrees of freedom in graphenic monolayers with defects, Nanotechnology. 27 (2016) 495703.

The species - temperature profiles thus obtained were interpolated to provide a number of data points sufficiently large for an accurate ANN training and testing.
The other half of the initial data set was used as ANN input and desired output, and presented to the training software, but the ANN coefficients were not be modified anymore.
The same initial data set was used for ANN training and to build up the ISAT table.
The overall accuracy is acceptable, but for some species there are regions in the flame where the ANN prediction differs from the direct integration data by as much as 15%.
Even if the amount of data obtained so far does not allow a conclusive evaluation of the algorithm, the current partial results can be considered encouraging.

The measured data are compared with the numerical results from geometrically linear/nonlinear finite element analyses.
Numerical prediction for the buckling load of the smart skin agreed well with the experimental data.
Reduction of RCS and development cost for a new aircraft as well can be obtained by installing the smart skin in the aircraft.
Fig. 4 shows three measured load-strain data obtained from the two strain gages.
By examining the load-strain data from the outer face the buckling loads are determined when the strains are abruptly changed.

The important data’s collected from PTP port container terminal located at Malaysia.
The input data that used in ANN obtained from Arena results.
Based on the data that given by PTP management, the annual productivity of PTP port container terminal is between 65% and 70 %.
Specifically ANNs are good for task involving some incomplete data sets or incomplete information, and for some cases with high complexity like port container terminal[9].
The results that obtained through these runs are used as input data to construct Artificial Neural Network (ANN).