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In order to promote emissions reductions, carbon emissions reductions shall be discounted.
Selected a representative and large power plant as a case, all the latest data are from the enterprise’s official website.
The main data are shown in Table 1.
Table 1 Data Descriptions of Selected Power Plant Variables/Parameters Value Variables/Parameters Value Reduction period (year) 5 Project’s life cycle (year) 20 Predicted emissions in 5 years (104ton) 450.09 Reduction emissions discountrate (%) 8 Permits distribution in 5 years (104ton) 546.38 Annual return ratio on retudtion investment (ton CO2-e/Yuan) 0.0038 Permits price (RMB Yuan) 25.68 Limits of (104Yuan) 5067.89~ (1) Considered that China's economic and social development plan is usually adjusted for every five years, the reduction period is also assumed as 5 years.
According to the above decision model and related data, the optimal range of emission reduction investment is 5067.89 to (unit: 104Yuan, see Table 1).

Based on the data stored in memory mode, this article proposes a new reduction algorithm.
First, the low-high column of data processing attributes: Because the data in memory stored in binary form, TableElement is short data type defined by the macro, which accounts for two-byte, 32-bit systems, a byte is 8, the low may represent 0-255.
Greater than 255 data, referred to as a high.
Step Four: Then, when all the data which is sequentially in the order of LSD (Least significant digital) of attribute reduction are thrown into the bucket, the initialized objIdx[] array is written the corresponding value, namely updated the data line number, it is sorted.
Subsequent authors will further develop applied research how to use the new algorithm for large data mining.

It was found that higher content of additives gave larger thermal reduction and larger microsphere (RM1) gave a larger thermal reduction than that of the smaller ones (RM2).
Reading data were recorded every 10 seconds until the reading temperature going to constant.
After that the reading data were collected every 5 minutes for 1 hour and 40 minutes.
This is in accordance with the surface temperature data which indicating that 28 %wt or more of TiO2 can provide sufficient solar reflection and enhances thermal reduction.
It is also suggesting that the single reflectance data is not suitable to represent the thermal protection property of the roof paint.

A private cloud is a proprietary network or a data center that supplies hosted services to a limited number of people.
Through the enterprise private cloud, a factory can obtain the device service, the software service, model service, the data service, the information service and knowledge service from the public cloud.
In addtion, the service cloud should supply safe data storage for the users to record, look up, manage, and reuse their manufacture process data.
According to the business requirements from energy-saving and emission reduction and the new related technologies in this area [7-9], these key technologies are: pinch technology for device improvement in energy-efficiency, the coupling modeling technology to describe the nature of the coupling relation between production and energy consumption, computer simulation for calculate the possible energy-consumption value by reproduction in a virtual environment, dispatch optimization technology, evaluation technology for energy-consumption efficiency, data mining technology for energy-saving oriented etc.
Conclusions Both of global climate improvement and manufacture cost-reduction require the people to make endeavor in work of energy-saving and emission-reduction.

Once data theft, it will bring immeasurable losses.
The chip achieves data transmission with standard physical interface and data transmission protocol.
In addition, the chip adopts 3DES method to encrypt data.
Research on computer network data security strategy [J].
Multisensor data fusion and its applications[M].

Technological Transformation for Loss Reduction in Power Networks in Low-carbon Economy Energy Saving and Consumption Reduction.
Technical transformation for loss reduction could help optimize, refine and quantify loss reduction measures.
Tab. 1 Projected National Percentages of Thermal Power and Average Coal Consumption of Electricity Generating (×10-6) Year 2013 2014 2015 2016 2017 2018 Percentage/% 80.58 79.81 78.93 77.95 76.84 76.12 Coal Consumption/t/kWh 335 332 329 326 322 320 Data from: http://news.bjx.com.cn, http://www.lib.hust.edu.cn/xueke/slsd/wszy/dl.htm, etc.
Tab. 2 Predicted Electricity Saving Amount (×10-6) and Transmission Loss in Distribution Network of the Studied Company Year 2014 2015 2016 2017 2018 2019 Electricity Saving Amount/kWh 32.28 40.56 45.34 49.76 52.18 54.45 Overall Transmission Loss/% 4.8 4.6 4.5 4.4 4.3 4.2 Data from: the studied power supply company.
With the above data and assumptions substituted in the emission reducing model introduced in the last section, the developing trend of low-carbon benefit of the company, by the implementation of technical transformation projects for loss reduction, in the next five years was plotted, as shown in following Figure 1.

Crash Reduction Factor(CRF) CRF (Crash Reduction Factor), also known as Crash Reduction Rate, refers to the percentage of reduction in the number of traffic accidents when taking a particular security measures in transport system[1].
The data required is: the average number of accidents in many years before the implementation of safeguard measures which is called aB, the average number of accidents in several years after the implementation of safeguard measures which is called aA.
A certain improvement measures have classified the value of CRF to different type of accidents, which not only improves the accuracy but also provides a variety of research required data.
General types of accidents are: All, Fatality or Injury, Property Damage Only, Head On, Rear End, Side Swipe, Left Turn, Right Turn, Right Angle, Fixed Object, Pedestrian, Red Light Run, Run Off Road, Wet , Night[3].Table1 shows the Statistic data of CRF channelization portion in Missouri State，U.S.A.[4] Table.1 Statistic Data of CRF Channelization Portion in Missouri State，U.S.A.
If it is got that the historical traffic accident data of the road which has been implemented a certain safeguard measure, the number of reduction of accidents can also be calculated after the implementation of a certain safeguard measure, and the calculation formula(Eq.  2) is used for caculating the number of reduction of accidents.

Data processing of the measured data is performed by computers, which are essentially digital data processing devices.
Data processing of uniform data is significantly easier and faster than data processing of nonuniform data because equal duration of time intervals between moments of measurements enables simplification of calculations performed during the data processing procedures.
However, when strong electromagnetic induction is present, long series of consecutive data are corrupted and lost during the data transmission process, and such data transmission losses are called heavy data transmission losses.
The received data in cases of heavy data transmission losses cannot be made uniform in a meaningful way.
It is obvious that data losses cause reduction of signal power and hence the reduction of spectral power, but even basic considerations show that the distribution of power losses depends on the signal spectra and duration and distribution in time of loss events.

The Investigation of Pipe Ends Formation under Reduction Mill Rolling D.
It is established that the greatest "contribution" to wall thickness data spread is made by the pipe facets, which is caused by the influence of the reduction regimes and the rolls calibration due to the metal flow into the tapers of groove.
The reduction regime is chosen so that the wall thickness increases from 4.5 to 4.8 mm.
Thus, at the rear end the average wall thickness is greater than at the front end, which corresponds to the literature data [2-8].
Summary In the course of the investigation of the longitudinal and transverse wall thickness variation of the oil-well tubing with the size of 60 × 5 mm rolled at EWPM 20-102 it was established that the greatest "contribution" to the wall thickness data spread is the pipe facetedness that is caused by the influence of the reduction regimes at the reduction mill and the rolls calibration due to the metal flow into the tapers of groove.

The kinetics showed the AO7 reduction rate can be greatly improved by the addition of sulfate and RF, thus it is possible to speed up the start-up of AO7 reduction system under appropriate condition.
For this reason, oxidation following reduction of the –N=N– is favored for the degradation of azo dye, with reduction being the rate-limiting step of the overall process.
The balance between AO7 and SA (data not shown) indicating that the reduction plays a major role in the AO7 decolourising process.
Data shown in Fig. 2 indicated that the absence and presence of RF made no big difference, while the existence of sulfate can greatly influence the variation of the electron donor.
The kinetics of AO7 reduction.