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Apparently, leakage and dynamic power reduction are two distinct sets of techniques.
Therefore, there exist chances for dynamic application of standby leakage reduction techniques together with dynamic power reduction techniques to reduce total power in active mode.
This results in the reduction of total power.
However, the above-mentioned total power reduction methods are not suitable for low power multipliers design.
Power consumption measurements were derived from the netlists with back-annotated parasitic, using value change dump (VCD) data from simulation of 10,000 random input signals.

Methodology According to the carbon balance method, a model is build to calculate the gasoline consumption of light-duty GVs using the emission data firstly.
Data source.
Fig. 1 The driving cycle The data collected by EV includes time, vehicle speed, battery working current and battery voltage et al.
The information collected by GV under the same driving condition contains time, vehicle speed and emission data (hydrocarbon, carbon dioxide and carbon monoxide emission rates) et al.
For GVs, gasoline consumption rates can be estimated by using the vehicle emission data.

Data and Method Evaluation method of eco-environmental quality.
Land use change/cover (LUCC) data in 2005 was acquired from Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences, while LUCC data in 2010 was interpreted by the team of Shanghai Key lab for Urban Ecological Processes and Eco-restoration, East China Normal University.
Both the raster (including NDVImax) and vector data(including water-net map)were used for griding calculation after extraction based on the administrative division data of the YRDUA.
With respect to statistical data (including annual emissions of pollutants and volume of water resources), we assigned them to their corresponding vector map layers of administrative units’ attributes in order to realize its spatiality.
Lei, Evaluation of the ecological environment of Shandong Province using MODIS data and GIS platform, Chinese J.

The cutoff wall initially becomes deeper, the reduction of seepage discharge is distinct to reach 24%, as the depth increases gradually, the seepage discharge reduces slowly and can only reach 7%.
According to the statistical data of 1981, one hundred accidents that occurred in 241 large reservoirs, 30%~40% has been caused by seepage control problem[1].
The calculations take zero elevation plane as datum plane of hydraulic head.The upstream and downstream water levels are respectively 2269.72m and 2262.50m in the normal operation of the dam.
The seepage discharge of cutoff wall at 10m has reduced 24% than the seepage discharge of no foundation cutoff wall, and there is only 7% reduction when the depth of cutoff wall is from 10m to 20m, then the depth increases to 30m, the reduction of foundation seepage is very weak.
The cutoff wall initially becomes deeper, the reduction of seepage discharge is distinct to reach 24%, as the depth increases gradually, the seepage discharge reduce slowly and can only reach 7% ,then the depth increases to 30m, the reduction of foundation seepage is very weak.

(d) shows that there is a certain error between RMS from simulation and calculation,this error becomes most evident when elevation is 15°,after that the difference decreases when elevation rises.But because magnitude of difference is 10-3mm which is too small,so simulation results and calculation results are consistent and it can prove correctness of vector superposition based on Eq.6.Data of RMS obtained by simulation is optimal.
(8) After obtaining error data by simulation,Eq.7 and Eq.8 can calculate efficiency and gain loss of 25m antenna,then obtain the influence of elevation on efficiency and gain loss,at last evaluate the electrical performance when working in different band.
After transformation for 25m antenna,it is possible to cover all nine bands.Since the change of gravity deformation is caused by changes of elevation,elevation of antenna is the parameter which can be directly displayed from computer,and RMS is difficult to measure.So for creating reference conditions for the compensation of antenna.This paper obtains data about under different bands electrical performance parameters changing with elevation,Table 4,Table 5 and Table 6 shows those data.
According to Eq.7 and Eq.8,model about change of gain loss/efficiency with elevation angle can be established.First step is doing data fitting and obtains a model by using Table 3. and this model describes the correspondence between RMS and elevation angle.Result is shown in Eq.9.Unit of error is meter
[4] Hu Kaiyu,Aili Yusup,Xu Xuelin,Xiang Binbin and Liu Qi,Accurate data fitting for adjustments of focus position coordinates applied to Cassegrain antenna’s sub-reflector compensation[J].

However, few data are yet available to support this assumption.
Wave reduction is expected to be reliant on the density of vegetation and the surge water level.
When the waves encounter the densest vegetation, the largest rates of wave reduction occur [13][11].
Whereas a 20 years Rhizophora forest can offer up to 98 % wave reduction.
A Laboratory Study on Wave Reduction by Mangrove Forests.

The activation energy for annealing behavior was calculated using DSC data.
The plates, 8mm in thickness, were rolled with the reduction of 85% at cryogenic temperature.
Since the DSC peak position depends on the heating rate [6], the measured data of the peak positions for the different heating rates of 1, 2, 4, 8, 16, 32℃/min were used.
The measured data for the first peak due to the precipitation, the second peak due to recovery and third peak due to recrystallization are listed in Table 1.
The present result showed that the Q value for precipitation coincided with the calculated data by Picu et al. [9].

To predict the fusibility of single and blended coals during combustion, thermodynamic equilibrium calculations are performed using the computer program FactSage 6.1 with thermodynamic data taken from the FACT databases [10].
For the case of PM1, the reduction of S , Si and Al are mainly responsible for the reduction of PM1.
Effects of mineral transformations on the reduction of PM2.5 during the combustion of coal blends Effects of mineral transformations on the reduction of PM1-2.5.
Effects of mineral transformations on the reduction of PM1.
Therefore, the reduction of PM1 can be attributed to the transformation of volatilized vapors.TEM images show that the transformations of the volatile element S,P in submicron particles lead to the reduction of PM1 during the combustion of 50/50 blends, as shown in Fig. 9a and b.

Fig.2: Data representation tasks of the energy data management cycle [4] Transparent energy data management requires functionalities of energy data acquisition.
Whenever possible, measurement data is matched with system data using a timestamp.
Both short-term measures that serve to remedy a deviation of a defined target value and measures for the long-term reduction of resource and energy requirements can be stored and assigned.
Hence, administrative data, descriptive data, structural data as well as technical data are covered with the model.
By merging existing data with energy measurement data, a holistic, product-related data model can be created.

WIM data were obtained from weigh stations located adjacent the bridge.
WIM data were obtained from weigh stations located adjacent the bridge.
At the final step, comparing the ambient truck characteristics of the SHM and WIM data is completed to identify ambient WIM trucks that closely match the characteristics from the SHM data.
In order to ensure that the SHM is able to appropriately detect damage of critical locations of the bridge, the field feasibility of the SHM including strain data collection, reduction, and evaluation processes was also validated in recent work (Phares et al. 2013).
The framework integrated Weight-In-Motion (WIM) data with strain data resulting from ambient trucks through a Structural Health Monitoring (SHM) system.