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The results show that between 2009 and 2020, the potential for electricity savings will reach about 1228.5TWh, and the potential reduction in carbon dioxide (CO2) emissions will be about 1020.5 million tons.
The predicted data and parameters are all obtained or calculated from the “International Energy Outlook 2009”.
Moreover, according to the planning data of energy efficiency measures in California, we calibrate the data of EPPs for the study.
Contrast of effect in 2030 Contrast of the planning value Power generation Installed capacity Total investment CO2 emissions Unit TWh GW hundred million U.S. dollars million tons Predicted value 5153 1201 4961 2702 IRSP planning value 4472 1070 4367 2430 Reduction 681 131 594 272 Note: The predicted values are obtained from the International Energy Outlook 2009.
Whatever, the case study for power planning in the U.S. is still a single-objective planning due to lack of enough data.

The reduction reaction is characterized by a distinct peak at -0.4 V.
Figure 2 shows the etch rate data for Ge versus SiGe (100) in 0.1 M HCl/10 mM H2O2 solution.
The etched samples showed an overall reduction of Ge (sub)oxides.
These data provide a link between the silicon bulk concentration and the oxide solubility: at least 50% Si is needed to passivate the surface by the formation of typical Si-O-Si oxide bridges.
The presented data provide starting point for understanding the link between Si surface chemistry and the etching kinetics of SiGe.

Analysis of similarity of machine elements with the use of Artificial Neural Networks The application of methods of similarity analysis results on a reduction of the diversity of elements produced.
Those are consecutively arranged phases of representation of input data, randomly distributed according to the uniform distribution with the assumption of a rectangular grid.
The first box (image) presents the distribution of learning data, and the following ones the successive phases of network learning.
The final box presents the full representation of the form created by the input data.
The primary advantage of the network is that it can, in the learning process, identify connections within input data on the basis of recognition of features that are similar in model or master parts.

Beside the numerical technique and element formulation, material data and boundary conditions are essential for successful computation.
In order to perform successful numerical simulation, additional material data are required.
Experimental data are necessary for the evaluation of the parameters in this model.
Therefore the data have to be used with sufficient consideration.
Therefore, they have to be determined reasonably by experimental data or reliable references.

DFSS is a methodology commonly used in the automotive industry to analyze simulation and measurement data.
DOE enables the construction of an efficient simulation experiment, which provides data to be generalized using a regression model, called here an optimization model.
The optimization model needs to be identified using data obtained by running the simulation model of a damper and a servo-hydraulic tester.
The model fit to simulation data achieved a high value of 94.29%.
Data acquisition was performed with an 8-channel ICP amplifier manufactured by LMS.

The factors affecting the accuracy of soil property measurement originate from soil heterogeneity [2], moisture content [3], soil texture [4,5], soil color [6], model size [7], sample pretreatment [8,9], data preprocessing [10,11], and calibration procedures [12].
Sudduth and Hummel compared the spectra in the range of 1640-2640 nm at 40-nm data spacing interval and those in the range of 1670-2630 nm at 60-nm data spacing interval for determining the optimum number of scans for soil OC prediction in 30 Illinois soil samples [18].
Furthermore, nor on an optimization process for wavelength reduction.
Spectral Pretreatment and Data Analysis.
After spectral preprocessing, the entire data set were divided into a calibration set (75%) and an independent validation set (25%).

Materials and methods Study Area Trang province was selected which represented Pacific white shrimp production of Thailand based on the data of Department of Fisheries [3].
Data Acquisition The determination of the random sample sizes of Krejcie and Morgan [4] was applied to calculate the number of samples in this study.
The data were obtained through a series of questionnaires filled out by the owners of 106 farms.
The questionnaire was based on inventory data for life cycle analysis [5,6].
Greenhouse gas emissions reduction and material flows.

The results of the experiment are the data of heat flux change, measured nearby thermo conductive inclusion (sensor 1) and “along the insulation”(sensor 2).
These data were applied for further comparison with the results of computer modelling.
Film coefficients for each surface were considered constant and computed from averaged data: [W/(m2∙ºC)] , [W/(m2∙ºC)].
Internal and external air temperature was varied in time in accordance with temperature sensors data of the full-scale experiment.
The computational results let us obtain the data about alteration of heat flux close to thermo conductive inclusion and “along insulation”.

The data set contains 20 specimens for a/W=0.5, and 12 specimens each for a/W=0.1 and 0.25 at each temperature.
Fracture toughness data obtained from the a/W=0.25 specimens are used to verify the calibrated model.
Figure 2 shows the comparisons between model predictions and the experimental data at 25ºC.
The dashed lines denote the 90% confidence limit for the estimate of rank probability of the experimental data.
Most experimental data fall within the 90% confidence bounds.

This unusual behavior was ascribed to the reduction of NiSnF4 complexes existing in the chloride-fluoride solution [2].
This is consistent with the LSV data (Fig.2), where limitining current for tin deposition was observed as a plateau below -0.8V.
Exemplary data are presented in Fig.10.
This is in the accordance with the potentiodynamic results indicating reduction of the tin ions under limiting current.
Potentiodynamic and potentiostatic measurements confirmed that reduction of Sn(II) ions run under limiting current corresponding to the release of the cation from gluconate complexes as a rate determining step.