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For these points, reusing knowledge from historical data becomes a natural option.
Based on company's sales records and product documentations, the links between customer needs and purchased products related to which product satisfies which customer can be identified and they are represented as {( * * ,s tr v )|∀s∈[1,S],t∈[1,T]}, which acts as the original data of knowledge discovery to product family planning in this research
Algorithm for Attribute Reduction.
With the definition of sorting capability and minimal reduction set, a hierarchical attribute reduction algorithm (Algorithm 2) is presented as follows.
Algorithm for Value Reduction.

Research on Peak-to-Average Power Ratio for OFDM System Hao Fang1, a, Wanhua Wei1,b 1Electronic And Information Enginerring College ,Wuhan Donghu University,China affhhaa@126.com , bweiwanhua@126.com Key words: OFDM, PAPR, SLM algorithm, PTS algorithm, Matlab Abstract: OFDM is a special multi-carrier modulation, its basic idea is to make high-speed transmission of data flow through the serial-parallel conversion and make it to be the low-speed transmission of data flow in a number of narrow-band orthogonal sub-channels.
As shown in Fig.2is symbol vector after serial-to-parallel conversion and baseband mapping, X1,X2,…,XV is data vector containing the same information in V channel, and processed after multiplying vector X to V channel, B=[b1,b2,…,bv] V random phase vector of with length of N evenly distributed on the complex vector within a 1 radius of circumference.
Firstly, data vector with sending terminal length of N is divided into v non-overlapping sub-vectors in accordance with adjacent segmentation, random segmentation or interleaving segmentation.
Modified selected mapping technique for PAPR reduction of coded OFDM signal.
Journal of Southeast University, 2005,21(2):123-126 [3]Xiaodong Li,LJ Cimini.Effects of clipping and filtering On the perforamance of OFDM.IEEE,Communications Letters,2007,2(5),pp:131-133 [4] H Ochiai, H Imai.On the distribution of the peak-to-average power ratio in OFDM signals.IEEE Trans on Comm, Feb 2008,49(2),pp:282-289 [5] S.H.Muller and J.B.Huber,“A Novel Peak Power Reduction Scheme for OFDM.“ Proc.IEEE.PIMRC‘97,Helsinki,Finland, Sept.2007,PP.1090-94

Based on the presented data, a possible growth model is proposed to clarify AIC mechanism.
The reduction of agglomerations on the a-Si surface is due to the diffusion of Al agglomerates into silicon.
From the XRD data, XRD signals at Si(111), (200), (311), Al (111) and Al (200) were detected which indicates the formation of nanocrystalline material.
Inspection of the data of Fig. 2 reveals that the AIC process has produced crystalline fraction at temperature above 500oC, with a peak centered at around 514 cm-1.
There is a clear tendency of reduction of resistivity as the annealing temperature increases.

The Chrastil model is used to correlate the experimental data.
The method of calculation the solubility data are by experimental and using Chrastil model [14] to correlate the data.
The process is done 3 times for each data and the oil yield is collected into the bottles.
To our knowledge, there is no previous reported solubility data for tamarind seed in supercritical CO2.
Then, all parameters value is determined through data regression.

Following this introduction, the model will be described; next we will introduce the data and sources for the study.
Model structure and data The objective function is to minimize the total discounted, cumulated cost of generation and interregional transmission for all six regions throughout the whole planning horizon.
In this study, emission reduction targets for 2030 are set according to relevant literature [4].China Electricity Council (CEC) proposed carbon intensity reduction targets in 2015 and 2020 according the “12th Five-Year "plan.
Data and source Regional electricity demand, generation and installed capacity are collected from power sector statistics, industrial report and other study [7] .Resource restrictions, nuclear and other renewable energy utilization restrictions are based on each province’s governmental statistics, development planning and techno-economic evaluations of renewable energy power technologies [8].
(In Chinese) [3] The notice of “12th five-year” plan for energy conservation and emissions reduction by The State Council of China: 2012 http://www.gov.cn/zwgk/2012-08/21/content_2207867.htm

Productive use of water resources Simulations From the 2008 Statistical Yearbook of Xinjiang and data of water resources of Xinjiang from 1999 to 2007. we can see that Xinjiang's annual economic growth rate and the first, second and tertiary industries and the average annual growth rate of water consumption per unit output value of its average annual growth rate of data from 1978 to 2007.
Based on the above data, this paper takes three kinds of hypothetical scenarios simulated in the Xinjiang Production and nature of water resources: water resources without control, the implementation of loose water consumption control, the implementation of strong control of water consumption.
Therefore, Xinjiang oasis should increase water conservation efforts. 4.2 Results of Assumption 2 Based on the above data and equation (5), the use of optimization modeling software lingo8.0, first obtain the optimal industrial structure, the magnitude of change (Table 2), and then get the following simulation results (Table 3).
Accordance with the scenario simulated, Xinjiang oasis will be substantial reduction in water consumption in the next five years (2010-2015), which indicaties in Xinjiang oasis is still in the water consumption of the extensive stage, water-saving potential is still great. 4.3Results of Assumption 3 Based on the above data and equation (5), the use of optimization modeling software lingo8.0, first obtain the optimal industrial structure, the magnitude of change (Table 4), and then get the following simulation results (see Table 5-1, 5-2).
By using DPS(Data Processing System) software to the raw data processing and on the use of standardized scoring method to experts, the impact factor of four weights to determine the economic goals of 0.3516, the industrial structure of 0.1429, the water use efficiency of 0.2028, 0.3026 fot the gross water consumption.

The reduction is more important at elevated temperature.
By turning a hard steel (AISI 4130), Ding [11] obtained a reduction of Kc about 20%.
This reduction became significant only when heating temperature exceeds 500°C.
Indeed, Kc reduction values reach from 13 to 34% for a temperature range of 500 to 750°C.
Tounsi, “ Identification of Material Constitutive Laws for Machining-Part II: Generation of the Constitutive Data and Validation of the Constitutive Law”, Journal of Manufacturing Science and Engineering, Vol. 132, N°5, 2010

Predicted results compared well with experimental test data.
However, relevant data and information from which a reliable assessment of structural capacity of corrosion damaged structures can be made is still lacking.
By analysing the test data and using regression analysis for different concrete cover thicknesses, they established a relationship as: ; where is the concrete cover; however the test data was based on rebar diameter of 20mm only.
The predicted maximum bond strength results were compared with the test data in Fig. 5, (a) and (b) for specimen with 12 mm and 25 mm diameters deformed bars, respectively.
It can be seen that the predicted bond strength compared reasonably well with the experimental test data.

The optimization results can also provide more reliable data to a customer.
Also, the values in the middle of the data in the DB are calculated by linear interpolation and retrieved in the optimization.
The optimizer uses the interpolated data retrieved from the DB in order to estimate the objective functions and constraints.
Initial design Variable & Constraints Interpolation (Objective Function) DOT(Optimization) Updated Design Variables Optimal Result End Optimization No Yes Thermal Analysis Database Design Variables Searched Data Fig. 5.
Since the optimization is done by data values retrieved from a constructed DB, it is required to check the optimized results with the results obtained from the direct calculation.

In this research, nanoscale iron particles were synthesized by reduction of Fe2SO4 7 H2O by NaBH4 at low temperature to avoid oxidation during the process.
Nanoscale zero- valent iron particles can be prepared in aqueous solution via reduction of ferric iron (Fe(III)) or ferrous iron (II) with sodium borohydride [4,5].
Results and Discussion In this study, zero-valent iron nanoparticles (6 – 40 nm) have been synthesised by the method of Fe2SO4 reduction using NaBH4 as a reducing agent at low temperature.
Surface area of zero valent iron nanoparticles was analyzed by data isotherm adsorption BET (Brunner Emmet Teller) using Nova instrument based on absorption of adsorbent on N2 gas.
Conclusion Zero-valent iron nanoparticles (6-40 nm) was synthesized by FeSO4.7H2O reduction using NaBH4 as a reducing agent at low temperature and high mixing speed (650 rpm).