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Carbon emission data of associated materials are obtained from PaLATE database.
The Life Cycle Cost Saving (LCCS)i% and the Life Cycle Carbon Reduction (LCCR)i%, expressed by Eq. (1) and Eq. (2) represent the percentage reduction of cost and carbon footprint while using mixture with i% of RAP rather than that of virgin mixture (0% of RAP added).
The database of PaLATE also includes carbon emission data for recycled materials and on-site recycling processes, which are not provided by other LCA tools.
The compositions of mixtures with different RAP contents (see Table 2) are calculated by an equation provided by Federal Highway Administration, Eq. (11), associated with the density data in the PaLATE.
For 40% RAP mixture, the reduction of carbon footprint is almost 30%.

Therefore the noise reduction of echo signal is an important step about the detection of the leaving time.
In view of the above problems, a noise reduction algorithm based on the minimum statistics noise estimation [4] is presented in this paper.
Composing of the test system The test system consists of an infrared launcher, a special radar, a power-supply and a data acquisition instrument.
The test sustem After putting the test device in the correct positions, when the first projectile fires, the emerging flame launch the infrared detecting instrument, data acquisition instrument begins to collect data of radar echo.
When the projectile enters the radar beam, a data acquisition instrument can collect data of the projectile echo until the end of the experiment.

The measurement time for each curve of infiltration capacity is about 100 minutes, the time interval of measurement is about 1-5 minutes, and the data number of each curve is about 30.
Thus, the total data number of measurement is about 720.
Based on these data, the infiltration capacity curves corresponding to different initial soil moisture content conditions for the three test grasslands are drawn and shown in Fig.1-3, where Fig.1 is for grassland A, Fig.2 is for grassland B and Fig.3 is for grassland C.
However, some data points having similar moisture content values occur abnormal, for instance, the initial infiltration capacity corresponding to the initial soil moisture content of 27.20% is larger than that corresponding to the value of 27.17% for grassland B.
Possible runoff reduction caused by infiltrating of LID-type road greenbelt.

The latter includes two methods: sensitivity extraction and data mining methods to select attributes.
Attribute reduction makes the amount of data of the diagnosis decreased, and the purpose of shortening diagnosis time is achieved.
The algorithm uses the data points as potential cluster center and calculates the mountain function value of each data point according to calculating the distance between the data points, and determines the number of clusters and cluster centers.
Therefore, there is a characteristic parameter for each type of fault patterns. 2.2 Attribute Selection Methods in Data Mining In the real world, a large database or data warehouse data is very large, attribute selection needs to be used and process data in data mining.
Thus the attribute selection method can be selected for bearing fault diagnosis feature extraction in data mining.

Introduction Data mining of the time series is the algorithms and implementation techniques to discover valuable regularity from a large number of time series data and it’s an important branch of data mining is widely used in the economic, scientific, industrial and many other fields.
In this study, the egg_full data set of the EEG Database is selected, all these data are come from experimental studies and each time series contains 256 data points.
The data in Table 1 also shows that this method can more accurately index the time series.
Event detection from time series data.
In proceeding of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining.

The reduction of tin ions according to [11] is a reversible diffusion-controlled process.
Polyanionic complexes and complexes with choline chloride reduction the most suitable form of linearization was the use of √τ↔1 / i coordinates, which corresponds to the reduction from the coating layer.
Based on a comparison of the data obtained, we propose a scheme of the process, including as the current density increases, the tin ions reduction directly from the electrolyte (1, Figure 3), the trichlorostanite complexes transition to the polyanionic coating layer (2, Fig. 3), followed by reduction (3 Fig. 3), the polyanionic layer conversion into a mixed one, with the participation of the choline cation (4, Figure 3), followed by restoration (5, Fig. 3): Fig. 3 Scheme of the process of reduction of tin ions from an ionic liquid At stage 5, at current densities of 20-30 mA/cm2, the electrocrystallization formed crystallites encapsulation conditions are created, which is a necessary condition for forming highly disperse metallic powders.
The tin ions reduction mechanism from a choline chloride based ionic liquid depends on the current density and includes: the trichlorostanite complexes reduction at current densities up to 5 mA/cm2; reduction from a polyanionic adsorbed layer at current densities of 5-12 mA/cm2 and reduction from a mixed layer including polyanions bound and by electrolyte ions at current densities exceeding 12 mA/cm2. 2.
The reduction rate from the polyanionic layer is less than the its growth rate, the reduction and growth rate for the mixed layer are comparable.

Nanostructured powders of cobalt and nickel, due to their high saturation induction, are used to make a magnetic liquids and materials, which is a part of the recording and data storage systems and they are widely applicable in biology and medicine.
According to the small-angle X-ray scattering data estimated on method [9], size contribution functions for nickel and cobalt particles have bimodal character (Fig. 3).
For the reduction of metal cobalt form cobalt carbonate the ratio υ(N2H4)/υ(Co)CoCO3 exceeds 15.
If the reduction agent’s concentrations decrease, the reaction decelerates considerably and the initial reagents might be reduced only partially.
Bakar, Effect of pH on Formation of Nickel Nanostructures through Chemical Reduction Method, Procedia Eng. 68 (2013) 43-48

Box 27272, mtawalbeh@sharjah.ac.ae Keywords: Magnesium production, Electrolytic reduction, Melt chlorination, Decomposition kinetic.
Magnesium is produced primarily by two methods: electrolytic reduction of magnesium chloride in molten salt and metallothermic reduction of magnesium oxide as in the Pidgeon and the Magnetherm processes [11].
Fortunately, the thermal reduction processes are progressively embargoed due to the growing awareness of their environmental impacts, high energy consumption along with their low efficiency [13].
The fitting of the experiments data for the first stage showed that the kinetics in this stage is controlled by the surface reactions and the rate of these reactions is given by the following Eq.: (5) Fig. 2: The change of mass ration of the MgCl2.2H2O granules in the molten electrolyte with time at different temperatures.
Hanson, “Structures of three dehydration products of bischofite from in situ synchrotron powder diffraction data (MgCl 2 · n H 2 O; n = 1, 2, 4),” Acta Crystallogr.

Mining Closed Weighed Frequent Patterns from A Sliding Window over Data Stream Ge Cuicui, Fu Xiufen Faculty of Computer, Guangdong University of Technology, Guangzhou,China gecuicui1988@163.com; Fuxf@gdut.edu.cn Keywords: Sliding window; Data stream; Closed weighted frequent pattern; DS_CRWF; Data mining Abstract.
Introduction Data stream is a sequence of massive data elements which is continuously generated and unbounded.
In sliding window, new data comes and old data eliminate continually, the data in sliding window not only growth but also reduction, so, it is more difficult to mine frequent patterns in sliding window.
The adopted data stream is the customer shopping data generated by the IBM synthetic data generator.
The specified data is T10I6D1000K, each basic window contains 50000 transactions over data stream.

Based on the data of China Statistical Yearbook and China Environmental Statistics Yearbook, the pollutants discharge and energy utilization efficiency, including technical efficiency (TE), pure technical efficiency (PTE), scale efficiency (SE) and returns to scale (RTS) of China’s industry and its sub-sectors were analyzed by constant returns to scale model (CRS) and variable returns to scale model (VRS) of non-parametric data envelopment analysis (DEA) method.
∑jλjxj-s-=θx0 (6) ∑jλj=1,λj≥0,j=1,2,…,n s-≥0,s+≥0 DEA method principles Data source.
In the evaluation of environmental efficiency, the input data were the GDP of industrial output and the pollutants emitted, including waste water, waste gas and solid waste, the three kinds of forms.
Annual industrial GDP is from the Statistical Yearbook of China[10], and the data during 2001 and 2009 was transformed into comparable prices in 2001.
The energy inputs was from the Energy Statistical Yearbook of China[13], and pollutants data was from Environment Statistical Yearbook of China[14].