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A comparative data depicting the efficiency of various microbes for reduction of Cr(VI) form chromite mine soil is presented in Table-1.
The reduction rate was relatively low during first 24 hours.
The reduction rate increased with increase in time duration.
The best growth and reduction was taken as pH 7, which facilitated ~90% hexavalent chromium reduction.
About 90% Cr(VI) reduction was observed at 35ºC in comparison with only 80% reduction at 25ºC (Fig. 8).

To date no experimental data is available about the dynamic temperature profile for refinery sludge gasification in an updraft gasifier.
In order to measure the temperature profile inside the gasifier and classify the gasification reactions zones, five type-K thermocouples connected to data logger (USB TC-08) and the readings of the temperature are logged in the computer.
In gasification, reduction reaction is the prefered.
Combustion is also important as it sustain the endothermic reduction reaction.
Obrenberger, “Updraft- Fixed Bed gasification of Softwood Bellets: Mathematical Modelling and Comparison with experimental data,” in European biomass Conference and Exhibition, 2009, no.

The results show that our model can both enhance learning performance and classification accuracy. 1 Feature Reduction based on Manifold Learning Since the original dimensionality of the feature space gathered from the primary image data is usually very large, which will seriously affect the performance and results of classification, dimensionality reduction for the original feature space is thus not a negligible phase.
Linear dimensionality reduction will usually satisfy the tasks of linear distributed reduction, but in nonlinear cases it will lose certain efficiency and accuracy.
The methods of nonlinear dimensionality reduction are hence widely introduced for such nonlinear reduction situations.
Its basic idea is that the overall information served by overlapping the local neighbors maintains the original topology structure of the primary image data, using local linear approximation to the overall linear to the global and meanwhile keeping the local geometry structures unchanged.
[9] Belkin M and Niyogi P, “Laplacian Eigenmaps for Dimensionality Reduction and Data Representation[J],” Neural Computation, 2003,15(6), pp. 1373–1396.

To implement such an analysis, energy data has to be related to the material flow states working, waiting and failed.
The needed energy data can be estimated or measured.
In the waiting state, the measured data showed a different pattern.
The corresponding data is illustrated in Table I.
Based on real measured energy data, the main energy consumers of the system were identified.

According to the information of water quality, hydrology data and the discharge distribution of the river, we chose COD, ammonia nitrogen and volatile phenol as the main control factors, and some formulas were used to calculate the water environmental capacity of COD, ammonia nitrogen and volatile phenol.
According to monitoring data of water quality, among pollutants discharged into the river, the proportion of COD, NH3-N and volatile phenol is relatively larger, so we select them as evaluation factors of the water environmental capacity, the limit value of water environmental quality standards as Table Ⅱ{TTP}8545 .
According to comparison with data of Fenhe River, the results are relatively reasonable.
Hydrological parameters selection The water environmental capacity is different on different reaches or it has different flow, 1980—2008 series’ data is used in this paper.
Pollutants reduction calculating.

The crystallographic texture was derived from EBSD data.
These data were collected from the center of the sheets thickness on longitudinal section.
Fig. 1 Optical micrograph in longitudinal sections before and after rolling: a) initial b) RT, 15% reduction, c) CT, 15% reduction, d) RT, 50% reduction and e) CT, 50% reduction.
Fig. 2 ϕ2 = 45°, 65° and 90° sections of ODFs before and after rolling: a) initial, b) RT, 15% reduction, c) RT, 50% reduction, d) CT, 15% reduction and e) CT, 50% reduction.( Levels: 1, 2…18.)
However, in present work, the β fiber consisted of a highest S, a medium B and a lowest C at 50% reduction and showed homogeneously at 15% reduction.

According to the low efficiency of vegetable leaf image data mining problems, proposed an improved algorithm based on Apriori algorithm to get greatly related groups.
(1).experimental environment: server: P42．0GHz, 4G memory, SQL Server2000; ; client: P4 3.9 GHz, 2G memory (2). the experimental data pretreatment: choose 563 slice of cucumber downy mildew blade using photoshop tools separation, a total of 2457 pieces of disease spot, disease spot image connected data of image region, Obtain the original data, the following table 1 Table 1 Cucumber leaf disease spot image characteristics disease spot Area A Color C Brightness B Location P Premeter G gray median H Shape R 0001 1.16 brown 70 central 1.23 146 elongatd 0002 1.24 Green 78 edge 1.17 161 triangle 0003 0.05 light 100 central 1.41 123 circular 0004 0.55 light green 91 edge 4.65 128 triangle (3), using the improved Apriori algorithm support degree or more than 30% of the maximum correlation set {ACHR}, showed that degree of victims has nothing to do with brightness, location, but relates with area of shape, color, grayscale average, significantly
But for image data preprocessing, how scientific dynamic partitioning discrete interval, and not to divide the discrete artificial fixed interval is thin, is not conducive to dig up rules, so using coarse intensive algorithm to conclude mining rules, make the mining results more scientific.
The concept of data mining, and technology [M].
Large data set based on rough sets and genetic algorithm of data mining application research [D].

This method makes full use of the advantage of" let the data speak".
As a method of dealing with imprecise, uncertain and incomplete data, rough set is only based on pure data to delete redundant information, and to compare the roughness of knowledge and importance.
In rough sets, the information table is used to describe the universe in the data set, the decision table is a kind of a decision attribute characteristics of information table.
Attribute reduction.
[4] Z Pawlak.Rough Sets,Theoretical Aspects of Reasoning about Data [M].Kluwer Academic Publishing,1991:9-51

Comparative study of various data conversion methods for torsion tests applied to a HSLA steel J.
Conversion of torsion data The conversion of torsion data to true stress, true strain and strain rate was conducted by means of the methods described previously.
The open circles are the experimental data and the lines correspond to predictions of Eq. 11.
The open circles are the experimental data and the lines represent the fit obtained by Eq. 11.
A good correlation is obtained indicating the quality of the data reduction process.

IF steel was homogeneously cold rolled between 30-95% reduction in thickness.
In addition to this a {411}<148> component began to intensify, reading 5R at 95% reduction.
At 50% reduction figure 2c complete α and γ fibres formed and strengthened with increasing reduction until 80%, figure 2e and g.
Grain B is an example which is of ~10µm thickness after 80% rolling reduction.
A TEM study, which is very challenging for such a thin material, might give the required misorientation data.