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Aerobic reduction of uranium(VI) by Acidithiobacillus thiooxidans cultures R.M.Gargarello1,2a, D.
Results so far obtained may suggest an indirect uranium reduction, mediated by metabolically produced reducing compounds.
This data is also supported by the fact that an alkaline pH value tends to stabilize reducing compounds, as previously reported by other authors [14] Fig. 1.
As can be seen in Fig. 3, addition of uranium slows down growing rate, but reducing compounds produced in the reaction media are enough for uranium(VI) reduction.
This phenomenon was produced by the so called indirect reduction, mediated by colloidal reducing compounds: polithionates.

The studied adsorption data fitted well to Langmuir adsorption model with the correlation coefficient 0.9947.
In the present investigation, the experimental data were tested with respect to the Langmuir isotherm.
The data obtained from the adsorption experiment conducted during the present investigation was fitted using different COD concentration into the isotherm equation.
The studied adsorption data fitted well to Langmuir adsorption model with the correlation coefficient 0.9947 in Fig. 6.
The studied adsorption data fitted well to Langmuir adsorption model with the correlation coefficient 0.9947.

It is recommended that the conductor should have a temperature-reduction value of 25℃ according to the conductor temperature-reduction value analyzed by amount of creep, which can be used for reference during line design and construction.
The software specially developed for a creep test is used to collect data in this test.
Data Analysis Method The relationship between creep elongation and time of stranded conductor changes exponential, and it is a linear relation on log-log coordinates.
Analyze test data to get the coefficient in creep equation; use the creep equation to calculate amount of conductor creep for 10 years or 30 years operation, and then offer a regression curve[7].
It can see temperature-reduction value of 5 types of conductors from Table 3.

Lossless data compression is a technique which is used in data compression algorithms to compress text data and also retrieve perfect original data from the compressed data.
The test data obtained will be having huge data.
The hybrid approach is combination of Dynamic Bit reduction method and Huffman coding to compress the text data.
Improved dynamic bit reduction algorithm was designed to improve the compression ratio and memory saving percentage for text data.
For instance, it could be interested in finding representatives for homogeneous groups (data reduction), in finding ‘natural clusters’ and their unknown properties (natural data types), in finding ‘useful’ and suitable groupings (useful data classes) or in finding unusual data objects.

Thermodynamic Modeling of Iron Ore Reduction Using Synthesis Gas A.N.
The thermodynamic model constructed corresponds to the literature and calculated data and can be used to optimize the reduction process under various production conditions.
In order to perform the requisite calculation, the following data is required: 1.
Furthermore, the database encompasses data on condensed carbon (graphite).
The graphical data indicate that the most significant components are Fe(s1), FeO(s2) and Fe3O4(s2), with a concentration exceeding 10-1 mol%.

Recently, data mining approaches have been successfully applied to industrial data analysis to derive useful and comprehensive knowledge [4].
One of the new data mining theories is the Rough Set Theory (RST) (Pawlak, 1982), which can be used for reduction of data sets, finding hidden data patterns and generation of decision rules.
In this study, we propose a systematic approach, including data preprocessing, data reduction, and rule generation, for selecting a group of attributes capable of representing mould risk assessment.
In our work, basic event’s historical data is organized into a decision table.
Some of those attribute data are incomplete, vague or ambivalent in Table 2.

First, the pre-system uses principal component model to convert properties of the source data of the basic window, and it plays a role of dimensionality reduction; Second, the post-system uses the density model to execute clustering operation; Finally, it uses the summary of data, generated in before two steps, to execute simply second clustering and update the clustering result, which fit the requirements of streaming data features.
The Description of a Dynamic Data Stream Model Data Stream.
,an), as the data come constantly , the old data are out from one end of the window and the new data enter from the other one end.
Dimension reduction has been one important research topic in the field of pattern recognition, machine learning, and multivariate data analysis.
Especially with the arrival of the information age, people get large amounts of data, high dimension, unstructured data has become easy increasingly, which makes data reduction has become more urgent.

The standard Gibbs free energy change can be calculated by using basic thermodynamics data [8] when the vanadium oxides were reduced by reducing agent ferrosilicon
Then it is more beneficial to the ferrosilicon reduction reaction.
After 3~5 minutes, prepare tapping after the self-reduction agglomerate are fully melting.
In the self-reduction agglomerate, the amount of reluctant ferrosilicon is surplus.
(In Chinese) [8] Yingjiao Liang, Yinchang Che: Thermodynamics Data Book of Inorganic Compound.

Leakage Process and Minimization--Transistor Stacking Effect, Data Retention Gated Ground Cache, Drowsy Cache Debasis Mukherjeea and B.V.R.
Data Retention Gated-Ground Cache and 3.
Data Retention Gated Ground Cache Data Retention Gated Ground cache is based on transistor stacking effect.
Data Retention Gated Ground Cache.
Obviously very low power supply is needed for passive operation where only data storage is done.

LFDA can preserve both manifold of data and discriminant information.
Dimensionality reduction is the transformation of high-dimensional data into a meaningful representation of reduced dimensionality.
In addition, LPP can only preserve the manifold of data and lose the discriminant information because LPP is an unsupervised dimensionality reduction algorithm.
Dimensionality reduction is performed to solve the problem of dimensionality curse which arises by high dimensional data at the stage of feature extraction.
[9] Masashi Sugiyama: Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis.