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To make up for a lack of past studies, based on the autoptical data in Dali river, a tributary river that has the most dams and reservoirs in He-Long region, analysis and studies were carried out on sediment reduction effects by soil retaining dams with different allocation proportion in this paper.
Sediment reduction by different types of dams is given in Table 1 [9].
In August 1994, the sediment transport was 100 million ton annually because of heavy rainstorm, on August 10, 1994, the daily sediment transport was 33.8 million ton, the annual sediment transport modulus is 25700t/km2; the annual flood sediment transport would be 118.7 million ton by restoring sediment reduction with slope surface measures, accounting for 48.1% and 57.1% of the annual sediment transport of 208 million ton by Wuding river that year, being the maximum value in the observed data series.
The maximum sediment reduction capability (sediment reduction capability) of different types of soil retaining dams correlates closely with flood season rainfall.
Within the sediment reduction capability of soil retaining dam, the sediment reduction increase with more rainfall and more incoming sediment in the flood season, having the feature of “the more incoming sediment is, the more sediment reduction will be”.

The main phase of the reduction product was Fe2B, FeB and SiC.
Though the best combination has in the nine experiments, but experiment were needed to increase some data to analyzing if samples has were reacted completely.
The main phase of the reduction product was Fe2B, FeB and SiC.
The main phase of the reduction product was Fe2B, FeB and SiC.
High-temperature reduction of ore comprising ludwigite[J].

To eliminate the redundancies, noises, incompletion, and inconsistencies in the data set of sluice monitoring, a method of data preprocessing to implement data mining is proposed by integrating the data preparation process in data mining and data warehouse [4].
Data preprocessing Before starting data mining, data preprocessing should be performed to eliminate or reduce the the redundant, noise, incomplete or inconsistent data.
Processing Data mart Monitoring DB Hydrological, meteorological DB Other data Cleaning Integration Transformation Reduction Data Warehouse Data sets Extraction Loading Input Output Fig. 1 The process of data preprocessing Beginning from selecting the data sources, the operation of data preprocessing searchs the existing rows (records) and columns (attributes) related to the data mining tasks, then performs data cleaning, integration, transformation, and reduction based on the result data sets after extraction, and in the last the process is finished when the data processed are stored in the specified data mining sets.
The data extraction and loading are the operations that must be implemented whatever the data are from any kinds of sources, but it is not necessary for data cleaning, integration, transformation, and reduction in the processing stages.
Only when the data preprocessing is finished can we get clear data to perform data mining operations.

The texture of the secondary recrystallized sample under 97.2% cold rolling reduction rate condition consists of {110}<112> orientation, which is quite different from Goss ({110}<001>) orientation obtained under lower cold rolling reduction rate conditions.
There are some experimental data demonstrating that these grain boundaries have high energy (HE) [3].
In order to change a primary recrystallized texture, it is effective to control cold rolling reduction rate.
The orientations after secondary recrystallization were measured by the back Laue reflection method, and its ODF was calculated from the secondary recrystallized orientation data using the series expansion method.
The sample under the 92.6% cold rolling reduction rate condition has highly oriented Goss texture.

The model fitted quite well with the collected data, suggesting that the model was applicable.
So it is greatly necessary to develop new technologies for the reduction and disposal of waste sludge.
Data processing and model evaluation Nonlinear fitting analysis and significance test were done using SPSS17.0, Origin 7.0 and 1st Opt software.
Fig.3 The uptake and release curve for TOC: a, the uptake curve; b, the release curve Fig.4 The uptake and release curve for TN: a, the uptake curve; b, the release curve Fig.5 The uptake and release curve for TP: a, the uptake curve; b, the release curve Non-linear fitting method and related equations (3) and (4) were applied to the experimental data.
Based on data given above, the specific release rate of pollutants by Tubificidae in dry weight could be calculated.

Quantitative data about crystal orientation within shear bands were taken from EBSD mapping data.
Crystal orientations within SBs were analyzed from EBSD data.
In each specimen, the EBSD mapping data in the vicinity of the largest angle of SBs were taken into account.
Reduction h1 q1 q2 h2 Fig. 4.
Change of SB angles during reduction.

Ferric iron reduction was observed in all incubation modes.
Members of genus Acidiphilium are known to couple the oxidation of glucose to the reduction of Fe(III) [4].
Values of redox potential were plotted against related Fe 3+/Fe 2+ data obtained from the chemical analysis of the solution for both iron species.
Bacterial growth and iron reduction is shown in Fig. 2.
Because the ferric iron reduction and Fe(III) coupled CO2 production were exponential, the corresponding rate constants were calculated from the respective slopes of lines on semilog plots (data not shown).

Introduction Air pollutant emission reduction effect is an important indicator for the evaluation of port enterprise, national and regional energy saving and emission reduction effectiveness.
Although Eq.1 is relatively simpler, there isstill a considerable amount of work needed not only having to investigate fuel or energy consumption of all national container port,but also to statistic and analyze the data.
Hi,j,k=l=0l(TEFi,l×Tj,k×10-4) (2) In Eq.2, Hi,j,k: same as formula-1; TEFi,l: the ithair pollutant’s emission factor per unit throughput of lthcontainer terminal handling facility(t/ TEU), estimated by fuel consumption method (Eq.1) using the actual investigation data of throughput, fuel or energy consumption in representative container terminal; Tj,k: the throughputamount of jth port’s container terminal in kth year (TEU/a).
Fig. 2 Container port throughput in 2012 and highway distributing minimum mileage Fig.3 Air pollutant emissions in 2013 from port handling and highway distributing In the calculation above mentioned, the state published data of coastal and inland river port throughput and unit energy consumption, container throughput, heavy truck and ordinary truck unit mileage energy consumption in 2013 [24], and container handling facilities energy consumption of unit throughput (coastal and inland river port in the same) estimated by this research investigation are used, respectively, into Eq.4 and Eq.5.
Evaluation of air pollutant emission reduction.According to the published data of coastal and inland river port cargo throughput and container throughput from 2001 to 2013, this research has obtained the non containerized cargo throughput of port, and the classification of statistics formula predicting throughput (Fig.5 to Fig.6(left)), in which the correlation coefficients range from 0.971 to 0.998.

Substituting the above control experiment data into the above formula, the value of the specific reaction rate at any temperature can be obtained.
Results and Discussion Preliminary Results After many experiments and adjustments, it was found that in the case of 200 pellets, the most consistent with the original experimental data.
The results obtained are not much different from the experimental data in the literature, which proves that this method is feasible.
The degree of reduction and the reduction time of the pellets are obtained in continuous time.
Melt reduction [M].

In order to solve the multidimensional data model and relational data model,query between the two-way data system, data cleansing, data conversion, distributed data accuracy and consistency control problem, this paper described the concept of grid related, the global data mining combined with local data mining is proposed based on local information based on the concept of a global grid of data mining algorithm, and the mining process was divided into ETI.
Data mining which is called Knowledge Discovery in Database is to extract or mine knowledge from large amounts of data.
Association analysis is to find association rules hidden in the data and the rules describe some interesting relations of the items in the given data set [2].
Shao :The reduction for two kind of generalized concept lattice.
Zhang :Knowledge Reduction in consistant decision formal context.