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Zhangjiakou rainfall data were analyzed for many years, the rainfall from April to October accounted for 94.10% of the rainfall throughout the year, the rainfall from May to October accounted for 87.48% of the rainfall throughout the year.
Seasonal reduction coefficient calculation often considers the comparison between the rainfall data in the months that daily temperature above 0℃ and the rainfall data throughout the year.
According to the actual monitoring data of many northern city, the water quality laws are roughly the same or similar, combined with shortage of water resources in Zhangjiakou, this paper suggests Zhangjiakou City initial abandon flow coefficient adopts 0.9.
According to the data, excluding the factors of seasonal reduction, rainwater runoff initial abandon and runoff coefficient, the average annual rainwater resources potential of this city is 8296.5 × 104 m³.
Through the analysis on the related data in study region, the calculation formulas of the storage tank capacity and annual rainwater collection are derived.

Based on the investigation data of Hg in Jiaozhou Bay waters in April, July and October 1986, this paper tried to analysis the horizontal, vertical and seasonal distributions of Hg in Jiaozhou Bay, and to reveal the pollution sources and transfer processes of Hg.
The water environmental capacity of Hg in Jiaozhou Bay had been unable to carry the emission load of Hg, and therefore the reduction of emission as well as the improvement of cyclic utilization ratio were the primary processes for Hg pollution prevention and reduction.
Based on the investigation data of Hg in waters in Jiaozhou bay in April, July and October 1986, the major purpose of this paper were to analysis the horizontal, vertical and seasonal distributions of Hg in Jiaozhou Bay, to reveal the aquatic transfer process of Hg, and to provide basis for decision-making of pollution control.
Data source.
The data was provided by North China Sea Environmental Monitoring Center.

Sample size determination was done from pilot study data and 3 specimens per group were determined to be adequate for statistical analysis.The specimens were placed individually in a 400 ml glass beaker (Pyrex; Dow Corning, Corning, NY), containing 200 ml of tap water.
For the flexural properties, data was analyzed using the non-parametric Kruskal-Wallis test.
When compared to the control, a total reduction of 94% to 98% biofilm was seen at 120 s for both 850 W and 1000 W for all four yeast species tested.This was the highest reduction seen from all the groups.
Control 850 W 1000 W 90 sec 120 sec 90 sec 120 sec 71.115 69.444 69.338 69.768 69.002 Discussion The data obtained from the study supported the rejection of the null hypotheses that the microwave did not reduce adhesion and biofilm quantities of the candida cells.
Although slight reduction in the flexural strength of the PMMA resin was noted when compared to the control specimens, the data was not statistically significant.

The kinetic characteristics of the reduction are determined, since they fix at which rate the plating can proceed.
Prior to the kinetic study, requiring measurements in solutions of varying composition, the thermodynamic data on complex formation between Ag + and −2 32OS ions are analysed.
The kinetic characteristics of the reduction are determined, since they fix at which rate the plating can proceed.
Prior to the kinetic study, requiring measurements in solutions of varying composition, the thermodynamic data on complex formation between Ag + and −2 32OS ions are analysed.
It can be seen that i-1 is a linear function of both concentration and rotation speed parameters, so it is clear that the reduction follows a mechanism involving mass and charge transfer steps.

In 2000, LLE dimension reduction method was put forward for nonlinear reduction on Science by Roweis [3].
Punctured Sphere simulation data set is simulated by PCA and LTSA algorithm based on Matlab.
Fig. 1a is punctured sphere data set, which is a data set with a hollow ball shape.
Fig. 3a is PCA feature extraction that different sample data are overlapping and mixed relatively, so it do not extract low dimensional manifold of the sample data.Clustering feature and inner structure are shown for the same kind sample data in Fig.3b.
Fig.4 Diagram of diesel engine wear fault recognition rate Conclusions It is concluded that LTSA feature extraction method is researched for vibration data reduction.

All the data were the average value of repeated tests for three times, SPSS20.0 was used to conduct data analysis, PhotoshopCS and Excel was used for the chart and diagrams.
Fig.2 showed with the increasing of the hydrolysis time, the reduction rate of α-lactalbumin antigen in three-groups hydrolates gradually increased.
Fig.3 showed that with the increasing of the hydrolysis time, the reduction rate of β-lactoglobulin antigen made by trypsin and flavourzyme gradually increased.During hydrolysis for the first forty minutes, the antigen of β-lactoglobulin reduced more quickly in several hydrolates, while forty minutes later, the reduction rate of β-lactoglobulin antigen became flat.
And after 40 minutes of trypsin hydrolysis, then flavourzyme was added, the reduction rate of β-lactoglobulin antigen increased more quickly.
After 60 minutes of hydrolysis, the β-lactoglobulin antigen reduction rate of three groups were 13%, 55%, 71% respectively.

Cost reduction in magnesium alloys would be expected with increasing the volume of magnesium alloys used.
However, the reduction in the cost of producing primary magnesium will have a direct effect on the volume of magnesium alloys used in automotive applications.
Cost reduction in magnesium alloys can also be achieved by increasing its recyclability.
Also, utilization of 2-cavity cold chamber die casting enhances productivity leading to cost reduction.
At the start of design, we gathered all the data available that could impact the magnesium SCLH module being developed.

For instance, data collection and transmission etc may introduce noise.
Therefore in practice we must carry out denoising before analyzing measurement data.
For general 0≤a≤1, The size of data estimated by this method is between soft, hard methods, so it is called soft and hard thresholds compromise methods.
Example analysis This section adopts a set of RTK dynamic deformation monitoring data observed by GPS continuously to carry out denoising experiment analysis.
GPS and GLONASS combined measurement and research on deformation monitoring data processing.

Introduction Mechanical property data of high temperature structural materials and mechanical behavior are important bases for material selection, structural integrity evaluation and life prediction of aircraft engines.
With the development of structural strength design of aircraft engines, the demand range for materials performance data used for aircraft engine design is consistently expanding, which requires in-depth studies of materials mechanical behavior and improvement in materials mechanical properties used in aircraft engine design.
The present data of stress rupture properties of single crystal superalloy were obtained through standard cylindrically specimen.
With the reduction of the section size of thin-walled specimens, the stress rupture lives of DD6 alloy decrease slightly.
All the data is smaller than 1, suggesting that the stress rupture lives of thin-walled specimens do not reach that of the standard cylindrically specimen, i.e., thin-walled debit exists in the machined thin-walled slab specimens of DD6 single crystal superalloy.

Solutions responsible for data acquisition for management purposes should constitute integrated system, combining data sources located in the lowest layers of the production system, middle-layer sub-systems responsible for the initial interpretation, data pre-processing and reduction of the amount of data, as well as communication interfaces and industrial database responsible for robust data archiving [4].
MIAS data sources.
The process of data integration can be divided into two phases – the pre-processing (basic data manipulation, such as formal verification of correctness, analog-to-digital conversion, digitization and reduction) and aggregation of information [11].
It allows significant reduction of data amount and facilitate further analysis due to initial interpretation of dispersed information [13].
Data archiving in MIAS.