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This study analyzes the characteristics and reasons of the eco-hydrological evolution based on the 1982 and 2006 phytoplankton and zooplankton survey data[8,9].
Water quality data is from 1998 to 2010, the rainfall data is from 1956 to 2010, and the measured runoff data of Bengbu Gate is from 1956 to 2000.
The reduction of dry season runoff and flood runoff in Bengbu Segment is one of the reasons for the reduction of organisms.
But the mechanism is not clear due to the lack of data in survey data of aquatic organisms.
Due to lack of monitoring data of the Huaihe River aquatic organisms, the article only analyzes the eco-hydrological evolution of Bengbu Segment resulting in some limitations, which needs increasing monitoring points and frequency to strengthen the monitoring efforts of aquatic organisms in the Huaihe River.

The bandgaps of the films were calculated using measured optical reflection data.
Another reduction peak was observed at -200 mV.
The bulk reduction occurs at -400 mV.
Elem Te Au Cd Total Cd/Te Wt% 5.46 88.25 6.27 100 At% 9.68 81.48 8.82 100 0.9 Fig. 9 is optical reflectance data from a 100-cycle CdTe film.
Fig. 10 is reflectance data from a 100-cycle PbTe film.

The test data included a concatenated body of text from the King James Bible.
Requisite data were prepared for running the CADWORK heuristic.
These together with the other data types were fed into CADWORK for the analysis.
CADWORK computes the distances between the bins and employs the MTM-2 data card to synthesize the time for the type of motion involved at each line of the methods analysis of operations data.
The data included anthropometric data of the operator; character positions in the keyboard layout regarded as bins; relationships matrix; and methods analysis of operations data for simulation of the cycle times employed for layout evaluation.

The data mining process including four basic steps, as shown in Fig.1: question definition, data preparation and pretreatment, data mining, result explanation and confirmation.
The magnanimous data is accumulated in electric power industry process, and the massive knowledge exist in the historical data.
The rough set method can process each kind of data, including the incomplete or imprecise data, as well as the data of numerous variables, and using the data it can also produce the model which consists of simple concepts and is easy to operate, and produce precise but easy to inspect and confirm rules.
The mining of data connection rules discovers useful connection or related relation between mass data item sets.
Turbine Classification Rules Based on Rough Set Reduction Algorithm Proposed the introduction of the rough set data mining prediction and diagnosis of turbine failure, to overcome the traditional expert system knowledge acquisition bottleneck problem.

The numerical simulation results were then compared with the experimental data reported by AL-Salehi et al. (1973).
Compared with the experimental data reported by AL-Salehi (1973), these results are still within the reasonable range.
Figure 1 shows the comparison between the rolling force in cold rolling and experimental data.
Comparison between the average rolling force from Units l to 4 and the experimental data obtained by Al-Salehi et al. (1973).
the average rolling force from(N/mm) reduction cold rolling the experimental data (%) results Load cell Integrated Calculated 14.17 718 637 767 622 21.86 1127 1048 1281 842 29.40 1646 1474 1569 1024 34.41 2035 1839 2l08 1161 Fig. 1.

Referring to the source of the research, the data are taken from two variable groups: latent variable and indicator variable.
The data collection techniques are field observation method, laboratory observation, interview, and scale method.
Results and Discussion In order to generate representative study result, two activities are exercised; data collection and measurement, and theoretical structural model development as a prototype model.
Data collection and measurement are to be the first step to generate more representative data for indicators in every latent variable.
The activity is firstly started by collecting dust from particular area or environment and compiling housing data.

It is important to note however that such microstructures are not uniform, although detailed experimental data concerning the dependence of the deformation microstructure on the crystal orientation are limited.
Microstructures in: (a) S region, 90% reduction; (b) B region, 90% reduction; (c) S region, 90% reduction (the high angle subgrains are highlighted); (d) 98% reduction sample.
These data show a small increase in the fraction of very low misorientations (from 2° to 3°).
One possibility is that this increase is related to the required minimum misorientation boundary definition threshold used in EBSD data analysis.
Further investigation of this will require comparison of EBSD data with transmission electron microscopy data from regions of similar crystal orientation.

Text Mining is a branch of Data Mining, but the biggest difference between them is the different operating object.
Now Text Mining has become one important branch of Data Mining and achieves significant results.
What’s more, SVM is the commonest used feature model. 3） Feature reduction.
And the experimental data is binary single labeled including 208 instances with 60 attributes.
Research on Web Text Data Mining: TongJi University.2006, In Chinese; [2] YUnlong Li.

Physical Size Reduction: Silicon Si PiN vs.
IGBT steady-state loss (PSS) uses VCE- IC curve fit data.
IF curve fit data.
(Left) Near Field Radiated EMI 10 db uV / Div 3 MHz / Div -10 db (3.1:1) reduction KE3 / SiC 150 kHz to 30 MHz 150 kHz to 30 MHz reduction with KE3 -SiC -10 db(3.1:1) -10 db (3.1:1) reduction KE3 / SiC 150 kHz to 30 MHz 150 kHz to 30 MHz reduction with KE3 -SiC -10 db(3.1:1) 150 kHz to 30 MHz reduction with KE3 -SiC -10 db(3.1:1) 3 MHz ~ 108 ns 3 MHz to 30 MHz reduction with KE3 -SiC -8 db(2.5:1) 3 MHz ~ 108 ns 3 MHz to 30 MHz reduction with KE3 -SiC -8 db(2.5:1) 3 MHz to 30 MHz reduction with KE3 -SiC -8 db(2.5:1) 10 MHz ~ 32ns - 6db (2:1) reduction KE3 / SiC 9-12 MHz - 6 db (2:1) KE3-SiC at 9-12 MHz reduction with -10 db (3.2:1) reduction with KE3 - SiC at 22 - 24 MHz 10 MHz ~ 32ns - 6db (2:1) reduction KE3 / SiC 9-12 MHz - 6 db (2:1) KE3-SiC at 9-12 MHz reduction with - 6db (2:1) reduction KE3 / SiC 9-12 MHz - 6 db (2:1) KE3-SiC at 9-12 MHz reduction with - 6db (2:1) reduction KE3 / SiC 9-12 MHz - 6 db (2:1) KE3
-SiC at 9-12 MHz reduction with -10 db (3.2:1) reduction with KE3 - SiC at 22 - 24 MHz - 6 db (2:1) reduction KE3 / SiC 18-22 MHz - 6 db (2:1) reduction KE3 / SiC 18 -22 MHz with at - 6db (2:1) reduction KE3 / SiC 9-12 MHz - 6 db (2:1 ) KE3 /SiC at 9 -12 MHz reduction with - 6 db (2:1) reduction KE3 / SiC 18-22 MHz - 6 db (2:1) reduction KE3 / SiC 18 -22 MHz with at - 6 db (2:1) reduction KE3 / SiC 18-22 MHz - 6 db (2:1) reduction KE3 / SiC 18 -22 MHz with at - 6db (2:1) reduction KE3 / SiC 9-12 MHz - 6 db (2:1 ) KE3 /SiC at 9 -12 MHz reduction with - 6db (2:1) reduction KE3 / SiC 9-12 MHz - 6 db (2:1 ) KE3 /SiC at 9 -12 MHz reduction with

Problems and Countermeasures of Air Conditioning Systems in Internet Data Centers in Xi’an city Xiaohong Yanga, Zhiwei Wangb, and Xiang Jingc Xi’an University of Architecture & Technology, Xi’an, Shaanxi, 710055, P.R.China a776995609@qq.com, bWzhiwei-atu@163.com, c410419290@qq.com Keywords: air distribution, high temperature alarm, system energy efficiency, internet data center, countermeasures Abstract.
Site investigation on air conditioning systems (ACS) in three internet data centers (IDCs) in Xi’an city was conducted, the relative data used for issues analysis was obtained from on-site testing of temperature field and velocity field in IDCs and online working meter on power distribution box of ACS and computer cracks, the field investigation found that there were three main problems: high temperature alarm of larger power electronic components in computer cracks; irrational air distribution around computer cracks; and not high energy efficiency of ACS.
They include the cold and hot air stream mixing, nonuniform of static pressure in underfloor plenum chamber, and air temperature rise of jet flow in upper air supply mode etc., and lead to the energy efficiency reduction of ACS.  
The improvement measures of system efficiency will be taken in three aspects: 1) a rational air distribution should be designed according to different demand of microthermal environment of CRs in IDCs; which involves air supply pattern selection and individuation demand satisfaction etc.; 2) for air cooling type of condenser with water spray, based on testing data from field or manufacturer, the optimal time of water spray and interval of twice spray can be determined for best heat exchange effectiveness; 3) by adding an independent fresh air system, outdoor air natural cooling resource can be fully used in transitional seasons and winter, thus electricity power consumption of refrigerator will greatly decreased during corresponding period; 4) by using water loop heat pump system, a great deal of heat from CRs in IDCs can be absorbed and transferred into a zone of heating demand or as a heat source for hot water supply in IDC building.
C&AC.21 (2007), p. 105-107 [4] Xiang Jing: Test and research on the Air-conditioning system in Internet Data Centers.