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This study is the beginning of attempts to obtain existing data of estrogens contamination in wastewater treatment plants (WWTPs) in Bangkok Metropolitan area.
However, the results in January 2011 showed very low concentrations though this period should reperesent for dry season according to rainfall data as well.
The explanation for this occurrence were still unclear and required more monitoring data for analysis.
During the March 2010 sampling, estrogens concentrations in all the samples were significantly higher than the data reported in other countries.
Monitoring data in surface waters in Bangkok area should be further investigated.

The parameters of the model were calibrated with measured data of the water depth, flow and water quality in Yuncheng reach of the Fen River.
In addition to the measured data of depth and flow rate of Liuquan section while the flow is 3.05m3/s in 2010, June 29, we got the major parameters in hydrodynamic model.
The data of outlets within the reach is shown in table 2.
According to the hydrologic data in 1988- 2009 of Chaizhuang and Hejin hydrologic station we chose 8.54m3/s as the flow value of normal year (P=50%), and 5.33m3/s for dry year (P=75%).
In this study, the corresponding degradation coefficient k was fixed according to the flow rate, for lacking of necessary data in relevant frequency years.

The goal of an ERP-based integrated information system is to make the system effective and efficient (e.g. by reducing data redundancy and maintaining the data flow through company).
However, this MRP procedure requires the declaration of period for data analysis and is based on the average demand for the assumed period.
The data set (52 weeks) has been divided into two sets: learning (L) – 42 samples and testing (T) – 10 samples.
The testing set includes data for each fifth week (i.e. 5, 10, 15, etc).
The material demand and inventory forecasts are based on the data from an ERP system and can be assigned to MRP procedure.

Data were drawn from the results of Jominy test as initial hardness [5].
Average error compared to the experimental data is 1,01 HRC. 2.
Average error compared to the experimental data is 1,08 HRC. 3.
Average error compared to the experimental data is 1,1 HRC. 4.
Average error compared to the experimental data is 0,88 HRC.

Table 1: Key data on the rheological preparation of the samples Sample No.
The data acquired for the study at hand was first median filtered and thresholded.
A top view on the volume data of the cylindrical samples is shown in Fig. 2.
The result for sample 4 compared to the original data is shown in Fig. 4.
After approximating the additional particle layer, it was removed from the volume data.

According to the empirical data [4-7], various carbon emission coefficients of carbon sources as in Table 1.
statistical standards, the textile carbon emission in Henan Province can not be directly compare with other provinces, but can compare the energy consumption (Million tons standard coal), then you can know the probably situation of carbon emission, each provincial energy consumption statistics data basically from 2007, therefore, this article only compare the two years data from 2007 to 2008.
The energy consumption intensity comparison in industry of Henan in 2008 Data: Henan Statistical Yearbook (2009), where the added value is industrial enterprises above designated size.
Comparison Characteristics in Henan The result in Fig. 1 show that, compared with the other three provinces, the energy consumption of textile in Henan in 2007 was less than Hunan and Jiangxi, however, the data in 2008 was 3 times in Jiangxi.
Journal of Safety and Envionment, 6, 88--91(2006) [7] Gang Wang, Xiao Feng.: CO2 emission reduction through energy integration.

Fixed several discharge conditions discharge, discharge data obtained, and then change the fixed discharge conditions, a series of data are then used as training samples to train the neural network.
First of all, all the data obtained are used to train the network, and the error is into the flat area after training network about 100.
After training completely network, the data during validating data extracting the pulse process parameters is entered into the network to obtain the output predicting value, so it is referred as predicting SOC. 1.
Comparing the predicted and experimental values Since the data are more, the maximum absolute error is less than 5%.
CHEN Shi at School of Chemical Engineering and Environment, Beijing Institute of Technology who supported the experiment data and case.

Large straightness error may result in the life reduction wear and rotational run-out.
Practical examples For comparative purposes, the measured data available in the literature [7] are selected.
Table 1 (a) and (b) show the measured data of the planar straightness error of a linear stage.
It is observed from table 3 that, although there is a disparity in the choice of the initial region, the evaluation results is not much difference for the same data.
For calculating convenience, take two endpoints of the measured data as the initial reference point and the initial regionis more than two times estimation error value of the measured.

The RFID transponder saves data in the chip, keeping away from stain and damage.
The data is password-protected and difficult to be falsified, which ensure its security.
The RFID reader will then read and decode the data, and send it to the higher level system through the communication interfaces[7].
When the battery compartment is in the induction area of the RFID reader, the reader will read ID information on the tag, communicate data with upper-level management unit, upload the collected ID data, execute the command issued by higher level management unit, and finally upload the ID information of the battery compartment to the monitoring system.
The uploaded data is the basis of unified battery compartment management.

The data layer is a database management system, which it defines and manages the contact of the database and the application and it is responsible for the management of read and write data in the database.
Diagnostic database established by the framework standard achieve a shared of diagnostic data formats and specifications.
As long as the data comply with the standard, the system can identify.
The data maintenance module is used by a modular structure of B/S mode, and developed by the VC language.
Only the client has installed data maintenance module subroutine and the users have been authenticated, they have permission to access the data maintenance module.