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The strain gauges, load cells and LVDTs were connected to the same data logger and the results recorded automatically at each load step.
Comparison of predictions of formula with experimental data.
The figures show that not only the predicted failure modes of prestressed beams are well agree with the actual failure modes, but also the predicted nominal moments are very close to the experimental data.
Because the experimental data is very limited and experimental beams are all over-reinforced prestressed sections, the equations of ideally reinforced section are not confirmed.
(e) The predicted nominal moments are very close to the experimental data.

This paper describes power reduction implementations inthe form of wireless data transmission along with real-time signal processing in the device, which are verified with three in vivo measurements.
During this period approximately 12 MB of raw data is gathered.
−2000 0 2000 Output [a.u]Accelerometer data (Implant) X Y Z −2000 0 2000 Output [a.u.]Accelerometer data (Receiver) X Y Z 0 1 2 3 4 20 30 40 Time [hours]Temperature [ ° C]Temperature Data Implant Receiver Fig. 5: (Top) 3-axis accelerometer data from the implant.
(Middle) 3-axis accelerometer data from the receiver.
It is equipped with a wireless data transmission and an optional real-time signal processing of the ECG data.

Annual average daily traffic (AADT) serves the important basic data in transportation sector.
Introduction Traffic volume is used as basic data for road and transportation planning, design and policymaking.
Short count is carried out in all roads estimated to require traffic volume data.
The results of analysis on traffic patterns for 365 days through the permanent count data showed that there are daily and monthly patterns in traffic data.
However, since there was less change in weekday traffic data, the short count is conducted on weekdays.

HUANG Yuanxiong HUANG [5] further simplified the method and put it contrasted with the monitoring data, finding it matches well.
Therefore the pile length reduction factor can be defined as Eq. 2.
There were few field monitoring data of bottom upheaval because of the difficulty of monitor.
Therefore, the upheaval of lattice column was referred as field monitoring data.
(3) The study of Shanghai Natural History Museum foundation pit showed that the bottom upheaval calculated by the correction method was more close to the monitoring data comparing with the upheaval calculated by method of residual stress.

The data tier comprised of social composition data, economic data, water resource data, eco-environmental data etc., and they are divided into two major categories - parameter and variable.
These data will be collated and transmitted to the constraint tier and objective tier.
There are mainly 74 input data and 44 output data in the model.
The flow data from 1956 to 2005 reflect that of long term average flow and the data from 1980 to 2005 reflect the conditions of relatively low flow in recent years.
Our future work will focus on strengthening the use of remote sensing of ET data.

But LPP cannot be achieved owing to singularity of matrices while the face training samples is deficient and face image data dimension is high.
It combines MMMC with 2DLPP algorithm and constructs a parameter for more optimization. 2DLDPP can further get data in between-class distance more close and in within-class distance more far.
Because MMMC can map the data into an optimal subspace for classification [22], the discriminability of the data will be improved greatly.
Niyogi, Laplacian eigenmaps for dimensionality reduction and data representation, Neural Comput. 15 (6) (2003) 1373-1396
[14] M.Belkin, P.Niyogi, Laplacian eigenmaps for dimensionality reduction and data representation, Neural Comput. 15 (6) (2003) 1373-1396

As a result, it is possible to improve significantly the metal structure at the same values of the forge reduction.
It is shown that the accumulated degree of shear deformation, as well as the uniformity of its distribution, is higher for the new method with the same reduction values.
The reduction per pass was equal to 0.21, where h0 and h1 are the initial and final height of the billet.
This circumstance must be taken into account when developing the technological process by calculating the metal damage ω by the formula where a and Λr are the empirical data of the materials, Λ is the measurement data of the accumulated degree of shear deformation from computer or mathematical simulation [21].

Because the dynamic soft reduction of continuous casting process is based on the computation of the solidification end point, using model to simulating the steel solidificating process is more and more interesting.
Using model to simulating the steel solidification process is more and more interesting, as the dynamic soft reduction of continuous casting process is based on the computation of the solidification end point [4-7].
The input data for the model is listed in table3.
Table 1 Physical parameter used in the calculation Coefficients , kg/m3 , kg/m3 , K , K , J/kg·K , J/kg·K Value 7600 7000 1671 1763 670 840 Table 2 Thermal parameter used in the calculation[1,2] Coefficients , W/m·K , W/m·K , W/m·K , W/m·K , W/m2K4 Value 29.31 23.26 3~7 353 11.1 5.67×10-8 0.7 Table 3 Input data for the model Parameters rd, m , m , m tmax, s Tin, K Tair, K Tcooled_sider, K Value 0.0072 0.005 0.005 10 1873 288 288 The results The calculation of the model is under the following 2 kinds of condition: (a) Setting ml=3, simulating the solidification of steel droplet with initial temperature of 1873 K on the 5 mm water cooled copper plate, Fig. 3.

Introduction This essay is the subject of national eleventh five support schemes: The analysis of key technology on emission reduction and comprehension utilization of solid waste from large iron ore mine (Number:2008BAB32B14).
Table 2 Granule composition of the tailings Sieve's diameter /mm Cumulative percentage retained 0 100 0.045 95.8 0.075 83.28 0.15 60.84 0.3 31.36 0.6 8.76 1.18 3.96 2.36 2.56 4.75 0 9.5 0 After putting the data of Table 2 into the Fineness Module formula (Standard GB/T14684-2001), we can easily calculate out the tailings' module: MX=1.07.
The data of strength of concrete with standard sand refer with Table6.
The data of strength of concrete with ultra-fine iron tailings refer with Table 7.
Acknowledgements This work was financially supported by the subject of national eleventh five support schemes: The analysis of key technology on emission reduction and comprehension utilization of solid waste from large iron ore mine (Number:2008BAB32B14).

Information Entropy Method for Evaluating Regional Earthquake Relative disaster-carrying Capability Juan Liu1, a, Jingyu Su1, b, Wei Wang1, c and Donghui Ma2, d 1Institute of Earthquake Resistance and Disaster Reduction, Beijing University of Technology, Beijing 100124, China 2 College of Architecture & Urban Planning, Beijing University of Technology, Beijing 100124, China alidongzhixue@sina.com, bjysu@bjut.edu.cn, chautww@126.com, dieemdh@163.com Keywords: information entropy method; earthquake relative disaster-carrying capability; evaluation method Abstract.
Only in this way, our country’s urban earthquake disaster reduction can develop evenly.
Disaster-carrying capability Disaster prevention ability Social factors Employment Education Medical treatment Social security Economic factors Disaster prevention investment Monitoring and forecasting facilities Environmental factors Environmental protection efforts Resilience capability Social factors Population density Population situation Economic factors Wealth density of fixed foundation Engineering anti-seismic capability Resilience of building structures Resilience of lifeline subsystems Lifeline system correlation Disaster response capacity Social factors Ability of medical assistance Government emergency response ability Economic factors Lifeline restore ability Internal and external communications developed degrees Drainage conditions Secondary disasters Environmental factors Temporary relief distribution center Disaster recovery capability Social factors Production and construction HR Economic factors Economic diversity Wealth savings Insurance Environmental factors Environmental quality Data
Processing We may get standardization of indicators according to standardize the original data matrices.
We adopt the basic data of the cities and standardize them to establish the characteristic matrix.Using the Eq.1～Eq.3, we can get the entropy and the information utility value , and use Eq.5 to work out each indicator’s weight .