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Saiang’s group has gotten valuable monitoring data by microseimic tests and conducted study on mechanical response of EDZ around shallow tunnel in brittle rock by assuming deformation modulus distributes linearly along radius from excavation surface, as shown in Fig.3.
Fig. 1 Regular partition and uniform reduction method Fig. 2 Regular partition and linear reduction method Irregular partition and uniform reduction method.
Irregular partition and uniform reduction method is to determine EDZ’s zones by numerical calculation or curve-fitting based on field tests data, such as deformation, acoustic velocity, and then, in every zone the parameters are uniformly reduced, as shown in Fig.4.
Whatever regular or irregular partition, mechanical parameter reduction is determined according to engineering analogy or limited local monitoring data, which is experience dependent and of uncertainty due to researcher’s engineering experience and knowledge.
Thus, monitoring data needs to be effectively and generally used and analyzed to find certain macro relationship between outer characteristics and inner mechanism of excavation response, and then establish a model to connect determinations of scope and parameter reduction of EDZ.

Investigation on vibration reduction effects of a new vibration isolation module for pier foundation Zhaoxia Ma 1,2, a, Weiping Xie 1,a and Guangcai Yan3,a 1 School of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan, China 2Hubei University of Technology Engineering and Technology College, Wuhan, China 3 Zhongnan Hospital of Wuhan University, Wuhan, China a zhaoxiama@foxmail.com Keywords: Vibration isolation module; vibration isolation; peak acceleration; vibration level Abstract.
The simulation and measured results suggest that vibration reduction effects are satisfactory.
The vertical, radial horizontal and tangential horizontal vibration levels of each measuring point under various working conditions were calculated and the vibration reduction effects of the vibration isolation module were evaluated.
The vibration levels can be calculated from the measured and numerical simulation calculated acceleration data of each measuring point.

A base case model is developed from the production data of an actual oil field to simulate the performance of the actual well without gas lift system.
However, injecting too much gas could possibly result in reduction of production rate (Blann and Williams, 1984).
Once the well system model has been tuned to the real field data, different scenario can be modeled to make further predictions of well performance.
Well test data was utilized to develop a base model with the inflow and outflow performance curve matched with the well performance.
With further increment of gas injection rate, a small reduction of oil rate was observed.

Several studies have produced estimates on the influence of rolling parameters [4, 5, 6], but there is still insufficient data for complete understanding of the limits of influence of process variables.
The EBSD data were acquired in a rectangular scan grid with a step size of 0.2µm and post processed with OIM-TSL® Data analysis version 4.6 software.
ACC ↗ Increasing reduction per pass ↘Decreasing reduction per pass 10%-21%-32%-35% 35%-32%-21%-10% High FRT Low FRT Fig. 1 Grain boundary maps of: (a) schedule I, (b) schedule III, (c) schedule (II), (d) schedule (IV) showing HAGB >15° misorientation (black) and LAGB >2- 15° (white) Figures 3 and 4 show grain size distribution maps obtained by the EBSD data.
Air ↗ Increasing reduction per pass ↘Decreasing reduction per pass 10%-21%-32%-35% 35%-32%-21%-10% High FRT Low FRT Fig. 2 Grain boundary maps of: (a) schedule V, (b) schedule VII, (c) schedule (VI), (d) schedule (VIII) showing HAGB >15° misorientation (black) and LAGB >2- 15° (white) From this data (Figs. 3 and 4) one can see that in general high FRT resulted in the prevalent presence of large grain size clusters.
The most striking observation which emerged from the data comparison (route V vs. route VII) and (route II vs. route IV) was that although the change from decreasing to increasing reduction per pass corresponded to a grain size refinement, no improvement in the yield and tensile strength was measured.

P_base express the base load of TOU, that is the original data of IEEE39bus model.
(3) In Eq.3, P_RTP is the load power; P_base is the original data in IEEE39bus model; Pri_rt is real time price; Pri_base is the average price of historical data.
In day-ahead part, we calculate OPF 96 times, to get the base data (OPF output).
Then, the DR parameters are set according to these base data.
In daily part, we calculate OPF for another 96times, to get the DRs operation data.

The present paper summarizes the data obtained during the about 10-year period of such operations and contains the main conclusions based on these data.
Data about the microflora of the wetland are shown in Table 1.
Content of radioactive elements and heavy metals in different plant species from the constructed wetland Typha latifolia Phragmites australis Scirpus lacustris Elements I II I II I II U, mg/kg 28 - 95 ND 12 - 82 ND 12 - 59 ND Ra-226, Bq/kg 35 - 145 ND 35 - 125 ND 23 - 82 ND Cu, mg/kg 55 - 194 7 41 - 140 5 23 - 71 3 Zn, mg/kg 32 - 225 5 64 - 203 7 44 - 114 3 Cd, mg/kg 5 - 23 ND 5 - 18 ND 3 - 10 ND Ni, mg/kg 21 - 100 3 18 - 79 3 14 - 59 3 Co, mg/kg 15 - 71 2 15 - 105 3 10 - 51 2 Pb, mg/kg 12 - 64 5 8 - 54 5 9 - 32 7 Mn, mg/kg 91 - 325 10 44 - 262 8 41 - 154 8 As, mg/kg 6 - 68 3 10 - 59 3 6 - 28 2 Notes: I - Data about plant specimens grown in the wetland; II - Data about plant specimens grown in a wetland non-polluted by radioactive elements and heavy metals.
The microbial sulphate reduction was a function of the concentration of organic monomers dissolved in the waters.
Under such conditions the microbial sulphate reduction still proceeded, although at much lower rates.

Data collection/site visit: Primary and secondary data were used in this study.
Secondary data were obtained from documents and publications on the subject of solid waste practice and management.
The field data acquisition was to establish the physical status of each site through visual observations as well as geographic coordinates.
No data on waste stream composition was undertaken in the field.
Waste characterisation data was established by using desktop information.

Since the influential factors of vacuum reduction of condenser are fuzzy and uncertain, the improved FAHP is used.
We can build a condenser vacuum reduction factors set and fault symptoms set [4].
So can establish a condenser vacuum reduction factors hierarchical analysis system.
The following is the test data: unit loads 600 MW, the exhaust steam temperature of low back pressure condenser dropping from 44℃ to 37℃, the vacuum degree rising from 93kPa to 95.6kPa, the coal consumption dropping from 240 t/h to 231 t/h.
For the failure instance in paper, the final result is "the reduction or interruption of the water pump seal water".

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.

Study on the Data-mining based Mechanism for the Occurrence of Ship Collision Accidents Caused by Human Factors Hongxi Li Dalian Maritime University, Dalian, Liaoning Province, 116026,China Keywords: Accidents Caused by Human Factors; Collision Accident; Bayesian Network; Data Mining; Cause Chain Abstract.
In order to effectively analyze the mechanism for the occurrence of ship collision accidents caused by human factors, an accident causing chain was constructed using the Bayesian network structure and the data mining algorithm.
Data mining algorithm.
Apriori algorithm can effectively mine the frequent item sets of the association rules and discover the associative, valuable data models from the mass data using the prior knowledge of frequent item sets.
Traffic Accident Analysis based on Data Mining Technology [J].