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According to the characteristics of environment emergency responding, attention should be paid to the following three points with respect to water environment model selection: 1)First, as emergency responding is pressing and complex, the decision makers must make relatively proper first response at the preliminary stage, and must get accurate pollution information later, so there must be a model system consisting of both quick engineering model and fine scientific model to meet the needs in different phases of emergency responding. 2)After an accident, the data required for model calculation such as the hydrological data, water quality data and pollution source data are not complete at first, but will be gradually obtained during the responding process.
Aiming at the characteristics of water pollution change, this paper uses a model of base state with amendments to build the space-time data model, and uses AcrSDE to realize management of the space-time data.
Basic Correction Model 2.4 Integration of water environment model and GIS The methods and degree of integration of water environment model and GIS depend on the complexity of the model objective, the requirement of the model for GIS functions of the base data, the practicability of the interface, the compatibility of the data model, the hardware environment, the system structure of GIS model software [12,13], and so on.
Pollution Situation in Scenario 3 The pollution data at the Baishiyao section in three scenarios are entered into the system for comparison analysis, as shown in Figure 6.
Comparison of Cadmium Concentration Change at Baishiyao Section in the Three Scenarios The comparison of the pollution data in Scenario 3 and the actually measured data (as shown in Figure 7) shows that the two change processes match very much, indicating that this study can accurately forecast and simulate pollution change after implementation of various emergency responding actions and can be used for actual emergency responding.

However, after 56 and 180 days, the reduction in elasticity rapidly occurs about 48% and 78%, respectively.
On the other hand, after 180 days the mortar prism was become soft and brittle causes no data could be recorded.
The reduction in modulus of elasticity of mortar occurs due mainly to the crystallization of sodium sulphate.
The reduction in compressive strength causes the reduction in elastic modulus.
Meanwhile for the 5% sulphate solution the reduction in elasticity recorded about 51% approximately.

In the recent years, the feature extraction algorithms based on manifold learning, which attempt to project the original data into a lower dimensional feature space by preserving the local neighborhood structure, have drawn much attention.
LDA produces an optimal representation with the restriction that the ratio of the trace of the between-class scatter and the trace of the within-class scatter is maximized when the given data are linearly distributed.
But, when the data is distributed in a nonlinear way, LDA may fail to discover essential data structures.
Another technique called Locality Preserving Projections (LPP) [2] has been proposed for dimensionality reduction, which can preserve the intrinsic geometry of data.
Then, we first maps the input data into a potentially much higher dimensional feature space by virtue of nonlinear kernel trick, and in such a way, nonlinear features are extracted by running KNSMFA.

In view of floods, based on the historical data, significant flooding appears to occur every 2 to 3 years and it always occurs 2-3 times a year.
However, the shape of simulated flow response still matches well to the measured data series, even though the model underestimated/overestimated some peaks.
The proposed reduction in daily reservoir release is intentionally varied by 10%, 30%, and 50% from its original daily outflow in order to create various “what if?”
However, the flow reduction is not significant at E.20A streamflow station as a result of substantial flows from unregulated tributaries.
Varying the reduction of both reservoir releases by 10%, 30%, and 50%, simulated discharges were decreased by 5.86%, 17.57%, and 26.59%, respectively.

We also demonstrate the involvement of NADH-dependent NRs in metal tolerance, silver reduction and nanoparticle synthesis.
The biomass turning brown-black as a result of reduction of silver is a convenient indication of the formation of silver nanoparticles.
Data is the enzyme activity in the presence of inhibitor as a percentage of that in controls where inhibitor was not added in presence of 0.5 mM AgNO3.
The reduction of nitrate to nitrite is catalyzed by NR using NADH as the electron donor, however in the presence of silver the electron is shuttled from NADH to reduction of silver ions [15].
SNPs synthesized in HNB exhibited a smaller size distribution between 2- 20 nm with an average particle size of 12 nm, which corresponds well with the XRD data.

Data processing and comparison further contribute to expand knowledge and appropriate personal database aiming material design, manufacturing error minimization and cost reduction.
The results can be exploited in manufacturing cost reduction while tailoring polymer based composite architectures for various structural applications.
Experimental plotted data corresponds to the second thermal runs.

The selection of reduction and oxidation catalyst is the key factor of the gas after-treatment process.
Additionally, vehicles’ own parameters (vehicle weight, engine displacement, front projection area, main reduction ratio, and gear reduction ratio and fuel type et al.) need to be considered together with factors above.
And then compare the experimental results with actual data measured on May 24th.
Compare real data of vehicle emissions with forecasting result The basic information of testing vehicle is listed in Table 4.
From Fig. 8, except that value of emissions data from Daoli Twelve Street to Zhaolin Street is far higher than forecasting result, others are satisfying.

Table 4 OCT of different WMA Mix type OCT[℃] Mix type OCT[℃] WMA(A) 121~124 WMA(B) 122~124 WMA(C) 121~123 WMA(D) 108~111 Compared with HMA, the OCT reduction of WMA(A),WMA(B) and WMA(C) in Table.4 are close, it is about 12~15℃, while the reduction of WMA(D) reaches to 26~27℃.
Above data are far less than advertise of additives producers, the reason is that producer’ data are commonly compared with the limit of actual construction.
OCT ranges of all WMA in Table.4 look narrower than that of the basic HMA, In fact, it’s the result that limited number of data is fitted to straight line.
The compactibility of WMA(D) significantly higher than others, and its OCT reduction reaches to 26~27℃.
By means of the equidensity-temperature method, the OCT reduction of WMA(A),WMA(B) and WMA(C) are similar and about 12~15℃, while the reduction of WMA(D) reaches to 26~27℃. ⅳ.

Collection Material Basic Data: This engineering project construction standard, project characteristic and so on.
Technical Data: This engineering project design document, construction organization plan, additional document by each kind of needed materials specification and quality technology target and so on.
Economic Data: Construction drawing budget, construction budget, cost plan and manpower and materials machine expense clear and so on, including: · Folk recipe and foundation engineering cost · Main body structural engineering cost · Costs and so on roofing waterproofing and heat insulation · Decoration repair project cost Functional Analysis and Appraises.
The cost reduction of the decoration engineering is 83 thousand yuans, the cost reduction of the structural engineering is 65 thousand yuans.
It is quite close to the target reduction cost.

Although there is a large scatter in the data, there is a definite stir volume (~ 10 mm2) at which h reaches hMax.
Mode 3 is similar to Mode 2 and the ratio of strength reduction (384 N/mm / 630 N/mm) is 0.61.
From the above discussion and from the overall data in Fig. 4a, the contribution to reduction in fracture strength due to FSLW by other deformation/fracture modes are seen to be at least as significant Mode 1 – fracture due to the presence of a hook.
Reduction due to Mode 1 only becomes significant when h reached a high and critical value.
Overall, increasing fracture strength by reducing the degree of softening may be seen as important as the reduction in hook size.