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No loss of original information, the influence index to water quality will be combined into a new group index to reflect the comprehensive index, in order to achieve dimension reduction, simplified data and improve the reliability analysis to the results.
（2）Acquisition data sample Supposing No. of sample is n, the collected index data can be got the matrix with chemotactic treatment: (2) Where Yij is the jth evaluation index for ith sample
（3）index Standardization Because dimensions of each index data are not consistent, and great difference, the index should be changed into the same measure indicators,to Make each index be comparable.
The data can be seen in table 1.
While principal component loading matrix is needed to make data in initial factor divide correponding eigenvalue.

This paper presents evidence on the issue in the behaviour of prepayment using data from the 7 banks in Taiwan.
This may result in a reduction of efficiency and a cost overrun.
[5] Leece, D. (2006) Testing a theoretical model of mortgage demand on UK data, Applied Economics, 38, 2037-2051
D. and Thibodeau, T. (1985) Estimation of mortgage default using disaggregate loan history data, AREUEA Journal, 15, 292-317
J. (1989) Mortgage borrower repayment behavior: a microeconomic analysis with Canadian adjustable rate mortgage data, Real Estate Economics, 17, 118-136.

DEM and meteorological data The resolution of DEM data in Yellow River delta was 400m×400m.
The meteorological data including: precipitation, evaporation, wind speed, wind direction, water temperature, and solar radiation. 2.
Verified data The verified data in this case come from a experiment of dredging that was carried out at the delta area of the Yellow River Estuary.
The measured data included the altitude of cross-sections, discharge and water levels.
It optimizes the agreement between the measured data and the simulated results from the model.

A set of determinations was made by which the experimental data were measured, stored and analyzed, so that a number of conclusions can be drawn regarding the power and control of such DC motors, under optimal operating and braking conditions.
After storing the data and drawing the temperature variation curves, interdependence analyses of the measured quantities can be performed.
In the figures included in Fig. 15 to Fig. 22 their interface with the settings corresponding to the data obtained from the experimental program is highlighted.
DMM interface for P4 voltage Conclusions The analysis of the experimental data revealed that there are important variations of the instantaneous speed in the shaft of the braking system and of the drive motor implicitly.
The data resulting from the experimental research can also be used by designers and manufacturers in the field of low power motors, from specific fields, where precise and reliable braking systems are required.

There are many successful policies for EC reduction and energy conservation.
For prediction problems, a supervised learning algorithm is often adopted for training the network to relate input data to output data.
Among the total numbers of data, the number for training and testing was 21 and 5, respectively.
Fig. 1 Variation of data.
[14] Data on http://web3.moeaboe.gov.tw/ECW/populace/home/Home.aspx

The data, which were monitored at 19 monitoring points from July to August in 2013, is calculated to assess the eutrophication level.
Two months of data were averaged for TN and TP of entire lake, the value is 2.954 and 0.249 respectively.
As to water quality method, it is easy to implement without complex data processing.
The data of TP, SD and Chl were used for quantitative evaluation of lakes eutrophication status.
It computed with the data of TP, TN, Chl, SD and CODMn to estimate the eutrophication level.

The tool will run through a python script that • Creates data layout for ABAQUS by generating .inp file in with the boundary conditions such as iso-statisme condition, clamping pressures, material properties and the corresponding cutting forces for line boring process are defined; • Starts ABAQUS software and runs .inp file on Renault’s calculation server; • Carries out the results post-processing in .dat file generated by ABAQUS software and presents them in an Excel file.
The tool can then be divided into three sub-routines (see Fig.2 and Fig.3): • Sub-routine corresponding to the entries that are defined in the Excel interface; • Sub-routine for the data layout and calculation defined by Python routines and ABAQUS solver; • Sub-routine corresponding to the outputs presented in an Excel file.
A comparative numerical study is performed between the cylinder block manufactured in the production line (type A) and the prototype one with mass reduction (Type B).
The same cutting condition is applied on both cylinder blocks: the one in the manufacturing lines (Type A) and the prototype one with 2 kg of mass reduction (M9TGEN4 light).
Thus, the mass reduction of 2 Kg did not have an important impact on the cylinder block’s rigidity.

They also exhibited a reduction in compressive and tensile strength development at early age (7 days), with an average drop of 6 percent in compressive strength for all grades.
The reason for the reduction could be adduced to the less specific gravity of lime (2.22) as compared with that of clay (2.5).
Cement reduction in construction industry translates to reduction in cost of construction and emission of CO2 gas. 5.0 Recommendation Having exhibited the highest compressive strength after 28 days curing period, it is recommended that the blend of 40%L:60%C be adopted to replace cement in mortar production for masonry, rendering, plastering and pointing applications References [1] Amankwah, E.O., Badiako, M., Kankam, C.K. (2014) “Influence of calcined clay pozzolana on strength characteristics of Portland cement concrete” International Journal of Materials Science and Applications, Vol. 3, Issue 6, pp. 410-419 [2] AL-Jumaily, I.A.S., Naji, N., Kareem, Q (2015) “An Overview on the Influence of Pozzolanic Materials on Properties of Concrete” International Journal of Enhanced Research in Science Technology and Engineering, Vol. 4, Issue 3, pp. 81-92 [3] Plenge, W.H (2002) “Roadmap 2030: The U.S Concrete Industry Technology Roadmap” Concrete Research and Education Foundation, U.S.A
ISBN 0-582-30094 [15] BS EN459-1: Building Lime: definition, specification and conformity criteria [16] BS EN 998-Part 2: Masonry Mortar [17] BS EN 998-Part 1: Rendering and Plastering Mortar [18] TARMAC- Technical Information (Lime Sand Mortars): Technical Data Sheet Lime Sand Mortars.
Product Data Sheet No. 120/01 [19] Indian Standards (IS 712- 1973): Standards for Specification for Building limes

Table 1 Computational values of Nux with respect to variations in n and Pr as compared with published data n Grubka & Bobba [51] Present study Pr=1 Pr=10 Pr=100 Pr=1 Pr=10 Pr=100 -2.0 -1.0000 -10.0000 -100.0000 -1.00000 -10.00000 -100.00000 -1.0 0.0000 0.0000 0.0000 0.00012 0.00000 0.00000 0.0 0.5820 2.3080 7.7657 0.58201 2.30800 7.76565 1.0 1.0000 3.7207 12.2940 1.00000 3.72067 12.29408 2.0 1.3333 4.7969 15.7120 1.33333 4.79687 15.71197 3.0 1.61534 5.6934 18.5516 1.61538 5.69338 18.55154 Table 2 Variations in m when other parameters are zero with respect to Cfx as compared with published data m Cortell [30] Fatunmbi et al. [32] Present 0.0 0.627547 0.627624 0.627563 0.2 0.766758 0.766901 0.766945 0.5 0.889477 0.889602 0.889552 1.0 1.000000 1.000052 1.000008 3.0 1.148588 1.148637 1.148601 10.0 1.234875 1.234913 1.234882 4 Presentation
Likewise, the reduction in the fluid flow owing to a rise in γ indicates that growth in γ compels a reduction in the transport field due to a fall in the yield stress as γ increases which in turn lowers the fluid flow.
Furthermore, growth in the Schmidt number Sc shrinks the solutal boundary layer structure which in turn dictates a reduction in the concentration profile as found in Fig. 8.
In this view, the Schmidt number Sc varies inversely to the coefficient of mass diffusivity and at such, a rise Sc propels a decrease in the nanoparticles concentration boundary layer and consequently compels a reduction in the nanoparticles concentration profiles.

The data related to consumption are converted into Terra Watt Hour from other units to unify the units and get a clearer result in the same unit.
PARAMETER DESCRIPTION Parameter Name of the Parameter Value d Natural acid rain rate 0.018 α Acid rain caused for fossil fuel consumption 1.534592e-06 β Acid rain reduction rate for using hydro-power 1.455974e-05 Γ Acid rain reduction rate for using wind power 3.368984e-05 R Fossil fuel consumption rate 0.012 K Carrying capacity of fossil fuel 955000 Φ Fossil fuel consumption reduction rate for using hydro-power 5e-6 Ψ Fossil fuel consumption reduction rate for using wind power 5e-6 Hydro power usage rate for other than electricity 0.001 Wind power usage rate for other than electricity 0.005 Fossil fuel to electricity gain percentage 0.04 Hydro-power to electricity gain percentage 0.8 Wind power to electricity gain percentage 0.65 Fig. 2.
These data show the impact of hydro and wind power energy in today’s real life world.