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Various thematic maps were prepared for the factors that influence groundwater such as rainfall, soil type, land use, slope and geology using satellite imageries, toposheets and data from Government and other organizations.
Pandey and Singh [4] developed an integrated Remote Sensing and GIS based methodology and tested for the evaluation of groundwater resources using IRS-LISS-II and LISS-III data along with other data sets to extract information on the hydrogeomorphic features of this hard rock terrain.
Data used and Methodology The Rainfall data for the study area was collected from the Public Works Department, Thanjavur.
Merged geocoded data of IRS P6 and IRS 1D (LISS IV and PAN Sensors) for the year 2004 in 1:50000 scale was acquired from National Remote Sensing Agency, Hyderabad to derive land use map of the study area.
The data gathered about the slope in the study area is fed into the ArcMap GIS software and a TIN map was created using which slope is generated using the same software.

After data input, this software finds the most advantageous structural variant in terms of environmental impacts, the most advantageous structural variant in terms of cost and the most advantageous variant overall.
Furthermore, the data for determining the minimum thickness of the cover layer (the minimum cover due to fire resistance, allowance in design for deviation) are entered.
Software user interface Subsequently, some other data are specified – especially the required service life of the structure.
Mainly the data from the CENIA database EPD [12] were used for the calculation.
This database contains data provided directly by building material manufacturers, therefore, the data can be considered credible.

Comparing thermal performance and indoor thermal environment before and after the improvement of traditional residential building envelope, the simulation result will also be produced by Autodesk Ecotect Analysis, which offered the basic data and theoretical foundation for the sustainable development of traditional residential buildings in north region of Shanxi Province.
As building energy consumption is much higher than construction energy consumption in general, the range of building energy conservation is confined to building energy consumption. [1] Through data collection and statistic analysis of Zhangs’ residential building energy consumption, the following conclusions can be made in Table 1.
Table 3 Building Heating Consumption and Coal Consumption for Heating Indexes (3) Serial Number Item Calculated Value（W/m2） Standard Limit（kg/m2） 1 Index of building heating consumption 16.3 20.8 2 Index of coal consumption for heating 12.7 13.5 The data shows that the energy consumption of the residence has reached the standard level of energy efficiency limits.
Fig. 3 Analysis on temperature of heat-gaining before and after the improvement According to Fig. 3, before the improvement, data points scatter around the white oblique line (the white line stands for relatively ideal temperature range), while after the transformation, data points are concentrated on the white line, which indicates that the temperature tends toward a steadily developed value.
Accordingly, the considerable reduction of building energy consumption and the better indoor thermal environment can be gained, which will finally contribute to the sustainable development of traditional residence in the northern region of Shanxi Province.

One-way analysis of variance (ANOVA) with Tukey’s pairwise multiple comparisons was used to analyze the data.
Data points with the same letters indicate that they were not statistically different from each other.
Data points with the same letters indicate that they were not statistically different from each other.
Data points with the same letters indicate that they were not statistically different from each other.
Data points with the same letters indicate that they were not statistically different from each other.

The data were then converted to yield per hm2.
Data analysis The data were analyzed with SPSS13.0 by one-way Anova with Bonferroni’s correction.
Treatment with 100 mmol/L NaCl for 2 weeks caused 43.9 and 62.5 % reduction in fresh weight of leaf blade for Jitianza 2 and Lvneng 1.
Data are the means of 7 replicates and vertical bars represented SD.
Table 1 Comparison of two sweet sorghum varieties in field Parameters Jitianza 2 Lvneng 1 Emergence rate (%) 75.34±5.26a 36.67±3.35b Plant height (cm) 3.98±0.13a 2.68±0.12b Stem width (cm) 2.25±0.17a 1.75±0.14b Number of leaf blade (slice) 17.3±0.67a 13.2±0.34b Total leaf area (m2) 0.66±0.02a 0.31±0.01b Stem stalk Yield (kg/hm2) 73815±57a 30450±40b Ear Yield (kg/hm2) 5760±28a 3390±13b Juice yield (%) 55.12±2.78a 38.78±3.23b Stalk Brix (%) 19.6±0.42a 16.3±0.34b Sugar content (%) 16.66±0.33a 13.86±0.27b Note: Different letter on the right of data indicated significant difference between Jitianza 2 and Lvneng 1 at P < 0.05 level, and data are the means of 20 replicates.

Statistical analysis (Kolmogorov-Smirnov and Kruskal-Wallis Statistic) was also carried out on the experimental data.
These tests were chosen to check if the data was normally distributed, a prerequisite for subsequent statistical analysis.
Pairwise comparisons of grouped data Cost Analysis The equipment set up and raw materials used where all locally sourced.
Multiple experiments per categorization was not carried out, leaving little opportunity to calculate error estimates in data.
b) The research data is statically proven as the Kruskal-Wallis test shows significant difference between untreated 50:50 mixture and the pretreated 75:25 mixture.

TRS data on cermets were collected and summarised in a separate table, too.
Calculation performed with data of Jonsson [38].
available ○ numerical data can be extracted from graphs X only graphs presented or no easy/accurate data extraction possible - no data ◊ not used in any graph due to missing or non-convertible HV/KIC units specifications (e.g.
It is assumed that these commercial grades are practically free of pores, whereas in case of literature data, porosity data were often not reported.
Several data of such a type of cermets were cited above (4.3.5).

This data is obtained by merging multiple data captured from different positions into single data and contains 98,624,222 points in total.
The voxelization enables easy data processing using connectivity.
In the case of the data in Figure 1, the total number of cells is 39,053,170 and the number of active cells which contains at least one point is 1,015,036, which leads to data reduction and makes computation much faster.
Cross sectional shape of H steel Figure 12 shows the results for the mechanical room data.
Results for oil rig data [1] T.

In order to find out the spatial shape of a granite rock-mass, three kinds of data can be used:1) The exploration data (drilling data, geological profile data, geological boundaries and so on); 2) The ridge extension line data applied to speculate the concealed granite rock-mass based on low magnetic-anomaly zone (T≤50nT) from high-precision magnetic-prospecting and Controlled-Source Audio-Frequency Magnetotelluric (CSAMT) results; 3) The other interpolated data of concealed granite rock-mass boundary on CSAMT comprehensive prospecting profile.
With these data, a simulating 3D model of the granite rock-mass can be established to provide basic data for prospecting deep concealed porphyry-polymetallic ore deposits.
“Digital deposit ” is a kind of 3D geological model on deposit scale, which is established on geological exploration data, including drilling core data, chemical analysis, some measured data on geological sections and so on.
The acquisition of 3D data: 3D spatial database (drilling data, the interpolated data of concealed boundary on the sections, the speculated data of concealed ridge-line extension) of the granite rock-mass is established with Micromine software by its 3D spatial superposition and positioning function.
The classification of the data: the measured and analysis data, speculated data, interpolated data. 3.2.1 The sources of the measured and analysis data of the granite rock-mass.

The datas are taken by the actual operation condition.
The observed data is then calculated and plotted on OEM performance curve.
If reduction in flow is continuous, at one point the blower operation will become unstable and a momentary flow reversal will take place.
[4] Performance curve of 1st Reactor Circulation gas Blower K-2201, Basic Data Book Unit Polypropylene vol.9, Pertamina, Palembang, Indonesia
(Bill) Forsthoffer, Forstoffer’s Rotating Equipment Handbooks vol 1., Elsevier, Oxford, 2005 [6] Compressor and Expander in Engeneering Data Book, 12th ed., Gas Processor Suppliers association, Oklahoma, 2004 [7] Ernest E.